diff options
Diffstat (limited to 'tests/nnapi')
178 files changed, 32248 insertions, 476 deletions
diff --git a/tests/nnapi/CMakeLists.txt b/tests/nnapi/CMakeLists.txt index d3c9cab4c..dc8ba865f 100644 --- a/tests/nnapi/CMakeLists.txt +++ b/tests/nnapi/CMakeLists.txt @@ -2,6 +2,20 @@ if (NOT BUILD_RUNTIME_NNAPI_TEST) return() endif(NOT BUILD_RUNTIME_NNAPI_TEST) +if (GENERATE_RUNTIME_NNAPI_TESTS) + set(GENERATOR_SCRIPT "${CMAKE_CURRENT_SOURCE_DIR}/specs/generate_test.sh") + file(GLOB_RECURSE TEST_SPECS "${CMAKE_CURRENT_SOURCE_DIR}/specs/*.mod.py") + set(GENERATED_CPPS "${CMAKE_CURRENT_SOURCE_DIR}/src/generated/all_generated_V1_1_cts_tests.cpp" + "${CMAKE_CURRENT_SOURCE_DIR}/src/generated/all_generated_V1_0_cts_tests.cpp" + "${CMAKE_CURRENT_SOURCE_DIR}/src/generated/all_generated_Ex_cts_tests.cpp") + add_custom_command(OUTPUT ${GENERATED_CPPS} + COMMAND ${GENERATOR_SCRIPT} + DEPENDS ${GENERATOR_SCRIPT} ${TEST_SPECS} + COMMENT "Generating runtime nnapi tests") + add_custom_target(generate-runtime-nnapi-tests + DEPENDS ${GENERATED_CPPS}) +endif(GENERATE_RUNTIME_NNAPI_TESTS) + # 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 @@ -12,45 +26,38 @@ 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() +set(RUNTIME_NNAPI_TEST_SRC ${RUNTIME_NNAPI_TEST_SRC} ${GENERATED_TEST_SRC}) add_executable(${RUNTIME_NNAPI_TEST} ${RUNTIME_NNAPI_TEST_SRC}) +if (GENERATE_RUNTIME_NNAPI_TESTS) + add_dependencies(${RUNTIME_NNAPI_TEST} generate-runtime-nnapi-tests) +endif(GENERATE_RUNTIME_NNAPI_TESTS) + nnfw_find_package(GTest) -set(RUNTIME_NNAPI_TEST_SRC_INC ${NNFW_INCLUDE_DIR} - ${CMAKE_CURRENT_SOURCE_DIR}/include +set(RUNTIME_NNAPI_TEST_SRC_INC ${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}) +elseif (BUILD_PURE_ARM_COMPUTE) + set(SKIPLIST_FILE_NAME ${RUNTIME_NNAPI_TEST}.skip.${TARGET_PLATFORM}.pacl) endif() +target_link_libraries(${RUNTIME_NNAPI_TEST} nnfw_lib_nnapi) target_link_libraries(${RUNTIME_NNAPI_TEST} gtest) target_link_libraries(${RUNTIME_NNAPI_TEST} ${LIB_PTHREAD} dl) -install(TARGETS nnapi_gtest DESTINATION unittest) +install(TARGETS ${RUNTIME_NNAPI_TEST} 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() +# Install skiplist file for target as backup +FILE(GLOB SKIPLIST_TARGET ${CMAKE_CURRENT_SOURCE_DIR}/${RUNTIME_NNAPI_TEST}.skip.${TARGET_PLATFORM}*) +FILE(GLOB SKIPLIST_NOARCH ${CMAKE_CURRENT_SOURCE_DIR}/${RUNTIME_NNAPI_TEST}.skip.noarch.*) +list(APPEND SKIPLIST_ALL_RUNTIME ${SKIPLIST_TARGET} ${SKIPLIST_NOARCH}) +install(FILES ${SKIPLIST_ALL_RUNTIME} DESTINATION unittest OPTIONAL) diff --git a/tests/nnapi/nnapi_gtest.skip.armv7l-linux b/tests/nnapi/nnapi_gtest.skip.armv7l-linux index 1c85418a9..124634a3f 100644 --- a/tests/nnapi/nnapi_gtest.skip.armv7l-linux +++ b/tests/nnapi/nnapi_gtest.skip.armv7l-linux @@ -1,44 +1,15 @@ -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 +# +# Following tests will be skipped on armv7l-linux +# +# Not support operations +GeneratedTests.lsh_projection* +GeneratedTests.mobilenet* +GeneratedTests.svdf* +GeneratedTests.batch_to_space* +GeneratedTests.space_to_batch* +# Unexpected result 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 +# Not support broadcast +GeneratedTests.logical_or_ex_broadcast_4D_2D +# Unsupported optional input that has shape +GeneratedTests.lstm2* diff --git a/tests/nnapi/nnapi_gtest.skip.armv7l-linux.acl_neon b/tests/nnapi/nnapi_gtest.skip.armv7l-linux.acl_neon new file mode 100644 index 000000000..964696245 --- /dev/null +++ b/tests/nnapi/nnapi_gtest.skip.armv7l-linux.acl_neon @@ -0,0 +1,30 @@ +# +# Following tests will be skipped on armv7l-linux acl_neon +# +# Not support operations +TrivialTest.BroadcastMulTwo +GeneratedTests.depth_to_space* +GeneratedTests.dequantize +GeneratedTests.embedding_lookup* +GeneratedTests.hashtable_lookup* +GeneratedTests.lsh_projection* +GeneratedTests.mobilenet* +GeneratedTests.reduce_min* +GeneratedTests.space_to_depth* +GeneratedTests.svdf* +GeneratedTests.batch_to_space* +GeneratedTests.space_to_batch* +GeneratedTests.cast_ex* +GeneratedTests.gather_ex* +GeneratedTests.reduce_max_ex* +GeneratedTests.topk_v2* +# Unexpected result +GeneratedTests.pack* +# Float error +GeneratedTests.exp_ex_1D_float +GeneratedTests.exp_ex_2D_float +# Unsupported optional input that has shape +GeneratedTests.lstm2* +# Unsupported data type +GeneratedTests.argmax_ex_int32 +GeneratedTests.argmax_ex_neg_axis_int32 diff --git a/tests/nnapi/nnapi_gtest.skip.armv7l-linux.neurun b/tests/nnapi/nnapi_gtest.skip.armv7l-linux.cpu index 59957ba60..39b7271ec 100644 --- a/tests/nnapi/nnapi_gtest.skip.armv7l-linux.neurun +++ b/tests/nnapi/nnapi_gtest.skip.armv7l-linux.cpu @@ -9,44 +9,16 @@ 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.add_broadcast* +GeneratedTests.add_quant* 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 @@ -54,21 +26,17 @@ GeneratedTests.embedding_lookup_4d_nnfw GeneratedTests.equal_ex* GeneratedTests.exp_ex* GeneratedTests.floor_ +GeneratedTests.greater_equal_ex* GeneratedTests.hashtable_lookup* GeneratedTests.l2_normalization* GeneratedTests.l2_pool* GeneratedTests.local_response_norm* +GeneratedTests.less_ex* 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* @@ -98,18 +66,10 @@ GeneratedTests.transpose* GeneratedTests.cast_ex* GeneratedTests.gather_ex* GeneratedTests.strided_slice_ex* -GeneratedTests.tensorflowmax_ex* +GeneratedTests.reduce_max_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* diff --git a/tests/nnapi/nnapi_gtest.skip.armv7l-linux.pacl b/tests/nnapi/nnapi_gtest.skip.armv7l-linux.pacl new file mode 100644 index 000000000..c6c4f2694 --- /dev/null +++ b/tests/nnapi/nnapi_gtest.skip.armv7l-linux.pacl @@ -0,0 +1,38 @@ +GeneratedTests.greater_equal_ex* +GeneratedTests.less_ex* +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.prelu_ex_quant8_1 +GeneratedTests.prelu_ex_broadcast_quant8_1 +GeneratedTests.svdf* +GeneratedTests.transpose_conv_ex_float_4 +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-tizen b/tests/nnapi/nnapi_gtest.skip.armv7l-tizen index ebb51de94..124634a3f 100644 --- a/tests/nnapi/nnapi_gtest.skip.armv7l-tizen +++ b/tests/nnapi/nnapi_gtest.skip.armv7l-tizen @@ -1,47 +1,15 @@ -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 +# +# Following tests will be skipped on armv7l-linux +# +# Not support operations +GeneratedTests.lsh_projection* +GeneratedTests.mobilenet* +GeneratedTests.svdf* +GeneratedTests.batch_to_space* +GeneratedTests.space_to_batch* +# Unexpected result GeneratedTests.pack* +# Not support broadcast +GeneratedTests.logical_or_ex_broadcast_4D_2D +# Unsupported optional input that has shape +GeneratedTests.lstm2* diff --git a/tests/nnapi/nnapi_gtest.skip.noarch.interp b/tests/nnapi/nnapi_gtest.skip.noarch.interp new file mode 100644 index 000000000..6f97b8235 --- /dev/null +++ b/tests/nnapi/nnapi_gtest.skip.noarch.interp @@ -0,0 +1,72 @@ +# +# Following tests will be skipped on x86_64-linux interpreter +# +# Not support operations +TrivialTest.Broadcast* +GeneratedTests.avg_pool_quant* +GeneratedTests.concat_quant* +GeneratedTests.conv_quant* +GeneratedTests.depthwise_conv2d_quant* +GeneratedTests.fully_connected_quant* +GeneratedTests.max_pool_quant* +GeneratedTests.softmax_quant* +GeneratedTests.add_broadcast* +GeneratedTests.add_quant* +GeneratedTests.argmax* +GeneratedTests.depth_to_space* +GeneratedTests.dequantize +GeneratedTests.embedding_lookup +GeneratedTests.embedding_lookup_2d_nnfw +GeneratedTests.embedding_lookup_4d_nnfw +GeneratedTests.equal_ex* +GeneratedTests.exp_ex* +GeneratedTests.floor_ +GeneratedTests.greater_equal_ex* +GeneratedTests.hashtable_lookup* +GeneratedTests.l2_normalization* +GeneratedTests.l2_pool* +GeneratedTests.local_response_norm* +GeneratedTests.less_ex* +GeneratedTests.logical_and_ex* +GeneratedTests.logical_or_ex* +GeneratedTests.logistic* +GeneratedTests.lsh_projection* +GeneratedTests.lstm* +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.reduce_max_ex* +GeneratedTests.reduce_sum_ex* +GeneratedTests.topk_v2* +# Unexpected result +GeneratedTests.split* +GeneratedTests.transpose_conv* +GeneratedTests.pack* +GeneratedTests.unpack* +GeneratedTests.logical_not_ex* diff --git a/tests/nnapi/nnapi_gtest.skip.x86_64-linux b/tests/nnapi/nnapi_gtest.skip.x86_64-linux index e87c4acf6..901a10391 100644 --- a/tests/nnapi/nnapi_gtest.skip.x86_64-linux +++ b/tests/nnapi/nnapi_gtest.skip.x86_64-linux @@ -1,88 +1,78 @@ # # 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 +# Not support operations +TrivialTest.AddTwo +TrivialTest.AddThree +TrivialTest.BroadcastAddTwo +TrivialTest.BroadcastMulTwo +ValidationTestCompilation.SetPreference +ValidationTestCompilation.CreateExecution +ValidationTestCompilation.Finish +ValidationTestExecution.SetInput +ValidationTestExecution.SetOutput +ValidationTestExecution.SetInputFromMemory +ValidationTestExecution.SetOutputFromMemory +ValidationTestExecution.StartCompute +ValidationTestExecution.EventWait +GeneratedTests.add_broadcast* +GeneratedTests.add_quant* +GeneratedTests.argmax* +GeneratedTests.depth_to_space* +GeneratedTests.depthwise_conv2d_quant8* GeneratedTests.dequantize GeneratedTests.embedding_lookup -GeneratedTests.exp_ex_1D_float +GeneratedTests.embedding_lookup_2d_nnfw +GeneratedTests.embedding_lookup_4d_nnfw GeneratedTests.equal_ex* -GeneratedTests.exp_ex_2D_float +GeneratedTests.exp_ex* 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.greater_equal_ex* +GeneratedTests.hashtable_lookup* +GeneratedTests.l2_normalization* +GeneratedTests.l2_pool* +GeneratedTests.less* +GeneratedTests.local_response_norm* 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.logical_or_ex* +GeneratedTests.logistic* +GeneratedTests.lsh_projection* +GeneratedTests.lstm* +GeneratedTests.mobilenet* +GeneratedTests.mul* 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.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.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.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.reduce_max_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.topk_v2* +# Unexpected result GeneratedTests.split* GeneratedTests.transpose_conv* GeneratedTests.pack* GeneratedTests.unpack* +GeneratedTests.logical_not_ex* diff --git a/tests/nnapi/nnapi_test_generator/android-p/README.md b/tests/nnapi/nnapi_test_generator/android-p/README.md new file mode 100644 index 000000000..469e467ea --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/README.md @@ -0,0 +1,11 @@ +# nnapi test generator + +_nnapi test generator_ aims at generating NN public C API tests. + +To generate tests, run the following command. + +``` +$ tests/nnapi/specs/generate_test.sh +``` + +Original code is at https://android.googlesource.com/platform/frameworks/ml/+/efd22b6. diff --git a/tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h b/tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h new file mode 100644 index 000000000..1fcb0d661 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h @@ -0,0 +1,209 @@ +/* + * 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. + */ + +/* Header-only library for various helpers of test harness + * See frameworks/ml/nn/runtime/test/TestGenerated.cpp for how this is used. + */ +#ifndef ANDROID_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H +#define ANDROID_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H + +#include <gtest/gtest.h> + +#include <cmath> +#include <functional> +#include <map> +#include <tuple> +#include <vector> + +namespace generated_tests { + +constexpr const size_t gMaximumNumberOfErrorMessages = 10; + +typedef std::map<int, std::vector<float>> Float32Operands; +typedef std::map<int, std::vector<int32_t>> Int32Operands; +typedef std::map<int, std::vector<uint8_t>> Quant8Operands; +typedef std::tuple<Float32Operands, // ANEURALNETWORKS_TENSOR_FLOAT32 + Int32Operands, // ANEURALNETWORKS_TENSOR_INT32 + Quant8Operands // ANEURALNETWORKS_TENSOR_QUANT8_ASYMM + > + MixedTyped; +typedef std::pair<MixedTyped, MixedTyped> MixedTypedExampleType; + +template <typename T> +struct MixedTypedIndex {}; + +template <> +struct MixedTypedIndex<float> { + static constexpr size_t index = 0; +}; +template <> +struct MixedTypedIndex<int32_t> { + static constexpr size_t index = 1; +}; +template <> +struct MixedTypedIndex<uint8_t> { + static constexpr size_t index = 2; +}; + +// Go through all index-value pairs of a given input type +template <typename T> +inline void for_each(const MixedTyped& idx_and_data, + std::function<void(int, const std::vector<T>&)> execute) { + for (auto& i : std::get<MixedTypedIndex<T>::index>(idx_and_data)) { + execute(i.first, i.second); + } +} + +// non-const variant of for_each +template <typename T> +inline void for_each(MixedTyped& idx_and_data, + std::function<void(int, std::vector<T>&)> execute) { + for (auto& i : std::get<MixedTypedIndex<T>::index>(idx_and_data)) { + execute(i.first, i.second); + } +} + +// internal helper for for_all +template <typename T> +inline void for_all_internal( + MixedTyped& idx_and_data, + std::function<void(int, void*, size_t)> execute_this) { + for_each<T>(idx_and_data, [&execute_this](int idx, std::vector<T>& m) { + execute_this(idx, static_cast<void*>(m.data()), m.size() * sizeof(T)); + }); +} + +// Go through all index-value pairs of all input types +// expects a functor that takes (int index, void *raw data, size_t sz) +inline void for_all(MixedTyped& idx_and_data, + std::function<void(int, void*, size_t)> execute_this) { + for_all_internal<float>(idx_and_data, execute_this); + for_all_internal<int32_t>(idx_and_data, execute_this); + for_all_internal<uint8_t>(idx_and_data, execute_this); +} + +// Const variant of internal helper for for_all +template <typename T> +inline void for_all_internal( + const MixedTyped& idx_and_data, + std::function<void(int, const void*, size_t)> execute_this) { + for_each<T>(idx_and_data, [&execute_this](int idx, const std::vector<T>& m) { + execute_this(idx, static_cast<const void*>(m.data()), m.size() * sizeof(T)); + }); +} + +// Go through all index-value pairs (const variant) +// expects a functor that takes (int index, const void *raw data, size_t sz) +inline void for_all( + const MixedTyped& idx_and_data, + std::function<void(int, const void*, size_t)> execute_this) { + for_all_internal<float>(idx_and_data, execute_this); + for_all_internal<int32_t>(idx_and_data, execute_this); + for_all_internal<uint8_t>(idx_and_data, execute_this); +} + +// Helper template - resize test output per golden +template <typename ty, size_t tuple_index> +void resize_accordingly_(const MixedTyped& golden, MixedTyped& test) { + std::function<void(int, const std::vector<ty>&)> execute = + [&test](int index, const std::vector<ty>& m) { + auto& t = std::get<tuple_index>(test); + t[index].resize(m.size()); + }; + for_each<ty>(golden, execute); +} + +inline void resize_accordingly(const MixedTyped& golden, MixedTyped& test) { + resize_accordingly_<float, 0>(golden, test); + resize_accordingly_<int32_t, 1>(golden, test); + resize_accordingly_<uint8_t, 2>(golden, test); +} + +template <typename ty, size_t tuple_index> +void filter_internal(const MixedTyped& golden, MixedTyped* filtered, + std::function<bool(int)> is_ignored) { + for_each<ty>(golden, + [filtered, &is_ignored](int index, const std::vector<ty>& m) { + auto& g = std::get<tuple_index>(*filtered); + if (!is_ignored(index)) g[index] = m; + }); +} + +inline MixedTyped filter(const MixedTyped& golden, + std::function<bool(int)> is_ignored) { + MixedTyped filtered; + filter_internal<float, 0>(golden, &filtered, is_ignored); + filter_internal<int32_t, 1>(golden, &filtered, is_ignored); + filter_internal<uint8_t, 2>(golden, &filtered, is_ignored); + return filtered; +} + +// Compare results +#define VECTOR_TYPE(x) \ + typename std::tuple_element<x, MixedTyped>::type::mapped_type +#define VALUE_TYPE(x) VECTOR_TYPE(x)::value_type +template <size_t tuple_index> +void compare_( + const MixedTyped& golden, const MixedTyped& test, + std::function<void(VALUE_TYPE(tuple_index), VALUE_TYPE(tuple_index))> + cmp) { + for_each<VALUE_TYPE(tuple_index)>( + golden, + [&test, &cmp](int index, const VECTOR_TYPE(tuple_index) & m) { + const auto& test_operands = std::get<tuple_index>(test); + const auto& test_ty = test_operands.find(index); + ASSERT_NE(test_ty, test_operands.end()); + for (unsigned int i = 0; i < m.size(); i++) { + SCOPED_TRACE(testing::Message() + << "When comparing element " << i); + cmp(m[i], test_ty->second[i]); + } + }); +} +#undef VALUE_TYPE +#undef VECTOR_TYPE +inline void compare(const MixedTyped& golden, const MixedTyped& test, float fpRange = 1e-5f) { + size_t totalNumberOfErrors = 0; + compare_<0>(golden, test, [&totalNumberOfErrors, fpRange](float g, float t) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(g, t, fpRange); + } + if (std::abs(g - t) > fpRange) { + totalNumberOfErrors++; + } + }); + compare_<1>(golden, test, [&totalNumberOfErrors](int32_t g, int32_t t) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_EQ(g, t); + } + if (g != t) { + totalNumberOfErrors++; + } + }); + compare_<2>(golden, test, [&totalNumberOfErrors](uint8_t g, uint8_t t) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(g, t, 1); + } + if (std::abs(g - t) > 1) { + totalNumberOfErrors++; + } + }); + EXPECT_EQ(size_t{0}, totalNumberOfErrors); +} + +}; // namespace generated_tests + +#endif // ANDROID_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H diff --git a/tests/nnapi/nnapi_test_generator/android-p/slicing.py b/tests/nnapi/nnapi_test_generator/android-p/slicing.py new file mode 100644 index 000000000..f08e9d1a1 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/slicing.py @@ -0,0 +1,167 @@ +#!/usr/bin/python3 + +# Copyright 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. +"""Slicing the input Model file + +Invoked by ml/nn/runtime/test/specs/slicing.sh; this Python code is +not intended to be invoked directly by the users. See that script for +details on how to use the slicing tool is used. + +This script does the following work: + +Perform a topological sort similar to the test generator, except that: +* It would stop at the N-th operation it encounters, and +* Rename the output of the N-th operation to a model output, and +* Name that as the output of the model. +* Also only inputs and weights used by the submodel would be emitted. + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import argparse +from functools import reduce +import math +import os +import struct +import sys +import contextlib +import test_generator +import pprint +# Stuff from test generator +from test_generator import Example +from test_generator import Float32Scalar +from test_generator import Input +from test_generator import Int32Scalar +from test_generator import Internal +from test_generator import Model +from test_generator import Output +from test_generator import Parameter +from test_generator import smart_open + + +# Take a model from command line +def import_source(): + parser = argparse.ArgumentParser() + parser.add_argument("spec", help="the spec file") + parser.add_argument( + "-n", "--number", + help="number of operations in the sliced model. Default = 1", + default=1) + parser.add_argument( + "-m", "--model", help="the output model file", default="-") + parser.add_argument( + "-e", "--example", help="the output example file", default="-") + args = parser.parse_args() + + if os.path.exists(args.spec): + test_generator.FileNames.SpecFile = os.path.basename(args.spec) + exec (open(args.spec).read()) + + return (args.model, args.example, args.number) + + +# Slice till the Nth op the topological sort finds +# the output of that op becomes the output of the model +class slicing: + + def __init__(self, threshold): + self.__nr_op_seen = 0 + self.__threshold = threshold + self.__last_outs = [] + self.__all_formatted_ops = [] + self.__referenced_operands = set() + + def format_as_py_op(self, op): + try: + fmt = op.PyDefinition() + except AttributeError: # not an op, but things like weights + return True + if fmt is not None: + self.__nr_op_seen += 1 + if self.__nr_op_seen > self.__threshold: + return False + self.__last_outs = op.outs + for o in op.ins: + self.__referenced_operands.add(o) + for o in op.outs: + self.__referenced_operands.add(o) + self.__all_formatted_ops.append("model = model.%s" % fmt) + return True + + def dump(self, model_file): + for x in self.__all_formatted_ops: + print(x, file=model_file) + + def dump_example(self, example_file): + override = {} + # Make alias for the output variable + for lo in self.__last_outs: + override[lo.get_name()] = lo.type.get_nr_elements() + alias_def = """\ +# Alias for the output variable {operand_name} +aliased_output{number} = {operand_name} +""" + op = { + 'operand_name': lo.get_name(), + 'number': 0 # only support one output as of now + } + print (alias_def.format(**op), file=example_file) + Example.py_dump(example_file, override, self.__referenced_operands) + + def format_operands(self): + # Dump operand definitions + op_definitions = [] + for o in test_generator.Operand.operands.objects(): + if o not in self.__referenced_operands: + continue + ty = o.type + raw_shape = ty.get_raw_shape() + op_def = """{op_name} = {operand}("{op_name}", "{element_type}", "{shape}" """ + if isinstance(o, test_generator.Parameter): + op_def += """, {initializer})""" + init = o.initializer + py_operand_name = "Parameter" + else: + op_def += ")" + init = [] + py_operand_name = "IgnoredOutput" if o in set( + self.__last_outs) else o.__class__.__name__ + + op = { + "element_type": ty.get_element_type(), + "shape": ty.get_raw_shape(), + "op_name": o.get_name(), + "operand": py_operand_name, + "initializer": init + } + op_definitions.append(op_def.format(**op)) + return "\n".join(op_definitions) + + +if __name__ == "__main__": + (model, example, number) = import_source() + s = slicing(int(number)) + + with smart_open(model) as model_file: + spec_file = " (from: %s)" % (test_generator.FileNames.SpecFile) + print("# Generated file%s. Do not edit" % (spec_file), file=model_file) + print("model = Model()", file=model_file) + test_generator.TopologicalSort(lambda x: s.format_as_py_op(x)) + print(s.format_operands(), file=model_file) + s.dump(model_file) + with smart_open(example) as example_file: + s.dump_example(example_file) diff --git a/tests/nnapi/nnapi_test_generator/android-p/test_generator.py b/tests/nnapi/nnapi_test_generator/android-p/test_generator.py new file mode 100644 index 000000000..922ef7754 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/test_generator.py @@ -0,0 +1,775 @@ +#!/usr/bin/python3 + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# Copyright 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. + +"""NN model compiler + +Compile models and examples into NDK-based CTS unit tests +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import argparse +from functools import reduce +import math +import os +import struct +import sys +import contextlib +import pprint +import re + +@contextlib.contextmanager +def smart_open(filename=None): + if filename and filename != '-': + fh = open(filename, 'w') + else: + fh = sys.stdout + + try: + yield fh + finally: + if fh is not sys.stdout: + fh.close() + +class Phase(object): + def __init__(self): + self.__objects = [] + self.__contents = [] + self.__dict_of_objects = {} + + def append(self, obj, x): + self.__objects.append(obj) + self.__contents.append(x) + self.__dict_of_objects[obj.ID()] = obj + + def dump(self, filename): + for x in self.__contents: + print (" " + x + ";", file=filename) + + def objects(self): + return self.__objects + + def search(self, i): + return self.__dict_of_objects[i] + +# Tracking objects inside a model with a not necessarily unique name and +# an unique number +class NamedObject(object): + __serial = 0 + + def __init__(self, name = "NamedObject"): + self.__name = name + self.__id = NamedObject.serial() + NamedObject.__serial += 1 + + def ID(self): + return self.__id + + def serial(): + return NamedObject.__serial + + def get_name(self): + return self.__name + + def __str__(self): + return self.get_name() + + def __hash__(self): + return self.__id + +# Object that can be traversed during topological sorting phase +class Traversable(object): + def traversable(self): + return True + +class Nontraversable(object): + def traversable(self): + return False + +# Object that can take input from other objects +class Uses(object): + all_uses = set() + def __init__(self, ins = []): + self.ins = ins.copy() + Uses.all_uses.add(self) + for i in ins: + i.outs.append(self) + +# Object that other objects takes its definition from +class Definitions(object): + def __init__(self, outs = []): + self.outs = outs.copy() + for o in outs: + o.ins.append(self) + +class TypeLookup: + __type_lookup = { + "INT32": "int32_t", + "UINT32": "uint32_t", + "FLOAT32": "float", + "TENSOR_INT32": "int32_t", + "TENSOR_FLOAT32": "float", + "TENSOR_QUANT8_ASYMM": "uint8_t", +# "OEM_SCALAR": this is service-defined. + "TENSOR_OEM_BYTE": "uint8_t", + } + + def get_cpptype(nnapi_type): + return TypeLookup.__type_lookup[nnapi_type] + + def is_float(nnapi_type): + return TypeLookup.get_cpptype(nnapi_type) == "float" + + def get_size(nnapi_type): + return 1 if TypeLookup.get_cpptype(nnapi_type) == "uint8_t" else 4 + + +class Type(object): + __types = {} + __type_serial = 0 # types have their own numbering + def __init__(self, vt = None, shape = None): + self.__vt = vt + self.__shape = shape + if vt is None or shape is None: + self.__name = None + return + + key = str(self) + if key not in Type.__types: + self.__id = Type.__type_serial + Type.__types[str(self)] = self + Type.__type_serial += 1 + else: + self.__id = Type.__types[key].__id + self.__name = "type" + str(self.__id) + + def get_shape(self): + return self.__shape + + def get_element_type(self): + return self.__vt + + def get_name(self): + return self.__name + + def __str__(self): + return (", ".join([self.__vt, self.__shape])) + + def __hash__(self): + return self.__id + + def dump(filename): + for key, value in sorted(Type.__types.items()): + print (" OperandType " + str(value.__name) + "(Type::" + str(key) + ");", file=filename) + + def get_raw_shape(self): + return self.__shape + + def get_parsed_shape(self): + # Parse shape + if (self.__shape != "" and self.__shape != "{}"): + left, sep, right = self.__shape.partition('{') + real_shape, sep, right = right.partition('}') + shape = [int(x) for x in real_shape.split(",")] + # left now looks like "0.0f, 127.5f, " + scale, sep, zero_point = right.rpartition(',') + if scale == "": + if zero_point == "": + return real_shape, "0", "0" + return real_shape, zero_point, "0" + left, sep, scale = scale.partition(',') + return real_shape, scale.replace("f", ""), zero_point + else: + return "", "0", "0" + + def get_nr_elements(self): + # Parse shape + nr_elements = 1 + real_shape, scale, zero_point = self.get_parsed_shape() + + if (real_shape != "" and real_shape != "{}"): + shape = [int(x) for x in real_shape.split(",")] + nr_elements = reduce((lambda x, y: x*y), shape) + return nr_elements + + def get_size(self): + element_size = TypeLookup.get_size(self.__vt) + return self.get_nr_elements() * element_size + +# A value is a typed, named object +class Value(NamedObject): + def __init__(self, name, vt): + NamedObject.__init__(self, name) + self.type = vt + +# An operand that can be fed into operations. Also, an operand is always +# declared before operations. +class Operand(Value): + # All operand declarations in string + operands = Phase() + + def __init__(self, name, vt): + Value.__init__(self, name, vt) + def_string = ( + "auto " + self.get_name() + " = "\ + "model->addOperand(&" + vt.get_name() + ")") + Operand.operands.append(self, def_string) + + # By default, produce nothing (when asked by the Topological Sort phase) + def Definition(self): + pass + + def Reference(self): + return NamedObject.__str__(self) + + # Print a set of operands in curly braces + def print_operands(operands): + return [ x.Reference() for x in operands ] + + # Defined with the model or not + def is_weight(self): + return False + +# A user-declared input operand +class Input(Operand, Definitions, Traversable): + # for enumerating inputs + __next_number = 0 + # Holds reference to all Inputs; used by Topoligcal sort as starting nodes. + __inputs = set() + + def __init__(self, name, vt, shape, increase_next_number=True): + Operand.__init__(self, name, Type(vt, shape)) + Definitions.__init__(self) + Input.__inputs.add(self) + self.number = Input.__next_number + if increase_next_number is True: + Input.__next_number += 1 + + def lifetime(self): + return "MODEL_INPUT" + + def is_internal(self): + return False + + def get_inputs(exclude_internal = None): + if exclude_internal is not None: + external = { x for x in Input.__inputs if not x.is_internal() } + return external + else: + return Input.__inputs + +# A user-declared output operand +class Output(Operand, Uses, Nontraversable): + # for enumerating outputs + __next_number = 0 + __outputs = [] + + def __init__(self, name, vt, shape): + Operand.__init__(self, name, Type(vt, shape)) + Uses.__init__(self) + Output.__outputs.append(self) + self.number = Output.__next_number + Output.__next_number += 1 + + def lifetime(self): + return "MODEL_OUTPUT" + + # return all unique outputs in the original order + def get_outputs(): + saw = set() + unique = [x for x in Output.__outputs if x not in saw and (saw.add(x) or True)] + return unique + +# An output that we don't want to compare the results +class IgnoredOutput(Output): + __ignored = set() + def __init__(self, name, vt, shape): + Output.__init__(self, name, vt, shape) + IgnoredOutput.__ignored.add(self) + def gen_ignored(): + ignored_func = """ +bool is_ignored(int i) { + static std::set<int> ignore = {%s}; + return ignore.find(i) != ignore.end(); +}""" % ", ".join([str(x.number) for x in IgnoredOutput.__ignored]) + return ignored_func + +class ModelArgument: + __arguments = [] + + def __init__(self, arg_type, arg_name): + self.__arg_type = arg_type + self.__arg_name = arg_name + ModelArgument.__arguments.append(" ".join([arg_type, arg_name])) + + def get_arg_type(self): + return self.__arg_type + + def get_arg_name(self): + return self.__arg_name + + def get_arguments(): + return ModelArgument.__arguments + + def lifetime(self): + return "CONSTANT_COPY" + +# Print in C float literal format +def pretty_print_as_float(x): + s = str(float(x)) + if s.find(".") >= 0 or s.find("e") >= 0: + return s + "f" + else: + return s + ".0f" + +class Parameter(Input): + # TODO seems wrong that's an Input. + def __init__(self, name, vt, shape, initializer): + Input.__init__(self, name, vt, shape, False) + self.initializer = initializer + self.cpptype = TypeLookup.get_cpptype(vt) + def is_internal(self): + return True + def Definition(self): + init_name = self.get_name() + "_init" + initializer = [str(x) for x in self.initializer] + if self.cpptype == "float": + initializer = [ pretty_print_as_float(x) for x in initializer] + init = self.cpptype + " " + init_name + "[]" + init = "static " + init + " = {" + ", ".join(initializer) + "};" + args = [ self.get_name(), init_name, + "sizeof(" + self.cpptype + ") * " + str(len(self.initializer)) ] + stmt = "\n ".join([init, + "model->setOperandValue(" + ", ".join(args)+");"]) + return stmt + def is_weight(self): + return True + def lifetime(self): + if Configuration.useSHM(): + return "CONSTANT_REFERENCE" + else: + return "CONSTANT_COPY" + +class Int32Scalar(Parameter): + def __init__(self, name, value): + Parameter.__init__(self, name, "INT32", "{}", [value]) + +class Float32Scalar(Parameter): + def __init__(self, name, value): + Parameter.__init__(self, name, "FLOAT32", "{}", [value]) + +# A compiler-generated intermediate result from an operation +class IntermediateResult(Operand, Definitions, Uses, Traversable): + def __init__(self, src: Value): + tmp_name = "tmp" + str(NamedObject.serial()) + Operand.__init__(self, tmp_name, src.type) + Definitions.__init__(self) + Uses.__init__(self, [src]) + + def lifetime(self): + return "TEMPORARY_VARIABLE" + +# An explicitly declared intermediate result +class Internal(Operand, Definitions, Uses, Traversable): + def __init__(self, name, vt, shape): + Operand.__init__(self, name, Type(vt, shape)) + Definitions.__init__(self) + Uses.__init__(self) + + def lifetime(self): + return "TEMPORARY_VARIABLE" + +# An operation in a model +class Operation(Definitions, Uses, Traversable): + def __init__(self, optype, ins, outs): + self.type = ins[0].type + Definitions.__init__(self, outs) + Uses.__init__(self, ins) + self.optype = optype + + def __str__(self): + inputs = [ str(x) for x in self.ins ] + return "Operation:" + self.optype + " " + ", ".join(inputs) + + def Reference(self): + return "operation" + str(self.ID()); + + def Definition(self): + inputs = Operand.print_operands(self.ins); + outputs = Operand.print_operands(self.outs); + if re.search('_EX$', self.optype): + return "model->addOperationEx(ANEURALNETWORKS_"+self.optype+", " + \ + "{"+", ".join(inputs)+"}, {" + ", ".join(outputs) + "});" + else: + return "model->addOperation(ANEURALNETWORKS_"+self.optype+", " + \ + "{"+", ".join(inputs)+"}, {" + ", ".join(outputs) + "});" + + # Get Python-ish dump for the op + def PyDefinition(self): + py_op_string = """Operation("{optype}", {inputs}).To({outputs})""" + inputs = [str(x) for x in Operand.print_operands(self.ins)] + inputs = ", ".join(inputs) + assert len(self.outs) <= 1 + outputs = str(Operand.print_operands(self.outs)[0]) + ops = {"optype": self.optype, "inputs": inputs, "outputs": outputs} + return py_op_string.format(**ops) + +# Main interface +class Model(object): + __isRelaxed = False + + def __init__(self): + self.__currentOp = None + + # TODO turn this into generic binary operations + def Add(self, i1: Value, i2 = None) -> Operation: + ins = [i1] + if i2 is not None: + ins.append(i2) + if self.__currentOp is not None: + ir = IntermediateResult(self.__currentOp) + self.__currentOp = ir + ins.append(self.__currentOp) + + op = Operation("ADD", ins, []) + + self.__currentOp = op + return self + + def Operation(self, op_name, *args): + ins = [i for i in args] + outs = [] + op = Operation(op_name, ins, outs) + self.__currentOp = op + return self + + def RawAdd(self, i1: Value, i2: Value, o = None) -> Operation: + ins = [i1, i2] + outs = [] + if o is not None: + outs = [o] + op = Operation("ADD", ins, outs) + + self.__currentOp = op + return self + + # See CpuExecutor::executeOperation() for the arguments of each op + def AveragePool(self, input, padding, stride_width, stride_height, filter_width, filter_height, activation): + ins = [input, padding, stride_width, + stride_height, filter_width, filter_height, activation] + outs = [] + op = Operation("AVERAGE_POOL_2D", ins, outs) + self.__currentOp = op + return self + + def Concatenation(self, *args): + ins = [i for i in args] + outs = [] + op = Operation("CONCATENATION", ins, outs) + self.__currentOp = op + return self + + def Conv(self, filter, bias, input, padding, stride_width, stride_height, activation): + ins = [filter, bias, input, padding, stride_width, + stride_height, activation] + outs = [] + op = Operation("CONV_2D", ins, outs) + self.__currentOp = op + return self + + def DepthWiseConv(self, filter, bias, input, padding, stride_width, stride_height, depth_multiplier, activation): + ins = [filter, bias, input, padding, stride_width, + stride_height, depth_multiplier, activation] + outs = [] + op = Operation("DEPTHWISE_CONV_2D", ins, outs) + self.__currentOp = op + return self + + def FullyConnected(self, input, weights, bias, activation): + ins = [input, weights, bias, activation] + outs = [] + op = Operation("FULLY_CONNECTED", ins, outs) + self.__currentOp = op + return self + + def Logistic(self, input): + ins = [input] + outs = [] + op = Operation("LOGISTIC", ins, outs) + self.__currentOp = op + return self + + def L2Pool(self, input, padding, stride_width, stride_height, filter_width, filter_height, activation): + ins = [input, padding, stride_width, + stride_height, filter_width, filter_height, activation] + outs = [] + op = Operation("L2_POOL_2D", ins, outs) + self.__currentOp = op + return self + + def MaxPool(self, input, padding, stride_width, stride_height, filter_width, filter_height, activation): + ins = [input, padding, stride_width, + stride_height, filter_width, filter_height, activation] + outs = [] + op = Operation("MAX_POOL_2D", ins, outs) + self.__currentOp = op + return self + + def SoftMax(self, input, beta): + ins = [input, beta] + outs = [] + op = Operation("SOFTMAX", ins, outs) + self.__currentOp = op + return self + + def Reshape(self, input, shape): + ins = [input, shape] + outs = [] + op = Operation("RESHAPE", ins, outs) + self.__currentOp = op + return self + + def Out(self, o): + if (type(o) is list or type(o) is tuple): + for i in o: + self.__currentOp.outs.append(i) + i.ins.append(self.__currentOp) + else: + self.__currentOp.outs.append(o) + o.ins.append(self.__currentOp) + return self + + def To(self, o:Value): + ret = Model.Out(self, o) + self.__currentOp = None + return self + + def RelaxedExecution(self, isRelaxed): + Model.__isRelaxed = isRelaxed + return self + + def isRelaxed(): + return Model.__isRelaxed + + +class FileNames: + SpecFile = "" + +class Example(): + __examples = [] + def __init__(self, list_of_examples): + Example.__examples.append(list_of_examples) + + def dump_dict(d): + ret = [] + for k, v in d.items(): + key = str(k) + suffix = "f" + if type(k) is not int: + key = str(k.number) + if not TypeLookup.is_float(k.type.get_element_type()): + suffix = "" + init = ", ".join( + [str(i) + (suffix if str(i).find(".") != -1 else "") for i in v]) + ret.append("{%s, {%s}}" % (key, init)) + return ", ".join(ret) + + def dump_mixed_types(d): + ret = [] + + float32_dict = {} + int32_dict = {} + uint8_dict = {} + + for k, v in d.items(): + key_id = k.ID() if type(k) is not int else k + ty = Operand.operands.search(key_id).type.get_element_type() + # find out type of the operand addressed by the key + if (ty == "TENSOR_FLOAT32"): + float32_dict[k] = v + elif (ty == "TENSOR_INT32"): + int32_dict[k] = v + elif (ty == "TENSOR_OEM_BYTE"): + uint8_dict[k] = v + elif (ty == "TENSOR_QUANT8_ASYMM"): + uint8_dict[k] = v + else: + print ("Unhandled type %s"%ty, file = sys.stderr) + assert 0 and "unsupported example type" + + tuple_init = """\ +{{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{{float32_dict}}}, + // int -> INT32 map + {{{int32_dict}}}, + // int -> QUANT8_ASYMM map + {{{uint8_dict}}} +}}""" + tuple_contents = { + 'float32_dict': Example.dump_dict(float32_dict), + 'int32_dict': Example.dump_dict(int32_dict), + 'uint8_dict': Example.dump_dict(uint8_dict) + } + return tuple_init.format(**tuple_contents) + + + def dump(example_file): + if len(Example.__examples) > 0: + spec_file = " (from: %s)" % (FileNames.SpecFile) + print ('// Generated file%s. Do not edit' % (spec_file), + file = example_file) + for i, o in Example.__examples: + print ('// Begin of an example', file = example_file) + print ('{', file = example_file) + inputs = Example.dump_mixed_types(i) + outputs = Example.dump_mixed_types(o) + print ('//Input(s)\n%s,' % inputs , file = example_file) + print ('//Output(s)\n%s' % outputs, file = example_file) + print ('}, // End of an example', file = example_file) + + # Similar to dump_dict, but in python. Used by the slicing tool + # if referenced is not None, only print operands that are present there + def py_dump_dict(d, referenced): + ret = [] + for k, v in d.items(): + if referenced != None and k not in referenced: + continue + key = str(k) + init = pprint.pformat(v) + ret.append("%s: %s" % (key, init)) + return ", ".join(ret) + + # similar to dump, but in python. Used by the slicing tool + # if referenced is not None, only print operands that are present there + def py_dump(example_file, override, referenced): + if len(Example.__examples) > 0: + example_no = 0 + example_template = """\ +input{no} = {{{inputs}}} +# Only executed during data collection phase +if collecting_data is True: + Example((input{no}, {{{outputs}}})) +""" + for i, o in Example.__examples: + print ('# Begin of an example', file = example_file) + inputs = Example.py_dump_dict(i, referenced) + output_list = [] + for k, v in override.items(): + output_list.append("%s: [0] * %d" % (k, v)) + outputs = ",".join(output_list) + + # TODO: handle >1 outputs + for k, v in o.items(): + assert k.number == 0 + example_contents = { + 'no': example_no, + 'inputs': inputs, + 'outputs': outputs + } + print (example_template.format(**example_contents), file = example_file) + + +def TopologicalSort(format_op): + start = Input.get_inputs().copy() + deps = { x: set(x.ins) for x in Uses.all_uses } + + while len(start) > 0: + cur = start.pop() + if format_op(cur) is False: + return + distinct_outs = set(cur.outs) + for o in distinct_outs: + deps[o].remove(cur) + if len(deps[o]) == 0 and o.traversable(): + start.add(o) + +class Configuration: + use_shm_for_weights = False + def useSHM(): + return Configuration.use_shm_for_weights + +# Take a model from command line +def import_source(): + parser = argparse.ArgumentParser() + parser.add_argument("spec", help="the spec file") + parser.add_argument( + "-m", "--model", help="the output model file", default="-") + parser.add_argument( + "-e", "--example", help="the output example file", default="-") + args = parser.parse_args() + + if os.path.exists(args.spec): + FileNames.SpecFile = os.path.basename(args.spec) + exec (open(args.spec).read()) + + return (args.model, args.example) + + +def print_cts_op(model_file, op): + fmt = op.Definition() + if fmt is not None: + print (" %s" % fmt, file = model_file) + return True + +if __name__ == '__main__': + (model, example) = import_source() + # Boilerplate + args = "" + if len(ModelArgument.get_arguments()) > 0: + args = ", " + ", ".join(ModelArgument.get_arguments()) + + print("Output CTS model: %s" % model, file=sys.stderr) + print("Output example:" + example, file=sys.stderr) + + with smart_open(model) as model_file: + spec_file = " (from: %s)" % (FileNames.SpecFile) + + print ('// Generated file%s. Do not edit'%(spec_file), file = model_file) + print ("void CreateModel(Model *model" + args + ") {", file=model_file) + + # Phase 0: types + Type.dump(model_file) + # Phase 1: add operands + print (" // Phase 1, operands", file=model_file) + Operand.operands.dump(model_file) + + # Phase 2: operations + print (" // Phase 2, operations", file=model_file) + TopologicalSort(lambda x: print_cts_op(model_file, x)) + + # Phase 3: add inputs and outputs + print (" // Phase 3, inputs and outputs", file=model_file) + inputs = Operand.print_operands(Input.get_inputs(True)); + outputs = Operand.print_operands(Output.get_outputs()); + print (" model->identifyInputsAndOutputs(\n" + + " {"+", ".join(inputs)+"},\n {" + ", ".join(outputs) + "});", + file=model_file) + + # Phase 4: set relaxed execution if needed + if (Model.isRelaxed()): + print (" // Phase 4: set relaxed execution", file=model_file) + print (" model->relaxComputationFloat32toFloat16(true);", file=model_file) + + # Boilerplate + print (" assert(model->isValid());", file=model_file); + print ("}", file=model_file) + print (IgnoredOutput.gen_ignored(), file=model_file) + + with smart_open(example) as example_file: + Example.dump(example_file) diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/conv_1_h3_w2_SAME.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/conv_1_h3_w2_SAME.mod.py new file mode 100644 index 000000000..8e93749e2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/conv_1_h3_w2_SAME.mod.py @@ -0,0 +1,11 @@ +i4 = Int32Scalar("b4", 1) +i5 = Int32Scalar("b5", 1) +i6 = Int32Scalar("b6", 1) +i7 = Int32Scalar("b7", 0) +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # input 0 +i3 = Output("op3", "TENSOR_FLOAT32", "{1, 8, 8, 1}") # output 0 +i0 = Parameter("op0", "TENSOR_FLOAT32", "{1, 3, 2, 3}", [-0.966213, -0.467474, -0.82203, -0.579455, 0.0278809, -0.79946, -0.684259, 0.563238, 0.37289, 0.738216, 0.386045, -0.917775, 0.184325, -0.270568, 0.82236, 0.0973683, -0.941308, -0.144706]) # parameters +i1 = Parameter("op1", "TENSOR_FLOAT32", "{1}", [0]) # parameters +model = Model() +model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) + diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/stdout.txt.expect new file mode 100644 index 000000000..47d92b6b8 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_conv/stdout.txt.expect @@ -0,0 +1,41 @@ +// 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/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/depthwise_conv.bin.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/depthwise_conv.bin.mod.py new file mode 100644 index 000000000..8738ee01b --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/depthwise_conv.bin.mod.py @@ -0,0 +1,11 @@ +model = Model() +i4 = Int32Scalar("b4", 1) +i5 = Int32Scalar("b5", 1) +i6 = Int32Scalar("b6", 1) +i7 = Int32Scalar("b7", 1) +i8 = Int32Scalar("b8", 0) +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # input 0 +i3 = Output("op3", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # output 0 +i0 = Parameter("op0", "TENSOR_FLOAT32", "{1, 1, 1, 3}", [-0.966213, -0.467474, -0.82203]) # parameters +i1 = Parameter("op1", "TENSOR_FLOAT32", "{3}", [0, 0, 0]) # parameters +model = model.DepthWiseConv(i2, i0, i1, i4, i5, i6, i7, i8).To(i3) diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/stdout.txt.expect new file mode 100644 index 000000000..9a22cc3e3 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_depthwise_conv/stdout.txt.expect @@ -0,0 +1,43 @@ +// Generated file (from: depthwise_conv.bin.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/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/explicit_add.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/explicit_add.mod.py new file mode 100644 index 000000000..b1c8f99a4 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/explicit_add.mod.py @@ -0,0 +1,7 @@ +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # input 0 +i2 = Output("op2", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # output 0 +i0 = Internal("op0", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # intermediate result +model = Model() +model = model.RawAdd(i1, i1).To(i0) +model = model.RawAdd(i0, i1).To(i2) + diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/stdout.txt.expect new file mode 100644 index 000000000..1221b7bda --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_explicit/stdout.txt.expect @@ -0,0 +1,21 @@ +// Generated file (from: explicit_add.mod.py). Do not edit +void CreateModel(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 8, 8, 3}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type0); + auto op0 = model->addOperand(&type0); + // Phase 2, operations + model->addOperation(ANEURALNETWORKS_ADD, {op1, op1}, {op0}); + model->addOperation(ANEURALNETWORKS_ADD, {op0, 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/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/addfloat.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/addfloat.mod.py new file mode 100644 index 000000000..f0e4f0430 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/addfloat.mod.py @@ -0,0 +1,8 @@ +# model +i1 = Input("operand1","TENSOR_FLOAT32", "{3,4}") +i2 = Input("operand2","TENSOR_FLOAT32", "{3,4}") +i3 = Input("operand3","TENSOR_FLOAT32", "{3,4}") +o = Output("operand4","TENSOR_FLOAT32", "{3,4}") + +Model().Add(i1, i2).Add(i3).Out(o) + diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/stdout.txt.expect new file mode 100644 index 000000000..eb8cc146b --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_float/stdout.txt.expect @@ -0,0 +1,23 @@ +// Generated file (from: addfloat.mod.py). Do not edit +void CreateModel(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {3,4}); + // Phase 1, operands + auto operand1 = model->addOperand(&type0); + auto operand2 = model->addOperand(&type0); + auto operand3 = model->addOperand(&type0); + auto operand4 = model->addOperand(&type0); + auto tmp4 = model->addOperand(&type0); + // Phase 2, operations + model->addOperation(ANEURALNETWORKS_ADD, {operand1, operand2}, {tmp4}); + model->addOperation(ANEURALNETWORKS_ADD, {operand3, tmp4}, {operand4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {operand1, operand2, operand3}, + {operand4}); + assert(model->isValid()); +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/addfloat.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/addfloat.mod.py new file mode 100644 index 000000000..dbe7701a1 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/addfloat.mod.py @@ -0,0 +1,22 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2}") # a vector of 2 float32s +i2 = Input("op2", "TENSOR_FLOAT32", "{2}") # another vector of 2 float32s +b0 = Int32Scalar("b0", 0) # an int32_t scalar bias +i3 = Output("op3", "TENSOR_FLOAT32", "{2}") +model = model.Operation("ADD", i1, i2, b0).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0], + i2: # input 1 + [3.0, 4.0]} + +output0 = {i3: # output 0 + [4.0, 6.0]} + +# Instantiate an example +Example((input0, output0)) + + + diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/stdout.txt.expect new file mode 100644 index 000000000..e3d2af3fa --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_full/stdout.txt.expect @@ -0,0 +1,46 @@ +// Generated file (from: addfloat.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 b0 = model->addOperand(&type1); + auto op3 = model->addOperand(&type0); + // Phase 2, operations + static int32_t b0_init[] = {0}; + model->setOperandValue(b0, b0_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, b0}, {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(); +} +// Generated file (from: addfloat.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/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/lstm.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/lstm.mod.py new file mode 100644 index 000000000..cb1bf6010 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/lstm.mod.py @@ -0,0 +1,161 @@ +# +# 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. +# + +# LSTM Test: No Cifg, No Peephole, No Projection, and No Clipping. + +model = Model() + +n_batch = 1 +n_input = 2 +# n_cell and n_output have the same size when there is no projection. +n_cell = 4 +n_output = 4 + +input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input)) + +input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) + +recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) + +cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}") +cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{0}") +cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{0}") + +input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) + +projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}") +projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}") + +output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) +cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) + +activation_param = Input("activation_param", "TENSOR_INT32", "{1}") +cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}") +proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}") + +scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4))) +output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) +cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) + +model = model.Operation("LSTM", + input, + + input_to_input_weights, + input_to_forget_weights, + input_to_cell_weights, + input_to_output_weights, + + recurrent_to_input_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 +).To([scratch_buffer, output_state_out, cell_state_out, output]) + +# Example 1. Input in operand 0, +input0 = {input_to_input_weights: [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524], + input_to_forget_weights: [0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212], + input_to_cell_weights: [-0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778], + input_to_output_weights: [-0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578], + + input_gate_bias: [0.,0.,0.,0.], + forget_gate_bias: [1.,1.,1.,1.], + cell_gate_bias: [0.,0.,0.,0.], + output_gate_bias: [0.,0.,0.,0.], + + recurrent_to_input_weights: [ + -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, + -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, + -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296], + + recurrent_to_cell_weights: [ + -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, + -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, + -0.46367589, 0.26016325, -0.03894562, -0.16368064], + + recurrent_to_forget_weights: [ + -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, + -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, + 0.28053468, 0.01560611, -0.20127171, -0.01140004], + + recurrent_to_output_weights: [ + 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, + 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, + -0.51818722, -0.15390486, 0.0468148, 0.39922136], + + cell_to_input_weights: [], + cell_to_forget_weights: [], + cell_to_output_weights: [], + + projection_weights: [], + projection_bias: [], + + activation_param: [4], # Tanh + cell_clip_param: [0.], + proj_clip_param: [0.], +} + +# Instantiate examples +# TODO: Add more examples after fixing the reference issue +test_inputs = [ + [2., 3.], +# [3., 4.],[1., 1.] +] +golden_outputs = [ + [-0.02973187, 0.1229473, 0.20885126, -0.15358765,], +# [-0.03716109, 0.12507336, 0.41193449, -0.20860538], +# [-0.15053082, 0.09120187, 0.24278517, -0.12222792] +] + +for (input_tensor, output_tensor) in zip(test_inputs, golden_outputs): + output0 = { + scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], + cell_state_out: [ 0 for x in range(n_batch * n_cell) ], + output_state_out: [ 0 for x in range(n_batch * n_output) ], + output: output_tensor + } + input0[input] = input_tensor + input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ] + input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ] + Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/stdout.txt.expect new file mode 100644 index 000000000..2ba320d77 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/stdout.txt.expect @@ -0,0 +1,75 @@ +// Generated file (from: lstm.mod.py). Do not edit +void CreateModel(Model *model) { + 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 type8(Type::TENSOR_FLOAT32, {1}); + OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); + OperandType type4(Type::TENSOR_FLOAT32, {4}); + OperandType type7(Type::TENSOR_INT32, {1}); + // 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 + 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, activation_param, cell_clip_param, proj_clip_param}, + {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(); +} +// 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}}, {21, {0.0f}}, {22, {0.0f}}}, + // int -> INT32 map + {{20, {4}}}, + // 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, {-0.02973187f, 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/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/averpoolfloat.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/averpoolfloat.mod.py new file mode 100644 index 000000000..17d6e0a4f --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/averpoolfloat.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "0.0f, 127.5f, {1, 2, 2, 1}") +cons1 = Int32Scalar("cons1", 1) +act = Int32Scalar("act", 0) +o = Output("op3", "TENSOR_QUANT8_ASYMM", "0.0f, 127.5f, {1, 2, 2, 1}") +model = model.Operation("AVERAGE_POOL", i1, cons1, cons1, cons1, cons1, cons1, act).To(o) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4]} + +output0 = {o: # output 0 + [1, 2, 3, 4]} + +# Instantiate an example +Example((input0, output0)) + + + diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/stdout.txt.expect new file mode 100644 index 000000000..b4632d34d --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_avgpool/stdout.txt.expect @@ -0,0 +1,48 @@ +// Generated file (from: averpoolfloat.mod.py). Do not edit +void CreateModel(Model *model) { + OperandType type1(Type::INT32, {}); + OperandType type0(Type::TENSOR_QUANT8_ASYMM, 0.0f, 127.5f, {1, 2, 2, 1}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto cons1 = 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 act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_AVERAGE_POOL, {op1, cons1, 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(); +} +// Generated file (from: averpoolfloat.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/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/quantized.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/quantized.mod.py new file mode 100644 index 000000000..7ef623513 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/quantized.mod.py @@ -0,0 +1,11 @@ +i4 = Int32Scalar("b4", 2) +i5 = Int32Scalar("b5", 2) +i6 = Int32Scalar("b6", 2) +i7 = Int32Scalar("b7", 0) +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}") # input 0 +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{1, 1, 1, 1}") # output 0 +i0 = Parameter("op0", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}", [1, 1, 1, 1]) # parameters +i1 = Parameter("op1", "TENSOR_INT32", "{1}", [0]) # parameters +model = Model() +model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) + diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/stdout.txt.expect new file mode 100644 index 000000000..6b28bdd54 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_quantized_conv/stdout.txt.expect @@ -0,0 +1,40 @@ +// Generated file (from: quantized.mod.py). Do not edit +void CreateModel(Model *model) { + OperandType type0(Type::INT32, {}); + OperandType type3(Type::TENSOR_INT32, {1}); + OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}); + OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 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(&type1); + auto op1 = model->addOperand(&type3); + // Phase 2, operations + static int32_t b4_init[] = {2}; + model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1); + static int32_t b5_init[] = {2}; + model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1); + static int32_t b6_init[] = {2}; + 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 uint8_t op0_init[] = {1, 1, 1, 1}; + model->setOperandValue(op0, op0_init, sizeof(uint8_t) * 4); + static int32_t op1_init[] = {0}; + model->setOperandValue(op1, op1_init, sizeof(int32_t) * 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/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/stdout.txt.expect new file mode 100644 index 000000000..14cd4f99d --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/stdout.txt.expect @@ -0,0 +1,93 @@ +// Generated code. Do not edit +// Create the model +Model createTestModel() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 16}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 1}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::ADD, + .inputs = {0, 2, 1}, + .outputs = {3}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {3}; + std::vector<uint8_t> operandValues = { + 0, 0, 0, 0, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} +// Generated file (from: vts_full.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, {6.0f, 8.0f, 10.0f, 12.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/vts_full.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/vts_full.mod.py new file mode 100644 index 000000000..4ad3b2e4b --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_full/vts_full.mod.py @@ -0,0 +1,19 @@ +# Force VTS mode +Configuration.vts = True +# model +model = Model() +i0 = Input("operand0","TENSOR_FLOAT32", "{1, 2, 2, 1}") +b0 = Int32Scalar("b0", 0) +p0 = Parameter("p0", "TENSOR_FLOAT32", "{1, 2, 2, 1}", [5.0, 6.0, 7.0, 8.0]) +o = Output("out","TENSOR_FLOAT32", "{1, 2, 2, 1}") + +model.Operation("ADD", i0, p0, b0).To(o) + +input0 = {i0: # input 0 + [1.0, 2.0, 3.0, 4.0]} + +output0 = {o: # output 0 + [6.0, 8.0, 10.0, 12.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/addfloat.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/addfloat.mod.py new file mode 100644 index 000000000..976cb35ec --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/addfloat.mod.py @@ -0,0 +1,12 @@ +# Force VTS mode +Configuration.vts = True +# model +i1 = Input("operand1","TENSOR_FLOAT32", "{3,4}") +i2 = Input("operand2","TENSOR_FLOAT32", "{3,4}") +i3 = Input("operand3","TENSOR_FLOAT32", "{3,4}") +Parameter("p1", "TENSOR_QUANT8_ASYMM", "{1, 2, 3}", [1, 2, 3, 4, 5, 6]) +Parameter("p2", "TENSOR_FLOAT32", "{}", [42.0]) +o = Output("operand4","TENSOR_FLOAT32", "{3,4}") + +Model().Add(i1, i2).Add(i3).Out(o) + diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/stdout.txt.expect new file mode 100644 index 000000000..2e74d1fc0 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_vts_operands/stdout.txt.expect @@ -0,0 +1,103 @@ +// Generated code. Do not edit +// Create the model +Model createTestModel() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {3,4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {3,4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {3,4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 3}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 6}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 6, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {3,4}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {3,4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::TEMPORARY_VARIABLE, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::ADD, + .inputs = {0, 1}, + .outputs = {6}, + }, + { + .type = OperationType::ADD, + .inputs = {2, 6}, + .outputs = {5}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1, 2}; + const std::vector<uint32_t> outputIndexes = {5}; + std::vector<uint8_t> operandValues = { + 1, 2, 3, 4, 5, 6, 0, 0, 40, 66 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/stderr.txt.expect new file mode 100644 index 000000000..c5a6e36b9 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/stderr.txt.expect @@ -0,0 +1,2 @@ +Output CTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/stdout.txt.expect new file mode 100644 index 000000000..fa67d68ac --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/stdout.txt.expect @@ -0,0 +1,51 @@ +// Generated file (from: weird_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 b0 = model->addOperand(&type1); + auto tmp = model->addOperand(&type0); + auto tmp2 = model->addOperand(&type0); + auto op3 = model->addOperand(&type0); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static int32_t b0_init[] = {0}; + model->setOperandValue(b0, b0_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, b0}, {tmp}); + model->addOperation(ANEURALNETWORKS_ADD, {tmp, op2, b0}, {tmp2}); + model->addOperation(ANEURALNETWORKS_ADD, {tmp2, op4, b0}, {op3}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2, op4}, + {op3}); + assert(model->isValid()); +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} +// Generated file (from: weird_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}}, {2, {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, {9.0f, 12.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/weird_add.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/weird_add.mod.py new file mode 100644 index 000000000..a230267a4 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_weird/weird_add.mod.py @@ -0,0 +1,29 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2}") # a vector of 2 float32s +i2 = Input("op2", "TENSOR_FLOAT32", "{2}") # another vector of 2 float32s +b0 = Int32Scalar("b0", 0) # an int32_t scalar bias +tmp = Internal("tmp", "TENSOR_FLOAT32", "{2}") +tmp2 = Internal("tmp2", "TENSOR_FLOAT32", "{2}") +o3 = Output("op3", "TENSOR_FLOAT32", "{2}") +i4 = Input("op4", "TENSOR_FLOAT32", "{2}") # another vector of 2 float32s +model = model.Operation("ADD", i1, i2, b0).To(tmp) +model = model.Operation("ADD", tmp, i2, b0).To(tmp2) +model = model.Operation("ADD", tmp2, i4, b0).To(o3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0], + i2: # input 1 + [3.0, 4.0], + i4: # input 4 + [5.0, 6.0]} + +output0 = {o3: # output 0 + [9.0, 12.0]} + +# Instantiate an example +Example((input0, output0)) + + + diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/test.py b/tests/nnapi/nnapi_test_generator/android-p/tests/test.py new file mode 100644 index 000000000..c987cf680 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/test.py @@ -0,0 +1,328 @@ +#!/usr/bin/python3 + +# Copyright 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. + + +"""NN Model Test Compiler Test. + +Runs subdirectories of tests for the test generator/compiler. +""" + +import filecmp +import glob +import os +import re +import shutil +import subprocess +import sys +import unittest + + +__author__ = 'Android' + + +DOTTED_LINE = '................' + +class OrigFile: + OrigDir = None + +class TestGeneratorTests(unittest.TestCase): + """Class to contain all the unittest test cases. + + Tests will be dynamically added to this class as methods. + No static tests, so this class is initially empty. + + """ + pass + + +def GenerateTests(dir_name): + """Creates a test method that can be added as method to GenerateTests.""" + cwd = os.getcwd() + def Test(self): + os.chdir(cwd) + ExecTest(dir_name, self) + return Test + + +def AddUnitTests(test_dirs): + """Adds a test to Tests for each directory in test_dirs.""" + + for t in test_dirs: + # Must start with 'test_' according to unittest + test_name = 'test_%s' % t + test = GenerateTests(t) + # Add test as method to TestGeneratorTests with test_name as method name + setattr(TestGeneratorTests, test_name, test) + + +class Options(object): + verbose = 0 + cleanup = 1 + update_cts = 0 + zero_return = 0 + + +def CompareFiles(actual, expect): + """Compares actual and expect for equality.""" + if not os.path.isfile(actual): + if Options.verbose: + print ('Could not find %s' % actual) + return False + if not os.path.isfile(expect): + if Options.verbose: + print ('Could not find %s' % expect) + return False + + return filecmp.cmp(actual, expect, False) + + +def CopyIfDifferent(src, dst): + """Updates dst if it is different from src.""" + if not CompareFiles(src, dst): + if Options.verbose: + print ('Copying from %s to %s' % (src, dst)) + shutil.copyfile(src, dst) + + +def GetCommandLineArgs(filename): + """Extracts command line arguments from first comment line in a file.""" + f = open(filename, 'r') + line = f.readline() + f.close() + if line[0] == '/' and line[1] == '/': + return line[2:].strip() + else: + return '' + + +def ReadFileToStr(filename): + """Returns contents of file as a str.""" + with open(filename, 'r') as f: + return f.read() + + +def ReportIfDifferFromExpected(tests, name, file1, file2): + """Fails tests if file1 and file2 differ.""" + if not CompareFiles(file1, file2): + if Options.verbose: + err_message = ('%s is different:\n' + 'expected:\n%s\n%s%s\n\n' + 'actual:\n%s\n%s%s\n') % ( + name, + DOTTED_LINE, ReadFileToStr(file1), DOTTED_LINE, + DOTTED_LINE, ReadFileToStr(file2), DOTTED_LINE) + else: + err_message = '%s is different' % name + tests.fail(err_message) + + +def GetRSFiles(): + """Returns a list of files in cwd with extension '.rs' or '.fs'.""" + rs_files = glob.glob('*.mod.py') + rs_files.sort() + return rs_files + + +def GetOutDir(): + return os.path.abspath(os.path.join(OrigFile.OrigDir, "../")) + + +# Declare/define cache variable for GetOutDir to cache results +# This way we only need to call subprocesses once to get the directory +GetOutDir.cache = None + + +def CreateCmd(run_vts): + """Creates the test command to run for the current test.""" + cmd_string = ('%s/%s_generator.py' + ) % (GetOutDir(), "test" if not run_vts else "vts") + base_args = cmd_string.split() + rs_files = GetRSFiles() + + # Extra command line arguments can be placed as // comments at the start of + # any .rs file. We automatically bundle up all of these extra args and invoke + # llvm-rs-cc with them. + extra_args_str = '' + for rs_file in rs_files: + extra_args_str += GetCommandLineArgs(rs_file) + extra_args = extra_args_str.split() + + args = base_args + extra_args + rs_files + return args + +def Cleanup(): + """Cleans up the cwd of any tmp files created in current test.""" + try: + os.remove('stdout.txt') + os.remove('stderr.txt') + shutil.rmtree('tmp/') + except OSError: + pass + + +def CheckTestResult(dir_name, subprocess_ret, tests, args): + """Checks the result of the subprocess command to see if it passed/failed. + + If dir_name starts with 'F_', then subprocess is expected to fail. + If it instead succeeded, then this test is failed. + Vice versa with a dir_name starting with 'P_'. + + Args: + dir_name: name of current directory/test name + subprocess_ret: return code of subprocess + tests: unittest, call tests.fail(reason) when failure + args: the arguments for the command that was run + """ + if dir_name[0:2] == 'F_': + if subprocess_ret == 0: + if Options.verbose: + err_message = ('Command (%s) passed on invalid input\n' + 'stdout:\n%s\n%s%s\n') % ( + ' '.join(args), + DOTTED_LINE, ReadFileToStr('stdout.txt'), DOTTED_LINE + ) + else: + err_message = 'Command passed on invalid input' + tests.fail(err_message) + elif dir_name[0:2] == 'P_': + if subprocess_ret != 0: + if Options.verbose: + err_message = ('Command (%s) failed on valid input\n' + 'stderr:\n%s\n%s%s\n') % ( + ' '.join(args), + DOTTED_LINE, ReadFileToStr('stderr.txt'), DOTTED_LINE + ) + else: + err_message = 'Command failed on valid input' + tests.fail(err_message) + else: + tests.fail('Invalid test name: ' + dir_name + + ', should start with F_ or P_') + + + +def ExecTest(dir_name, tests): + """Executes an test generator test from dir_name.""" + + os.chdir(dir_name) + stdout_file = open('stdout.txt', 'w+') + stderr_file = open('stderr.txt', 'w+') + run_vts = (dir_name[2:5] == 'vts') + args = CreateCmd(run_vts) + + if Options.verbose > 1: + print ('Executing:', ' '.join(args)) + + # Execute the command and check the resulting shell return value. + # All tests that are expected to FAIL have directory names that + # start with 'F_'. Other tests that are expected to PASS have + # directory names that start with 'P_'. + ret = 0 + try: + ret = subprocess.call(args, stdout=stdout_file, stderr=stderr_file) + except OSError: + tests.fail('subprocess.call failed: ' + ' '.join(args)) + + stdout_file.close() + stderr_file.close() + + CheckTestResult(dir_name, ret, tests, args) + + ReportIfDifferFromExpected(tests, 'stdout', 'stdout.txt.expect', 'stdout.txt') + ReportIfDifferFromExpected(tests, 'stderr', 'stderr.txt.expect', 'stderr.txt') + + if Options.cleanup: + Cleanup() + + +def Usage(): + """Print out usage information.""" + print ('Usage: %s [OPTION]... [TESTNAME]...' + 'Renderscript Compiler Test Harness\n' + 'Runs TESTNAMEs (all tests by default)\n' + 'Available Options:\n' + ' -h, --help Help message\n' + ' -n, --no-cleanup Don\'t clean up after running tests\n' + ' -v, --verbose Verbose output. Enter multiple -v to get more verbose.\n' + ' -z, --zero-return Return 0 as exit code no matter if tests fail. Required for TreeHugger.\n' + ) % (sys.argv[0]), + return + + +def main(): + """Runs the unittest suite. + + Parses command line arguments, adds test directories as tests. + + Returns: + 0 if '-z' flag is set. + Else unittest.main() returns with its own error code. + """ + + OrigFile.OrigDir = os.path.dirname(os.path.abspath(__file__)) + # Chdir to the directory this file is in since tests are in this directory + os.chdir(OrigFile.OrigDir) + files = [] + for arg in sys.argv[1:]: + if arg in ('-h', '--help'): + Usage() + return 0 + elif arg in ('-n', '--no-cleanup'): + Options.cleanup = 0 + elif arg in ('-u', '--update-cts'): + Options.update_cts = 1 + elif arg in ('-v', '--verbose'): + Options.verbose += 1 + elif arg in ('-z', '--zero-return'): + Options.zero_return = 1 + else: + # Test list to run + if os.path.isdir(arg): + files.append(arg) + else: + print >> sys.stderr, 'Invalid test or option: %s' % arg + return 1 + + if not files: + file_names = os.listdir('.') + # Test names must start with 'F_' or 'P_' + # 'F_' tests are expected to fail + # 'P_' tests are expected to pass + for f in file_names: + if os.path.isdir(f) and (f[0:2] == 'F_' or f[0:2] == 'P_'): + files.append(f) + files.sort() + + AddUnitTests(files) + + # verbosity=2 is necessary for PythonUnitTestRunner to parse the results + # Otherwise verbosity does not matter + # If Options.zero_return is set, do not let unittest.main() exit + # This is necessary in TreeHugger to distinguish between failing tests and + # failing to execute the python script + # If Options.zero_return is not set, let unittest.main() exit + # In this case it will return a non-zero code if any tests fail + unittest_exit = Options.zero_return == 0 + unittest.main(verbosity=2, + argv=[sys.argv[0]] + ['TestGeneratorTests'], + exit=unittest_exit) + + return 0 + + +if __name__ == '__main__': + sys.exit(main()) + diff --git a/tests/nnapi/nnapi_test_generator/android-p/vts_generator.py b/tests/nnapi/nnapi_test_generator/android-p/vts_generator.py new file mode 100644 index 000000000..ab34e2bda --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/vts_generator.py @@ -0,0 +1,247 @@ +#!/usr/bin/python3 + +# Copyright 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. +"""VTS testcase generator + +Implements VTS test backend. Shares most logic with the CTS test +generator. Invoked by ml/nn/runtime/test/specs/generate_vts_tests.sh; +See that script for details on how this script is used. + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import argparse +from functools import reduce +import math +import os +import struct +import sys +import contextlib +import test_generator +import pprint +# Stuff from test generator +from test_generator import Configuration +from test_generator import Example +from test_generator import Float32Scalar +from test_generator import IgnoredOutput +from test_generator import Input +from test_generator import Int32Scalar +from test_generator import Internal +from test_generator import Model +from test_generator import Operand +from test_generator import Output +from test_generator import Parameter +from test_generator import smart_open + +# Take a model from command line +def import_source(): + parser = argparse.ArgumentParser() + parser.add_argument("spec", help="the spec file") + parser.add_argument( + "-m", "--model", help="the output model file", default="-") + parser.add_argument( + "-e", "--example", help="the output example file", default="-") + args = parser.parse_args() + + if os.path.exists(args.spec): + test_generator.FileNames.SpecFile = os.path.basename(args.spec) + exec (open(args.spec).read()) + + return (args.model, args.example) + +# Generate operands in VTS format +def generate_vts_operands(): + # Dump operand definitions + op_def = """\ + {{ + .type = OperandType::{operand_type}, + .dimensions = {shape}, + .numberOfConsumers = {no_consumers}, + .scale = {scale}, + .zeroPoint = {zero_point}, + .lifetime = OperandLifeTime::{lifetime}, + .location = {{.poolIndex = 0, .offset = {offset}, .length = {length}}}, + }}""" + offset = 0 + op_definitions = [] + for o in Operand.operands.objects(): + ty = o.type + no_consumers = len(o.outs) if o.traversable() else 0 + lifetime = o.lifetime() + length = ty.get_size() if o.is_weight() else 0 + real_shape, scale, zero_point = ty.get_parsed_shape() + scale = float(scale) + zero_point = int(zero_point) + op = { + "operand_type": ty.get_element_type(), + "shape": "{%s}" % real_shape, + "no_consumers": no_consumers, + "scale": test_generator.pretty_print_as_float(scale), + "zero_point": str(int(zero_point)), + "lifetime": lifetime, + "offset": offset if o.is_weight() else 0, + "length": length + } + offset += length + op_definitions.append(op_def.format(**op)) + + op_vec = """\ + const std::vector<Operand> operands = {{ +{0} + }};""".format(",\n".join(op_definitions)) + return op_vec + +# Generate VTS operand values +def generate_vts_operand_values(): + weights = [o for o in Operand.operands.objects() if o.is_weight()] + binit = [] + for w in weights: + ty = w.type.get_element_type() + if ty == "TENSOR_QUANT8_ASYMM": + binit += w.initializer + elif ty in {"TENSOR_FLOAT32", "FLOAT32", "TENSOR_INT32", "INT32"}: + fmt = "f" if (ty == "TENSOR_FLOAT32" or ty == "FLOAT32") else "i" + for f in w.initializer: + binit += [int(x) for x in struct.pack(fmt, f)] + else: + assert 0 and "Unsupported VTS operand type" + + init_defs = ", ".join([str(x) for x in binit]) + if (init_defs != ""): + init_defs = "\n %s\n " % init_defs + byte_vec_fmt = """{%s}""" % init_defs + return byte_vec_fmt + +# Generate VTS operations +class VTSOps(object): + vts_ops = [] + def generate_vts_operation(op): + try: + opcode =op.optype + except AttributeError: # not an op, but things like weights + return + op_fmt = """\ + {{ + .type = OperationType::{op_code}, + .inputs = {{{ins}}}, + .outputs = {{{outs}}}, + }}""" + op_content = { + 'op_code': op.optype, + 'op_type': op.type.get_element_type(), + 'ins': ", ".join([str(x.ID()) for x in op.ins]), + 'outs': ", ".join([str(x.ID()) for x in op.outs]), + } + VTSOps.vts_ops.append(op_fmt.format(**op_content)) + return True + +def generate_vts_operations(model_file): + test_generator.TopologicalSort(lambda x: VTSOps.generate_vts_operation(x)) + return ",\n".join(VTSOps.vts_ops) + + +def generate_vts_model(model_file): + operand_values_fmt = "" + if Configuration.useSHM(): + # Boilerplate code for passing weights in shared memory + operand_values_fmt = """\ + std::vector<uint8_t> operandValues = {{}}; + const uint8_t data[] = {operand_values}; + + // Allocate segment of android shared memory, wrapped in hidl_memory. + // This object will be automatically freed when sharedMemory is destroyed. + hidl_memory sharedMemory = allocateSharedMemory(sizeof(data)); + + // Mmap ashmem into usable address and hold it within the mappedMemory object. + // MappedMemory will automatically munmap the memory when it is destroyed. + sp<IMemory> mappedMemory = mapMemory(sharedMemory); + + if (mappedMemory != nullptr) {{ + // Retrieve the mmapped pointer. + uint8_t* mappedPointer = + static_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer())); + + if (mappedPointer != nullptr) {{ + // Acquire the write lock for the shared memory segment, upload the data, + // and release the lock. + mappedMemory->update(); + std::copy(data, data + sizeof(data), mappedPointer); + mappedMemory->commit(); + }} + }} + + const std::vector<hidl_memory> pools = {{sharedMemory}}; +""" + else: + # Passing weights via operandValues + operand_values_fmt = """\ + std::vector<uint8_t> operandValues = {operand_values}; + const std::vector<hidl_memory> pools = {{}}; +""" + + operand_values_val = { + 'operand_values': generate_vts_operand_values() + } + operand_values = operand_values_fmt.format(**operand_values_val) + # operand_values = operand_values_fmt + model_fmt = """\ +// Generated code. Do not edit +// Create the model +Model createTestModel() {{ +{operand_decls} + + const std::vector<Operation> operations = {{ +{operations} + }}; + + const std::vector<uint32_t> inputIndexes = {{{input_indices}}}; + const std::vector<uint32_t> outputIndexes = {{{output_indices}}}; +{operand_values} + return {{ + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools,{relaxed_field} + }}; +}} +""" + model = { + "operations": generate_vts_operations(sys.stdout), + "operand_decls": generate_vts_operands(), + "operand_values": operand_values, + "output_indices": ", ".join([str(i.ID()) for i in Output.get_outputs()]), + "input_indices": ", ".join([str(i.ID()) for i in Input.get_inputs(True)]), + "relaxed_field": + "\n .relaxComputationFloat32toFloat16 = true," if (Model.isRelaxed()) else "" + } + print(model_fmt.format(**model), file = model_file) + +def generate_vts(model_file): + generate_vts_model(model_file) + print (IgnoredOutput.gen_ignored(), file=model_file) + +if __name__ == "__main__": + (model, example) = import_source() + print("Output VTS model: %s" % model, file=sys.stderr) + print("Output example:" + example, file=sys.stderr) + + with smart_open(model) as model_file: + generate_vts(model_file) + with smart_open(example) as example_file: + Example.dump(example_file) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/README.md b/tests/nnapi/nnapi_test_generator/android-q-beta/README.md new file mode 100644 index 000000000..62a6978cd --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/README.md @@ -0,0 +1,408 @@ +# NN-API Test Generator + +Original code is at https://android.googlesource.com/platform/frameworks/ml/+/ee61649/nn/tools/test_generator/ + +### Fix for neurun + +- Update path in this README.md file for neurun NNAPI frontend test + - `nn/runtime/test/specs/` => `tests/nnapi/specs/` + - $ANDROID_BUILD_TOP/frameworks/ml/nn/runtime/test/specs => $NNAS_PROJECT_PATH/tests/nnapi/specs + - Rebuild with mm afterwards => Rebuild afterwards (mm is not supported) + +--- + +# Using the NN-API Test Generator + +## Prerequisites + +- Python3 +- Numpy + +## Writing a Test Specification + +You should create new test specs in `tests/nnapi/specs/<version>/` and name it with `.mod.py` suffix, so that other tools can automatically update the unit tests. + +### Specifying Operands + +#### Syntax + +``` +OperandType(name, (type, shape, <optional scale, zero point>), <optional initializer>) +``` + +For example, + +```Python +# p1 is a 2-by-2 fp matrix parameter, with value [1, 2; 3, 4] +p1 = Parameter("param", ("TENSOR_FLOAT32", [2, 2]), [1, 2, 3, 4]) + +# i1 is a quantized input of shape (2, 256, 256, 3), with scale = 0.5, zero point = 128 +i1 = Input("input", ("TENSOR_QUANT8_ASYMM", [2, 256, 256, 3], 0.5, 128)) + +# p2 is an Int32 scalar with value 1 +p2 = Int32Scalar("act", 1) +``` + +#### OperandType + +There are currently 10 operand types supported by the test generator. + +- Input +- Output + * IgnoredOutput, will not compare results in the test +- Parameter + * Int32Scalar, shorthand for parameter with type INT32 + * Float32Scalar, shorthand for parameter with type FLOAT32 + * Int32Vector, shorthand for 1-D TENSOR_INT32 parameter + * Float32Vector, shorthand for 1-D TENSOR_FLOAT32 parameter +- Internal, for model with multiple operations + +### Specifying Models + +#### Instantiate a model + +```Python +# Instantiate a model +model = Model() + +# Instantiate a model with a name +model2 = Model("model_name") +``` + +#### Add an operation + +``` +model.Operation(optype, i1, i2, ...).To(o1, o2, ...) +``` + +For example, + +```Python +model.Operation("ADD", i1, i2, act).To(o1) +``` + +#### Use implicit operands + +Simple scalar and 1-D vector parameters can now be directly passed to Operation constructor, and test generator will deduce the operand type from the value provided. + +```Python +model.Operation("MEAN", i1, [1], 0) # axis = [1], keep_dims = 0 +``` + +Note that, for fp values, the initializer should all be Python fp numbers, e.g. use `1.0` or `1.` instead of `1` for implicit fp operands. + +### Specifying Inputs and Expected Outputs + +The combination of inputs and expected outputs is called an example for a given model. An example is defined like + +```Python +# Example 1, separate dictionary for inputs and outputs +input1 = { + i1: [1, 2], + i2: [3, 4] +} +output1 = {o1: [4, 6]} + +# Example 2, combined dictionary +example2_values = { + i1: [5, 6], + i2: [7, 8], + o1: [12, 14] +} + +# Instantiate an example +Example((input1, output1), example2_values) +``` + +By default, examples will be attached to the most recent instantiated model. You can explicitly specify the target model, and optionally, the example name by + +```Python +Example((input1, output1), example2_values, model=model, name="example_name") +``` + +### Specifying Variations + +You can add variations to the example so that the test generator can automatically create multiple tests. Currently, 6 types of variation are supported: + +- DefaultVariation, i.e. no variation +- DataTypeConverter +- DataLayoutConverter +- AxisConverter +- RelaxedModeConverter +- ParameterAsInputConverter +- ActivationConverter + +#### DataTypeConverter + +Convert input/parameter/output to the specified type, e.g. float32 -> quant8. The target data type for each operand to transform has to be explicitly specified. It is the spec writer's responsibility to ensure such conversion is valid. + +```Python +converter = DataTypeConverter(name="variation_name").Identify({ + op1: (target_type, target_scale, target_zero_point), + op2: (target_type, target_scale, target_zero_point), + ... +}) +``` + +#### DataLayoutConverter + +Convert input/parameter/output between NHWC and NCHW. The caller need to provide a list of target operands to transform, and also the data layout parameter to set. + +```Python +converter = DataLayoutConverter(target_data_layout, name="variation_name").Identify( + [op1, op2, ..., layout_parameter] +) +``` + +#### AxisConverter + +Transpose a certain axis in input/output to target position, and optionally remove some axis. The caller need to provide a list of target operands to transform, and also the axis parameter to set. + +```Python +converter = AxisConverter(originalAxis, targetAxis, dimension, drop=[], name="variation_name").Identify( + [op1, op2, ..., axis_parameter] +) +``` + +This model variation is for ops that apply calculation along certain axis, such as L2_NORMALIZATION, SOFTMAX, and CHANNEL_SHUFFLE. For example, consider L2_NORMALIZATION with input of shape [2, 3, 4, 5] along the last axis, i.e. axis = -1. The output shape would be the same as input. We can create a new model which will do the calculation along axis 0 by transposing input and output shape to [5, 2, 3, 4] and modify the axis parameter to 0. Such converter can be defined as + +```Python +toAxis0 = AxisConverter(-1, 0, 4).Identify([input, output, axis]) +``` + +The target axis can also be negative to test the negative indexing + +```Python +toAxis0 = AxisConverter(-1, -4, 4).Identify([input, output, axis]) +``` + +Consider the same L2_NORMALIZATION example, we can also create a new model with input/output of 2D shape [4, 5] by removing the first two dimension. This is essentially doing `new_input = input[0,0,:,:]` in numpy. Such converter can be defined as + +```Python +toDim2 = AxisConverter(-1, -1, 4, drop=[0, 1]).Identify([input, output, axis]) +``` + +If transposition and removal are specified at the same time, the converter will do transposition first and then remove the axis. For example, the following converter will result in shape [5, 4] and axis 0. + +```Python +toDim2Axis0 = AxisConverter(-1, 2, 4, drop=[0, 1]).Identify([input, output, axis]) +``` + +#### RelaxedModeConverter + +Convert the model to enable/disable relaxed computation. + +```Python +converter = RelaxedModeConverter(is_relaxed, name="variation_name") +``` + +#### ParameterAsInputConverter + +Convert a certain parameter to model input, e.g. weight in CONV_2D. The caller need to provide a list of target operands to convert. + +```Python +converter = ParameterAsInputConverter(name="variation_name").Identify( + [op1, op2, ...] +) +``` + +#### ActivationConverter + +Convert the output by certain activation, the original activation is assumed to be NONE. The caller need to provide a list of target operands to transform, and also the activation parameter to set. + +```Python +converter = ActivationConverter(name="variation_name").Identify( + [op1, op2, ..., act_parameter] +) +``` + +#### Add variation to example + +Each example can have multiple groups of variations, and if so, will take the cartesian product of the groups. For example, suppose we declare a model with two groups, and each group has two variations: `[[default, nchw], [default, relaxed, quant8]]`. This will result in 6 examples: `[default, default], [default, relaxed], [default, quant8], [nchw, default], [nchw, relaxed], [nchw, quant8]`. + +Use `AddVariations` to add a group of variations to the example + +```Python +# Add two groups of variations [default, nchw] and [default, relaxed, quant8] +example.AddVariations(nchw).AddVariations(relaxed, quant8) +``` + +By default, when you add a group of variation, a unnamed default variation will be automatically included in the list. You can name the default variation by + +```Python +example.AddVariations(nchw, defaultName="nhwc").AddVariations(relaxed, quant8) +``` + +Also, you can choose not to include default by + +```Python +# Add two groups of variations [nchw] and [default, relaxed, quant8] +example.AddVariations(nchw, includeDefault=False).AddVariations(relaxed, quant8) +``` + +The example above will result in 3 examples: `[nchw, default], [nchw, relaxed], [nchw, quant8]`. + +#### Some helper functions + +The test generator provides several helper functions or shorthands to add commonly used group of variations. + +```Python +# Each following group of statements are equivalent + +# DataTypeConverter +example.AddVariations(DataTypeConverter().Identify({op1: "TENSOR_FLOAT16", ...})) +example.AddVariations("float16") # will apply to every TENSOR_FLOAT32 operands + +example.AddVariations(DataTypeConverter().Identify({op1: "TENSOR_INT32", ...})) +example.AddVariations("int32") # will apply to every TENSOR_FLOAT32 operands + +# DataLayoutConverter +example.AddVariations(DataLayoutConverter("nchw").Identify(op_list)) +example.AddVariations(("nchw", op_list)) +example.AddNchw(*op_list) + +# AxisConverter +# original axis and dim are deduced from the op_list +example.AddVariations(*[AxisConverter(origin, t, dim).Identify(op_list) for t in targets]) +example.AddAxis(targets, *op_list) + +example.AddVariations(*[ + AxisConverter(origin, t, dim).Identify(op_list) for t in range(dim) + ], includeDefault=False) +example.AddAllPositiveAxis(*op_list) + +example.AddVariations(*[ + AxisConverter(origin, t, dim).Identify(op_list) for t in range(-dim, dim) + ], includeDefault=False) +example.AddAllAxis(*op_list) + +drop = list(range(dim)) +drop.pop(origin) +example.AddVariations(*[ + AxisConverter(origin, origin, dim, drop[0:(dim-i)]).Identify(op_list) for i in dims]) +example.AddDims(dims, *op_list) + +example.AddVariations(*[ + AxisConverter(origin, origin, dim, drop[0:i]).Identify(op_list) for i in range(dim)]) +example.AddAllDims(dims, *op_list) + +example.AddVariations(*[ + AxisConverter(origin, j, dim, range(i)).Identify(op_list) \ + for i in range(dim) for j in range(i, dim) + ], includeDefault=False) +example.AddAllDimsAndPositiveAxis(dims, *op_list) + +example.AddVariations(*[ + AxisConverter(origin, k, dim, range(i)).Identify(op_list) \ + for i in range(dim) for j in range(i, dim) for k in [j, j - dim] + ], includeDefault=False) +example.AddAllDimsAndAxis(dims, *op_list) + +# ParameterAsInputConverter +example.AddVariations(ParameterAsInputConverter().Identify(op_list)) +example.AddVariations(("as_input", op_list)) +example.AddInput(*op_list) + +# RelaxedModeConverter +example.Addvariations(RelaxedModeConverter(True)) +example.AddVariations("relaxed") +example.AddRelaxed() + +# ActivationConverter +example.AddVariations(ActivationConverter("relu").Identify(op_list)) +example.AddVariations(("relu", op_list)) +example.AddRelu(*op_list) + +example.AddVariations( + ActivationConverter("relu").Identify(op_list), + ActivationConverter("relu1").Identify(op_list), + ActivationConverter("relu6").Identify(op_list)) +example.AddVariations( + ("relu", op_list), + ("relu1", op_list), + ("relu6", op_list)) +example.AddAllActivations(*op_list) +``` + +### Specifying the Model Version + +If not explicitly specified, the minimal required HAL version will be inferred from the path, e.g. the models defined in `tests/nnapi/specs/V1_0/add.mod.py` will all have version `V1_0`. However there are several exceptions that a certain operation is under-tested in previous version and more tests are added in a later version. In this case, two methods are provided to set the version manually. + +#### Set the version when creating the model + +Use `IntroducedIn` to set the version of a model. All variations of the model will have the same version. + +```Python +model_V1_0 = Model().IntroducedIn("V1_0") +... +# All variations of model_V1_0 will have the same version V1_0. +Example(example, model=model_V1_0).AddVariations(var0, var1, ...) +``` + +#### Set the version overrides + +Use `Example.SetVersion` to override the model version for specific tests. The target tests are specified by names. This method can also override the version specified by `IntroducedIn`. + +```Python +Example.SetVersion(<version>, testName0, testName1, ...) +``` + +This is useful when only a subset of variations has a different version. + +### Creating negative tests + +Negative test, also known as validation test, is a testing method that supplies invalid model or request, and expects the target framework or driver to fail gracefully. You can use `ExpectFailure` to tag a example as invalid. + +```Python +Example.ExpectFailure() +``` + +### A Complete Example + +```Python +# Declare input, output, and parameters +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 4, 1}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{1, 3, 3, 1}", [1, 4, 7, 2, 5, 8, 3, 6, 9]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [-200]) +act = Int32Scalar("act", 0) +o1 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 4, 1}") + +# Instantiate a model and add CONV_2D operation +# Use implicit parameter for implicit padding and strides +Model().Operation("CONV_2D", i1, f1, b1, 1, 1, 1, act, layout).To(o1) + +# Additional data type +quant8 = DataTypeConverter().Identify({ + i1: ("TENSOR_QUANT8_ASYMM", 0.5, 127), + f1: ("TENSOR_QUANT8_ASYMM", 0.5, 127), + b1: ("TENSOR_INT32", 0.25, 0), + o1: ("TENSOR_QUANT8_ASYMM", 1.0, 50) +}) + +# Instantiate an example +example = Example({ + i1: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], + o1: [0, 0, 0, 0, 35, 112, 157, 0, 0, 34, 61, 0] +}) + +# Only use NCHW data layout +example.AddNchw(i1, f1, o1, layout, includeDefault=False) + +# Add two more groups of variations +example.AddInput(f1, b1).AddVariations("relaxed", quant8).AddAllActivations(o1, act) +``` + +The spec above will result in 24 tests. + +## Generate Tests + +Once you have your model ready, run + +``` +$NNAS_PROJECT_PATH/tests/nnapi/specs/generate_test.sh +$NNAS_PROJECT_PATH/tests/nnapi/specs/generate_vts_test.sh +``` + +It will read and generate all CTS/VTS unit tests based on spec files in `tests/nnapi/specs/V1_*/*` if needed. CTS test generator is able to identify which spec files are modified since last generation and only regenerate those files to reduce compilation time. To force a regeneration, use `-f` flag. The VTS test generator will regenerate tests targeting the latest HAL version by default. Pass the `all` positional argument to override. + +Rebuild afterwards. diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/cts_generator.py b/tests/nnapi/nnapi_test_generator/android-q-beta/cts_generator.py new file mode 100644 index 000000000..bb65762fc --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/cts_generator.py @@ -0,0 +1,314 @@ +#!/usr/bin/python3 + +# Copyright 2018, 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. + +"""CTS testcase generator + +Implements CTS test backend. Invoked by ml/nn/runtime/test/specs/generate_tests.sh; +See that script for details on how this script is used. + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import argparse +import math +import os +import re +import sys +import traceback + +# Stuff from test generator +import test_generator as tg +from test_generator import ActivationConverter +from test_generator import BoolScalar +from test_generator import Configuration +from test_generator import DataTypeConverter +from test_generator import DataLayoutConverter +from test_generator import Example +from test_generator import Float16Scalar +from test_generator import Float32Scalar +from test_generator import Float32Vector +from test_generator import GetJointStr +from test_generator import IgnoredOutput +from test_generator import Input +from test_generator import Int32Scalar +from test_generator import Int32Vector +from test_generator import Internal +from test_generator import Model +from test_generator import Operand +from test_generator import Output +from test_generator import Parameter +from test_generator import ParameterAsInputConverter +from test_generator import RelaxedModeConverter +from test_generator import SmartOpen +from test_generator import SymmPerChannelQuantParams + +def IndentedPrint(s, indent=2, *args, **kwargs): + print('\n'.join([" " * indent + i for i in s.split('\n')]), *args, **kwargs) + +# Take a model from command line +def ParseCmdLine(): + parser = argparse.ArgumentParser() + parser.add_argument("spec", help="the spec file/directory") + parser.add_argument( + "-m", "--model", help="the output model file/directory", default="-") + parser.add_argument( + "-e", "--example", help="the output example file/directory", default="-") + parser.add_argument( + "-t", "--test", help="the output test file/directory", default="-") + parser.add_argument( + "-f", "--force", help="force to regenerate all spec files", action="store_true") + args = parser.parse_args() + tg.FileNames.InitializeFileLists(args.spec, args.model, args.example, args.test) + Configuration.force_regenerate = args.force + +def NeedRegenerate(): + if not all(os.path.exists(f) for f in \ + [tg.FileNames.modelFile, tg.FileNames.exampleFile, tg.FileNames.testFile]): + return True + specTime = os.path.getmtime(tg.FileNames.specFile) + 10 + modelTime = os.path.getmtime(tg.FileNames.modelFile) + exampleTime = os.path.getmtime(tg.FileNames.exampleFile) + testTime = os.path.getmtime(tg.FileNames.testFile) + if all(t > specTime for t in [modelTime, exampleTime, testTime]): + return False + return True + +# Write headers for generated files, which are boilerplate codes only related to filenames +def InitializeFiles(model_fd, example_fd, test_fd): + specFileBase = os.path.basename(tg.FileNames.specFile) + fileHeader = """\ +// Generated from {spec_file} +// DO NOT EDIT +// clang-format off +#include "{header}" +""" + print(fileHeader.format(spec_file=specFileBase, header="TestGenerated.h"), file=test_fd) + print(fileHeader.format(spec_file=specFileBase, header="TestGenerated.h"), file=model_fd) + print(fileHeader.format(spec_file=specFileBase, header="TestHarness.h"), file=example_fd) + +# Dump is_ignored function for IgnoredOutput +def DumpCtsIsIgnored(model, model_fd): + isIgnoredTemplate = """\ +bool {is_ignored_name}(int i) {{ + static std::set<int> ignore = {{{ignored_index}}}; + return ignore.find(i) != ignore.end(); +}} +""" + print(isIgnoredTemplate.format( + ignored_index=tg.GetJointStr(model.GetIgnoredOutputs(), method=lambda x: str(x.index)), + is_ignored_name=str(model.isIgnoredFunctionName)), file=model_fd) + +# Dump Model file for Cts tests +def DumpCtsModel(model, model_fd): + assert model.compiled + if model.dumped: + return + namespace = "generated_tests::{spec_name}".format(spec_name=tg.FileNames.specName) + print("namespace {namespace} {{\n".format(namespace=namespace), file=model_fd) + print("void %s(Model *model) {"%(model.createFunctionName), file=model_fd) + + # Phase 0: types + for t in model.GetTypes(): + if t.scale == 0.0 and t.zeroPoint == 0 and t.extraParams is None: + typeDef = "OperandType %s(Type::%s, %s);"%(t, t.type, t.GetDimensionsString()) + else: + if t.extraParams is None or t.extraParams.hide: + typeDef = "OperandType %s(Type::%s, %s, %s, %d);"%( + t, t.type, t.GetDimensionsString(), tg.PrettyPrintAsFloat(t.scale), t.zeroPoint) + else: + assert t.type == "TENSOR_QUANT8_SYMM_PER_CHANNEL", "Unexpected model configuration. " \ + "Extra params are currently expected for " \ + "TENSOR_QUANT8_SYMM_PER_CHANNEL operand type. " + assert t.scale == 0.0 and t.zeroPoint == 0, "Scale and zero point are always zero for " \ + "TENSOR_QUANT8_SYMM_PER_CHANNEL operands" + typeDef = "OperandType %s(Type::%s, %s, %s);"%( + t, t.type, t.GetDimensionsString(), t.extraParams.GetConstructor()) + + IndentedPrint(typeDef, file=model_fd) + + # Phase 1: add operands + print(" // Phase 1, operands", file=model_fd) + for op in model.operands: + IndentedPrint("auto %s = model->addOperand(&%s);"%(op, op.type), file=model_fd) + + # Phase 2: operations + print(" // Phase 2, operations", file=model_fd) + for p in model.GetParameters(): + paramDef = "static %s %s[] = %s;\nmodel->setOperandValue(%s, %s, sizeof(%s) * %d);"%( + p.type.GetCppTypeString(), p.initializer, p.GetListInitialization(), p, + p.initializer, p.type.GetCppTypeString(), p.type.GetNumberOfElements()) + IndentedPrint(paramDef, file=model_fd) + for op in model.operations: + IndentedPrint("model->addOperation(ANEURALNETWORKS_%s, {%s}, {%s});"%( + op.optype, tg.GetJointStr(op.ins), tg.GetJointStr(op.outs)), file=model_fd) + + # Phase 3: add inputs and outputs + print (" // Phase 3, inputs and outputs", file=model_fd) + IndentedPrint("model->identifyInputsAndOutputs(\n {%s},\n {%s});"%( + tg.GetJointStr(model.GetInputs()), tg.GetJointStr(model.GetOutputs())), file=model_fd) + + # Phase 4: set relaxed execution if needed + if (model.isRelaxed): + print (" // Phase 4: set relaxed execution", file=model_fd) + print (" model->relaxComputationFloat32toFloat16(true);", file=model_fd) + + print (" assert(model->isValid());", file=model_fd) + print ("}\n", file=model_fd) + DumpCtsIsIgnored(model, model_fd) + print("}} // namespace {namespace}".format(namespace=namespace), file=model_fd) + model.dumped = True + +def DumpMixedType(operands, feedDict): + supportedTensors = [ + "DIMENSIONS", + "TENSOR_FLOAT32", + "TENSOR_INT32", + "TENSOR_QUANT8_ASYMM", + "TENSOR_OEM_BYTE", + "TENSOR_QUANT16_SYMM", + "TENSOR_FLOAT16", + "TENSOR_BOOL8", + "TENSOR_QUANT8_SYMM_PER_CHANNEL", + "TENSOR_QUANT16_ASYMM", + "TENSOR_QUANT8_SYMM", + ] + typedMap = {t: [] for t in supportedTensors} + FeedAndGet = lambda op, d: op.Feed(d).GetListInitialization() + # group the operands by type + for operand in operands: + try: + typedMap[operand.type.type].append(FeedAndGet(operand, feedDict)) + typedMap["DIMENSIONS"].append("{%d, {%s}}"%( + operand.index, GetJointStr(operand.dimensions))) + except KeyError as e: + traceback.print_exc() + sys.exit("Cannot dump tensor of type {}".format(operand.type.type)) + mixedTypeTemplate = """\ +{{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> Dimensions map + .operandDimensions = {{{dimensions_map}}}, + // int -> FLOAT32 map + .float32Operands = {{{float32_map}}}, + // int -> INT32 map + .int32Operands = {{{int32_map}}}, + // int -> QUANT8_ASYMM map + .quant8AsymmOperands = {{{uint8_map}}}, + // int -> QUANT16_SYMM map + .quant16SymmOperands = {{{int16_map}}}, + // int -> FLOAT16 map + .float16Operands = {{{float16_map}}}, + // int -> BOOL8 map + .bool8Operands = {{{bool8_map}}}, + // int -> QUANT8_SYMM_PER_CHANNEL map + .quant8ChannelOperands = {{{int8_map}}}, + // int -> QUANT16_ASYMM map + .quant16AsymmOperands = {{{uint16_map}}}, + // int -> QUANT8_SYMM map + .quant8SymmOperands = {{{quant8_symm_map}}}, +}}""" + return mixedTypeTemplate.format( + dimensions_map=tg.GetJointStr(typedMap.get("DIMENSIONS", [])), + float32_map=tg.GetJointStr(typedMap.get("TENSOR_FLOAT32", [])), + int32_map=tg.GetJointStr(typedMap.get("TENSOR_INT32", [])), + uint8_map=tg.GetJointStr(typedMap.get("TENSOR_QUANT8_ASYMM", []) + + typedMap.get("TENSOR_OEM_BYTE", [])), + int16_map=tg.GetJointStr(typedMap.get("TENSOR_QUANT16_SYMM", [])), + float16_map=tg.GetJointStr(typedMap.get("TENSOR_FLOAT16", [])), + int8_map=tg.GetJointStr(typedMap.get("TENSOR_QUANT8_SYMM_PER_CHANNEL", [])), + bool8_map=tg.GetJointStr(typedMap.get("TENSOR_BOOL8", [])), + uint16_map=tg.GetJointStr(typedMap.get("TENSOR_QUANT16_ASYMM", [])), + quant8_symm_map=tg.GetJointStr(typedMap.get("TENSOR_QUANT8_SYMM", [])) + ) + +# Dump Example file for Cts tests +def DumpCtsExample(example, example_fd): + namespace = "generated_tests::{spec_name}".format(spec_name=tg.FileNames.specName) + print("namespace {namespace} {{\n".format(namespace=namespace), file=example_fd) + print("std::vector<::test_helper::MixedTypedExample>& get_%s() {" % (example.examplesName), file=example_fd) + print("static std::vector<::test_helper::MixedTypedExample> %s = {" % (example.examplesName), file=example_fd) + for inputFeedDict, outputFeedDict in example.feedDicts: + print ('// Begin of an example', file = example_fd) + print ('{\n.operands = {', file = example_fd) + inputs = DumpMixedType(example.model.GetInputs(), inputFeedDict) + outputs = DumpMixedType(example.model.GetOutputs(), outputFeedDict) + print ('//Input(s)\n%s,' % inputs , file = example_fd) + print ('//Output(s)\n%s' % outputs, file = example_fd) + print ('},', file = example_fd) + if example.expectedMultinomialDistributionTolerance is not None: + print ('.expectedMultinomialDistributionTolerance = %f' % + example.expectedMultinomialDistributionTolerance, file = example_fd) + print ('}, // End of an example', file = example_fd) + print("};", file=example_fd) + print("return %s;" % (example.examplesName), file=example_fd) + print("};", file=example_fd) + print("\n}} // namespace {namespace}".format(namespace=namespace), file=example_fd) + +# Dump Test file for Cts tests +def DumpCtsTest(example, test_fd): + namespace = "generated_tests::{spec_name}".format(spec_name=tg.FileNames.specName) + testTemplate = """\ +namespace {namespace} {{ + +void {create_model_name}(Model *model); +bool {is_ignored_name}(int); +std::vector<::test_helper::MixedTypedExample>& get_{examples_name}(); + +TEST_F({test_case_name}, {test_name}) {{ + execute({create_model_name}, + {is_ignored_name}, + get_{examples_name}()); +}} + +}} // namespace {namespace} +""" + if example.model.version is not None and not example.expectFailure: + testTemplate += """\ +TEST_AVAILABLE_SINCE({version}, {test_name}, {namespace}::{create_model_name})\n""" + + if example.expectFailure: + testCaseName = "GeneratedValidationTests" + elif example.model.hasDynamicOutputShape: + testCaseName = "DynamicOutputShapeTest" + else: + testCaseName = "GeneratedTests" + + print(testTemplate.format( + test_case_name=testCaseName, + test_name=str(example.testName), + namespace=namespace, + create_model_name=str(example.model.createFunctionName), + is_ignored_name=str(example.model.isIgnoredFunctionName), + examples_name=str(example.examplesName), + version=example.model.version), file=test_fd) + +if __name__ == '__main__': + ParseCmdLine() + while tg.FileNames.NextFile(): + if Configuration.force_regenerate or NeedRegenerate(): + print("Generating CTS tests from spec %s" % tg.FileNames.specFile, file=sys.stderr) + exec(open(tg.FileNames.specFile, "r").read()) + with SmartOpen(tg.FileNames.modelFile) as model_fd, \ + SmartOpen(tg.FileNames.exampleFile) as example_fd, \ + SmartOpen(tg.FileNames.testFile) as test_fd: + InitializeFiles(model_fd, example_fd, test_fd) + Example.DumpAllExamples( + DumpModel=DumpCtsModel, model_fd=model_fd, + DumpExample=DumpCtsExample, example_fd=example_fd, + DumpTest=DumpCtsTest, test_fd=test_fd) + else: + print("Skip file: %s" % tg.FileNames.specFile, file=sys.stderr) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/include/TestHarness.h b/tests/nnapi/nnapi_test_generator/android-q-beta/include/TestHarness.h new file mode 100644 index 000000000..3b4b26b16 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/include/TestHarness.h @@ -0,0 +1,426 @@ +/* + * 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. + */ + +/* Header-only library for various helpers of test harness + * See frameworks/ml/nn/runtime/test/TestGenerated.cpp for how this is used. + */ +#ifndef ANDROID_FRAMEWORKS_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H +#define ANDROID_FRAMEWORKS_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H + +#include <gmock/gmock-matchers.h> +#include <gtest/gtest.h> + +#include <cmath> +#include <functional> +#include <map> +#include <tuple> +#include <vector> + +namespace test_helper { + +constexpr const size_t gMaximumNumberOfErrorMessages = 10; + +// TODO: Figure out the build dependency to make including "CpuOperationUtils.h" work. +inline void convertFloat16ToFloat32(const _Float16* input, std::vector<float>* output) { + for (size_t i = 0; i < output->size(); ++i) { + (*output)[i] = static_cast<float>(input[i]); + } +} + +// This class is a workaround for two issues our code relies on: +// 1. sizeof(bool) is implementation defined. +// 2. vector<bool> does not allow direct pointer access via the data() method. +class bool8 { + public: + bool8() : mValue() {} + /* implicit */ bool8(bool value) : mValue(value) {} + inline operator bool() const { return mValue != 0; } + + private: + uint8_t mValue; +}; + +static_assert(sizeof(bool8) == 1, "size of bool8 must be 8 bits"); + +typedef std::map<int, std::vector<uint32_t>> OperandDimensions; +typedef std::map<int, std::vector<float>> Float32Operands; +typedef std::map<int, std::vector<int32_t>> Int32Operands; +typedef std::map<int, std::vector<uint8_t>> Quant8AsymmOperands; +typedef std::map<int, std::vector<int16_t>> Quant16SymmOperands; +typedef std::map<int, std::vector<_Float16>> Float16Operands; +typedef std::map<int, std::vector<bool8>> Bool8Operands; +typedef std::map<int, std::vector<int8_t>> Quant8ChannelOperands; +typedef std::map<int, std::vector<uint16_t>> Quant16AsymmOperands; +typedef std::map<int, std::vector<int8_t>> Quant8SymmOperands; +struct MixedTyped { + static constexpr size_t kNumTypes = 9; + OperandDimensions operandDimensions; + Float32Operands float32Operands; + Int32Operands int32Operands; + Quant8AsymmOperands quant8AsymmOperands; + Quant16SymmOperands quant16SymmOperands; + Float16Operands float16Operands; + Bool8Operands bool8Operands; + Quant8ChannelOperands quant8ChannelOperands; + Quant16AsymmOperands quant16AsymmOperands; + Quant8SymmOperands quant8SymmOperands; +}; +typedef std::pair<MixedTyped, MixedTyped> MixedTypedExampleType; + +// Mixed-typed examples +typedef struct { + MixedTypedExampleType operands; + // Specifies the RANDOM_MULTINOMIAL distribution tolerance. + // If set to greater than zero, the input is compared as log-probabilities + // to the output and must be within this tolerance to pass. + float expectedMultinomialDistributionTolerance = 0.0; +} MixedTypedExample; + +// Go through all index-value pairs of a given input type +template <typename T> +inline void for_each(const std::map<int, std::vector<T>>& idx_and_data, + std::function<void(int, const std::vector<T>&)> execute) { + for (auto& i : idx_and_data) { + execute(i.first, i.second); + } +} + +// non-const variant of for_each +template <typename T> +inline void for_each(std::map<int, std::vector<T>>& idx_and_data, + std::function<void(int, std::vector<T>&)> execute) { + for (auto& i : idx_and_data) { + execute(i.first, i.second); + } +} + +// Go through all index-value pairs of a given input type +template <typename T> +inline void for_each(const std::map<int, std::vector<T>>& golden, + std::map<int, std::vector<T>>& test, + std::function<void(int, const std::vector<T>&, std::vector<T>&)> execute) { + for_each<T>(golden, [&test, &execute](int index, const std::vector<T>& g) { + auto& t = test[index]; + execute(index, g, t); + }); +} + +// Go through all index-value pairs of a given input type +template <typename T> +inline void for_each( + const std::map<int, std::vector<T>>& golden, const std::map<int, std::vector<T>>& test, + std::function<void(int, const std::vector<T>&, const std::vector<T>&)> execute) { + for_each<T>(golden, [&test, &execute](int index, const std::vector<T>& g) { + auto t = test.find(index); + ASSERT_NE(t, test.end()); + execute(index, g, t->second); + }); +} + +// internal helper for for_all +template <typename T> +inline void for_all_internal(std::map<int, std::vector<T>>& idx_and_data, + std::function<void(int, void*, size_t)> execute_this) { + for_each<T>(idx_and_data, [&execute_this](int idx, std::vector<T>& m) { + execute_this(idx, static_cast<void*>(m.data()), m.size() * sizeof(T)); + }); +} + +// Go through all index-value pairs of all input types +// expects a functor that takes (int index, void *raw data, size_t sz) +inline void for_all(MixedTyped& idx_and_data, + std::function<void(int, void*, size_t)> execute_this) { + for_all_internal(idx_and_data.float32Operands, execute_this); + for_all_internal(idx_and_data.int32Operands, execute_this); + for_all_internal(idx_and_data.quant8AsymmOperands, execute_this); + for_all_internal(idx_and_data.quant16SymmOperands, execute_this); + for_all_internal(idx_and_data.float16Operands, execute_this); + for_all_internal(idx_and_data.bool8Operands, execute_this); + for_all_internal(idx_and_data.quant8ChannelOperands, execute_this); + for_all_internal(idx_and_data.quant16AsymmOperands, execute_this); + for_all_internal(idx_and_data.quant8SymmOperands, execute_this); + static_assert(9 == MixedTyped::kNumTypes, + "Number of types in MixedTyped changed, but for_all function wasn't updated"); +} + +// Const variant of internal helper for for_all +template <typename T> +inline void for_all_internal(const std::map<int, std::vector<T>>& idx_and_data, + std::function<void(int, const void*, size_t)> execute_this) { + for_each<T>(idx_and_data, [&execute_this](int idx, const std::vector<T>& m) { + execute_this(idx, static_cast<const void*>(m.data()), m.size() * sizeof(T)); + }); +} + +// Go through all index-value pairs (const variant) +// expects a functor that takes (int index, const void *raw data, size_t sz) +inline void for_all(const MixedTyped& idx_and_data, + std::function<void(int, const void*, size_t)> execute_this) { + for_all_internal(idx_and_data.float32Operands, execute_this); + for_all_internal(idx_and_data.int32Operands, execute_this); + for_all_internal(idx_and_data.quant8AsymmOperands, execute_this); + for_all_internal(idx_and_data.quant16SymmOperands, execute_this); + for_all_internal(idx_and_data.float16Operands, execute_this); + for_all_internal(idx_and_data.bool8Operands, execute_this); + for_all_internal(idx_and_data.quant8ChannelOperands, execute_this); + for_all_internal(idx_and_data.quant16AsymmOperands, execute_this); + for_all_internal(idx_and_data.quant8SymmOperands, execute_this); + static_assert( + 9 == MixedTyped::kNumTypes, + "Number of types in MixedTyped changed, but const for_all function wasn't updated"); +} + +// Helper template - resize test output per golden +template <typename T> +inline void resize_accordingly_(const std::map<int, std::vector<T>>& golden, + std::map<int, std::vector<T>>& test) { + for_each<T>(golden, test, + [](int, const std::vector<T>& g, std::vector<T>& t) { t.resize(g.size()); }); +} + +template <> +inline void resize_accordingly_<uint32_t>(const OperandDimensions& golden, + OperandDimensions& test) { + for_each<uint32_t>( + golden, test, + [](int, const std::vector<uint32_t>& g, std::vector<uint32_t>& t) { t = g; }); +} + +inline void resize_accordingly(const MixedTyped& golden, MixedTyped& test) { + resize_accordingly_(golden.operandDimensions, test.operandDimensions); + resize_accordingly_(golden.float32Operands, test.float32Operands); + resize_accordingly_(golden.int32Operands, test.int32Operands); + resize_accordingly_(golden.quant8AsymmOperands, test.quant8AsymmOperands); + resize_accordingly_(golden.quant16SymmOperands, test.quant16SymmOperands); + resize_accordingly_(golden.float16Operands, test.float16Operands); + resize_accordingly_(golden.bool8Operands, test.bool8Operands); + resize_accordingly_(golden.quant8ChannelOperands, test.quant8ChannelOperands); + resize_accordingly_(golden.quant16AsymmOperands, test.quant16AsymmOperands); + resize_accordingly_(golden.quant8SymmOperands, test.quant8SymmOperands); + static_assert(9 == MixedTyped::kNumTypes, + "Number of types in MixedTyped changed, but resize_accordingly function wasn't " + "updated"); +} + +template <typename T> +void filter_internal(const std::map<int, std::vector<T>>& golden, + std::map<int, std::vector<T>>* filtered, std::function<bool(int)> is_ignored) { + for_each<T>(golden, [filtered, &is_ignored](int index, const std::vector<T>& m) { + auto& g = *filtered; + if (!is_ignored(index)) g[index] = m; + }); +} + +inline MixedTyped filter(const MixedTyped& golden, std::function<bool(int)> is_ignored) { + MixedTyped filtered; + filter_internal(golden.operandDimensions, &filtered.operandDimensions, is_ignored); + filter_internal(golden.float32Operands, &filtered.float32Operands, is_ignored); + filter_internal(golden.int32Operands, &filtered.int32Operands, is_ignored); + filter_internal(golden.quant8AsymmOperands, &filtered.quant8AsymmOperands, is_ignored); + filter_internal(golden.quant16SymmOperands, &filtered.quant16SymmOperands, is_ignored); + filter_internal(golden.float16Operands, &filtered.float16Operands, is_ignored); + filter_internal(golden.bool8Operands, &filtered.bool8Operands, is_ignored); + filter_internal(golden.quant8ChannelOperands, &filtered.quant8ChannelOperands, is_ignored); + filter_internal(golden.quant16AsymmOperands, &filtered.quant16AsymmOperands, is_ignored); + filter_internal(golden.quant8SymmOperands, &filtered.quant8SymmOperands, is_ignored); + static_assert(9 == MixedTyped::kNumTypes, + "Number of types in MixedTyped changed, but compare function wasn't updated"); + return filtered; +} + +// Compare results +template <typename T> +void compare_(const std::map<int, std::vector<T>>& golden, + const std::map<int, std::vector<T>>& test, std::function<void(T, T)> cmp) { + for_each<T>(golden, test, [&cmp](int index, const std::vector<T>& g, const std::vector<T>& t) { + for (unsigned int i = 0; i < g.size(); i++) { + SCOPED_TRACE(testing::Message() + << "When comparing output " << index << " element " << i); + cmp(g[i], t[i]); + } + }); +} + +// TODO: Allow passing accuracy criteria from spec. +// Currently we only need relaxed accuracy criteria on mobilenet tests, so we return the quant8 +// tolerance simply based on the current test name. +inline int getQuant8AllowedError() { + const ::testing::TestInfo* const testInfo = + ::testing::UnitTest::GetInstance()->current_test_info(); + const std::string testCaseName = testInfo->test_case_name(); + const std::string testName = testInfo->name(); + // We relax the quant8 precision for all tests with mobilenet: + // - CTS/VTS GeneratedTest and DynamicOutputShapeTest with mobilenet + // - VTS CompilationCachingTest and CompilationCachingSecurityTest except for TOCTOU tests + if (testName.find("mobilenet") != std::string::npos || + (testCaseName.find("CompilationCaching") != std::string::npos && + testName.find("TOCTOU") == std::string::npos)) { + return 2; + } else { + return 1; + } +} + +inline void compare(const MixedTyped& golden, const MixedTyped& test, float fpAtol = 1e-5f, + float fpRtol = 1e-5f) { + int quant8AllowedError = getQuant8AllowedError(); + for_each<uint32_t>( + golden.operandDimensions, test.operandDimensions, + [](int index, const std::vector<uint32_t>& g, const std::vector<uint32_t>& t) { + SCOPED_TRACE(testing::Message() + << "When comparing dimensions for output " << index); + EXPECT_EQ(g, t); + }); + size_t totalNumberOfErrors = 0; + compare_<float>(golden.float32Operands, test.float32Operands, + [&totalNumberOfErrors, fpAtol, fpRtol](float expected, float actual) { + // Compute the range based on both absolute tolerance and relative tolerance + float fpRange = fpAtol + fpRtol * std::abs(expected); + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(expected, actual, fpRange); + } + if (std::abs(expected - actual) > fpRange) { + totalNumberOfErrors++; + } + }); + compare_<int32_t>(golden.int32Operands, test.int32Operands, + [&totalNumberOfErrors](int32_t expected, int32_t actual) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_EQ(expected, actual); + } + if (expected != actual) { + totalNumberOfErrors++; + } + }); + compare_<uint8_t>(golden.quant8AsymmOperands, test.quant8AsymmOperands, + [&totalNumberOfErrors, quant8AllowedError](uint8_t expected, uint8_t actual) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(expected, actual, quant8AllowedError); + } + if (std::abs(expected - actual) > quant8AllowedError) { + totalNumberOfErrors++; + } + }); + compare_<int16_t>(golden.quant16SymmOperands, test.quant16SymmOperands, + [&totalNumberOfErrors](int16_t expected, int16_t actual) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(expected, actual, 1); + } + if (std::abs(expected - actual) > 1) { + totalNumberOfErrors++; + } + }); + compare_<_Float16>(golden.float16Operands, test.float16Operands, + [&totalNumberOfErrors, fpAtol, fpRtol](_Float16 expected, _Float16 actual) { + // Compute the range based on both absolute tolerance and relative + // tolerance + float fpRange = fpAtol + fpRtol * std::abs(static_cast<float>(expected)); + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(expected, actual, fpRange); + } + if (std::abs(static_cast<float>(expected - actual)) > fpRange) { + totalNumberOfErrors++; + } + }); + compare_<bool8>(golden.bool8Operands, test.bool8Operands, + [&totalNumberOfErrors](bool expected, bool actual) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_EQ(expected, actual); + } + if (expected != actual) { + totalNumberOfErrors++; + } + }); + compare_<int8_t>(golden.quant8ChannelOperands, test.quant8ChannelOperands, + [&totalNumberOfErrors, &quant8AllowedError](int8_t expected, int8_t actual) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(expected, actual, quant8AllowedError); + } + if (std::abs(static_cast<int>(expected) - static_cast<int>(actual)) > + quant8AllowedError) { + totalNumberOfErrors++; + } + }); + compare_<uint16_t>(golden.quant16AsymmOperands, test.quant16AsymmOperands, + [&totalNumberOfErrors](int16_t expected, int16_t actual) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(expected, actual, 1); + } + if (std::abs(expected - actual) > 1) { + totalNumberOfErrors++; + } + }); + compare_<int8_t>(golden.quant8SymmOperands, test.quant8SymmOperands, + [&totalNumberOfErrors, quant8AllowedError](int8_t expected, int8_t actual) { + if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) { + EXPECT_NEAR(expected, actual, quant8AllowedError); + } + if (std::abs(static_cast<int>(expected) - static_cast<int>(actual)) > + quant8AllowedError) { + totalNumberOfErrors++; + } + }); + + static_assert(9 == MixedTyped::kNumTypes, + "Number of types in MixedTyped changed, but compare function wasn't updated"); + EXPECT_EQ(size_t{0}, totalNumberOfErrors); +} + +// Calculates the expected probability from the unnormalized log-probability of +// each class in the input and compares it to the actual ocurrence of that class +// in the output. +inline void expectMultinomialDistributionWithinTolerance(const MixedTyped& test, + const MixedTypedExample& example) { + // TODO: These should be parameters but aren't currently preserved in the example. + const int kBatchSize = 1; + const int kNumClasses = 1024; + const int kNumSamples = 128; + + std::vector<int32_t> output = test.int32Operands.at(0); + std::vector<int> class_counts; + class_counts.resize(kNumClasses); + for (int index : output) { + class_counts[index]++; + } + std::vector<float> input; + Float32Operands float32Operands = example.operands.first.float32Operands; + if (!float32Operands.empty()) { + input = example.operands.first.float32Operands.at(0); + } else { + std::vector<_Float16> inputFloat16 = example.operands.first.float16Operands.at(0); + input.resize(inputFloat16.size()); + convertFloat16ToFloat32(inputFloat16.data(), &input); + } + for (int b = 0; b < kBatchSize; ++b) { + float probability_sum = 0; + const int batch_index = kBatchSize * b; + for (int i = 0; i < kNumClasses; ++i) { + probability_sum += expf(input[batch_index + i]); + } + for (int i = 0; i < kNumClasses; ++i) { + float probability = + static_cast<float>(class_counts[i]) / static_cast<float>(kNumSamples); + float probability_expected = expf(input[batch_index + i]) / probability_sum; + EXPECT_THAT(probability, + ::testing::FloatNear(probability_expected, + example.expectedMultinomialDistributionTolerance)); + } + } +} + +}; // namespace test_helper + +#endif // ANDROID_FRAMEWORKS_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/spec_visualizer.py b/tests/nnapi/nnapi_test_generator/android-q-beta/spec_visualizer.py new file mode 100644 index 000000000..73a9628b7 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/spec_visualizer.py @@ -0,0 +1,266 @@ +#!/usr/bin/python3 + +# Copyright 2019, 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. + +"""Spec Visualizer + +Visualize python spec file for test generator. +Invoked by ml/nn/runtime/test/specs/visualize_spec.sh; +See that script for details on how this script is used. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import argparse +import json +import os +import sys +from string import Template + +# Stuff from test generator +import test_generator as tg +from test_generator import ActivationConverter +from test_generator import BoolScalar +from test_generator import Configuration +from test_generator import DataTypeConverter +from test_generator import DataLayoutConverter +from test_generator import Example +from test_generator import Float16Scalar +from test_generator import Float32Scalar +from test_generator import Float32Vector +from test_generator import GetJointStr +from test_generator import IgnoredOutput +from test_generator import Input +from test_generator import Int32Scalar +from test_generator import Int32Vector +from test_generator import Internal +from test_generator import Model +from test_generator import Operand +from test_generator import Output +from test_generator import Parameter +from test_generator import ParameterAsInputConverter +from test_generator import RelaxedModeConverter +from test_generator import SymmPerChannelQuantParams + + +TEMPLATE_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), "spec_viz_template.html") +global_graphs = dict() + + +def FormatArray(data, is_scalar=False): + if is_scalar: + assert len(data) == 1 + return str(data[0]) + else: + return "[%s]" % (", ".join(str(i) for i in data)) + + +def FormatDict(data): + return "<br/>".join("<b>%s:</b> %s"%(k.capitalize(), v) for k, v in data.items()) + + +def GetOperandInfo(op): + op_info = {"lifetime": op.lifetime, "type": op.type.type} + + if not op.type.IsScalar(): + op_info["dimensions"] = FormatArray(op.type.dimensions) + + if op.type.scale != 0: + op_info["scale"] = op.type.scale + op_info["zero point"] = op.type.zeroPoint + if op.type.type == "TENSOR_QUANT8_SYMM_PER_CHANNEL": + op_info["scale"] = FormatArray(op.type.extraParams.scales) + op_info["channel dim"] = op.type.extraParams.channelDim + + return op_info + + +def FormatOperand(op): + # All keys and values in op_info will appear in the tooltip. We only display the operand data + # if the length is less than 10. This should be convenient enough for most parameters. + op_info = GetOperandInfo(op) + if isinstance(op, Parameter) and len(op.value) <= 10: + op_info["data"] = FormatArray(op.value, op.type.IsScalar()) + + template = "<span class='tooltip'><span class='tooltipcontent'>{tooltip_content}</span><a href=\"{inpage_link}\">{op_name}</a></span>" + return template.format( + op_name=str(op), + tooltip_content=FormatDict(op_info), + inpage_link="#details-operands-%d" % (op.model_index), + ) + + +def GetSubgraph(example): + """Produces the nodes and edges information for d3 visualization.""" + + node_index_map = {} + topological_order = [] + + def AddToTopologicalOrder(op): + if op not in node_index_map: + node_index_map[op] = len(topological_order) + topological_order.append(op) + + # Get the topological order, both operands and operations are treated the same. + # Given that the example.model.operations is already topologically sorted, here we simply + # iterate through and insert inputs and outputs. + for op in example.model.operations: + for i in op.ins: + AddToTopologicalOrder(i) + AddToTopologicalOrder(op) + for o in op.outs: + AddToTopologicalOrder(o) + + # Assign layers to the nodes. + layers = {} + for node in topological_order: + layers[node] = max([layers[i] for i in node.ins], default=-1) + 1 + for node in reversed(topological_order): + layers[node] = min([layers[o] for o in node.outs], default=layers[node]+1) - 1 + num_layers = max(layers.values()) + 1 + + # Assign coordinates to the nodes. Nodes are equally spaced. + CoordX = lambda index: (index + 0.5) * 200 # 200px spacing horizontally + CoordY = lambda index: (index + 0.5) * 100 # 100px spacing vertically + coords = {} + layer_cnt = [0] * num_layers + for node in topological_order: + coords[node] = (CoordX(layer_cnt[layers[node]]), CoordY(layers[node])) + layer_cnt[layers[node]] += 1 + + # Create edges and nodes dictionaries for d3 visualization. + OpName = lambda idx: "operation%d" % idx + edges = [] + nodes = [] + for ind, op in enumerate(example.model.operations): + for tensor in op.ins: + edges.append({ + "source": str(tensor), + "target": OpName(ind) + }) + for tensor in op.outs: + edges.append({ + "target": str(tensor), + "source": OpName(ind) + }) + nodes.append({ + "index": ind, + "id": OpName(ind), + "name": op.optype, + "group": 2, + "x": coords[op][0], + "y": coords[op][1], + }) + + for ind, op in enumerate(example.model.operands): + nodes.append({ + "index": ind, + "id": str(op), + "name": str(op), + "group": 1, + "x": coords[op][0], + "y": coords[op][1], + }) + + return {"nodes": nodes, "edges": edges} + + +# The following Get**Info methods will each return a list of dictionaries, +# whose content will appear in the tables and sidebar views. +def GetConfigurationsInfo(example): + return [{ + "relaxed": str(example.model.isRelaxed), + "use shared memory": str(tg.Configuration.useSHM()), + "expect failure": str(example.expectFailure), + }] + + +def GetOperandsInfo(example): + ret = [] + for index, op in enumerate(example.model.operands): + ret.append({ + "index": index, + "name": str(op), + "group": "operand" + }) + ret[-1].update(GetOperandInfo(op)) + if isinstance(op, Parameter): + ret[-1]["data"] = FormatArray(op.value, op.type.IsScalar()) + elif isinstance(op, Input): + ret[-1]["data"] = FormatArray(example.feedDicts[0][0][op], op.type.IsScalar()) + elif isinstance(op, Output) and not isinstance(op, IgnoredOutput): + ret[-1]["data"] = FormatArray(example.feedDicts[0][1][op], op.type.IsScalar()) + return ret + + +def GetOperationsInfo(example): + return [{ + "index": index, + "name": op.optype, + "group": "operation", + "opcode": op.optype, + "inputs": ", ".join(FormatOperand(i) for i in op.ins), + "outputs": ", ".join(FormatOperand(o) for o in op.outs), + } for index,op in enumerate(example.model.operations)] + + +# TODO: Remove the unused fd from the parameter. +def ProcessExample(example, fd): + """Process an example and save the information into the global dictionary global_graphs.""" + + global global_graphs + print(" Processing variation %s" % example.testName) + global_graphs[str(example.testName)] = { + "subgraph": GetSubgraph(example), + "details": { + "configurations": GetConfigurationsInfo(example), + "operands": GetOperandsInfo(example), + "operations": GetOperationsInfo(example) + } + } + + +def DumpHtml(spec_file, out_file): + """Dump the final HTML file by replacing entries from a template file.""" + + with open(TEMPLATE_FILE, "r") as template_fd: + html_template = template_fd.read() + + with open(out_file, "w") as out_fd: + out_fd.write(Template(html_template).substitute( + spec_name=os.path.basename(spec_file), + graph_dump=json.dumps(global_graphs), + )) + + +def ParseCmdLine(): + parser = argparse.ArgumentParser() + parser.add_argument("spec", help="the spec file") + parser.add_argument("-o", "--out", help="the output html path", default="out.html") + args = parser.parse_args() + tg.FileNames.InitializeFileLists(args.spec, "-", "-", "-") + tg.FileNames.NextFile() + return os.path.abspath(args.spec), os.path.abspath(args.out) + + +if __name__ == '__main__': + spec_file, out_file = ParseCmdLine() + print("Visualizing from spec: %s" % spec_file) + exec(open(spec_file, "r").read()) + Example.DumpAllExamples(DumpExample=ProcessExample, example_fd=0) + DumpHtml(spec_file, out_file) + print("Output HTML file: %s" % out_file) + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/spec_viz_template.html b/tests/nnapi/nnapi_test_generator/android-q-beta/spec_viz_template.html new file mode 100644 index 000000000..befe469dc --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/spec_viz_template.html @@ -0,0 +1,438 @@ +<!DOCTYPE html> +<html> +<head> + <title>$spec_name</title> + <style> + body { + font-family: "Roboto", sans-serif; + margin: 0; + height: 100%; + background-color: rgb(61, 65, 77); + } + + #main { + width: 62%; + transition: 0.5s; + } + + #main h1 { + padding: 20px; + color: #eee; + font-size: 24px; + } + + .subgraph h3 { + text-transform: capitalize; + } + + .subgraph { + padding: 20px; + margin: 20px; + border-radius: 10px; + background-color: #fff; + } + + .subgraph table { + border-collapse: collapse; + border-spacing: 0; + } + + .subgraph thead { + background-color: rgb(61, 65, 77); + color: white; + text-transform: capitalize; + } + + .subgraph tbody tr:nth-child(odd) { + background-color: #f2f2f2; + } + + .subgraph tbody tr:hover { + background-color: #d8d8d8; + } + + .subgraph td { + border: 1px solid #ddd; + padding: 8px; + } + + .subgraph select { + font-weight: bold; + text-transform: uppercase; + font-size: 18px; + color: black; + } + + .subgraph svg { + background: white; + border: 1px solid #ccc; + } + + .subgraph .edges line { + stroke: #333; + } + + .subgraph .nodes text { + color: black; + pointer-events: none; + font-family: sans-serif; + font-size: 11px; + } + + #sidebar { + height: 100%; + width: 38%; + position: fixed; + z-index: 1; + top: 0; + right: 0; + background-color: #eee; + overflow-x: hidden; + transition: 0.5s; + border-left: 1px solid #ccc; + } + + #sidebar #sidebar-main { + padding: 50px; + } + + #sidebar h1 { + margin-top: 6px; + margin-bottom: 24px; + font-weight: bold; + font-size: 18px; + text-transform: uppercase; + } + + #sidebar .subtitle { + margin-bottom: 6px; + border-bottom: 1px solid #ccc; + padding-bottom: 4px; + font-weight: bold; + font-size: 12px; + text-transform: uppercase; + color: #555; + } + + #sidebar .property { + display: block; + margin-bottom: 16px; + } + + #sidebar .property_title { + float: left; + width: 80px; + margin-top: 0; + padding-top: 10px; + font-weight: bold; + font-size: 12px; + text-transform: uppercase; + color: #555; + } + + #sidebar .property_text { + margin-top: 8px; + margin-left: 100px; + border: 1px solid #ccc; + border-radius: 2px; + padding: 8px; + font-size: 14px; + background-color: #fff; + } + + #sidebar .closebtn { + position: absolute; + top: 0; + right: 25px; + font-size: 36px; + margin-left: 50px; + text-decoration: none; + color: #555; + } + + .tooltip { + color: blue; + } + + .tooltip .tooltipcontent { + visibility: hidden; + color: black; + background-color: #eee; + margin-top: 18px; + padding: 5px; + border: 1px solid #ccc; + border-radius: 4px; + position: absolute; + z-index: 1; + } + + .tooltip:hover .tooltipcontent { + visibility: visible; + } + </style> + <link href="https://fonts.googleapis.com/css?family=Roboto&display=swap" rel="stylesheet" /> + <script src="https://d3js.org/d3.v4.min.js"></script> + <script> + graphs = $graph_dump; + </script> +</head> + +<body> + <div id="main"> + <h1>$spec_name</h1> + <div class="subgraph" id="main-subgraph"> + <label for="main-selector">Choose a subgraph: </label> + <select id="main-selector" onchange="renderSubgraph(this.value)"></select> + <div id="main-tables"></div> + <h3>Visual Graph</h3> + <svg id="subgraph-svg" width="100%" height="720"></svg> + </div> + </div> + + <div id="sidebar"> + <div id="sidebar-main"> + </div> + </div> + + <script> + // Render the sidebar view of a given node object. + // The node must have "name" and "group" fields available. + function renderSidebar(node) { + var sidebar = document.getElementById("sidebar-main"); + sidebar.innerHTML = ""; + if (node == null) return; + + // Sidebar subtitle -- text taken from node.group. + var subtitle = document.createElement("p"); + subtitle.classList.add("subtitle"); + subtitle.innerHTML = node.group; + sidebar.appendChild(subtitle); + + // Sidebar title -- text taken from node.name. + var title = document.createElement("h1"); + title.innerHTML = node.name; + sidebar.appendChild(title); + + // List all the other fields in sidebar. + var ignoredFields = ["name", "group"]; + for (var property in node) { + if (ignoredFields.includes(property)) continue; + + var propertyTitle = document.createElement("h2"); + propertyTitle.classList.add("property_title"); + propertyTitle.innerHTML = property; + + var propertyText = document.createElement("p"); + propertyText.classList.add("property_text"); + propertyText.innerHTML = node[property]; + + var propertyDiv = document.createElement("div"); + propertyDiv.classList.add("property"); + propertyDiv.appendChild(propertyTitle); + propertyDiv.appendChild(propertyText); + sidebar.appendChild(propertyDiv); + } + } + + // Render the SVG DAG visualization, from TFLite graph visualizer. + // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/visualize.py + // + // The node coordiates are pre-calculated from the python visualizer. + function renderSvg(subgraph) { + var data = graphs[subgraph]["subgraph"]; + var svg = d3.select("#subgraph-svg"); + svg.selectAll("*").remove(); + var width = svg.attr("width"); + var height = svg.attr("height"); + // Make the graph scrollable. + svg = svg.call(d3.zoom().on("zoom", function () { + svg.attr("transform", d3.event.transform); + })).append("g"); + var color = d3.scaleOrdinal(d3.schemeDark2); + var simulation = d3.forceSimulation() + .force("link", d3.forceLink().id(function (d) { return d.id; })) + .force("charge", d3.forceManyBody()) + .force("center", d3.forceCenter(0.5 * width, 0.5 * height)); + var edge = svg.append("g").attr("class", "edges").selectAll("line") + .data(data.edges).enter().append("path").attr("stroke", "black").attr("fill", "none") + // Make the node group + var node = svg.selectAll(".nodes") + .data(data.nodes) + .enter().append("g") + .attr("x", function (d) { return d.x }) + .attr("y", function (d) { return d.y }) + .attr("transform", function (d) { + return "translate( " + d.x + ", " + d.y + ")" + }) + .attr("class", "nodes") + .call(d3.drag() + .on("start", function (d) { + if (!d3.event.active) simulation.alphaTarget(1.0).restart(); + d.fx = d.x; d.fy = d.y; + }) + .on("drag", function (d) { + d.fx = d3.event.x; d.fy = d3.event.y; + }) + .on("end", function (d) { + if (!d3.event.active) simulation.alphaTarget(0); + d.fx = d.fy = null; + })); + // Within the group, draw a box for the node position and text + // on the side. + var node_width = 150; + var node_height = 30; + node.append("rect") + .attr("r", "5px") + .attr("width", function (d) { return d.group == 1 ? node_width : node_width + 50; }) + .attr("height", node_height) + .attr("rx", function (d) { return d.group == 1 ? 1 : 10; }) + .attr("stroke", "#000000") + .attr("fill", function (d) { return d.group == 1 ? "#dddddd" : "#000000"; }) + .attr("onclick", function (d) { + return "renderSidebar(graphs." + subgraph + ".details." + + (d.group == 1 ? "operands" : "operations") + "[" + + d.index.toString() + "])"; + }); + node.append("text") + .text(function (d) { return d.name; }) + .attr("x", 5) + .attr("y", 20) + .attr("fill", function (d) { return d.group == 1 ? "#000000" : "#eeeeee"; }) + // Setup force parameters and update position callback + var node = svg.selectAll(".nodes") + .data(data.nodes); + // Bind the links + var name_to_g = {} + node.each(function (data, index, nodes) { + name_to_g[data.id] = this; + }); + function proc(w, t) { + return parseInt(w.getAttribute(t)); + } + edge.attr("d", function (d) { + function lerp(t, a, b) { + return (1.0 - t) * a + t * b; + } + var x1 = proc(name_to_g[d.source], "x") + node_width / 2; + var y1 = proc(name_to_g[d.source], "y") + node_height; + var x2 = proc(name_to_g[d.target], "x") + node_width / 2; + var y2 = proc(name_to_g[d.target], "y"); + var s = "M " + x1 + " " + y1 + + " C " + x1 + " " + lerp(.5, y1, y2) + + " " + x2 + " " + lerp(.5, y1, y2) + + " " + x2 + " " + y2 + return s; + }); + } + + // Open a new window and present the full text data. + function showFullData(data) { + window.open().document.write(data); + } + + // Renders a single table. + function renderTable(title, data, headers) { + var parent = document.getElementById("main-tables"); + + // Create heading. + var heading = document.createElement("h3"); + heading.innerHTML = title; + parent.appendChild(heading); + + // Filter out headers that do not appear in any data element. + headers = headers.filter(function (key) { + return data.some(function (elem) { return key in elem; }); + }); + + // Render the table headers. + var table = document.createElement("table"); + let header = table.createTHead().insertRow(); + for (let key of headers) { header.insertCell().innerHTML = key; } + + // Render the table body. + // Since the "data" field could be very large, we omit the full content and + // append a "View Full" button to the end. + var omittableFields = ["data"]; + let body = table.createTBody(); + for (const [index, elem] of data.entries()) { + let row = body.insertRow(); + row.id = "details-" + title.toLowerCase() + "-" + index.toString(); + + for (let key of headers) { + var cell = row.insertCell(); + var data = key in elem ? elem[key] : "-"; + if (omittableFields.includes(key) && data.length > 100) { + // If the data exceeds the length limit, only print the first 80 and + // the last 20 characters. + data = data.substring(0, 80) + " ... " + + data.substring(data.length - 20, data.length) + " "; + cell.innerHTML = data; + + // Append a "View Full" button to the end. + var href = document.createElement("a"); + href.innerHTML = "View Full"; + href.href = "javascript:void(0)"; + href.onclick = function () { showFullData(elem[key]); }; + cell.appendChild(href); + } else { + cell.innerHTML = data; + } + } + } + parent.appendChild(table); + } + + function renderTables(subgraph) { + document.getElementById("main-tables").innerHTML = ""; + renderTable("Configurations", graphs[subgraph].details.configurations, [ + "relaxed", + "use shared memory", + "expect failure" + ]); + renderTable("Operands", graphs[subgraph].details.operands, [ + "index", + "name", + "type", + "dimensions", + "scale", + "zero point", + "channel dim", + "lifetime", + "data" + ]); + renderTable("Operations", graphs[subgraph].details.operations, [ + "index", + "opcode", + "inputs", + "outputs" + ]); + } + + // Re-render all the information related to a subgraph. + // Invoked everytime when the main-selector changes. + function renderSubgraph(subgraph) { + renderTables(subgraph); + renderSvg(subgraph); + renderSidebar(null); // Clear sidebar. + } + + // Renders the main-selector and the first subgraph choice in the main-selector. + // Only invoked once when the page gets loaded the first time. + function renderMain() { + var selector = document.getElementById("main-selector"); + var first = true; + for (var subgraph in graphs) { + var option = document.createElement("option"); + option.value = subgraph; + option.text = subgraph; + selector.appendChild(option); + if (first) { + first = false; + renderSubgraph(subgraph); + } + } + } + renderMain(); + </script> +</body> +</html> diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/test_generator.py b/tests/nnapi/nnapi_test_generator/android-q-beta/test_generator.py new file mode 100644 index 000000000..f49385c78 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/test_generator.py @@ -0,0 +1,1236 @@ +#!/usr/bin/python3 + +# Copyright 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. + +"""NN model compiler + +Contain classes definition and utilify functions for compiling models and +examples into NDK-based CTS and VTS unit tests. + +Used by cts_generator.py, vts_generator.py, and spec_visualizer.py +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import argparse +import copy +from functools import reduce +import itertools +import math +import os +import re +import struct +import sys +import contextlib +import pprint +import numpy as np + +def GetJointStr(l, sep=", ", method=str): + return sep.join([method(i) for i in l]) + +# Print in C float literal format +def PrettyPrintAsFloat(x): + s = str(float(x)) + if s.find(".") >= 0 or s.find("e") >= 0: + return s + "f" + else: + return s + ".0f" + +# Transform from original type to float32 +def Dequantize(v, ty): + v -= ty.zeroPoint + if ty.scale != 0: + v *= ty.scale + if isinstance(ty.extraParams, SymmPerChannelQuantParams): + v *= ty.extraParams.GetScalesBroadcastArray(ty.dimensions) + return v + +# Transform float32 to target data type +def Quantize(v, ty): + if ty.scale != 0: + v /= ty.scale + if isinstance(ty.extraParams, SymmPerChannelQuantParams): + v = v / ty.extraParams.GetScalesBroadcastArray(ty.dimensions) + v += ty.zeroPoint + if not ty.IsFloat(): + v = np.round(v) + v = int(v) if np.isscalar(v) else v.astype(int) + if ty.type == "TENSOR_QUANT8_ASYMM": + v = np.minimum(np.maximum(v, 0), 255) + elif ty.type == "TENSOR_QUANT16_ASYMM": + v = np.minimum(np.maximum(v, 0), 65535) + elif ty.type == "TENSOR_QUANT8_SYMM_PER_CHANNEL": + v = np.minimum(np.maximum(v, -127), 127) + elif ty.type == "UINT32": + v = np.maximum(v, 0) + return v + +@contextlib.contextmanager +def SmartOpen(filename=None, mode="w"): + if filename and filename != '-': + fh = open(filename, mode) + else: + fh = sys.stdout + + try: + yield fh + finally: + if fh is not sys.stdout: + fh.close() + +# Tracking objects inside a model with a unique name +class NamedObject: + existingNames = set() + + def __init__(self, *args, sep="_", showZero=False, startsFrom=0, skipRenaming=False): + name = GetJointStr([i for i in args if i is not None and i != ""], sep=sep) + if skipRenaming: + self.name = name + return + # make the name unique by renaming with a suffix number + uniqueName = name if showZero is False else name + sep + str(startsFrom) + while uniqueName in self.__class__.existingNames: + startsFrom += 1 + uniqueName = name + sep + str(startsFrom) + self.__class__.existingNames.add(uniqueName) + self.name = uniqueName + + def __str__(self): + return self.name + __repr__ = __str__ + + # Since names are unique, objects with the same name are considered equal + def __eq__(self, other): + return isinstance(other, NamedObject) and self.name == other.name + + def __ne__(self, other): + return not self.__eq__(other) + + def __hash__(self): + return hash(self.name) + + def __lt__(self, other): + return self.name < other.name + +# Types, operands should all have a unique name since they share the same namespace +class NamedVariable(NamedObject): + existingNames = set() + def __init__(self, *args, sep="_", showZero=False, startsFrom=0, skipRenaming=False): + NamedObject.__init__(self, *args, sep=sep, showZero=showZero, + startsFrom=startsFrom, skipRenaming=skipRenaming) + +# Global variables in the spec namespace such as CreateModel, is_ignored, and examples +class GlobalVariable(NamedVariable): + def __init__(self, *args, skipRenaming=False): + NamedObject.__init__(self, *args, startsFrom=1, skipRenaming=skipRenaming) + +# Each test should have a unique name, but will not conflict with variables +class NamedTest(NamedObject): + existingNames = set() + def __init__(self, *args, startsFrom=0, skipRenaming=False): + NamedObject.__init__(self, *args, startsFrom=1, skipRenaming=skipRenaming) + +class Type(NamedVariable): + typesMap = dict() + typeLookup = { + "INT32": "int32_t", + "UINT32": "uint32_t", + "FLOAT32": "float", + "FLOAT16": "_Float16", + "TENSOR_INT32": "int32_t", + "TENSOR_FLOAT16": "_Float16", + "TENSOR_FLOAT32": "float", + "TENSOR_QUANT8_ASYMM": "uint8_t", + "TENSOR_QUANT8_SYMM": "int8_t", + "BOOL": "bool8", + "TENSOR_QUANT16_ASYMM": "uint16_t", + "TENSOR_QUANT16_SYMM": "int16_t", + "TENSOR_BOOL8": "bool8", + "TENSOR_QUANT8_SYMM_PER_CHANNEL": "int8_t", +# "OEM_SCALAR": this is service-defined. + "TENSOR_OEM_BYTE": "uint8_t", + } + + # types are named as "type0", "type1", ... + def __init__(self, vt, dimensions, scale, zeroPoint, name="type", skipRenaming=False, + extraParams=None): + NamedVariable.__init__(self, name, sep="", showZero=True, skipRenaming=skipRenaming) + self.type = vt + self.dimensions = dimensions + self.scale = float(scale) + self.zeroPoint = int(zeroPoint) + self.extraParams = extraParams + + # Factory for Type object, only create a new Type if requested type does + # not have a match with all existing types + @staticmethod + def GetType(vt, dimensions, scale=0, zeroPoint=0, extraParams=None): + key = ",".join([vt, str(dimensions), str(scale), str(zeroPoint), str(extraParams)]) + if key not in Type.typesMap: + Type.typesMap[key] = Type(vt, dimensions, scale, zeroPoint, extraParams=extraParams) + return Type.typesMap[key] + + @staticmethod + def GetAllTypes(): + # sort to ensure a stable order when dumping the code + return sorted(Type.typesMap.values()) + + # For backward-compatibility + @staticmethod + def GetTypeFromString(vt, shape, extraParams=None): + dimensions, scale, zeroPoint = Type.GetParsedShape(shape) + scale = float(scale) + zeroPoint = int(zeroPoint) + return Type.GetType(vt, dimensions, scale, zeroPoint, extraParams) + + # For backward-compatibility + @staticmethod + def GetParsedShape(shape): + # Parse shape + if (shape != "" and shape != "{}"): + left, sep, right = shape.partition('{') + real_shape, sep, right = right.partition('}') + shape = [int(x) for x in real_shape.split(",")] + # left now looks like "0.0f, 127.5f, " + scale, sep, zero_point = right.rpartition(',') + if scale == "": + if zero_point == "": + return shape, "0", "0" + return shape, zero_point, "0" + left, sep, scale = scale.partition(',') + return shape, scale.replace("f", ""), zero_point + else: + return [], "0", "0" + + def GetNumberOfElements(self): + return reduce(lambda x,y: x*y, self.dimensions, 1) + + def GetCppTypeString(self): + return Type.typeLookup[self.type] + + def IsFloat(self): + return self.GetCppTypeString() in ["float", "_Float16"] + + def IsBool(self): + return self.GetCppTypeString() == "bool8" + + def IsScalar(self): + return not self.type.startswith("TENSOR_") + + def GetElementByteSize(self): + cppTypeString = self.GetCppTypeString() + if cppTypeString in ["uint8_t", "int8_t", "bool8"]: + return 1 + elif cppTypeString in ["int16_t", "uint16_t", "_Float16"]: + return 2 + else: + return 4 + + def GetByteSize(self): + return self.GetElementByteSize() * self.GetNumberOfElements() + + def GetDimensionsString(self): + return "{" + GetJointStr(self.dimensions) + "}" + + def GetSignatureTuple(self): + return (self.type, self.dimensions, self.scale, self.zeroPoint) + + def ToUnspecifiedDim(self): + return Type.GetType(self.type, [0] * len(self.dimensions), self.scale, self.zeroPoint) + +# To track implicitly convertible parameter types +class ImplicitParameter(): + @staticmethod + def ImplicitConvertion(value): + if isinstance(value, Operand): + return value + for implicitType in ImplicitParameter.__subclasses__(): + if implicitType.IsCompatible(value): + return implicitType("param", value) + assert False, "%s not supported for implicit parameter"%value + + +# ExtraParams with per-channel quantization. +class SymmPerChannelQuantParams(): + def __init__(self, channelDim, scales, hide = False): + self.channelDim = channelDim + self.scales = scales + self.hide = hide + + def GetScalesBroadcastArray(self, dimensions): + bshape = [1] * len(dimensions) + bshape[self.channelDim] = len(self.scales) + return np.array(self.scales).reshape(bshape) + + def GetConstructor(self): + return "SymmPerChannelQuantParams({%s},%d)" % ( + ", ".join(str(x) + "f" for x in self.scales), self.channelDim) + + def GetVtsSetter(self): + return "channelQuant" + + def GetVtsConstructor(self): + return "SymmPerChannelQuantParams{.scales={%s}, .channelDim=%d}" % ( + ", ".join(str(x) + "f" for x in self.scales), self.channelDim) + + +# An operand that can be fed into operations. Also, an operand is always +# declared before operations. +class Operand(NamedVariable): + + def __init__(self, name, opType, value, backward=None, skipRenaming=False, extraParams=None): + NamedVariable.__init__(self, name, sep="", skipRenaming=skipRenaming) + if type(opType) is str: + self.type = Type.GetTypeFromString(opType, value, extraParams) + value = backward + else: + self.type = Type.GetType(*opType, extraParams=extraParams) + self.SetValue(value) + self.dimensions = self.type.dimensions + self.lifetime = "TEMPORARY_VARIABLE" + self.model_index = None + self.ins = [] + self.outs = [] + + def SetValue(self, value): + self.value = value if type(value) is list or type(value) is tuple else [value] + return self + + def SetValueFromNumpy(self, value): + self.value = value.flatten().tolist() + return self + + def GetValueAsNumpy(self): + return np.array(self.value).reshape(self.type.dimensions) + + # Print value as cpp-style list initialization + def GetListInitialization(self): + assert self.value is not None, \ + "Trying to print operand %s with None value"%(str(self)) + if self.type.IsFloat(): + return "{%s}"%(GetJointStr(self.value, method=PrettyPrintAsFloat)) + elif self.type.IsBool(): + return "{%s}"%(GetJointStr(self.value, method=lambda v: "true" if v else "false")) + else: + return "{%s}"%(GetJointStr(self.value, method=lambda x: str(int(x)))) + + def ToUnspecifiedDim(self): + self.dimensions = self.type.dimensions + self.type = self.type.ToUnspecifiedDim() + +# Base class of user-defined input/output operand +class InOut(Operand): + + def __init__(self, name, opType, backward=None, skipRenaming=False, extraParams=None): + Operand.__init__(self, name, opType, backward, None, skipRenaming=skipRenaming, extraParams=extraParams) + self.lifetime = "MODEL_INPUT" + self.index = 0 + + def Feed(self, value): + self.SetValue(value[self] if type(value) is dict else value) + return self + + def GetListInitialization(self): + return "{%d, %s}"%(self.index, super().GetListInitialization()) + +# A user-declared input operand +class Input(InOut): + def __init__(self, name, opType, backward=None, skipRenaming=False, extraParams=None): + InOut.__init__(self, name, opType, backward, skipRenaming=skipRenaming, extraParams=extraParams) + self.lifetime = "MODEL_INPUT" + +# A user-declared output operand +class Output(InOut): + def __init__(self, name, opType, backward=None, skipRenaming=False): + InOut.__init__(self, name, opType, backward, skipRenaming=skipRenaming) + self.lifetime = "MODEL_OUTPUT" + +# An output that we don't want to compare the results +class IgnoredOutput(Output): + def __init__(self, name, opType, backward=None, skipRenaming=False): + Output.__init__(self, name, opType, backward, skipRenaming=skipRenaming) + self.lifetime = "MODEL_OUTPUT" + def Feed(self, value): + numElements = reduce(lambda x,y: x*y, self.dimensions, 1) + self.value = [0 for x in range(numElements)] + return self + +# An explicitly declared parameter +class Parameter(Operand): + def __init__(self, name, opType, value, backward=None, skipRenaming=False, extraParams=None): + Operand.__init__(self, name, opType, value, backward, skipRenaming=skipRenaming, + extraParams=extraParams) + self.initializer = NamedVariable(str(self) + "_init") + self.lifetime = "CONSTANT_REFERENCE" if Configuration.useSHM() else "CONSTANT_COPY" + +# A shortcut for parameters of INT32 +class Int32Scalar(Parameter, ImplicitParameter): + def __init__(self, name, value): + Parameter.__init__(self, name, ("INT32", []), int(value)) + @staticmethod + def IsCompatible(value): + return type(value) is int + +# A shortcut for parameters of FLOAT16 +class Float16Scalar(Parameter, ImplicitParameter): + def __init__(self, name, value): + Parameter.__init__(self, name, ("FLOAT16", []), float(value)) + @staticmethod + def IsCompatible(value): + return False + +# A shortcut for parameters of FLOAT32 +class Float32Scalar(Parameter, ImplicitParameter): + def __init__(self, name, value): + Parameter.__init__(self, name, ("FLOAT32", []), float(value)) + @staticmethod + def IsCompatible(value): + return type(value) is float + +# A shortcut for parameters of BOOL +class BoolScalar(Parameter, ImplicitParameter): + def __init__(self, name, value): + Parameter.__init__(self, name, ("BOOL", []), bool(value)) + @staticmethod + def IsCompatible(value): + return type(value) is bool + +# A shortcut for parameter of 1-D TENSOR_INT32 +class Int32Vector(Parameter, ImplicitParameter): + def __init__(self, name, value): + Parameter.__init__(self, name, ("TENSOR_INT32", [len(value)]), [int(v) for v in value]) + @staticmethod + def IsCompatible(value): + if type(value) is not list and type(value) is not tuple: + return False + return all(type(i) is int for i in value) + +# A shortcut for parameter of 1-D TENSOR_FLOAT32 +class Float32Vector(Parameter, ImplicitParameter): + def __init__(self, name, value): + Parameter.__init__(self, name, ("TENSOR_FLOAT32", [len(value)]), [float(v) for v in value]) + @staticmethod + def IsCompatible(value): + if type(value) is not list and type(value) is not tuple: + return False + return all(type(i) is float for i in value) + +# An explicitly declared intermediate result +class Internal(Operand): + def __init__(self, name, opType, backward=None, skipRenaming=False): + Operand.__init__(self, name, opType, backward, None, skipRenaming=skipRenaming) + self.lifetime = "TEMPORARY_VARIABLE" + +# An operation in a model, does not need a name +class Operation: + + def __init__(self, optype, ins, outs): + self.optype = optype + self.SetInputs(ins) + self.SetOutputs(outs) + + # for the ease of debugging + def __str__(self): + insString = GetJointStr(self.ins) + outsString = GetJointStr(self.outs) + return "Operation %s: [%s] -> [%s]"%(self.optype, insString, outsString) + __repr__ = __str__ + + def SetInputs(self, ins): + self.ins = [ImplicitParameter.ImplicitConvertion(i) for i in ins] + return self + + def SetOutputs(self, outs): + self.outs = list(outs) + return self + +# Main interface +class Model: + models = list() + + def __init__(self, name=None): + self.name = name + self.operations = [] + self.operands = [] + self.isRelaxed = False + self.compiled = False + self.dumped = False + self.hasDynamicOutputShape = False + self.version = FileNames.version + Model.models.append(self) + + def WithSuffix(self, *args): + self.createFunctionName = GlobalVariable("CreateModel", self.name, *args) + self.createTestFunctionName = GlobalVariable("createTestModel", self.name, *args) + self.isIgnoredFunctionName = GlobalVariable("is_ignored", self.name, *args) + return self + + def AddOperation(self, operation): + self.operations.append(operation) + for i in operation.ins: + if i not in self.operands: + self.operands.append(i) + for o in operation.outs: + if o not in self.operands: + self.operands.append(o) + return self + + def Operation(self, op_name, *args): + return self.AddOperation(Operation(op_name, args, [])) + + def To(self, *args): + assert len(self.operations) > 0 + if type(args[0]) is tuple or type(args[0]) is list: + outs = args[0] + else: + outs = args + self.operations[-1].SetOutputs(outs) + for o in outs: + if o not in self.operands: + self.operands.append(o) + return self + + def RelaxedExecution(self, isRelaxed): + self.isRelaxed = isRelaxed + return self + + def TestDynamicOutputShape(self, hasDynamicOutputShape): + self.hasDynamicOutputShape = hasDynamicOutputShape + return self + + # Sets the version of the model in compliance tests. Set to None to disable the test. + def IntroducedIn(self, ver): + self.version = ver + return self + + def GetTypes(self): + return sorted(list(set(op.type for op in self.operands))) + + def GetInputs(self): + return [i for i in self.operands if isinstance(i, Input)] + + def GetOutputs(self): + return [o for o in self.operands if isinstance(o, Output)] + + def GetInputsIndex(self): + return [i for i,op in enumerate(self.operands) if isinstance(op, Input)] + + def GetOutputsIndex(self): + return [o for o,op in enumerate(self.operands) if isinstance(op, Output)] + + def GetIndexOfOperands(self, operands): + return [self.operands.index(i) for i in operands] + + def GetIgnoredOutputs(self): + return [o for o in self.operands if isinstance(o, IgnoredOutput)] + + def GetParameters(self): + return [p for p in self.operands if isinstance(p, Parameter)] + + def GetEquivalentOperands(self, targets): + return [self.operands[self.operands.index(t)] for t in targets] + + def UpdateEquivalentOperands(self, targets): + for t in targets: + self.operands[self.operands.index(t)] = t + return self + + def SetOperandIndex(self): + for ind, i in enumerate(self.GetInputs()): + i.index = ind + for ind, o in enumerate(self.GetOutputs()): + o.index = ind + for ind, op in enumerate(self.operands): + op.model_index = ind + return self + + def SetOperandInsAndOuts(self): + for op in self.operands: + op.ins = list() + op.outs = list() + for op in self.operations: + op.ins = self.GetEquivalentOperands(op.ins) + op.outs = self.GetEquivalentOperands(op.outs) + for i in op.ins: + i.outs.append(op) + for o in op.outs: + o.ins.append(op) + return self + + def TopologicalSortHelper(self, op, deps, visited): + if op in visited: + assert op not in deps, "Cycle detected in the graph" + else: + visited.add(op) + for i in deps[op]: + self.TopologicalSortHelper(i, deps, visited) + self.operations.append(op) + deps.pop(op) + + # Topological sort of the operations, and detect if there is a cycle is the graph + def TopologicalSort(self): + deps = {op: list() for op in self.operations} + [deps[o].append(i) for op in self.operands for o in op.outs for i in op.ins] + operations = self.operations.copy() + self.operations = [] + visited = set() + for op in operations: + self.TopologicalSortHelper(op, deps, visited) + + def SetOutputUnspecified(self): + for op in self.operands: + op.dimensions = op.type.dimensions + if self.hasDynamicOutputShape: + for op in self.GetOutputs(): + op.ToUnspecifiedDim() + return self + + def Compile(self): + if self.compiled: + return self + self.SetOperandIndex() + self.SetOperandInsAndOuts() + self.TopologicalSort() + self.SetOutputUnspecified() + # Do not check compliance for relaxed mode and dynamic output shape tests. + if self.isRelaxed or self.hasDynamicOutputShape: + self.IntroducedIn(None) + self.compiled = True + return self + +# To track implicitly convertible variation types +class ImplicitVariation: + @staticmethod + def ImplicitConvertion(value): + if isinstance(value, ModelVariation): + return value + for implicitType in ImplicitVariation.__subclasses__(): + value = value if type(value) is tuple or type(value) is list else [value] + if implicitType.IsCompatible(value[0]): + var = implicitType(value[0]) + if len(value) > 1: + var.Identify(*value[1:]) + return var + assert False, "%s not supported for implicit variation"%value[0] + +# The base class for model variations +class ModelVariation: + + def __init__(self, name=None): + self.targetOperands = {} + self.name = name + + def ApplyToHelper(self, model, args, feedDicts, transform): + opVarList = [] + for op in model.GetEquivalentOperands(sorted(args.keys())): + opVar = op + feedDictsVar = [] + if isinstance(op, Input) or isinstance(op, Output): + for feedDict in feedDicts: + op_tmp = copy.deepcopy(op) + if op_tmp in feedDict[0]: + opVar = transform(op_tmp.Feed(feedDict[0]), args[op_tmp]) + elif op_tmp in feedDict[1]: + opVar = transform(op_tmp.Feed(feedDict[1]), args[op_tmp]) + else: + assert False + feedDictsVar.append(opVar.value) + assert type(op) == type(opVar), "Can not handle %s -> %s"%(type(op), type(opVar)) + else: + opVar = transform(op, args[op]) + # handle Parameter -> Input + if isinstance(opVar, Input) or isinstance(opVar, Output): + feedDictsVar = [opVar.value] * len(feedDicts) + if isinstance(opVar, Input) or isinstance(opVar, Output): + for feedDict, feedDictVar in zip(feedDicts, feedDictsVar): + if opVar in feedDict[1]: + feedDict[1][opVar] = feedDictVar + else: + feedDict[0][opVar] = feedDictVar + opVarList.append(opVar) + return opVarList + + # Make a deepcopy of the model and feedDicts, and apply the change + def ApplyTo(self, modelOrigin, feedDictsOrigin): + model, feedDicts = copy.deepcopy((modelOrigin, feedDictsOrigin)) + model.compiled = False + model.dumped = False + + if not self.targetOperands: + self.AutoIdentify(model) + + # get transformed operands and update feedDicts + operandsVar = self.ApplyToHelper( + model, self.targetOperands, feedDicts, self.TransformOperand) + + model = self.TransformModel(model) + model.UpdateEquivalentOperands(operandsVar) + return model, feedDicts + + def IdentifyOperands(self, args=None): + if args is None: + return self + self.targetOperands = args if type(args) is dict else {i: None for i in args} + return self + + def Identify(self, operandArgs=None, paramArgs=None): + self.IdentifyOperands(operandArgs) + return self + + # Set variation to its default name + def SetToDefaultName(self): + self.name = "" + return self + + # Automatically select the target operand list + def AutoIdentify(self, model): + return self + + # Transform operands that are marked by IdentifyOperands() + def TransformOperand(self, op, arg=None): + return op + + # Transform the model + def TransformModel(self, model): + return model + +# Default variation that does nothing +class DefaultVariation(ModelVariation): + + def __init__(self, name=None): + ModelVariation.__init__(self, name=name) + +# Convert operand data type +class DataTypeConverter(ModelVariation, ImplicitVariation): + + def __init__(self, targetType=None, name=None): + ModelVariation.__init__(self, name=name) + if targetType is not None: + assert DataTypeConverter.IsCompatible(targetType) + self.targetType = targetType + + @staticmethod + def IsCompatible(value): + return value.lower() in ["float16", "int32"] + + def SetToDefaultName(self): + if self.targetType is not None: + self.name = self.targetType.lower() + return self + # get all target types + targetTypes = list(zip(*self.targetOperands.values()))[0] + if "TENSOR_QUANT8_SYMM_PER_CHANNEL" in targetTypes: + self.name = "channelQuant8" + elif "TENSOR_QUANT8_ASYMM" in targetTypes: + self.name = "quant8" + elif "TENSOR_INT32" in targetTypes: + self.name = "int32" + elif "TENSOR_FLOAT16" in targetTypes: + self.name = "float16" + else: + self.name = "float32" + return self + + def AutoIdentify(self, model): + if self.targetType is not None: + # By default, select all the float32 tensors/scalars + targets = {op: ["TENSOR_" + self.targetType.upper()] \ + for op in model.operands if op.type.type == "TENSOR_FLOAT32"} + targets.update({op: [self.targetType.upper()] \ + for op in model.operands if op.type.type == "FLOAT32"}) + self.Identify(targets) + return self + + def TransformOperand(self, op, arg=None): + if len(arg) == 1: + typeTuple = (arg[0], op.type.dimensions) + else: + typeTuple = (arg[0], op.type.dimensions, *arg[1:]) + # To handle Internal operands + if op.value is None or op.type.GetNumberOfElements() == 0: + op.type = Type.GetType(*typeTuple) + else: + v = Dequantize(op.GetValueAsNumpy().astype(np.float32), op.type) + op.type = Type.GetType(*typeTuple) + v = Quantize(v, op.type) + op.SetValueFromNumpy(v) + return op + +# Convert model to turn on/off relaxed computation +class RelaxedModeConverter(ModelVariation, ImplicitVariation): + + def __init__(self, isRelaxed=True, name=None): + ModelVariation.__init__(self, name=name) + if isinstance(isRelaxed, bool): + self.isRelaxed = isRelaxed + else: + assert RelaxedModeConverter.IsCompatible(isRelaxed.lower()) + self.isRelaxed = True + + @staticmethod + def IsCompatible(value): + return value.lower() in ["relaxed"] + + def SetToDefaultName(self): + self.name = "relaxed" if self.isRelaxed else "float" + return self + + def TransformModel(self, model): + model.RelaxedExecution(self.isRelaxed) + return model + +# Convert data layout between "NHWC" amd "NCHW" +class DataLayoutConverter(ModelVariation, ImplicitVariation): + + def __init__(self, targetLayout="nchw", name=None): + ModelVariation.__init__(self, name=name) + self.targetLayout = targetLayout.lower() + assert DataLayoutConverter.IsCompatible(self.targetLayout) + self.perm = (0, 3, 1, 2) if self.targetLayout == "nchw" else (0, 2, 3, 1) + self.param = True if self.targetLayout == "nchw" else False + + @staticmethod + def IsCompatible(value): + return value.lower() in ["nhwc", "nchw"] + + def SetToDefaultName(self): + self.name = self.targetLayout + return self + + def TransformOperand(self, op, arg=None): + if len(op.type.dimensions) == 4: + # To handle Internal operands + if op.value is not None and op.type.GetNumberOfElements() != 0: + op.SetValueFromNumpy(op.GetValueAsNumpy().transpose(self.perm)) + newDim = [op.type.dimensions[i] for i in self.perm] + op.type = Type.GetType(op.type.type, newDim, op.type.scale, op.type.zeroPoint) + elif len(op.type.dimensions) == 1 and len(op.value) == 4: + op.SetValueFromNumpy(op.GetValueAsNumpy()[list(self.perm)]) + elif op.type.type == "BOOL": + op.SetValue(self.param) + else: + assert False, "%s not supported by DataLayoutConverter"%op + return op + +# Convert data by tansposing and removing axis +class AxisConverter(ModelVariation): + + def __init__(self, origin, target, dim, drop=[], name=None): + ModelVariation.__init__(self, name=name) + self.origin = origin + self.target = target + assert all(i >= -dim and i < dim for i in [self.origin, self.target]) + self.dim = dim + self.perm = list(range(dim)) + self.perm.insert(target if target >= 0 else target + dim, self.perm.pop(origin)) + self.drop = [drop] if type(drop) is int else list(drop) + assert all(i >= -dim and i < dim for i in self.drop) + self.drop = [i if i >= 0 else i + dim for i in self.drop] + assert target not in self.drop and target + dim not in self.drop + + def SetToDefaultName(self): + axis = self.target if self.target >= 0 else self.target + self.dim + axis -= sum(i < axis for i in self.drop) + neg = "" if self.target >= 0 else "_neg" + self.name = "dim%d_axis%d%s"%(self.dim - len(self.drop), axis, neg) + return self + + def TransposeAxis(self, op): + if op.type.type == "INT32": + op.SetValue(self.target) + elif len(op.type.dimensions) == self.dim: + # To handle Internal operands + if op.value is not None: + op.SetValueFromNumpy(op.GetValueAsNumpy().transpose(self.perm)) + newDim = [op.type.dimensions[i] for i in self.perm] + op.type = Type.GetType(op.type.type, newDim, op.type.scale, op.type.zeroPoint) + else: + assert False, "%s not supported by AxisConverter"%op + return op + + def RemoveAxis(self, op): + if op.type.type == "INT32": + if op.value[0] >= 0: + op.SetValue(op.value[0] - sum(i < op.value[0] for i in self.drop)) + else: + op.SetValue(op.value[0] + sum(i > (op.value[0] + self.dim) for i in self.drop)) + elif len(op.type.dimensions) == self.dim: + if op.value is not None: + val = op.GetValueAsNumpy() + for i in sorted(self.drop, reverse=True): + val = np.take(val, 0, axis=i) + op.SetValueFromNumpy(val) + newDim = [op.type.dimensions[i] for i in range(self.dim) if i not in self.drop] + op.type = Type.GetType(op.type.type, newDim, op.type.scale, op.type.zeroPoint) + else: + assert False, "%s not supported by AxisConverter"%op + return op + + def TransformOperand(self, op, arg=None): + op = self.TransposeAxis(op) + op = self.RemoveAxis(op) + return op + +# Convert a Parameter to Input +class ParameterAsInputConverter(ModelVariation, ImplicitVariation): + + def __init__(self, arg="as_input", prefix="weight", name=None): + ModelVariation.__init__(self, name=name) + assert ParameterAsInputConverter.IsCompatible(arg.lower()) + self.prefix = prefix + + @staticmethod + def IsCompatible(value): + return value.lower() in ["as_input"] + + def SetToDefaultName(self): + self.name = self.prefix + "_as_input" + return self + + def TransformOperand(self, op, arg=None): + assert isinstance(op, Parameter), "%s cannot be converted to Input."%type(op) + newop = Input(op.name, op.type.GetSignatureTuple(), skipRenaming=True, extraParams=op.type.extraParams) + newop.SetValue(op.value) + return newop + +# Convert Output based on activation +class ActivationConverter(ModelVariation, ImplicitVariation): + # (Enum, low, high) + actMap = { + "none": (0, None, None), + "relu": (1, 0.0, None), + "relu1": (2, -1.0, 1.0), + "relu6": (3, 0.0, 6.0), + } + def __init__(self, act="relu", name=None): + ModelVariation.__init__(self, name=name) + self.act = act.lower() + assert ActivationConverter.IsCompatible(self.act) + self.enum = ActivationConverter.actMap[self.act][0] + self.low = ActivationConverter.actMap[self.act][1] + self.high = ActivationConverter.actMap[self.act][2] + + @staticmethod + def IsCompatible(value): + return value.lower() in ActivationConverter.actMap.keys() + + def SetToDefaultName(self): + self.name = self.act + return self + + def TransformOperand(self, op, arg=None): + if op.type.type == "INT32": # activation enum + return op.SetValue(self.enum) + else: + assert isinstance(op, Output) + v = op.GetValueAsNumpy() + if self.low is not None: + low = Quantize(self.low, op.type) + v = np.maximum(v, low) + if self.high is not None: + high = Quantize(self.high, op.type) + v = np.minimum(v, high) + return op.SetValueFromNumpy(v) + +class DynamicOutputShapeConverter(ModelVariation): + def __init__(self, name=None): + ModelVariation.__init__(self, name=name) + + def SetToDefaultName(self): + self.name = "dynamic_output_shape" + return self + + def TransformModel(self, model): + model.TestDynamicOutputShape(True) + return model + +# An example is always attached to a model, and could have multiple variations +class Example: + examples = [] + versionOverrides = {} + + def __init__(self, *args, model=None, name=None): + self.model = Model.models[-1] if model is None else model + self.name = name + self.expectedMultinomialDistributionTolerance = None + self.expectFailure = False + self.feedDicts = [] + for feedDict in args: + if type(feedDict) is tuple or type(feedDict) is list: + self.feedDicts.append(feedDict) + elif type(feedDict) is dict: + self.feedDicts.append(( + {i: feedDict[i] for i in self.model.GetInputs()}, + {o: feedDict[o] for o in self.model.GetOutputs()} + )) + else: + assert False + if Configuration.test_dynamic_output_shape: + self.variations = [[DefaultVariation(), DynamicOutputShapeConverter()]] + else: + self.variations = [] + Example.examples.append(self) + + @staticmethod + def SetVersion(ver, *args): + for name in args: + Example.versionOverrides[name] = ver + + # Main entrance of test generator + @staticmethod + def DumpAllExamples(DumpModel=None, model_fd=None, + DumpExample=None, example_fd=None, + DumpTest=None, test_fd=None): + Example.CombineAllExamples() + for example in Example.examples: + example.Dump(DumpModel, model_fd, DumpExample, example_fd, DumpTest, test_fd) + + # Combine examples with the same model, same name, and same set of variations + @staticmethod + def CombineAllExamples(): + modelMap = {} + newExamples = [] + for example in Example.examples: + key = (example.model, example.name, tuple(tuple(e) for e in example.variations)) + if key in modelMap: + modelMap[key].Combine(example) + else: + modelMap[key] = example + newExamples.append(example) + Example.examples = newExamples + + def AddVariations(self, *args, includeDefault=True, defaultName=None): + self.variations.append([DefaultVariation(defaultName)] if includeDefault else []) + self.variations[-1].extend(ImplicitVariation.ImplicitConvertion(i) for i in args) + return self + + def AddNchw(self, *args, includeDefault=True, defaultName="nhwc"): + var = DataLayoutConverter("nchw").Identify(args) + self.AddVariations(var, includeDefault=includeDefault, defaultName=defaultName) + return self + + def AddRelaxed(self, isRelaxed=True, includeDefault=True, defaultName=None): + var = RelaxedModeConverter(isRelaxed) + self.AddVariations(var, includeDefault=includeDefault, defaultName=defaultName) + return self + + def AddInput(self, *args, includeDefault=True, defaultName=None): + var = ParameterAsInputConverter().Identify(args) + self.AddVariations(var, includeDefault=includeDefault, defaultName=defaultName) + return self + + def AddRelu(self, *args, includeDefault=True, defaultName=None): + var = ActivationConverter("relu").Identify(args) + self.AddVariations(var, includeDefault=includeDefault, defaultName=defaultName) + return self + + def AddAllActivations(self, *args): + var = [ActivationConverter(i).Identify(args) + for i in sorted(ActivationConverter.actMap.keys())] + self.AddVariations(*var, includeDefault=False) + return self + + def GuessOriginalAxisAndDim(self, *args): + origin = None + dim = None + for arg in args: + if arg.type.type == "INT32": + origin = arg.value[0] + else: + if dim is None: + dim = len(arg.type.dimensions) + else: + assert dim == len(arg.type.dimensions) + assert dim is not None + origin = dim - 1 if origin is None else origin + origin = origin + dim if origin < 0 else origin + return origin, dim + + def AddAxis(self, axis, *args, includeDefault=True, defaultName=None): + origin, dim = self.GuessOriginalAxisAndDim(*args) + axis = [axis] if type(axis) is int else list(axis) + var = [AxisConverter(origin, a, dim).Identify(args) for a in axis] + self.AddVariations(*var, includeDefault=includeDefault, defaultName=defaultName) + return self + + def AddAllPositiveAxis(self, *args): + origin, dim = self.GuessOriginalAxisAndDim(*args) + var = [AxisConverter(origin, a, dim).Identify(args) for a in range(dim)] + self.AddVariations(*var, includeDefault=False) + return self + + def AddAllAxis(self, *args): + origin, dim = self.GuessOriginalAxisAndDim(*args) + var = [AxisConverter(origin, a, dim).Identify(args) for a in range(-dim, dim)] + self.AddVariations(*var, includeDefault=False) + return self + + def AddDims(self, dims, *args, includeDefault=True, defaultName=None): + origin, dim = self.GuessOriginalAxisAndDim(*args) + dims = [dims] if type(dims) is int else list(dims) + drop = list(range(dim)) + drop.pop(origin) + var = [AxisConverter(origin, origin, dim, drop[0:(dim-i)]).Identify(args) for i in dims] + self.AddVariations(*var, includeDefault=includeDefault, defaultName=defaultName) + return self + + def AddAllDims(self, *args): + origin, dim = self.GuessOriginalAxisAndDim(*args) + drop = list(range(dim)) + drop.pop(origin) + var = [AxisConverter(origin, origin, dim, drop[0:i]).Identify(args) for i in range(dim)] + self.AddVariations(*var, includeDefault=False) + return self + + def AddAllDimsAndPositiveAxis(self, *args): + origin, dim = self.GuessOriginalAxisAndDim(*args) + var = [AxisConverter(origin, j, dim, range(i)).Identify(args) \ + for i in range(dim) for j in range(i, dim)] + self.AddVariations(*var, includeDefault=False) + return self + + def AddAllDimsAndAxis(self, *args): + origin, dim = self.GuessOriginalAxisAndDim(*args) + var = [AxisConverter(origin, k, dim, range(i)).Identify(args) \ + for i in range(dim) for j in range(i, dim) for k in [j, j - dim]] + self.AddVariations(*var, includeDefault=False) + return self + + def Combine(self, other): + assert self.model is other.model, "Only examples targetting the same model can be combined" + assert tuple(self.variations) == tuple(other.variations), \ + "Only examples with the same set of variations can be combined" + assert self.name == other.name, "Only examples with the same name can be combined" + self.feedDicts.extend(other.feedDicts) + return self + + def Dump(self, DumpModel, model_fd, DumpExample, example_fd, DumpTest, test_fd): + [v.SetToDefaultName() for vs in self.variations for v in vs if v.name is None] + for variationList in itertools.product(*self.variations): + # Apply variations + modelOrigin, feedDictsOrigin = self.model, self.feedDicts + self.model, self.feedDicts = copy.deepcopy((self.model, self.feedDicts)) + for variation in variationList: + self.model, self.feedDicts = variation.ApplyTo(self.model, self.feedDicts) + # Concat names for test and examples + varNames = [v.name for v in variationList] + self.testName = NamedTest(FileNames.specName, self.model.name, self.name, *varNames) + self.examplesName = GlobalVariable("examples", self.model.name, self.name, *varNames) + if str(self.testName) in Example.versionOverrides: + self.model.IntroducedIn(Example.versionOverrides[str(self.testName)]) + self.model.WithSuffix(*varNames).Compile() + # Dump files + if DumpModel is not None and model_fd is not None: + DumpModel(self.model, model_fd) + if DumpExample is not None and example_fd is not None: + DumpExample(self, example_fd) + if DumpTest is not None and test_fd is not None: + DumpTest(self, test_fd) + # Restore model and feedDicts before variation + self.model = modelOrigin + self.feedDicts = feedDictsOrigin + return self + + # Specifies the RANDOM_MULTINOMIAL distribution tolerance. + # If set to greater than zero, the input is compared as log-probabilities + # to the output and must be within this tolerance to pass. + def WithMultinomialDistributionTolerance(self, expectedTolerance): + assert self.expectFailure is False + self.expectedMultinomialDistributionTolerance = expectedTolerance + return self + + # Specifies that this example is expected to fail during compilation or execution. + def ExpectFailure(self): + assert self.expectedMultinomialDistributionTolerance is None + self.expectFailure = True + return self + +class FileNames: + specFiles = [] + specNames = [] + modelFiles = [] + exampleFiles = [] + testFiles = [] + specFile = "" + specName = "" + modelFile = "" + exampleFile = "" + testFile = "" + version = "" + fileIndex = 0 + + @staticmethod + def InitializeFileLists(spec, model, example, test): + # get all spec files and target files + if os.path.isfile(spec): + FileNames.specFiles = [os.path.abspath(spec)] + elif os.path.isdir(spec): + FileNames.specFiles = sorted([os.path.abspath(os.path.join(spec, f)) + for f in os.listdir(spec) if f.endswith(".mod.py")]) + else: + assert False, "%s is neither a file or a directory"%spec + FileNames.specNames = [re.sub(r"\..*", "", os.path.basename(f)) + for f in FileNames.specFiles] + FileNames.modelFiles = FileNames.ParseTargetFiles(model, ".model.cpp") + FileNames.exampleFiles = FileNames.ParseTargetFiles(example, ".example.cpp") + FileNames.testFiles = FileNames.ParseTargetFiles(test, ".mod.py.cpp") + + @staticmethod + def ParseTargetFiles(arg, ext): + numFiles = len(FileNames.specFiles) + absPath = os.path.abspath(arg) + if os.path.isdir(arg): + target = [os.path.join(absPath, f + ext) for f in FileNames.specNames] + elif arg == "-": + target = ["-"] * numFiles + else: + target = [absPath] * numFiles + return target + + @staticmethod + def NextFile(): + if FileNames.fileIndex >= len(FileNames.specFiles): + return False + FileNames.specFile = FileNames.specFiles[FileNames.fileIndex] + FileNames.specName = FileNames.specNames[FileNames.fileIndex] + FileNames.modelFile = FileNames.modelFiles[FileNames.fileIndex] + FileNames.exampleFile = FileNames.exampleFiles[FileNames.fileIndex] + FileNames.testFile = FileNames.testFiles[FileNames.fileIndex] + FileNames.fileIndex += 1 + NamedObject.existingNames = set() + NamedVariable.existingNames = set() + NamedTest.existingNames = set() + Type.typesMap = dict() + Model.models = list() + Example.examples = list() + Configuration.use_shm_for_weights = False + + # Extract version from absolute file path. + versionMatch = re.findall(r"/V\d_\d/", FileNames.specFile) + if len(versionMatch) == 1: + FileNames.version = versionMatch[0].strip('/') + else: + FileNames.version = None + return True + +class Configuration: + use_shm_for_weights = False + force_regenerate = False + test_dynamic_output_shape = True + + @staticmethod + def useSHM(): + return Configuration.use_shm_for_weights diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/lstm_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/lstm_float.mod.py new file mode 100644 index 000000000..60eec8280 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/lstm_float.mod.py @@ -0,0 +1,145 @@ +# Copyright 2018, 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. + +model = Model() + +n_batch = 1 +n_input = 2 +# n_cell and n_output have the same size when there is no projection. +n_cell = 4 +n_output = 4 + +input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input)) + +input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) + +recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) + +cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}") +cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{0}") +cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{0}") + +input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) + +projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}") +projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}") + +output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) +cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) + +activation_param = Int32Scalar("activation_param", 4) # Tanh +cell_clip_param = Float32Scalar("cell_clip_param", 0.) +proj_clip_param = Float32Scalar("proj_clip_param", 0.) + +scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4))) +output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) +cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) + +model = model.Operation("LSTM", + input, + + input_to_input_weights, + input_to_forget_weights, + input_to_cell_weights, + input_to_output_weights, + + recurrent_to_input_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 +).To([scratch_buffer, output_state_out, cell_state_out, output]) +model = model.RelaxedExecution(True) + +# Example 1. Input in operand 0, +input0 = {input_to_input_weights: [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524], + input_to_forget_weights: [0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212], + input_to_cell_weights: [-0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778], + input_to_output_weights: [-0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578], + + input_gate_bias: [0.,0.,0.,0.], + forget_gate_bias: [1.,1.,1.,1.], + cell_gate_bias: [0.,0.,0.,0.], + output_gate_bias: [0.,0.,0.,0.], + + recurrent_to_input_weights: [ + -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, + -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, + -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296], + + recurrent_to_cell_weights: [ + -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, + -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, + -0.46367589, 0.26016325, -0.03894562, -0.16368064], + + recurrent_to_forget_weights: [ + -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, + -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, + 0.28053468, 0.01560611, -0.20127171, -0.01140004], + + recurrent_to_output_weights: [ + 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, + 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, + -0.51818722, -0.15390486, 0.0468148, 0.39922136], + + cell_to_input_weights: [], + cell_to_forget_weights: [], + cell_to_output_weights: [], + + projection_weights: [], + projection_bias: [], +} + +test_input = [2., 3.] +output_state = [0, 0, 0, 0] +cell_state = [0, 0, 0, 0] +golden_output = [-0.02973187, 0.1229473, 0.20885126, -0.15358765,] +output0 = { + scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], + cell_state_out: [ -0.145439, 0.157475, 0.293663, -0.277353 ], + output_state_out: [ -0.0297319, 0.122947, 0.208851, -0.153588 ], + output: golden_output +} +input0[input] = test_input +input0[output_state_in] = output_state +input0[cell_state_in] = cell_state +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/stdout.txt.expect new file mode 100644 index 000000000..5fdca97d1 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_float/stdout.txt.expect @@ -0,0 +1,107 @@ +// clang-format off +// Generated file (from: lstm_float.mod.py). Do not edit +// clang-format off +// Generated file (from: lstm_float.mod.py). Do not edit +// clang-format off +// Generated file (from: lstm_float.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace lstm_float { +// Generated lstm_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace lstm_float + +void CreateModel(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); + OperandType type3(Type::TENSOR_FLOAT32, {0}); + OperandType type4(Type::TENSOR_FLOAT32, {4}); + OperandType type5(Type::TENSOR_FLOAT32, {0, 0}); + OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); + OperandType type7(Type::INT32, {}); + OperandType type8(Type::FLOAT32, {}); + OperandType type9(Type::TENSOR_FLOAT32, {1, 16}); + // 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}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored(int i) { + static std::set<int> ignore = {0}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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, {0.0f, 0.0f, 0.0f, 0.0f}}, {19, {0.0f, 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, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, lstm_float) { + execute(lstm_float::CreateModel, + lstm_float::is_ignored, + lstm_float::examples); +} + +#include "../generated/tests/lstm_float.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/depthwise_conv2d_quant8.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/depthwise_conv2d_quant8.mod.py new file mode 100644 index 000000000..051780877 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/depthwise_conv2d_quant8.mod.py @@ -0,0 +1,43 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 2}, 0.5f, 0") +f1 = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 2}, 0.5f, 0") +b1 = Input("op3", "TENSOR_INT32", "{2}, 0.25f, 0") +pad0 = Int32Scalar("pad0", 0) +act = Int32Scalar("act", 0) +stride = Int32Scalar("stride", 1) +cm = Int32Scalar("channelMultiplier", 1) +output = Output("op4", "TENSOR_QUANT8_ASYMM", "{1,1,1,2}, 1.f, 0") + +model = model.Operation("DEPTHWISE_CONV_2D", + i1, f1, b1, + pad0, pad0, pad0, pad0, + stride, stride, + cm, act).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [4, 16, 4, 32, 4, 64, 4, 128], + f1: + [2, 4, 2, 0, 2, 2, 2, 0], + b1: + [0, 0]} +# (i1 (depthconv) f1) +output0 = {output: # output 0 + [8, 48]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/stdout.txt.expect new file mode 100644 index 000000000..9c54412de --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_backward_compatibility_quant8/stdout.txt.expect @@ -0,0 +1,82 @@ +// clang-format off +// Generated file (from: depthwise_conv2d_quant8.mod.py). Do not edit +// clang-format off +// Generated file (from: depthwise_conv2d_quant8.mod.py). Do not edit +// clang-format off +// Generated file (from: depthwise_conv2d_quant8.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace depthwise_conv2d_quant8 { +// Generated depthwise_conv2d_quant8 test +#include "-" +// Generated model constructor +#include "-" +} // namespace depthwise_conv2d_quant8 + +void CreateModel(Model *model) { + OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 0); + OperandType type1(Type::TENSOR_INT32, {2}, 0.25f, 0); + OperandType type2(Type::INT32, {}); + OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 2}, 1.0f, 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 stride = model->addOperand(&type2); + auto channelMultiplier = model->addOperand(&type2); + auto act = 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 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); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_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(); +} + +std::vector<MixedTypedExample> examples = { +// 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 +}; + +TEST_F(GeneratedTests, depthwise_conv2d_quant8) { + execute(depthwise_conv2d_quant8::CreateModel, + depthwise_conv2d_quant8::is_ignored, + depthwise_conv2d_quant8::examples); +} + +#include "../generated/tests/depthwise_conv2d_quant8.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/conv_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/conv_float.mod.py new file mode 100644 index 000000000..f6b3e89ca --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/conv_float.mod.py @@ -0,0 +1,35 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 3, 1}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{1, 2, 2, 1}", [.25, .25, .25, .25]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) +pad0 = Int32Scalar("pad0", 0) +act = Int32Scalar("act", 0) +stride = Int32Scalar("stride", 1) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}") + +model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) +model = model.RelaxedExecution(True) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]} + +output0 = {output: # output 0 + [.875, .875, .875, .875]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/stdout.txt.expect new file mode 100644 index 000000000..cb6c58a3c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_float/stdout.txt.expect @@ -0,0 +1,85 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +void CreateModel(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 3, 1}); + OperandType type1(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto pad0 = model->addOperand(&type3); + auto stride = model->addOperand(&type3); + auto act = 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 stride_init[] = {1}; + model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_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}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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 +}; + +TEST_F(GeneratedTests, conv_float) { + execute(conv_float::CreateModel, + conv_float::is_ignored, + conv_float::examples); +} + +#include "../generated/tests/conv_float.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/mean_implicit.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/mean_implicit.mod.py new file mode 100644 index 000000000..be7b22bdb --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/mean_implicit.mod.py @@ -0,0 +1,41 @@ +# Copyright 2018, 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. + +i0 = Input("i0", ("TENSOR_FLOAT32", [2, 2])) # input 0 + +o1 = Output("o1", ("TENSOR_FLOAT32", [1, 2])) # output for model1 +o2 = Output("o2", ("TENSOR_FLOAT32", [2, 1])) # output for model2 +o3 = Output("o3", ("TENSOR_FLOAT32", [1])) # output for model3 + +model1 = Model().Operation("MEAN", i0, [0], 1).To(o1) # along axis 0, keep_dim=True +model2 = Model().Operation("MEAN", i0, [1], 1).To(o2) # along axis 1, keep_dim=True +model3 = Model().Operation("MEAN", i0, [0, 1], 0).To(o3) # along both axis, keep_dim=False + +inputs1 = {i0: [1, 2, 3, 4]} +outputs11 = {o1: [4, 6]} +outputs12 = {o2: [3, 7]} +outputs13 = {o3: [10]} + +inputs2 = {i0: [-1, -2, -3, -4]} +outputs21 = {o1: [-4, -6]} +outputs22 = {o2: [-3, -7]} +outputs23 = {o3: [-10]} + +Example((inputs1, outputs11), model=model1) +Example((inputs1, outputs12), model=model2) +Example((inputs1, outputs13), model=model3) + +Example((inputs2, outputs21), model=model1) +Example((inputs2, outputs22), model=model2) +Example((inputs2, outputs23), model=model3) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/stdout.txt.expect new file mode 100644 index 000000000..feebbcc81 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_parameter/stdout.txt.expect @@ -0,0 +1,262 @@ +// clang-format off +// Generated file (from: mean_implicit.mod.py). Do not edit +// clang-format off +// Generated file (from: mean_implicit.mod.py). Do not edit +// clang-format off +// Generated file (from: mean_implicit.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace mean_implicit { +// Generated mean_implicit test +#include "-" +// Generated model constructor +#include "-" +} // namespace mean_implicit + +void CreateModel(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {1, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {2, 1}); + OperandType type3(Type::TENSOR_FLOAT32, {1}); + OperandType type4(Type::TENSOR_INT32, {1}); + OperandType type5(Type::INT32, {}); + OperandType type6(Type::TENSOR_INT32, {2}); + // Phase 1, operands + auto i0 = model->addOperand(&type0); + auto param = model->addOperand(&type4); + auto param1 = model->addOperand(&type5); + auto o1 = model->addOperand(&type1); + // Phase 2, operations + static int32_t param_init[] = {0}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_MEAN, {i0, param, param1}, {o1}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {i0}, + {o1}); + assert(model->isValid()); +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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, {4.0f, 6.0f}}}, + // 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, {-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, {-4.0f, -6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, mean_implicit) { + execute(mean_implicit::CreateModel, + mean_implicit::is_ignored, + mean_implicit::examples); +} + +void CreateModel_2(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {1, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {2, 1}); + OperandType type3(Type::TENSOR_FLOAT32, {1}); + OperandType type4(Type::TENSOR_INT32, {1}); + OperandType type5(Type::INT32, {}); + OperandType type6(Type::TENSOR_INT32, {2}); + // Phase 1, operands + auto i0 = model->addOperand(&type0); + auto param2 = model->addOperand(&type4); + auto param3 = model->addOperand(&type5); + auto o2 = model->addOperand(&type2); + // Phase 2, operations + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t param3_init[] = {1}; + model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_MEAN, {i0, param2, param3}, {o2}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {i0}, + {o2}); + assert(model->isValid()); +} + +bool is_ignored_2(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_2 = { +// 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, {3.0f, 7.0f}}}, + // 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, {-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, {-3.0f, -7.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, mean_implicit_2) { + execute(mean_implicit::CreateModel_2, + mean_implicit::is_ignored_2, + mean_implicit::examples_2); +} + +void CreateModel_3(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {1, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {2, 1}); + OperandType type3(Type::TENSOR_FLOAT32, {1}); + OperandType type4(Type::TENSOR_INT32, {1}); + OperandType type5(Type::INT32, {}); + OperandType type6(Type::TENSOR_INT32, {2}); + // Phase 1, operands + auto i0 = model->addOperand(&type0); + auto param4 = model->addOperand(&type6); + auto param5 = model->addOperand(&type5); + auto o3 = model->addOperand(&type3); + // Phase 2, operations + static int32_t param4_init[] = {0, 1}; + model->setOperandValue(param4, param4_init, sizeof(int32_t) * 2); + static int32_t param5_init[] = {0}; + model->setOperandValue(param5, param5_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_MEAN, {i0, param4, param5}, {o3}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {i0}, + {o3}); + assert(model->isValid()); +} + +bool is_ignored_3(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_3 = { +// 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, {10.0f}}}, + // 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, {-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, {-10.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, mean_implicit_3) { + execute(mean_implicit::CreateModel_3, + mean_implicit::is_ignored_3, + mean_implicit::examples_3); +} + +#include "../generated/tests/mean_implicit.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/conv_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/conv_float.mod.py new file mode 100644 index 000000000..826f390c0 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/conv_float.mod.py @@ -0,0 +1,52 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [-200]) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +act = Int32Scalar("act", 0) # None activation +layout = Int32Scalar("layout", 0) # NHWC + +model = model.Operation("CONV_2D", i1, f1, b1, 1, 1, 1, act, layout).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8]} + +output0 = {output: # output 0 + [204, 120, 94, 104, 70, 164, 23, 112]} + +quant8 = DataTypeConverter().Identify({ + i1: ("TENSOR_QUANT8_ASYMM", 0.5, 128), + f1: ("TENSOR_QUANT8_ASYMM", 0.25, 128), + b1: ("TENSOR_INT32", 0.125, 0), + output: ("TENSOR_QUANT8_ASYMM", 2, 100) +}) + +# Instantiate an example +Example( + (input0, output0) +).AddVariations( + ("NCHW", [i1, f1, output], [layout]) +).AddVariations( + ("relu", [output], [act]), + ("relu6", [output], [act]), + includeDefault=False +).AddVariations( + ("as_input", [f1]) +).AddVariations( + "relaxed", quant8 +) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/stdout.txt.expect new file mode 100644 index 000000000..7d3cba69a --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_implicit_variation/stdout.txt.expect @@ -0,0 +1,1848 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +void CreateModel_relu(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu) { + execute(conv_float::CreateModel_relu, + conv_float::is_ignored_relu, + conv_float::examples_relu); +} + +void CreateModel_relu_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_relaxed) { + execute(conv_float::CreateModel_relu_relaxed, + conv_float::is_ignored_relu_relaxed, + conv_float::examples_relu_relaxed); +} + +void CreateModel_relu_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_quant8) { + execute(conv_float::CreateModel_relu_quant8, + conv_float::is_ignored_relu_quant8, + conv_float::examples_relu_quant8); +} + +void CreateModel_relu_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input) { + execute(conv_float::CreateModel_relu_weight_as_input, + conv_float::is_ignored_relu_weight_as_input, + conv_float::examples_relu_weight_as_input); +} + +void CreateModel_relu_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input_relaxed) { + execute(conv_float::CreateModel_relu_weight_as_input_relaxed, + conv_float::is_ignored_relu_weight_as_input_relaxed, + conv_float::examples_relu_weight_as_input_relaxed); +} + +void CreateModel_relu_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input_quant8) { + execute(conv_float::CreateModel_relu_weight_as_input_quant8, + conv_float::is_ignored_relu_weight_as_input_quant8, + conv_float::examples_relu_weight_as_input_quant8); +} + +void CreateModel_relu6(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6 = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6) { + execute(conv_float::CreateModel_relu6, + conv_float::is_ignored_relu6, + conv_float::examples_relu6); +} + +void CreateModel_relu6_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_relaxed) { + execute(conv_float::CreateModel_relu6_relaxed, + conv_float::is_ignored_relu6_relaxed, + conv_float::examples_relu6_relaxed); +} + +void CreateModel_relu6_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_quant8) { + execute(conv_float::CreateModel_relu6_quant8, + conv_float::is_ignored_relu6_quant8, + conv_float::examples_relu6_quant8); +} + +void CreateModel_relu6_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input) { + execute(conv_float::CreateModel_relu6_weight_as_input, + conv_float::is_ignored_relu6_weight_as_input, + conv_float::examples_relu6_weight_as_input); +} + +void CreateModel_relu6_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input_relaxed) { + execute(conv_float::CreateModel_relu6_weight_as_input_relaxed, + conv_float::is_ignored_relu6_weight_as_input_relaxed, + conv_float::examples_relu6_weight_as_input_relaxed); +} + +void CreateModel_relu6_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input_quant8) { + execute(conv_float::CreateModel_relu6_weight_as_input_quant8, + conv_float::is_ignored_relu6_weight_as_input_quant8, + conv_float::examples_relu6_weight_as_input_quant8); +} + +void CreateModel_nchw_relu(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu) { + execute(conv_float::CreateModel_nchw_relu, + conv_float::is_ignored_nchw_relu, + conv_float::examples_nchw_relu); +} + +void CreateModel_nchw_relu_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_relaxed) { + execute(conv_float::CreateModel_nchw_relu_relaxed, + conv_float::is_ignored_nchw_relu_relaxed, + conv_float::examples_nchw_relu_relaxed); +} + +void CreateModel_nchw_relu_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_quant8) { + execute(conv_float::CreateModel_nchw_relu_quant8, + conv_float::is_ignored_nchw_relu_quant8, + conv_float::examples_nchw_relu_quant8); +} + +void CreateModel_nchw_relu_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input, + conv_float::is_ignored_nchw_relu_weight_as_input, + conv_float::examples_nchw_relu_weight_as_input); +} + +void CreateModel_nchw_relu_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu_weight_as_input_relaxed, + conv_float::examples_nchw_relu_weight_as_input_relaxed); +} + +void CreateModel_nchw_relu_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu_weight_as_input_quant8, + conv_float::examples_nchw_relu_weight_as_input_quant8); +} + +void CreateModel_nchw_relu6(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6) { + execute(conv_float::CreateModel_nchw_relu6, + conv_float::is_ignored_nchw_relu6, + conv_float::examples_nchw_relu6); +} + +void CreateModel_nchw_relu6_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_relaxed) { + execute(conv_float::CreateModel_nchw_relu6_relaxed, + conv_float::is_ignored_nchw_relu6_relaxed, + conv_float::examples_nchw_relu6_relaxed); +} + +void CreateModel_nchw_relu6_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_quant8) { + execute(conv_float::CreateModel_nchw_relu6_quant8, + conv_float::is_ignored_nchw_relu6_quant8, + conv_float::examples_nchw_relu6_quant8); +} + +void CreateModel_nchw_relu6_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input, + conv_float::is_ignored_nchw_relu6_weight_as_input, + conv_float::examples_nchw_relu6_weight_as_input); +} + +void CreateModel_nchw_relu6_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu6_weight_as_input_relaxed, + conv_float::examples_nchw_relu6_weight_as_input_relaxed); +} + +void CreateModel_nchw_relu6_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu6_weight_as_input_quant8, + conv_float::examples_nchw_relu6_weight_as_input_quant8); +} + +#include "../generated/tests/conv_float.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/add_internal.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/add_internal.mod.py new file mode 100644 index 000000000..28c4afc52 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/add_internal.mod.py @@ -0,0 +1,71 @@ +# Copyright 2018, 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. + +model = Model() + +i0 = Input("i0", ("TENSOR_FLOAT32", [2])) # input 0 +i1 = Input("i1", ("TENSOR_FLOAT32", [2])) # input 0 +i2 = Input("i2", ("TENSOR_FLOAT32", [2])) # input 0 +i3 = Input("i3", ("TENSOR_FLOAT32", [2])) # input 0 +i4 = Input("i4", ("TENSOR_FLOAT32", [2])) # input 0 +i5 = Input("i5", ("TENSOR_FLOAT32", [2])) # input 0 +i6 = Input("i6", ("TENSOR_FLOAT32", [2])) # input 0 +i7 = Input("i7", ("TENSOR_FLOAT32", [2])) # input 0 +i8 = Input("i8", ("TENSOR_FLOAT32", [2])) # input 0 + +t0 = Internal("t0", ("TENSOR_FLOAT32", [2])) +t1 = Internal("t1", ("TENSOR_FLOAT32", [2])) +t2 = Internal("t2", ("TENSOR_FLOAT32", [2])) +t3 = Internal("t3", ("TENSOR_FLOAT32", [2])) +t4 = Internal("t4", ("TENSOR_FLOAT32", [2])) +t5 = Internal("t5", ("TENSOR_FLOAT32", [2])) +t6 = Internal("t6", ("TENSOR_FLOAT32", [2])) + +o0 = Output("o0", ("TENSOR_FLOAT32", [2])) +o1 = Output("o1", ("TENSOR_FLOAT32", [2])) +o2 = Output("o2", ("TENSOR_FLOAT32", [2])) + +p0 = Parameter("p0", ("TENSOR_FLOAT32", [2]), [0.0, 1.0]) +act = Int32Scalar("act", 0) + +model.Operation("ADD", o0, o1, act).To(o2) +model.Operation("ADD", p0, t5, act).To(t6) +model.Operation("ADD", i2, t0, act).To(t1) +model.Operation("ADD", i6, p0, act).To(t5) +model.Operation("ADD", i0, i1, act).To(t0) +model.Operation("ADD", t1, t3, act).To(t4) +model.Operation("ADD", t2, i5, act).To(t3) +model.Operation("ADD", t4, t6, act).To(o0) +model.Operation("ADD", i3, i4, act).To(t2) +model.Operation("ADD", i7, i8, act).To(o1) + +inputs = { + i0: [0, 0], + i1: [0, 0], + i2: [0, 0], + i3: [0, 0], + i4: [0, 0], + i5: [0, 0], + i6: [0, 0], + i7: [0, 0], + i8: [0, 0] +} + +outputs = { + o0: [0, 2], + o1: [0, 0], + o2: [0, 2] +} + +Example((inputs, outputs)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/stdout.txt.expect new file mode 100644 index 000000000..2e271c712 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_internal/stdout.txt.expect @@ -0,0 +1,98 @@ +// clang-format off +// Generated file (from: add_internal.mod.py). Do not edit +// clang-format off +// Generated file (from: add_internal.mod.py). Do not edit +// clang-format off +// Generated file (from: add_internal.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace add_internal { +// Generated add_internal test +#include "-" +// Generated model constructor +#include "-" +} // namespace add_internal + +void CreateModel(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {2}); + OperandType type1(Type::INT32, {}); + // Phase 1, operands + auto o0 = model->addOperand(&type0); + auto o1 = model->addOperand(&type0); + auto act = model->addOperand(&type1); + auto o2 = model->addOperand(&type0); + auto p0 = model->addOperand(&type0); + auto t5 = model->addOperand(&type0); + auto t6 = model->addOperand(&type0); + auto i2 = model->addOperand(&type0); + auto t0 = model->addOperand(&type0); + auto t1 = model->addOperand(&type0); + auto i6 = model->addOperand(&type0); + auto i0 = model->addOperand(&type0); + auto i1 = model->addOperand(&type0); + auto t3 = model->addOperand(&type0); + auto t4 = model->addOperand(&type0); + auto t2 = model->addOperand(&type0); + auto i5 = model->addOperand(&type0); + auto i3 = model->addOperand(&type0); + auto i4 = model->addOperand(&type0); + auto i7 = model->addOperand(&type0); + auto i8 = model->addOperand(&type0); + // Phase 2, operations + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static float p0_init[] = {0.0f, 1.0f}; + model->setOperandValue(p0, p0_init, sizeof(float) * 2); + model->addOperation(ANEURALNETWORKS_ADD, {i6, p0, act}, {t5}); + model->addOperation(ANEURALNETWORKS_ADD, {p0, t5, act}, {t6}); + model->addOperation(ANEURALNETWORKS_ADD, {i0, i1, act}, {t0}); + model->addOperation(ANEURALNETWORKS_ADD, {i2, t0, act}, {t1}); + model->addOperation(ANEURALNETWORKS_ADD, {i3, i4, act}, {t2}); + model->addOperation(ANEURALNETWORKS_ADD, {t2, i5, act}, {t3}); + model->addOperation(ANEURALNETWORKS_ADD, {t1, t3, act}, {t4}); + model->addOperation(ANEURALNETWORKS_ADD, {t4, t6, act}, {o0}); + model->addOperation(ANEURALNETWORKS_ADD, {i7, i8, act}, {o1}); + model->addOperation(ANEURALNETWORKS_ADD, {o0, o1, act}, {o2}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {i2, i6, i0, i1, i5, i3, i4, i7, i8}, + {o0, o1, o2}); + assert(model->isValid()); +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {0.0f, 0.0f}}, {1, {0.0f, 0.0f}}, {2, {0.0f, 0.0f}}, {3, {0.0f, 0.0f}}, {4, {0.0f, 0.0f}}, {5, {0.0f, 0.0f}}, {6, {0.0f, 0.0f}}, {7, {0.0f, 0.0f}}, {8, {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, {0.0f, 2.0f}}, {1, {0.0f, 0.0f}}, {2, {0.0f, 2.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, add_internal) { + execute(add_internal::CreateModel, + add_internal::is_ignored, + add_internal::examples); +} + +#include "../generated/tests/add_internal.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/conv_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/conv_float.mod.py new file mode 100644 index 000000000..61f7c92ee --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/conv_float.mod.py @@ -0,0 +1,61 @@ +# Copyright 2018, 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. + +model = Model("model_name") +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [-200]) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +act = Int32Scalar("act", 0) # None activation +layout = Int32Scalar("layout", 0) # NHWC +pad = Int32Scalar("param", 1) +stride0 = Int32Scalar("param1", 1) +stride1 = Int32Scalar("param2", 1) + +model = model.Operation("CONV_2D", i1, f1, b1, pad, stride0, stride1, act, layout).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8]} + +output0 = {output: # output 0 + [204, 120, 94, 104, 70, 164, 23, 112]} + +quant8 = DataTypeConverter(name="quantized").Identify({ + i1: ("TENSOR_QUANT8_ASYMM", 0.5, 128), + f1: ("TENSOR_QUANT8_ASYMM", 0.25, 128), + b1: ("TENSOR_INT32", 0.125, 0), + output: ("TENSOR_QUANT8_ASYMM", 2, 100) +}) +nchw = DataLayoutConverter("NCHW", name="nchw_layout").Identify([i1, f1, output], [layout]) +relu = ActivationConverter("relu", name="act").Identify([output], [act]) +relu6 = ActivationConverter("relu6").Identify([output], [act]) +weight_as_input = ParameterAsInputConverter(name="w_as_input").Identify([f1]) +relax = RelaxedModeConverter(True, name="float_relaxed") + +# Instantiate an example +# Will produce cartesian product of +# [nhwc, nchw_layout] * [act, relu6] * [w_as_param, w_as_input] * [float, float_relaxed, quantized] +# 24 variations +Example( + (input0, output0), name="example_name" +).AddVariations( + nchw, defaultName="nhwc" +).AddVariations( + relu, relu6, includeDefault=False +).AddVariations( + weight_as_input, defaultName="w_as_param" +).AddVariations( + relax, quant8, defaultName="float" +) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/stdout.txt.expect new file mode 100644 index 000000000..8cc78fe5a --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_naming/stdout.txt.expect @@ -0,0 +1,1848 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +void CreateModel_model_name_nhwc_act_w_as_param_float(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_act_w_as_param_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_param_float = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_act_w_as_param_float) { + execute(conv_float::CreateModel_model_name_nhwc_act_w_as_param_float, + conv_float::is_ignored_model_name_nhwc_act_w_as_param_float, + conv_float::examples_model_name_example_name_nhwc_act_w_as_param_float); +} + +void CreateModel_model_name_nhwc_act_w_as_param_float_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_act_w_as_param_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_param_float_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_act_w_as_param_float_relaxed) { + execute(conv_float::CreateModel_model_name_nhwc_act_w_as_param_float_relaxed, + conv_float::is_ignored_model_name_nhwc_act_w_as_param_float_relaxed, + conv_float::examples_model_name_example_name_nhwc_act_w_as_param_float_relaxed); +} + +void CreateModel_model_name_nhwc_act_w_as_param_quantized(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_act_w_as_param_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_param_quantized = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_act_w_as_param_quantized) { + execute(conv_float::CreateModel_model_name_nhwc_act_w_as_param_quantized, + conv_float::is_ignored_model_name_nhwc_act_w_as_param_quantized, + conv_float::examples_model_name_example_name_nhwc_act_w_as_param_quantized); +} + +void CreateModel_model_name_nhwc_act_w_as_input_float(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_act_w_as_input_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_input_float = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_act_w_as_input_float) { + execute(conv_float::CreateModel_model_name_nhwc_act_w_as_input_float, + conv_float::is_ignored_model_name_nhwc_act_w_as_input_float, + conv_float::examples_model_name_example_name_nhwc_act_w_as_input_float); +} + +void CreateModel_model_name_nhwc_act_w_as_input_float_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_act_w_as_input_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_input_float_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_act_w_as_input_float_relaxed) { + execute(conv_float::CreateModel_model_name_nhwc_act_w_as_input_float_relaxed, + conv_float::is_ignored_model_name_nhwc_act_w_as_input_float_relaxed, + conv_float::examples_model_name_example_name_nhwc_act_w_as_input_float_relaxed); +} + +void CreateModel_model_name_nhwc_act_w_as_input_quantized(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_act_w_as_input_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_input_quantized = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_act_w_as_input_quantized) { + execute(conv_float::CreateModel_model_name_nhwc_act_w_as_input_quantized, + conv_float::is_ignored_model_name_nhwc_act_w_as_input_quantized, + conv_float::examples_model_name_example_name_nhwc_act_w_as_input_quantized); +} + +void CreateModel_model_name_nhwc_relu6_w_as_param_float(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_relu6_w_as_param_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_param_float = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_relu6_w_as_param_float) { + execute(conv_float::CreateModel_model_name_nhwc_relu6_w_as_param_float, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_param_float, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_param_float); +} + +void CreateModel_model_name_nhwc_relu6_w_as_param_float_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_relu6_w_as_param_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_param_float_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_relu6_w_as_param_float_relaxed) { + execute(conv_float::CreateModel_model_name_nhwc_relu6_w_as_param_float_relaxed, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_param_float_relaxed, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_param_float_relaxed); +} + +void CreateModel_model_name_nhwc_relu6_w_as_param_quantized(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_relu6_w_as_param_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_param_quantized = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_relu6_w_as_param_quantized) { + execute(conv_float::CreateModel_model_name_nhwc_relu6_w_as_param_quantized, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_param_quantized, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_param_quantized); +} + +void CreateModel_model_name_nhwc_relu6_w_as_input_float(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_relu6_w_as_input_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_input_float = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_relu6_w_as_input_float) { + execute(conv_float::CreateModel_model_name_nhwc_relu6_w_as_input_float, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_input_float, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_input_float); +} + +void CreateModel_model_name_nhwc_relu6_w_as_input_float_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_relu6_w_as_input_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_input_float_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_relu6_w_as_input_float_relaxed) { + execute(conv_float::CreateModel_model_name_nhwc_relu6_w_as_input_float_relaxed, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_input_float_relaxed, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_input_float_relaxed); +} + +void CreateModel_model_name_nhwc_relu6_w_as_input_quantized(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nhwc_relu6_w_as_input_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_input_quantized = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nhwc_relu6_w_as_input_quantized) { + execute(conv_float::CreateModel_model_name_nhwc_relu6_w_as_input_quantized, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_input_quantized, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_input_quantized); +} + +void CreateModel_model_name_nchw_layout_act_w_as_param_float(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_act_w_as_param_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_param_float = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_act_w_as_param_float) { + execute(conv_float::CreateModel_model_name_nchw_layout_act_w_as_param_float, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_param_float, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_param_float); +} + +void CreateModel_model_name_nchw_layout_act_w_as_param_float_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_act_w_as_param_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_param_float_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_act_w_as_param_float_relaxed) { + execute(conv_float::CreateModel_model_name_nchw_layout_act_w_as_param_float_relaxed, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_param_float_relaxed, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_param_float_relaxed); +} + +void CreateModel_model_name_nchw_layout_act_w_as_param_quantized(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_act_w_as_param_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_param_quantized = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_act_w_as_param_quantized) { + execute(conv_float::CreateModel_model_name_nchw_layout_act_w_as_param_quantized, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_param_quantized, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_param_quantized); +} + +void CreateModel_model_name_nchw_layout_act_w_as_input_float(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_act_w_as_input_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_input_float = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_act_w_as_input_float) { + execute(conv_float::CreateModel_model_name_nchw_layout_act_w_as_input_float, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_input_float, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_input_float); +} + +void CreateModel_model_name_nchw_layout_act_w_as_input_float_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_act_w_as_input_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_input_float_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_act_w_as_input_float_relaxed) { + execute(conv_float::CreateModel_model_name_nchw_layout_act_w_as_input_float_relaxed, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_input_float_relaxed, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_input_float_relaxed); +} + +void CreateModel_model_name_nchw_layout_act_w_as_input_quantized(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_act_w_as_input_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_input_quantized = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_act_w_as_input_quantized) { + execute(conv_float::CreateModel_model_name_nchw_layout_act_w_as_input_quantized, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_input_quantized, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_input_quantized); +} + +void CreateModel_model_name_nchw_layout_relu6_w_as_param_float(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_param_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_param_float = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_relu6_w_as_param_float) { + execute(conv_float::CreateModel_model_name_nchw_layout_relu6_w_as_param_float, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_param_float, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_param_float); +} + +void CreateModel_model_name_nchw_layout_relu6_w_as_param_float_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_param_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_param_float_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_relu6_w_as_param_float_relaxed) { + execute(conv_float::CreateModel_model_name_nchw_layout_relu6_w_as_param_float_relaxed, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_param_float_relaxed, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_param_float_relaxed); +} + +void CreateModel_model_name_nchw_layout_relu6_w_as_param_quantized(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_param_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_param_quantized = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_relu6_w_as_param_quantized) { + execute(conv_float::CreateModel_model_name_nchw_layout_relu6_w_as_param_quantized, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_param_quantized, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_param_quantized); +} + +void CreateModel_model_name_nchw_layout_relu6_w_as_input_float(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_input_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_input_float = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_relu6_w_as_input_float) { + execute(conv_float::CreateModel_model_name_nchw_layout_relu6_w_as_input_float, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_input_float, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_input_float); +} + +void CreateModel_model_name_nchw_layout_relu6_w_as_input_float_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_input_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_input_float_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_relu6_w_as_input_float_relaxed) { + execute(conv_float::CreateModel_model_name_nchw_layout_relu6_w_as_input_float_relaxed, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_input_float_relaxed, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_input_float_relaxed); +} + +void CreateModel_model_name_nchw_layout_relu6_w_as_input_quantized(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_input_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_input_quantized = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_model_name_example_name_nchw_layout_relu6_w_as_input_quantized) { + execute(conv_float::CreateModel_model_name_nchw_layout_relu6_w_as_input_quantized, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_input_quantized, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_input_quantized); +} + +#include "../generated/tests/conv_float.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/conv_quant8.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/conv_quant8.mod.py new file mode 100644 index 000000000..f6b3e89ca --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/conv_quant8.mod.py @@ -0,0 +1,35 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 3, 1}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{1, 2, 2, 1}", [.25, .25, .25, .25]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) +pad0 = Int32Scalar("pad0", 0) +act = Int32Scalar("act", 0) +stride = Int32Scalar("stride", 1) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}") + +model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) +model = model.RelaxedExecution(True) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]} + +output0 = {output: # output 0 + [.875, .875, .875, .875]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/stdout.txt.expect new file mode 100644 index 000000000..e0195afb1 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_quant8/stdout.txt.expect @@ -0,0 +1,85 @@ +// clang-format off +// Generated file (from: conv_quant8.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_quant8.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_quant8.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace conv_quant8 { +// Generated conv_quant8 test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_quant8 + +void CreateModel(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 3, 1}); + OperandType type1(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto pad0 = model->addOperand(&type3); + auto stride = model->addOperand(&type3); + auto act = 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 stride_init[] = {1}; + model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_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}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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 +}; + +TEST_F(GeneratedTests, conv_quant8) { + execute(conv_quant8::CreateModel, + conv_quant8::is_ignored, + conv_quant8::examples); +} + +#include "../generated/tests/conv_quant8.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/conv_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/conv_float.mod.py new file mode 100644 index 000000000..c5ec8bf4e --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/conv_float.mod.py @@ -0,0 +1,44 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [-200]) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +act = Int32Scalar("act", 0) # None activation +layout = Int32Scalar("layout", 0) # NHWC +pad = Int32Scalar("param", 1) +stride0 = Int32Scalar("param1", 1) +stride1 = Int32Scalar("param2", 1) + +model = model.Operation("CONV_2D", i1, f1, b1, pad, stride0, stride1, act, layout).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8]} + +output0 = {output: # output 0 + [204, 120, 94, 104, 70, 164, 23, 112]} + +quant8 = DataTypeConverter().Identify({ + i1: ("TENSOR_QUANT8_ASYMM", 0.5, 128), + f1: ("TENSOR_QUANT8_ASYMM", 0.25, 128), + b1: ("TENSOR_INT32", 0.125, 0), + output: ("TENSOR_QUANT8_ASYMM", 2, 100) +}) + +# Instantiate an example +Example((input0, output0)).AddNchw(i1, f1, output, layout).AddAllActivations( + output, act).AddInput(f1).AddVariations(RelaxedModeConverter(True), quant8) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stderr.txt.expect new file mode 100644 index 000000000..7ac2240b2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stderr.txt.expect @@ -0,0 +1,3 @@ +Output CTS model: - +Output example:- +Output CTS test: - diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stdout.txt.expect new file mode 100644 index 000000000..87e74f722 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stdout.txt.expect @@ -0,0 +1,3688 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +void CreateModel_none(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_none(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none) { + execute(conv_float::CreateModel_none, + conv_float::is_ignored_none, + conv_float::examples_none); +} + +void CreateModel_none_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_none_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_relaxed) { + execute(conv_float::CreateModel_none_relaxed, + conv_float::is_ignored_none_relaxed, + conv_float::examples_none_relaxed); +} + +void CreateModel_none_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_none_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_quant8) { + execute(conv_float::CreateModel_none_quant8, + conv_float::is_ignored_none_quant8, + conv_float::examples_none_quant8); +} + +void CreateModel_none_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_none_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_weight_as_input) { + execute(conv_float::CreateModel_none_weight_as_input, + conv_float::is_ignored_none_weight_as_input, + conv_float::examples_none_weight_as_input); +} + +void CreateModel_none_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_none_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_weight_as_input_relaxed) { + execute(conv_float::CreateModel_none_weight_as_input_relaxed, + conv_float::is_ignored_none_weight_as_input_relaxed, + conv_float::examples_none_weight_as_input_relaxed); +} + +void CreateModel_none_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_none_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_weight_as_input_quant8) { + execute(conv_float::CreateModel_none_weight_as_input_quant8, + conv_float::is_ignored_none_weight_as_input_quant8, + conv_float::examples_none_weight_as_input_quant8); +} + +void CreateModel_relu(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu) { + execute(conv_float::CreateModel_relu, + conv_float::is_ignored_relu, + conv_float::examples_relu); +} + +void CreateModel_relu_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_relaxed) { + execute(conv_float::CreateModel_relu_relaxed, + conv_float::is_ignored_relu_relaxed, + conv_float::examples_relu_relaxed); +} + +void CreateModel_relu_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_quant8) { + execute(conv_float::CreateModel_relu_quant8, + conv_float::is_ignored_relu_quant8, + conv_float::examples_relu_quant8); +} + +void CreateModel_relu_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input) { + execute(conv_float::CreateModel_relu_weight_as_input, + conv_float::is_ignored_relu_weight_as_input, + conv_float::examples_relu_weight_as_input); +} + +void CreateModel_relu_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input_relaxed) { + execute(conv_float::CreateModel_relu_weight_as_input_relaxed, + conv_float::is_ignored_relu_weight_as_input_relaxed, + conv_float::examples_relu_weight_as_input_relaxed); +} + +void CreateModel_relu_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input_quant8) { + execute(conv_float::CreateModel_relu_weight_as_input_quant8, + conv_float::is_ignored_relu_weight_as_input_quant8, + conv_float::examples_relu_weight_as_input_quant8); +} + +void CreateModel_relu1(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu1(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1 = { +// 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}}}, + // 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1) { + execute(conv_float::CreateModel_relu1, + conv_float::is_ignored_relu1, + conv_float::examples_relu1); +} + +void CreateModel_relu1_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu1_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_relaxed = { +// 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}}}, + // 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_relaxed) { + execute(conv_float::CreateModel_relu1_relaxed, + conv_float::is_ignored_relu1_relaxed, + conv_float::examples_relu1_relaxed); +} + +void CreateModel_relu1_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu1_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_quant8) { + execute(conv_float::CreateModel_relu1_quant8, + conv_float::is_ignored_relu1_quant8, + conv_float::examples_relu1_quant8); +} + +void CreateModel_relu1_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu1_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_weight_as_input) { + execute(conv_float::CreateModel_relu1_weight_as_input, + conv_float::is_ignored_relu1_weight_as_input, + conv_float::examples_relu1_weight_as_input); +} + +void CreateModel_relu1_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu1_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_weight_as_input_relaxed) { + execute(conv_float::CreateModel_relu1_weight_as_input_relaxed, + conv_float::is_ignored_relu1_weight_as_input_relaxed, + conv_float::examples_relu1_weight_as_input_relaxed); +} + +void CreateModel_relu1_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu1_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_weight_as_input_quant8) { + execute(conv_float::CreateModel_relu1_weight_as_input_quant8, + conv_float::is_ignored_relu1_weight_as_input_quant8, + conv_float::examples_relu1_weight_as_input_quant8); +} + +void CreateModel_relu6(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6 = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6) { + execute(conv_float::CreateModel_relu6, + conv_float::is_ignored_relu6, + conv_float::examples_relu6); +} + +void CreateModel_relu6_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_relaxed) { + execute(conv_float::CreateModel_relu6_relaxed, + conv_float::is_ignored_relu6_relaxed, + conv_float::examples_relu6_relaxed); +} + +void CreateModel_relu6_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_quant8) { + execute(conv_float::CreateModel_relu6_quant8, + conv_float::is_ignored_relu6_quant8, + conv_float::examples_relu6_quant8); +} + +void CreateModel_relu6_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input) { + execute(conv_float::CreateModel_relu6_weight_as_input, + conv_float::is_ignored_relu6_weight_as_input, + conv_float::examples_relu6_weight_as_input); +} + +void CreateModel_relu6_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input_relaxed) { + execute(conv_float::CreateModel_relu6_weight_as_input_relaxed, + conv_float::is_ignored_relu6_weight_as_input_relaxed, + conv_float::examples_relu6_weight_as_input_relaxed); +} + +void CreateModel_relu6_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input_quant8) { + execute(conv_float::CreateModel_relu6_weight_as_input_quant8, + conv_float::is_ignored_relu6_weight_as_input_quant8, + conv_float::examples_relu6_weight_as_input_quant8); +} + +void CreateModel_nchw_none(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_none(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none) { + execute(conv_float::CreateModel_nchw_none, + conv_float::is_ignored_nchw_none, + conv_float::examples_nchw_none); +} + +void CreateModel_nchw_none_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_relaxed) { + execute(conv_float::CreateModel_nchw_none_relaxed, + conv_float::is_ignored_nchw_none_relaxed, + conv_float::examples_nchw_none_relaxed); +} + +void CreateModel_nchw_none_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_quant8) { + execute(conv_float::CreateModel_nchw_none_quant8, + conv_float::is_ignored_nchw_none_quant8, + conv_float::examples_nchw_none_quant8); +} + +void CreateModel_nchw_none_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_weight_as_input) { + execute(conv_float::CreateModel_nchw_none_weight_as_input, + conv_float::is_ignored_nchw_none_weight_as_input, + conv_float::examples_nchw_none_weight_as_input); +} + +void CreateModel_nchw_none_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_none_weight_as_input_relaxed, + conv_float::is_ignored_nchw_none_weight_as_input_relaxed, + conv_float::examples_nchw_none_weight_as_input_relaxed); +} + +void CreateModel_nchw_none_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {0}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_none_weight_as_input_quant8, + conv_float::is_ignored_nchw_none_weight_as_input_quant8, + conv_float::examples_nchw_none_weight_as_input_quant8); +} + +void CreateModel_nchw_relu(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu) { + execute(conv_float::CreateModel_nchw_relu, + conv_float::is_ignored_nchw_relu, + conv_float::examples_nchw_relu); +} + +void CreateModel_nchw_relu_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_relaxed) { + execute(conv_float::CreateModel_nchw_relu_relaxed, + conv_float::is_ignored_nchw_relu_relaxed, + conv_float::examples_nchw_relu_relaxed); +} + +void CreateModel_nchw_relu_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_quant8) { + execute(conv_float::CreateModel_nchw_relu_quant8, + conv_float::is_ignored_nchw_relu_quant8, + conv_float::examples_nchw_relu_quant8); +} + +void CreateModel_nchw_relu_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input, + conv_float::is_ignored_nchw_relu_weight_as_input, + conv_float::examples_nchw_relu_weight_as_input); +} + +void CreateModel_nchw_relu_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu_weight_as_input_relaxed, + conv_float::examples_nchw_relu_weight_as_input_relaxed); +} + +void CreateModel_nchw_relu_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu_weight_as_input_quant8, + conv_float::examples_nchw_relu_weight_as_input_quant8); +} + +void CreateModel_nchw_relu1(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1) { + execute(conv_float::CreateModel_nchw_relu1, + conv_float::is_ignored_nchw_relu1, + conv_float::examples_nchw_relu1); +} + +void CreateModel_nchw_relu1_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_relaxed) { + execute(conv_float::CreateModel_nchw_relu1_relaxed, + conv_float::is_ignored_nchw_relu1_relaxed, + conv_float::examples_nchw_relu1_relaxed); +} + +void CreateModel_nchw_relu1_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_quant8) { + execute(conv_float::CreateModel_nchw_relu1_quant8, + conv_float::is_ignored_nchw_relu1_quant8, + conv_float::examples_nchw_relu1_quant8); +} + +void CreateModel_nchw_relu1_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_weight_as_input) { + execute(conv_float::CreateModel_nchw_relu1_weight_as_input, + conv_float::is_ignored_nchw_relu1_weight_as_input, + conv_float::examples_nchw_relu1_weight_as_input); +} + +void CreateModel_nchw_relu1_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_relu1_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu1_weight_as_input_relaxed, + conv_float::examples_nchw_relu1_weight_as_input_relaxed); +} + +void CreateModel_nchw_relu1_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_relu1_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu1_weight_as_input_quant8, + conv_float::examples_nchw_relu1_weight_as_input_quant8); +} + +void CreateModel_nchw_relu6(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6) { + execute(conv_float::CreateModel_nchw_relu6, + conv_float::is_ignored_nchw_relu6, + conv_float::examples_nchw_relu6); +} + +void CreateModel_nchw_relu6_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_relaxed) { + execute(conv_float::CreateModel_nchw_relu6_relaxed, + conv_float::is_ignored_nchw_relu6_relaxed, + conv_float::examples_nchw_relu6_relaxed); +} + +void CreateModel_nchw_relu6_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_quant8) { + execute(conv_float::CreateModel_nchw_relu6_quant8, + conv_float::is_ignored_nchw_relu6_quant8, + conv_float::examples_nchw_relu6_quant8); +} + +void CreateModel_nchw_relu6_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input, + conv_float::is_ignored_nchw_relu6_weight_as_input, + conv_float::examples_nchw_relu6_weight_as_input); +} + +void CreateModel_nchw_relu6_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu6_weight_as_input_relaxed, + conv_float::examples_nchw_relu6_weight_as_input_relaxed); +} + +void CreateModel_nchw_relu6_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu6_weight_as_input_quant8, + conv_float::examples_nchw_relu6_weight_as_input_quant8); +} + +#include "../generated/tests/conv_float.mod.py.cpp" diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/lstm_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/lstm_float.mod.py new file mode 100644 index 000000000..60eec8280 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/lstm_float.mod.py @@ -0,0 +1,145 @@ +# Copyright 2018, 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. + +model = Model() + +n_batch = 1 +n_input = 2 +# n_cell and n_output have the same size when there is no projection. +n_cell = 4 +n_output = 4 + +input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input)) + +input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) +input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) + +recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) +recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) + +cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}") +cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{0}") +cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{0}") + +input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) +output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) + +projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}") +projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}") + +output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) +cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) + +activation_param = Int32Scalar("activation_param", 4) # Tanh +cell_clip_param = Float32Scalar("cell_clip_param", 0.) +proj_clip_param = Float32Scalar("proj_clip_param", 0.) + +scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4))) +output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) +cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) + +model = model.Operation("LSTM", + input, + + input_to_input_weights, + input_to_forget_weights, + input_to_cell_weights, + input_to_output_weights, + + recurrent_to_input_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 +).To([scratch_buffer, output_state_out, cell_state_out, output]) +model = model.RelaxedExecution(True) + +# Example 1. Input in operand 0, +input0 = {input_to_input_weights: [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524], + input_to_forget_weights: [0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212], + input_to_cell_weights: [-0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778], + input_to_output_weights: [-0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578], + + input_gate_bias: [0.,0.,0.,0.], + forget_gate_bias: [1.,1.,1.,1.], + cell_gate_bias: [0.,0.,0.,0.], + output_gate_bias: [0.,0.,0.,0.], + + recurrent_to_input_weights: [ + -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, + -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, + -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296], + + recurrent_to_cell_weights: [ + -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, + -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, + -0.46367589, 0.26016325, -0.03894562, -0.16368064], + + recurrent_to_forget_weights: [ + -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, + -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, + 0.28053468, 0.01560611, -0.20127171, -0.01140004], + + recurrent_to_output_weights: [ + 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, + 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, + -0.51818722, -0.15390486, 0.0468148, 0.39922136], + + cell_to_input_weights: [], + cell_to_forget_weights: [], + cell_to_output_weights: [], + + projection_weights: [], + projection_bias: [], +} + +test_input = [2., 3.] +output_state = [0, 0, 0, 0] +cell_state = [0, 0, 0, 0] +golden_output = [-0.02973187, 0.1229473, 0.20885126, -0.15358765,] +output0 = { + scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], + cell_state_out: [ -0.145439, 0.157475, 0.293663, -0.277353 ], + output_state_out: [ -0.0297319, 0.122947, 0.208851, -0.153588 ], + output: golden_output +} +input0[input] = test_input +input0[output_state_in] = output_state +input0[cell_state_in] = cell_state +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/stdout.txt.expect new file mode 100644 index 000000000..4ac72618c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_float/stdout.txt.expect @@ -0,0 +1,322 @@ +// clang-format off +// Generated file (from: lstm_float.mod.py). Do not edit +// clang-format off +// Generated file (from: lstm_float.mod.py). Do not edit +// Generated from: lstm_float.mod.py. +namespace lstm_float { +// Generated lstm_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace lstm_float + +// Create the model +Model createTestModel() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4, 4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4, 4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4, 4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4, 4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {0}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {0}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {0}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {0, 0}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {0}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 4}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::FLOAT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::FLOAT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 16}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 4}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 4}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 4}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::LSTM, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22}, + .outputs = {23, 24, 25, 26}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19}; + const std::vector<uint32_t> outputIndexes = {23, 24, 25, 26}; + std::vector<uint8_t> operandValues = { + 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored(int i) { + static std::set<int> ignore = {0}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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, {0.0f, 0.0f, 0.0f, 0.0f}}, {19, {0.0f, 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, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, lstm_float) { + generated_tests::Execute(device, + lstm_float::createTestModel, + lstm_float::is_ignored, + lstm_float::examples); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/depthwise_conv2d_quant8.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/depthwise_conv2d_quant8.mod.py new file mode 100644 index 000000000..051780877 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/depthwise_conv2d_quant8.mod.py @@ -0,0 +1,43 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 2}, 0.5f, 0") +f1 = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 2}, 0.5f, 0") +b1 = Input("op3", "TENSOR_INT32", "{2}, 0.25f, 0") +pad0 = Int32Scalar("pad0", 0) +act = Int32Scalar("act", 0) +stride = Int32Scalar("stride", 1) +cm = Int32Scalar("channelMultiplier", 1) +output = Output("op4", "TENSOR_QUANT8_ASYMM", "{1,1,1,2}, 1.f, 0") + +model = model.Operation("DEPTHWISE_CONV_2D", + i1, f1, b1, + pad0, pad0, pad0, pad0, + stride, stride, + cm, act).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [4, 16, 4, 32, 4, 64, 4, 128], + f1: + [2, 4, 2, 0, 2, 2, 2, 0], + b1: + [0, 0]} +# (i1 (depthconv) f1) +output0 = {output: # output 0 + [8, 48]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/stdout.txt.expect new file mode 100644 index 000000000..760430798 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_backward_compatibility_quant8/stdout.txt.expect @@ -0,0 +1,150 @@ +// clang-format off +// Generated file (from: depthwise_conv2d_quant8.mod.py). Do not edit +// clang-format off +// Generated file (from: depthwise_conv2d_quant8.mod.py). Do not edit +// Generated from: depthwise_conv2d_quant8.mod.py. +namespace depthwise_conv2d_quant8 { +// Generated depthwise_conv2d_quant8 test +#include "-" +// Generated model constructor +#include "-" +} // namespace depthwise_conv2d_quant8 + +// Create the model +Model createTestModel() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 4, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 2, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 1, 1, 2}, + .numberOfConsumers = 0, + .scale = 1.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::DEPTHWISE_CONV_2D, + .inputs = {0, 1, 2, 3, 3, 3, 3, 4, 4, 5, 6}, + .outputs = {7}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1, 2}; + const std::vector<uint32_t> outputIndexes = {7}; + std::vector<uint8_t> operandValues = { + 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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 +}; + +TEST_F(NeuralnetworksHidlTest, depthwise_conv2d_quant8) { + generated_tests::Execute(device, + depthwise_conv2d_quant8::createTestModel, + depthwise_conv2d_quant8::is_ignored, + depthwise_conv2d_quant8::examples); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/conv_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/conv_float.mod.py new file mode 100644 index 000000000..f6b3e89ca --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/conv_float.mod.py @@ -0,0 +1,35 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 3, 1}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{1, 2, 2, 1}", [.25, .25, .25, .25]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) +pad0 = Int32Scalar("pad0", 0) +act = Int32Scalar("act", 0) +stride = Int32Scalar("stride", 1) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}") + +model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) +model = model.RelaxedExecution(True) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]} + +output0 = {output: # output 0 + [.875, .875, .875, .875]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/stdout.txt.expect new file mode 100644 index 000000000..ad54e14e2 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_float/stdout.txt.expect @@ -0,0 +1,142 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// Generated from: conv_float.mod.py. +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +// Create the model +Model createTestModel() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 3, 3, 1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 4, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 2, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 1}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 3, 3, 3, 4, 4, 5}, + .outputs = {6}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {6}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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 +}; + +TEST_F(NeuralnetworksHidlTest, conv_float) { + generated_tests::Execute(device, + conv_float::createTestModel, + conv_float::is_ignored, + conv_float::examples); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/mean_implicit.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/mean_implicit.mod.py new file mode 100644 index 000000000..be7b22bdb --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/mean_implicit.mod.py @@ -0,0 +1,41 @@ +# Copyright 2018, 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. + +i0 = Input("i0", ("TENSOR_FLOAT32", [2, 2])) # input 0 + +o1 = Output("o1", ("TENSOR_FLOAT32", [1, 2])) # output for model1 +o2 = Output("o2", ("TENSOR_FLOAT32", [2, 1])) # output for model2 +o3 = Output("o3", ("TENSOR_FLOAT32", [1])) # output for model3 + +model1 = Model().Operation("MEAN", i0, [0], 1).To(o1) # along axis 0, keep_dim=True +model2 = Model().Operation("MEAN", i0, [1], 1).To(o2) # along axis 1, keep_dim=True +model3 = Model().Operation("MEAN", i0, [0, 1], 0).To(o3) # along both axis, keep_dim=False + +inputs1 = {i0: [1, 2, 3, 4]} +outputs11 = {o1: [4, 6]} +outputs12 = {o2: [3, 7]} +outputs13 = {o3: [10]} + +inputs2 = {i0: [-1, -2, -3, -4]} +outputs21 = {o1: [-4, -6]} +outputs22 = {o2: [-3, -7]} +outputs23 = {o3: [-10]} + +Example((inputs1, outputs11), model=model1) +Example((inputs1, outputs12), model=model2) +Example((inputs1, outputs13), model=model3) + +Example((inputs2, outputs21), model=model1) +Example((inputs2, outputs22), model=model2) +Example((inputs2, outputs23), model=model3) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/stdout.txt.expect new file mode 100644 index 000000000..f996d8470 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_parameter/stdout.txt.expect @@ -0,0 +1,381 @@ +// clang-format off +// Generated file (from: mean_implicit.mod.py). Do not edit +// clang-format off +// Generated file (from: mean_implicit.mod.py). Do not edit +// Generated from: mean_implicit.mod.py. +namespace mean_implicit { +// Generated mean_implicit test +#include "-" +// Generated model constructor +#include "-" +} // namespace mean_implicit + +// Create the model +Model createTestModel() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2}, + .numberOfConsumers = 3, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::MEAN, + .inputs = {0, 1, 2}, + .outputs = {3}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {3}; + std::vector<uint8_t> operandValues = { + 0, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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, {4.0f, 6.0f}}}, + // 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, {-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, {-4.0f, -6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, mean_implicit) { + generated_tests::Execute(device, + mean_implicit::createTestModel, + mean_implicit::is_ignored, + mean_implicit::examples); +} + +// Create the model +Model createTestModel_2() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2}, + .numberOfConsumers = 3, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 1}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::MEAN, + .inputs = {0, 1, 2}, + .outputs = {3}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {3}; + std::vector<uint8_t> operandValues = { + 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_2(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_2 = { +// 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, {3.0f, 7.0f}}}, + // 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, {-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, {-3.0f, -7.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, mean_implicit_2) { + generated_tests::Execute(device, + mean_implicit::createTestModel_2, + mean_implicit::is_ignored_2, + mean_implicit::examples_2); +} + +// Create the model +Model createTestModel_3() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2}, + .numberOfConsumers = 3, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 8}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::MEAN, + .inputs = {0, 1, 2}, + .outputs = {3}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {3}; + std::vector<uint8_t> operandValues = { + 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_3(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_3 = { +// 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, {10.0f}}}, + // 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, {-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, {-10.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, mean_implicit_3) { + generated_tests::Execute(device, + mean_implicit::createTestModel_3, + mean_implicit::is_ignored_3, + mean_implicit::examples_3); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/conv_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/conv_float.mod.py new file mode 100644 index 000000000..826f390c0 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/conv_float.mod.py @@ -0,0 +1,52 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [-200]) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +act = Int32Scalar("act", 0) # None activation +layout = Int32Scalar("layout", 0) # NHWC + +model = model.Operation("CONV_2D", i1, f1, b1, 1, 1, 1, act, layout).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8]} + +output0 = {output: # output 0 + [204, 120, 94, 104, 70, 164, 23, 112]} + +quant8 = DataTypeConverter().Identify({ + i1: ("TENSOR_QUANT8_ASYMM", 0.5, 128), + f1: ("TENSOR_QUANT8_ASYMM", 0.25, 128), + b1: ("TENSOR_INT32", 0.125, 0), + output: ("TENSOR_QUANT8_ASYMM", 2, 100) +}) + +# Instantiate an example +Example( + (input0, output0) +).AddVariations( + ("NCHW", [i1, f1, output], [layout]) +).AddVariations( + ("relu", [output], [act]), + ("relu6", [output], [act]), + includeDefault=False +).AddVariations( + ("as_input", [f1]) +).AddVariations( + "relaxed", quant8 +) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/stdout.txt.expect new file mode 100644 index 000000000..7e100da9b --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_implicit_variation/stdout.txt.expect @@ -0,0 +1,3548 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// Generated from: conv_float.mod.py. +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +// Create the model +Model createTestModel_relu() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu) { + generated_tests::Execute(device, + conv_float::createTestModel_relu, + conv_float::is_ignored_relu, + conv_float::examples_relu); +} + +// Create the model +Model createTestModel_relu_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_relaxed, + conv_float::is_ignored_relu_relaxed, + conv_float::examples_relu_relaxed); +} + +// Create the model +Model createTestModel_relu_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_quant8, + conv_float::is_ignored_relu_quant8, + conv_float::examples_relu_quant8); +} + +// Create the model +Model createTestModel_relu_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_weight_as_input, + conv_float::is_ignored_relu_weight_as_input, + conv_float::examples_relu_weight_as_input); +} + +// Create the model +Model createTestModel_relu_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_weight_as_input_relaxed, + conv_float::is_ignored_relu_weight_as_input_relaxed, + conv_float::examples_relu_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_relu_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_weight_as_input_quant8, + conv_float::is_ignored_relu_weight_as_input_quant8, + conv_float::examples_relu_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_relu6() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6 = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6, + conv_float::is_ignored_relu6, + conv_float::examples_relu6); +} + +// Create the model +Model createTestModel_relu6_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_relaxed, + conv_float::is_ignored_relu6_relaxed, + conv_float::examples_relu6_relaxed); +} + +// Create the model +Model createTestModel_relu6_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_quant8, + conv_float::is_ignored_relu6_quant8, + conv_float::examples_relu6_quant8); +} + +// Create the model +Model createTestModel_relu6_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_weight_as_input, + conv_float::is_ignored_relu6_weight_as_input, + conv_float::examples_relu6_weight_as_input); +} + +// Create the model +Model createTestModel_relu6_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_weight_as_input_relaxed, + conv_float::is_ignored_relu6_weight_as_input_relaxed, + conv_float::examples_relu6_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_relu6_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_weight_as_input_quant8, + conv_float::is_ignored_relu6_weight_as_input_quant8, + conv_float::examples_relu6_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_nchw_relu() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu, + conv_float::is_ignored_nchw_relu, + conv_float::examples_nchw_relu); +} + +// Create the model +Model createTestModel_nchw_relu_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_relaxed, + conv_float::is_ignored_nchw_relu_relaxed, + conv_float::examples_nchw_relu_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_quant8, + conv_float::is_ignored_nchw_relu_quant8, + conv_float::examples_nchw_relu_quant8); +} + +// Create the model +Model createTestModel_nchw_relu_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_weight_as_input, + conv_float::is_ignored_nchw_relu_weight_as_input, + conv_float::examples_nchw_relu_weight_as_input); +} + +// Create the model +Model createTestModel_nchw_relu_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu_weight_as_input_relaxed, + conv_float::examples_nchw_relu_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu_weight_as_input_quant8, + conv_float::examples_nchw_relu_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_nchw_relu6() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6, + conv_float::is_ignored_nchw_relu6, + conv_float::examples_nchw_relu6); +} + +// Create the model +Model createTestModel_nchw_relu6_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_relaxed, + conv_float::is_ignored_nchw_relu6_relaxed, + conv_float::examples_nchw_relu6_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu6_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_quant8, + conv_float::is_ignored_nchw_relu6_quant8, + conv_float::examples_nchw_relu6_quant8); +} + +// Create the model +Model createTestModel_nchw_relu6_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_weight_as_input, + conv_float::is_ignored_nchw_relu6_weight_as_input, + conv_float::examples_nchw_relu6_weight_as_input); +} + +// Create the model +Model createTestModel_nchw_relu6_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu6_weight_as_input_relaxed, + conv_float::examples_nchw_relu6_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu6_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu6_weight_as_input_quant8, + conv_float::examples_nchw_relu6_weight_as_input_quant8); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/add_internal.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/add_internal.mod.py new file mode 100644 index 000000000..28c4afc52 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/add_internal.mod.py @@ -0,0 +1,71 @@ +# Copyright 2018, 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. + +model = Model() + +i0 = Input("i0", ("TENSOR_FLOAT32", [2])) # input 0 +i1 = Input("i1", ("TENSOR_FLOAT32", [2])) # input 0 +i2 = Input("i2", ("TENSOR_FLOAT32", [2])) # input 0 +i3 = Input("i3", ("TENSOR_FLOAT32", [2])) # input 0 +i4 = Input("i4", ("TENSOR_FLOAT32", [2])) # input 0 +i5 = Input("i5", ("TENSOR_FLOAT32", [2])) # input 0 +i6 = Input("i6", ("TENSOR_FLOAT32", [2])) # input 0 +i7 = Input("i7", ("TENSOR_FLOAT32", [2])) # input 0 +i8 = Input("i8", ("TENSOR_FLOAT32", [2])) # input 0 + +t0 = Internal("t0", ("TENSOR_FLOAT32", [2])) +t1 = Internal("t1", ("TENSOR_FLOAT32", [2])) +t2 = Internal("t2", ("TENSOR_FLOAT32", [2])) +t3 = Internal("t3", ("TENSOR_FLOAT32", [2])) +t4 = Internal("t4", ("TENSOR_FLOAT32", [2])) +t5 = Internal("t5", ("TENSOR_FLOAT32", [2])) +t6 = Internal("t6", ("TENSOR_FLOAT32", [2])) + +o0 = Output("o0", ("TENSOR_FLOAT32", [2])) +o1 = Output("o1", ("TENSOR_FLOAT32", [2])) +o2 = Output("o2", ("TENSOR_FLOAT32", [2])) + +p0 = Parameter("p0", ("TENSOR_FLOAT32", [2]), [0.0, 1.0]) +act = Int32Scalar("act", 0) + +model.Operation("ADD", o0, o1, act).To(o2) +model.Operation("ADD", p0, t5, act).To(t6) +model.Operation("ADD", i2, t0, act).To(t1) +model.Operation("ADD", i6, p0, act).To(t5) +model.Operation("ADD", i0, i1, act).To(t0) +model.Operation("ADD", t1, t3, act).To(t4) +model.Operation("ADD", t2, i5, act).To(t3) +model.Operation("ADD", t4, t6, act).To(o0) +model.Operation("ADD", i3, i4, act).To(t2) +model.Operation("ADD", i7, i8, act).To(o1) + +inputs = { + i0: [0, 0], + i1: [0, 0], + i2: [0, 0], + i3: [0, 0], + i4: [0, 0], + i5: [0, 0], + i6: [0, 0], + i7: [0, 0], + i8: [0, 0] +} + +outputs = { + o0: [0, 2], + o1: [0, 0], + o2: [0, 2] +} + +Example((inputs, outputs)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/stdout.txt.expect new file mode 100644 index 000000000..c656504a1 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_internal/stdout.txt.expect @@ -0,0 +1,312 @@ +// clang-format off +// Generated file (from: add_internal.mod.py). Do not edit +// clang-format off +// Generated file (from: add_internal.mod.py). Do not edit +// Generated from: add_internal.mod.py. +namespace add_internal { +// Generated add_internal test +#include "-" +// Generated model constructor +#include "-" +} // namespace add_internal + +// Create the model +Model createTestModel() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 10, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 2, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 8}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::TEMPORARY_VARIABLE, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::TEMPORARY_VARIABLE, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::TEMPORARY_VARIABLE, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::TEMPORARY_VARIABLE, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::TEMPORARY_VARIABLE, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::TEMPORARY_VARIABLE, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::TEMPORARY_VARIABLE, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::ADD, + .inputs = {10, 4, 2}, + .outputs = {5}, + }, + { + .type = OperationType::ADD, + .inputs = {4, 5, 2}, + .outputs = {6}, + }, + { + .type = OperationType::ADD, + .inputs = {11, 12, 2}, + .outputs = {8}, + }, + { + .type = OperationType::ADD, + .inputs = {7, 8, 2}, + .outputs = {9}, + }, + { + .type = OperationType::ADD, + .inputs = {17, 18, 2}, + .outputs = {15}, + }, + { + .type = OperationType::ADD, + .inputs = {15, 16, 2}, + .outputs = {13}, + }, + { + .type = OperationType::ADD, + .inputs = {9, 13, 2}, + .outputs = {14}, + }, + { + .type = OperationType::ADD, + .inputs = {14, 6, 2}, + .outputs = {0}, + }, + { + .type = OperationType::ADD, + .inputs = {19, 20, 2}, + .outputs = {1}, + }, + { + .type = OperationType::ADD, + .inputs = {0, 1, 2}, + .outputs = {3}, + } + }; + + const std::vector<uint32_t> inputIndexes = {7, 10, 11, 12, 16, 17, 18, 19, 20}; + const std::vector<uint32_t> outputIndexes = {0, 1, 3}; + std::vector<uint8_t> operandValues = { + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 128, 63 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {0.0f, 0.0f}}, {1, {0.0f, 0.0f}}, {2, {0.0f, 0.0f}}, {3, {0.0f, 0.0f}}, {4, {0.0f, 0.0f}}, {5, {0.0f, 0.0f}}, {6, {0.0f, 0.0f}}, {7, {0.0f, 0.0f}}, {8, {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, {0.0f, 2.0f}}, {1, {0.0f, 0.0f}}, {2, {0.0f, 2.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, add_internal) { + generated_tests::Execute(device, + add_internal::createTestModel, + add_internal::is_ignored, + add_internal::examples); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/conv_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/conv_float.mod.py new file mode 100644 index 000000000..61f7c92ee --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/conv_float.mod.py @@ -0,0 +1,61 @@ +# Copyright 2018, 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. + +model = Model("model_name") +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [-200]) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +act = Int32Scalar("act", 0) # None activation +layout = Int32Scalar("layout", 0) # NHWC +pad = Int32Scalar("param", 1) +stride0 = Int32Scalar("param1", 1) +stride1 = Int32Scalar("param2", 1) + +model = model.Operation("CONV_2D", i1, f1, b1, pad, stride0, stride1, act, layout).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8]} + +output0 = {output: # output 0 + [204, 120, 94, 104, 70, 164, 23, 112]} + +quant8 = DataTypeConverter(name="quantized").Identify({ + i1: ("TENSOR_QUANT8_ASYMM", 0.5, 128), + f1: ("TENSOR_QUANT8_ASYMM", 0.25, 128), + b1: ("TENSOR_INT32", 0.125, 0), + output: ("TENSOR_QUANT8_ASYMM", 2, 100) +}) +nchw = DataLayoutConverter("NCHW", name="nchw_layout").Identify([i1, f1, output], [layout]) +relu = ActivationConverter("relu", name="act").Identify([output], [act]) +relu6 = ActivationConverter("relu6").Identify([output], [act]) +weight_as_input = ParameterAsInputConverter(name="w_as_input").Identify([f1]) +relax = RelaxedModeConverter(True, name="float_relaxed") + +# Instantiate an example +# Will produce cartesian product of +# [nhwc, nchw_layout] * [act, relu6] * [w_as_param, w_as_input] * [float, float_relaxed, quantized] +# 24 variations +Example( + (input0, output0), name="example_name" +).AddVariations( + nchw, defaultName="nhwc" +).AddVariations( + relu, relu6, includeDefault=False +).AddVariations( + weight_as_input, defaultName="w_as_param" +).AddVariations( + relax, quant8, defaultName="float" +) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/stdout.txt.expect new file mode 100644 index 000000000..892700036 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_naming/stdout.txt.expect @@ -0,0 +1,3548 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// Generated from: conv_float.mod.py. +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +// Create the model +Model createTestModel_model_name_nhwc_act_w_as_param_float() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nhwc_act_w_as_param_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_param_float = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_act_w_as_param_float) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_act_w_as_param_float, + conv_float::is_ignored_model_name_nhwc_act_w_as_param_float, + conv_float::examples_model_name_example_name_nhwc_act_w_as_param_float); +} + +// Create the model +Model createTestModel_model_name_nhwc_act_w_as_param_float_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_model_name_nhwc_act_w_as_param_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_param_float_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_act_w_as_param_float_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_act_w_as_param_float_relaxed, + conv_float::is_ignored_model_name_nhwc_act_w_as_param_float_relaxed, + conv_float::examples_model_name_example_name_nhwc_act_w_as_param_float_relaxed); +} + +// Create the model +Model createTestModel_model_name_nhwc_act_w_as_param_quantized() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nhwc_act_w_as_param_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_param_quantized = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_act_w_as_param_quantized) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_act_w_as_param_quantized, + conv_float::is_ignored_model_name_nhwc_act_w_as_param_quantized, + conv_float::examples_model_name_example_name_nhwc_act_w_as_param_quantized); +} + +// Create the model +Model createTestModel_model_name_nhwc_act_w_as_input_float() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nhwc_act_w_as_input_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_input_float = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_act_w_as_input_float) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_act_w_as_input_float, + conv_float::is_ignored_model_name_nhwc_act_w_as_input_float, + conv_float::examples_model_name_example_name_nhwc_act_w_as_input_float); +} + +// Create the model +Model createTestModel_model_name_nhwc_act_w_as_input_float_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_model_name_nhwc_act_w_as_input_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_input_float_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_act_w_as_input_float_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_act_w_as_input_float_relaxed, + conv_float::is_ignored_model_name_nhwc_act_w_as_input_float_relaxed, + conv_float::examples_model_name_example_name_nhwc_act_w_as_input_float_relaxed); +} + +// Create the model +Model createTestModel_model_name_nhwc_act_w_as_input_quantized() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nhwc_act_w_as_input_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_act_w_as_input_quantized = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_act_w_as_input_quantized) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_act_w_as_input_quantized, + conv_float::is_ignored_model_name_nhwc_act_w_as_input_quantized, + conv_float::examples_model_name_example_name_nhwc_act_w_as_input_quantized); +} + +// Create the model +Model createTestModel_model_name_nhwc_relu6_w_as_param_float() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nhwc_relu6_w_as_param_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_param_float = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_relu6_w_as_param_float) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_relu6_w_as_param_float, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_param_float, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_param_float); +} + +// Create the model +Model createTestModel_model_name_nhwc_relu6_w_as_param_float_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_model_name_nhwc_relu6_w_as_param_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_param_float_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_relu6_w_as_param_float_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_relu6_w_as_param_float_relaxed, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_param_float_relaxed, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_param_float_relaxed); +} + +// Create the model +Model createTestModel_model_name_nhwc_relu6_w_as_param_quantized() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nhwc_relu6_w_as_param_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_param_quantized = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_relu6_w_as_param_quantized) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_relu6_w_as_param_quantized, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_param_quantized, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_param_quantized); +} + +// Create the model +Model createTestModel_model_name_nhwc_relu6_w_as_input_float() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nhwc_relu6_w_as_input_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_input_float = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_relu6_w_as_input_float) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_relu6_w_as_input_float, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_input_float, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_input_float); +} + +// Create the model +Model createTestModel_model_name_nhwc_relu6_w_as_input_float_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_model_name_nhwc_relu6_w_as_input_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_input_float_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_relu6_w_as_input_float_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_relu6_w_as_input_float_relaxed, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_input_float_relaxed, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_input_float_relaxed); +} + +// Create the model +Model createTestModel_model_name_nhwc_relu6_w_as_input_quantized() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nhwc_relu6_w_as_input_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nhwc_relu6_w_as_input_quantized = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nhwc_relu6_w_as_input_quantized) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nhwc_relu6_w_as_input_quantized, + conv_float::is_ignored_model_name_nhwc_relu6_w_as_input_quantized, + conv_float::examples_model_name_example_name_nhwc_relu6_w_as_input_quantized); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_act_w_as_param_float() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nchw_layout_act_w_as_param_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_param_float = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_act_w_as_param_float) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_act_w_as_param_float, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_param_float, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_param_float); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_act_w_as_param_float_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_model_name_nchw_layout_act_w_as_param_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_param_float_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_act_w_as_param_float_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_act_w_as_param_float_relaxed, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_param_float_relaxed, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_param_float_relaxed); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_act_w_as_param_quantized() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nchw_layout_act_w_as_param_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_param_quantized = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_act_w_as_param_quantized) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_act_w_as_param_quantized, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_param_quantized, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_param_quantized); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_act_w_as_input_float() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nchw_layout_act_w_as_input_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_input_float = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_act_w_as_input_float) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_act_w_as_input_float, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_input_float, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_input_float); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_act_w_as_input_float_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_model_name_nchw_layout_act_w_as_input_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_input_float_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_act_w_as_input_float_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_act_w_as_input_float_relaxed, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_input_float_relaxed, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_input_float_relaxed); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_act_w_as_input_quantized() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nchw_layout_act_w_as_input_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_act_w_as_input_quantized = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_act_w_as_input_quantized) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_act_w_as_input_quantized, + conv_float::is_ignored_model_name_nchw_layout_act_w_as_input_quantized, + conv_float::examples_model_name_example_name_nchw_layout_act_w_as_input_quantized); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_relu6_w_as_param_float() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_param_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_param_float = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_relu6_w_as_param_float) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_relu6_w_as_param_float, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_param_float, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_param_float); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_relu6_w_as_param_float_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_param_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_param_float_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_relu6_w_as_param_float_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_relu6_w_as_param_float_relaxed, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_param_float_relaxed, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_param_float_relaxed); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_relu6_w_as_param_quantized() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_param_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_param_quantized = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_relu6_w_as_param_quantized) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_relu6_w_as_param_quantized, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_param_quantized, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_param_quantized); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_relu6_w_as_input_float() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_input_float(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_input_float = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_relu6_w_as_input_float) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_relu6_w_as_input_float, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_input_float, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_input_float); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_relu6_w_as_input_float_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_input_float_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_input_float_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_relu6_w_as_input_float_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_relu6_w_as_input_float_relaxed, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_input_float_relaxed, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_input_float_relaxed); +} + +// Create the model +Model createTestModel_model_name_nchw_layout_relu6_w_as_input_quantized() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_model_name_nchw_layout_relu6_w_as_input_quantized(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_model_name_example_name_nchw_layout_relu6_w_as_input_quantized = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_model_name_example_name_nchw_layout_relu6_w_as_input_quantized) { + generated_tests::Execute(device, + conv_float::createTestModel_model_name_nchw_layout_relu6_w_as_input_quantized, + conv_float::is_ignored_model_name_nchw_layout_relu6_w_as_input_quantized, + conv_float::examples_model_name_example_name_nchw_layout_relu6_w_as_input_quantized); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/conv_quant8.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/conv_quant8.mod.py new file mode 100644 index 000000000..f6b3e89ca --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/conv_quant8.mod.py @@ -0,0 +1,35 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 3, 1}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{1, 2, 2, 1}", [.25, .25, .25, .25]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) +pad0 = Int32Scalar("pad0", 0) +act = Int32Scalar("act", 0) +stride = Int32Scalar("stride", 1) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}") + +model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) +model = model.RelaxedExecution(True) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]} + +output0 = {output: # output 0 + [.875, .875, .875, .875]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/stdout.txt.expect new file mode 100644 index 000000000..0df371ba0 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_quant8/stdout.txt.expect @@ -0,0 +1,142 @@ +// clang-format off +// Generated file (from: conv_quant8.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_quant8.mod.py). Do not edit +// Generated from: conv_quant8.mod.py. +namespace conv_quant8 { +// Generated conv_quant8 test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_quant8 + +// Create the model +Model createTestModel() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 3, 3, 1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 4, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 2, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 1}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 3, 3, 3, 4, 4, 5}, + .outputs = {6}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {6}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples = { +// 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 +}; + +TEST_F(NeuralnetworksHidlTest, conv_quant8) { + generated_tests::Execute(device, + conv_quant8::createTestModel, + conv_quant8::is_ignored, + conv_quant8::examples); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/conv_float.mod.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/conv_float.mod.py new file mode 100644 index 000000000..c95696203 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/conv_float.mod.py @@ -0,0 +1,46 @@ +# Copyright 2018, 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. + +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +f1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1]) +b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [-200]) +output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +act = Int32Scalar("act", 0) # None activation +layout = Int32Scalar("layout", 0) # NHWC +pad = Int32Scalar("param", 1) +stride0 = Int32Scalar("param1", 1) +stride1 = Int32Scalar("param2", 1) + +model = model.Operation("CONV_2D", i1, f1, b1, pad, stride0, stride1, act, layout).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8]} + +output0 = {output: # output 0 + [204, 120, 94, 104, 70, 164, 23, 112]} + +quant8 = DataTypeConverter().Identify({ + i1: ("TENSOR_QUANT8_ASYMM", 0.5, 128), + f1: ("TENSOR_QUANT8_ASYMM", 0.25, 128), + b1: ("TENSOR_INT32", 0.125, 0), + output: ("TENSOR_QUANT8_ASYMM", 2, 100) +}) +relu = ActivationConverter("relu").Identify([output], [act]) +relu6 = ActivationConverter("relu6").Identify([output], [act]) + +# Instantiate an example +Example((input0, output0)).AddNchw(i1, f1, output, layout).AddAllActivations( + output, act).AddInput(f1).AddVariations(RelaxedModeConverter(True), quant8) diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/stderr.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/stderr.txt.expect new file mode 100644 index 000000000..3decb4c1c --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/stderr.txt.expect @@ -0,0 +1,2 @@ +Output VTS model: - +Output example:- diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/stdout.txt.expect new file mode 100644 index 000000000..6ae6401bf --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_vts_variation/stdout.txt.expect @@ -0,0 +1,7084 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// Generated from: conv_float.mod.py. +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +// Create the model +Model createTestModel_none() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_none(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_none) { + generated_tests::Execute(device, + conv_float::createTestModel_none, + conv_float::is_ignored_none, + conv_float::examples_none); +} + +// Create the model +Model createTestModel_none_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_none_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_none_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_none_relaxed, + conv_float::is_ignored_none_relaxed, + conv_float::examples_none_relaxed); +} + +// Create the model +Model createTestModel_none_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_none_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_none_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_none_quant8, + conv_float::is_ignored_none_quant8, + conv_float::examples_none_quant8); +} + +// Create the model +Model createTestModel_none_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_none_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_none_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_none_weight_as_input, + conv_float::is_ignored_none_weight_as_input, + conv_float::examples_none_weight_as_input); +} + +// Create the model +Model createTestModel_none_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_none_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_none_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_none_weight_as_input_relaxed, + conv_float::is_ignored_none_weight_as_input_relaxed, + conv_float::examples_none_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_none_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_none_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_none_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_none_weight_as_input_quant8, + conv_float::is_ignored_none_weight_as_input_quant8, + conv_float::examples_none_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_relu() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu) { + generated_tests::Execute(device, + conv_float::createTestModel_relu, + conv_float::is_ignored_relu, + conv_float::examples_relu); +} + +// Create the model +Model createTestModel_relu_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_relaxed, + conv_float::is_ignored_relu_relaxed, + conv_float::examples_relu_relaxed); +} + +// Create the model +Model createTestModel_relu_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_quant8, + conv_float::is_ignored_relu_quant8, + conv_float::examples_relu_quant8); +} + +// Create the model +Model createTestModel_relu_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_weight_as_input, + conv_float::is_ignored_relu_weight_as_input, + conv_float::examples_relu_weight_as_input); +} + +// Create the model +Model createTestModel_relu_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_weight_as_input_relaxed, + conv_float::is_ignored_relu_weight_as_input_relaxed, + conv_float::examples_relu_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_relu_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu_weight_as_input_quant8, + conv_float::is_ignored_relu_weight_as_input_quant8, + conv_float::examples_relu_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_relu1() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu1(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1 = { +// 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}}}, + // 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu1) { + generated_tests::Execute(device, + conv_float::createTestModel_relu1, + conv_float::is_ignored_relu1, + conv_float::examples_relu1); +} + +// Create the model +Model createTestModel_relu1_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu1_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_relaxed = { +// 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}}}, + // 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu1_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu1_relaxed, + conv_float::is_ignored_relu1_relaxed, + conv_float::examples_relu1_relaxed); +} + +// Create the model +Model createTestModel_relu1_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu1_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu1_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu1_quant8, + conv_float::is_ignored_relu1_quant8, + conv_float::examples_relu1_quant8); +} + +// Create the model +Model createTestModel_relu1_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu1_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu1_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_relu1_weight_as_input, + conv_float::is_ignored_relu1_weight_as_input, + conv_float::examples_relu1_weight_as_input); +} + +// Create the model +Model createTestModel_relu1_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu1_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu1_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu1_weight_as_input_relaxed, + conv_float::is_ignored_relu1_weight_as_input_relaxed, + conv_float::examples_relu1_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_relu1_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu1_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu1_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu1_weight_as_input_quant8, + conv_float::is_ignored_relu1_weight_as_input_quant8, + conv_float::examples_relu1_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_relu6() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6 = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6, + conv_float::is_ignored_relu6, + conv_float::examples_relu6); +} + +// Create the model +Model createTestModel_relu6_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128, 64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 224, 64, 0, 0, 192, 64, 0, 0, 160, 64, 0, 0, 128, 64, 0, 0, 64, 64, 0, 0, 0, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_relaxed = { +// 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}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_relaxed, + conv_float::is_ignored_relu6_relaxed, + conv_float::examples_relu6_relaxed); +} + +// Create the model +Model createTestModel_relu6_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_quant8, + conv_float::is_ignored_relu6_quant8, + conv_float::examples_relu6_quant8); +} + +// Create the model +Model createTestModel_relu6_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_weight_as_input, + conv_float::is_ignored_relu6_weight_as_input, + conv_float::examples_relu6_weight_as_input); +} + +// Create the model +Model createTestModel_relu6_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_relaxed = { +// 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}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_weight_as_input_relaxed, + conv_float::is_ignored_relu6_weight_as_input_relaxed, + conv_float::examples_relu6_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_relu6_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_quant8 = { +// 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, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_relu6_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_relu6_weight_as_input_quant8, + conv_float::is_ignored_relu6_weight_as_input_quant8, + conv_float::examples_relu6_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_nchw_none() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_none(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_none) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_none, + conv_float::is_ignored_nchw_none, + conv_float::examples_nchw_none); +} + +// Create the model +Model createTestModel_nchw_none_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_none_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_none_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_none_relaxed, + conv_float::is_ignored_nchw_none_relaxed, + conv_float::examples_nchw_none_relaxed); +} + +// Create the model +Model createTestModel_nchw_none_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_none_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_none_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_none_quant8, + conv_float::is_ignored_nchw_none_quant8, + conv_float::examples_nchw_none_quant8); +} + +// Create the model +Model createTestModel_nchw_none_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_none_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_none_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_none_weight_as_input, + conv_float::is_ignored_nchw_none_weight_as_input, + conv_float::examples_nchw_none_weight_as_input); +} + +// Create the model +Model createTestModel_nchw_none_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_none_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_none_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_none_weight_as_input_relaxed, + conv_float::is_ignored_nchw_none_weight_as_input_relaxed, + conv_float::examples_nchw_none_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_nchw_none_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_none_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_none_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_none_weight_as_input_quant8, + conv_float::is_ignored_nchw_none_weight_as_input_quant8, + conv_float::examples_nchw_none_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_nchw_relu() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu, + conv_float::is_ignored_nchw_relu, + conv_float::examples_nchw_relu); +} + +// Create the model +Model createTestModel_nchw_relu_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_relaxed, + conv_float::is_ignored_nchw_relu_relaxed, + conv_float::examples_nchw_relu_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_quant8, + conv_float::is_ignored_nchw_relu_quant8, + conv_float::examples_nchw_relu_quant8); +} + +// Create the model +Model createTestModel_nchw_relu_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_weight_as_input, + conv_float::is_ignored_nchw_relu_weight_as_input, + conv_float::examples_nchw_relu_weight_as_input); +} + +// Create the model +Model createTestModel_nchw_relu_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu_weight_as_input_relaxed, + conv_float::examples_nchw_relu_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu_weight_as_input_quant8, + conv_float::examples_nchw_relu_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_nchw_relu1() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu1(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu1) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu1, + conv_float::is_ignored_nchw_relu1, + conv_float::examples_nchw_relu1); +} + +// Create the model +Model createTestModel_nchw_relu1_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu1_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu1_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu1_relaxed, + conv_float::is_ignored_nchw_relu1_relaxed, + conv_float::examples_nchw_relu1_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu1_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu1_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu1_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu1_quant8, + conv_float::is_ignored_nchw_relu1_quant8, + conv_float::examples_nchw_relu1_quant8); +} + +// Create the model +Model createTestModel_nchw_relu1_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu1_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu1_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu1_weight_as_input, + conv_float::is_ignored_nchw_relu1_weight_as_input, + conv_float::examples_nchw_relu1_weight_as_input); +} + +// Create the model +Model createTestModel_nchw_relu1_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu1_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu1_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu1_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu1_weight_as_input_relaxed, + conv_float::examples_nchw_relu1_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu1_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu1_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu1_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu1_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu1_weight_as_input_quant8, + conv_float::examples_nchw_relu1_weight_as_input_quant8); +} + +// Create the model +Model createTestModel_nchw_relu6() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6, + conv_float::is_ignored_nchw_relu6, + conv_float::examples_nchw_relu6); +} + +// Create the model +Model createTestModel_nchw_relu6_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 64}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 64, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 68, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 72, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 76, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 80, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 84, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 128, 63, 0, 0, 64, 64, 0, 0, 160, 64, 0, 0, 224, 64, 0, 0, 0, 64, 0, 0, 128, 64, 0, 0, 192, 64, 0, 0, 0, 65, 0, 0, 0, 65, 0, 0, 192, 64, 0, 0, 128, 64, 0, 0, 0, 64, 0, 0, 224, 64, 0, 0, 160, 64, 0, 0, 64, 64, 0, 0, 128, 63, 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_relaxed, + conv_float::is_ignored_nchw_relu6_relaxed, + conv_float::examples_nchw_relu6_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu6_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 16}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 24, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 28, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 32, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 36, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132, 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_quant8, + conv_float::is_ignored_nchw_relu6_quant8, + conv_float::examples_nchw_relu6_quant8); +} + +// Create the model +Model createTestModel_nchw_relu6_weight_as_input() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_weight_as_input) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_weight_as_input, + conv_float::is_ignored_nchw_relu6_weight_as_input, + conv_float::examples_nchw_relu6_weight_as_input); +} + +// Create the model +Model createTestModel_nchw_relu6_weight_as_input_relaxed() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_FLOAT32, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 0, 0, 72, 195, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + .relaxComputationFloat32toFloat16 = true, + }; +} + +bool is_ignored_nchw_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_weight_as_input_relaxed) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu6_weight_as_input_relaxed, + conv_float::examples_nchw_relu6_weight_as_input_relaxed); +} + +// Create the model +Model createTestModel_nchw_relu6_weight_as_input_quant8() { + const std::vector<Operand> operands = { + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 1, + .scale = 0.5f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {2, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 0.25f, + .zeroPoint = 128, + .lifetime = OperandLifeTime::MODEL_INPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + }, + { + .type = OperandType::TENSOR_INT32, + .dimensions = {1}, + .numberOfConsumers = 1, + .scale = 0.125f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 0, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 4, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 8, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 12, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 16, .length = 4}, + }, + { + .type = OperandType::INT32, + .dimensions = {}, + .numberOfConsumers = 1, + .scale = 0.0f, + .zeroPoint = 0, + .lifetime = OperandLifeTime::CONSTANT_COPY, + .location = {.poolIndex = 0, .offset = 20, .length = 4}, + }, + { + .type = OperandType::TENSOR_QUANT8_ASYMM, + .dimensions = {1, 2, 2, 2}, + .numberOfConsumers = 0, + .scale = 2.0f, + .zeroPoint = 100, + .lifetime = OperandLifeTime::MODEL_OUTPUT, + .location = {.poolIndex = 0, .offset = 0, .length = 0}, + } + }; + + const std::vector<Operation> operations = { + { + .type = OperationType::CONV_2D, + .inputs = {0, 1, 2, 3, 4, 5, 6, 7}, + .outputs = {8}, + } + }; + + const std::vector<uint32_t> inputIndexes = {0, 1}; + const std::vector<uint32_t> outputIndexes = {8}; + std::vector<uint8_t> operandValues = { + 192, 249, 255, 255, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0 + }; + const std::vector<hidl_memory> pools = {}; + + return { + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools, + }; +} + +bool is_ignored_nchw_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_quant8 = { +// 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, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(NeuralnetworksHidlTest, conv_float_nchw_relu6_weight_as_input_quant8) { + generated_tests::Execute(device, + conv_float::createTestModel_nchw_relu6_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu6_weight_as_input_quant8, + conv_float::examples_nchw_relu6_weight_as_input_quant8); +} + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/test.py b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/test.py new file mode 100644 index 000000000..248fae3a4 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/test.py @@ -0,0 +1,328 @@ +#!/usr/bin/python3 + +# Copyright 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. + + +"""NN Model Test Compiler Test. + +Runs subdirectories of tests for the test generator/compiler. +""" + +import filecmp +import glob +import os +import re +import shutil +import subprocess +import sys +import unittest + + +__author__ = 'Android' + + +DOTTED_LINE = '................' + +class OrigFile: + OrigDir = None + +class TestGeneratorTests(unittest.TestCase): + """Class to contain all the unittest test cases. + + Tests will be dynamically added to this class as methods. + No static tests, so this class is initially empty. + + """ + pass + + +def GenerateTests(dir_name): + """Creates a test method that can be added as method to GenerateTests.""" + cwd = os.getcwd() + def Test(self): + os.chdir(cwd) + ExecTest(dir_name, self) + return Test + + +def AddUnitTests(test_dirs): + """Adds a test to Tests for each directory in test_dirs.""" + + for t in test_dirs: + # Must start with 'test_' according to unittest + test_name = 'test_%s' % t + test = GenerateTests(t) + # Add test as method to TestGeneratorTests with test_name as method name + setattr(TestGeneratorTests, test_name, test) + + +class Options(object): + verbose = 0 + cleanup = 1 + update_cts = 0 + zero_return = 0 + + +def CompareFiles(actual, expect): + """Compares actual and expect for equality.""" + if not os.path.isfile(actual): + if Options.verbose: + print ('Could not find %s' % actual) + return False + if not os.path.isfile(expect): + if Options.verbose: + print ('Could not find %s' % expect) + return False + + return filecmp.cmp(actual, expect, False) + + +def CopyIfDifferent(src, dst): + """Updates dst if it is different from src.""" + if not CompareFiles(src, dst): + if Options.verbose: + print ('Copying from %s to %s' % (src, dst)) + shutil.copyfile(src, dst) + + +def GetCommandLineArgs(filename): + """Extracts command line arguments from first comment line in a file.""" + f = open(filename, 'r') + line = f.readline() + f.close() + if line[0] == '/' and line[1] == '/': + return line[2:].strip() + else: + return '' + + +def ReadFileToStr(filename): + """Returns contents of file as a str.""" + with open(filename, 'r') as f: + return f.read() + + +def ReportIfDifferFromExpected(tests, name, file1, file2): + """Fails tests if file1 and file2 differ.""" + if not CompareFiles(file1, file2): + if Options.verbose: + err_message = ('%s is different:\n' + 'expected:\n%s\n%s%s\n\n' + 'actual:\n%s\n%s%s\n') % ( + name, + DOTTED_LINE, ReadFileToStr(file1), DOTTED_LINE, + DOTTED_LINE, ReadFileToStr(file2), DOTTED_LINE) + else: + err_message = '%s is different' % name + tests.fail(err_message) + + +def GetRSFiles(): + """Returns a list of files in cwd with extension '.rs' or '.fs'.""" + rs_files = glob.glob('*.mod.py') + rs_files.sort() + return rs_files + + +def GetOutDir(): + return os.path.abspath(os.path.join(OrigFile.OrigDir, "../")) + + +# Declare/define cache variable for GetOutDir to cache results +# This way we only need to call subprocesses once to get the directory +GetOutDir.cache = None + + +def CreateCmd(run_vts): + """Creates the test command to run for the current test.""" + cmd_string = ('%s/%s_generator.py' + ) % (GetOutDir(), "cts" if not run_vts else "vts") + base_args = cmd_string.split() + rs_files = GetRSFiles() + + # Extra command line arguments can be placed as // comments at the start of + # any .rs file. We automatically bundle up all of these extra args and invoke + # llvm-rs-cc with them. + extra_args_str = '' + for rs_file in rs_files: + extra_args_str += GetCommandLineArgs(rs_file) + extra_args = extra_args_str.split() + + args = base_args + extra_args + rs_files + return args + +def Cleanup(): + """Cleans up the cwd of any tmp files created in current test.""" + try: + os.remove('stdout.txt') + os.remove('stderr.txt') + shutil.rmtree('tmp/') + except OSError: + pass + + +def CheckTestResult(dir_name, subprocess_ret, tests, args): + """Checks the result of the subprocess command to see if it passed/failed. + + If dir_name starts with 'F_', then subprocess is expected to fail. + If it instead succeeded, then this test is failed. + Vice versa with a dir_name starting with 'P_'. + + Args: + dir_name: name of current directory/test name + subprocess_ret: return code of subprocess + tests: unittest, call tests.fail(reason) when failure + args: the arguments for the command that was run + """ + if dir_name[0:2] == 'F_': + if subprocess_ret == 0: + if Options.verbose: + err_message = ('Command (%s) passed on invalid input\n' + 'stdout:\n%s\n%s%s\n') % ( + ' '.join(args), + DOTTED_LINE, ReadFileToStr('stdout.txt'), DOTTED_LINE + ) + else: + err_message = 'Command passed on invalid input' + tests.fail(err_message) + elif dir_name[0:2] == 'P_': + if subprocess_ret != 0: + if Options.verbose: + err_message = ('Command (%s) failed on valid input\n' + 'stderr:\n%s\n%s%s\n') % ( + ' '.join(args), + DOTTED_LINE, ReadFileToStr('stderr.txt'), DOTTED_LINE + ) + else: + err_message = 'Command failed on valid input' + tests.fail(err_message) + else: + tests.fail('Invalid test name: ' + dir_name + + ', should start with F_ or P_') + + + +def ExecTest(dir_name, tests): + """Executes an test generator test from dir_name.""" + + os.chdir(dir_name) + stdout_file = open('stdout.txt', 'w+') + stderr_file = open('stderr.txt', 'w+') + run_vts = (dir_name[2:5] == 'vts') + args = CreateCmd(run_vts) + + if Options.verbose > 1: + print ('Executing:', ' '.join(args)) + + # Execute the command and check the resulting shell return value. + # All tests that are expected to FAIL have directory names that + # start with 'F_'. Other tests that are expected to PASS have + # directory names that start with 'P_'. + ret = 0 + try: + ret = subprocess.call(args, stdout=stdout_file, stderr=stderr_file) + except OSError: + tests.fail('subprocess.call failed: ' + ' '.join(args)) + + stdout_file.close() + stderr_file.close() + + CheckTestResult(dir_name, ret, tests, args) + + ReportIfDifferFromExpected(tests, 'stdout', 'stdout.txt.expect', 'stdout.txt') + ReportIfDifferFromExpected(tests, 'stderr', 'stderr.txt.expect', 'stderr.txt') + + if Options.cleanup: + Cleanup() + + +def Usage(): + """Print out usage information.""" + print ('Usage: %s [OPTION]... [TESTNAME]...' + 'Renderscript Compiler Test Harness\n' + 'Runs TESTNAMEs (all tests by default)\n' + 'Available Options:\n' + ' -h, --help Help message\n' + ' -n, --no-cleanup Don\'t clean up after running tests\n' + ' -v, --verbose Verbose output. Enter multiple -v to get more verbose.\n' + ' -z, --zero-return Return 0 as exit code no matter if tests fail. Required for TreeHugger.\n' + ) % (sys.argv[0]), + return + + +def main(): + """Runs the unittest suite. + + Parses command line arguments, adds test directories as tests. + + Returns: + 0 if '-z' flag is set. + Else unittest.main() returns with its own error code. + """ + + OrigFile.OrigDir = os.path.dirname(os.path.abspath(__file__)) + # Chdir to the directory this file is in since tests are in this directory + os.chdir(OrigFile.OrigDir) + files = [] + for arg in sys.argv[1:]: + if arg in ('-h', '--help'): + Usage() + return 0 + elif arg in ('-n', '--no-cleanup'): + Options.cleanup = 0 + elif arg in ('-u', '--update-cts'): + Options.update_cts = 1 + elif arg in ('-v', '--verbose'): + Options.verbose += 1 + elif arg in ('-z', '--zero-return'): + Options.zero_return = 1 + else: + # Test list to run + if os.path.isdir(arg): + files.append(arg) + else: + print >> sys.stderr, 'Invalid test or option: %s' % arg + return 1 + + if not files: + file_names = os.listdir('.') + # Test names must start with 'F_' or 'P_' + # 'F_' tests are expected to fail + # 'P_' tests are expected to pass + for f in file_names: + if os.path.isdir(f) and (f[0:2] == 'F_' or f[0:2] == 'P_'): + files.append(f) + files.sort() + + AddUnitTests(files) + + # verbosity=2 is necessary for PythonUnitTestRunner to parse the results + # Otherwise verbosity does not matter + # If Options.zero_return is set, do not let unittest.main() exit + # This is necessary in TreeHugger to distinguish between failing tests and + # failing to execute the python script + # If Options.zero_return is not set, let unittest.main() exit + # In this case it will return a non-zero code if any tests fail + unittest_exit = Options.zero_return == 0 + unittest.main(verbosity=2, + argv=[sys.argv[0]] + ['TestGeneratorTests'], + exit=unittest_exit) + + return 0 + + +if __name__ == '__main__': + sys.exit(main()) + diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/vts_generator.py b/tests/nnapi/nnapi_test_generator/android-q-beta/vts_generator.py new file mode 100644 index 000000000..2aa8731a0 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/vts_generator.py @@ -0,0 +1,361 @@ +#!/usr/bin/python3 + +# Copyright 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. +"""VTS testcase generator + +Implements VTS test backend. Shares most logic with the CTS test +generator. Invoked by ml/nn/runtime/test/specs/generate_vts_tests.sh; +See that script for details on how this script is used. + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import argparse +from functools import reduce +import math +import numpy as np +import os +import re +import struct +import contextlib +import pprint + +# Stuff from test generator +import test_generator as tg +from test_generator import ActivationConverter +from test_generator import BoolScalar +from test_generator import Configuration +from test_generator import DataTypeConverter +from test_generator import DataLayoutConverter +from test_generator import Example +from test_generator import Float16Scalar +from test_generator import Float32Scalar +from test_generator import Float32Vector +from test_generator import IgnoredOutput +from test_generator import Input +from test_generator import Int32Scalar +from test_generator import Int32Vector +from test_generator import Internal +from test_generator import Model +from test_generator import Operand +from test_generator import Output +from test_generator import Parameter +from test_generator import ParameterAsInputConverter +from test_generator import RelaxedModeConverter +from test_generator import SmartOpen +from test_generator import SymmPerChannelQuantParams + +# Dumping methods that shared with CTS generator +from cts_generator import DumpCtsIsIgnored + + +# TODO: Make this part of tg.Configuration? +target_hal_version = None + + +# Take a model from command line +def ParseCmdLine(): + parser = argparse.ArgumentParser() + parser.add_argument("spec", help="the spec file") + parser.add_argument( + "-m", "--model", help="the output model file", default="-") + parser.add_argument( + "-t", "--test", help="the output test file", default="-") + parser.add_argument( + "--target_hal_version", + help="the HAL version of the output", + required=True, + choices=["V1_0", "V1_1", "V1_2"]) + args = parser.parse_args() + example = "-" # VTS generator does not generate examples. See cts_generator.py. + tg.FileNames.InitializeFileLists( + args.spec, args.model, example, args.test) + global target_hal_version + target_hal_version = args.target_hal_version + +# Generate operands in VTS format +def generate_vts_operands(model): + # Dump operand definitions + op_def = """\ + {{ + .type = OperandType::{operand_type}, + .dimensions = {shape}, + .numberOfConsumers = {no_consumers}, + .scale = {scale}, + .zeroPoint = {zero_point}, + .lifetime = OperandLifeTime::{lifetime}, + .location = {{.poolIndex = 0, .offset = {offset}, .length = {length}}},{extraParams} + }}""" + offset = 0 + op_definitions = [] + extra_params_definitions = [] + for index, o in enumerate(model.operands): + length = o.type.GetByteSize() if isinstance(o, Parameter) else 0 + add_extra_params = o.type.extraParams is not None and not o.type.extraParams.hide + op = { + "operand_type": o.type.type, + "shape": o.type.GetDimensionsString(), + "no_consumers": len(o.outs), + "scale": tg.PrettyPrintAsFloat(o.type.scale), + "zero_point": str(int(o.type.zeroPoint)), + "lifetime": o.lifetime, + "offset": offset if isinstance(o, Parameter) else 0, + "length": length, + "extraParams": "" if not add_extra_params else "\n .extraParams = std::move(extraParams%d)," % (index,), + } + offset += length + op_definitions.append(op_def.format(**op)) + + extra_params_def = """\ + Operand::ExtraParams extraParams{index}; + extraParams{index}.{setMethodName}({param}); +""" + + if add_extra_params: + ep = o.type.extraParams + op = { + "index": index, + "setMethodName": ep.GetVtsSetter(), + "param": ep.GetVtsConstructor(), + } + extra_params_definitions.append(extra_params_def.format(**op)) + + op_vec = """{0}\ + const std::vector<Operand> operands = {{ +{1} + }};""".format(",\n".join(extra_params_definitions), ",\n".join(op_definitions)) + return op_vec + +# Generate VTS operand values +def generate_vts_operand_values(operands): + weights = [o for o in operands if isinstance(o, Parameter)] + binit = [] + for w in weights: + ty = w.type.type + if ty == "TENSOR_QUANT8_ASYMM": + binit += w.value + elif ty == "TENSOR_QUANT8_SYMM_PER_CHANNEL" or ty == "TENSOR_QUANT8_SYMM": + binit += [struct.pack("b", value)[0] for value in w.value] + elif ty == "BOOL" or ty == "TENSOR_BOOL8": + binit += [1 if x else 0 for x in w.value] + elif ty == "TENSOR_FLOAT16" or ty == "FLOAT16": + for f in w.value: + # The pack format for float16 is not available until Python 3.6. + binit += [int(x) for x in np.float16(f).tostring()] + elif ty in {"TENSOR_FLOAT32", "FLOAT32", "TENSOR_INT32", "INT32", "TENSOR_QUANT16_ASYMM"}: + if ty in ["TENSOR_FLOAT32", "FLOAT32"]: + fmt = "f" + elif ty in ["TENSOR_INT32", "INT32"]: + fmt = "i" + elif ty == "TENSOR_QUANT16_ASYMM": + fmt = "H" + for f in w.value: + binit += [int(x) for x in struct.pack(fmt, f)] + else: + assert 0 and "Unsupported VTS operand type" + + init_defs = ", ".join([str(x) for x in binit]) + if (init_defs != ""): + init_defs = "\n %s\n " % init_defs + byte_vec_fmt = """{%s}""" % init_defs + return byte_vec_fmt + +# Generate VTS operations +def generate_vts_operation(op, model): + op_fmt = """\ + {{ + .type = OperationType::{op_code}, + .inputs = {{{ins}}}, + .outputs = {{{outs}}}, + }}""" + op_content = { + 'op_code': op.optype, + 'ins': tg.GetJointStr(model.GetIndexOfOperands(op.ins)), + 'outs': tg.GetJointStr(model.GetIndexOfOperands(op.outs)) + } + return op_fmt.format(**op_content) + +def generate_vts_operations(model): + vts_ops = [generate_vts_operation(op, model) for op in model.operations] + return ",\n".join(vts_ops) + +def generate_vts_model(model, model_file): + operand_values_fmt = "" + if Configuration.useSHM(): + # Boilerplate code for passing weights in shared memory + operand_values_fmt = """\ + std::vector<uint8_t> operandValues = {{}}; + const uint8_t data[] = {operand_values}; + + // Allocate segment of android shared memory, wrapped in hidl_memory. + // This object will be automatically freed when sharedMemory is destroyed. + hidl_memory sharedMemory = ::android::nn::allocateSharedMemory(sizeof(data)); + + // Mmap ashmem into usable address and hold it within the mappedMemory object. + // MappedMemory will automatically munmap the memory when it is destroyed. + sp<::android::hidl::memory::V1_0::IMemory> mappedMemory = mapMemory(sharedMemory); + + if (mappedMemory != nullptr) {{ + // Retrieve the mmapped pointer. + uint8_t* mappedPointer = + static_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer())); + + if (mappedPointer != nullptr) {{ + // Acquire the write lock for the shared memory segment, upload the data, + // and release the lock. + mappedMemory->update(); + std::copy(data, data + sizeof(data), mappedPointer); + mappedMemory->commit(); + }} + }} + + const std::vector<hidl_memory> pools = {{sharedMemory}}; +""" + else: + # Passing weights via operandValues + operand_values_fmt = """\ + std::vector<uint8_t> operandValues = {operand_values}; + const std::vector<hidl_memory> pools = {{}}; +""" + + operand_values_val = { + 'operand_values': generate_vts_operand_values(model.operands) + } + operand_values = operand_values_fmt.format(**operand_values_val) + # operand_values = operand_values_fmt + model_fmt = """\ +// Create the model +Model {create_test_model_name}() {{ +{operand_decls} + + const std::vector<Operation> operations = {{ +{operations} + }}; + + const std::vector<uint32_t> inputIndexes = {{{input_indices}}}; + const std::vector<uint32_t> outputIndexes = {{{output_indices}}}; +{operand_values} + return {{ + .operands = operands, + .operations = operations, + .inputIndexes = inputIndexes, + .outputIndexes = outputIndexes, + .operandValues = operandValues, + .pools = pools,{relaxed_field} + }}; +}} +""" + model_dict = { + "hal_version": target_hal_version, + "create_test_model_name": str(model.createTestFunctionName), + "operations": generate_vts_operations(model), + "operand_decls": generate_vts_operands(model), + "operand_values": operand_values, + "output_indices": tg.GetJointStr(model.GetOutputsIndex()), + "input_indices": tg.GetJointStr(model.GetInputsIndex()), + "relaxed_field": + "\n .relaxComputationFloat32toFloat16 = true," if (model.isRelaxed) else "" + } + print(model_fmt.format(**model_dict), file = model_file) + +def generate_vts(model, model_file): + assert model.compiled + # Do not generate DynamicOutputShapeTest for pre-1.2 VTS. + if model.hasDynamicOutputShape and target_hal_version < "V1_2": + return + namespace = "android::hardware::neuralnetworks::{hal_version}::generated_tests::{spec_name}".format(spec_name=tg.FileNames.specName, hal_version=target_hal_version) + print("namespace {namespace} {{\n".format(namespace=namespace), file=model_file) + generate_vts_model(model, model_file) + DumpCtsIsIgnored(model, model_file) + print("}} // namespace {namespace}".format(namespace=namespace), file=model_file) + +def generate_vts_test(example, test_file): + # Do not generate DynamicOutputShapeTest for pre-1.2 VTS. + if example.model.hasDynamicOutputShape and target_hal_version < "V1_2": + return + + generated_vts_namespace = "android::hardware::neuralnetworks::{hal_version}::generated_tests::{spec_name}".format(spec_name=tg.FileNames.specName, hal_version=target_hal_version) + generated_cts_namespace = "generated_tests::{spec_name}".format(spec_name=tg.FileNames.specName) + testTemplate = """\ +namespace {generated_cts_namespace} {{ + +std::vector<::test_helper::MixedTypedExample>& get_{examples_name}(); + +}} // namespace {generated_cts_namespace} + +namespace {generated_vts_namespace} {{ + +Model {create_model_name}(); +bool {is_ignored_name}(int); +""" + + if not example.expectFailure: + testTemplate += """ +TEST_F({test_case_name}, {test_name}) {{ + Execute(device, + {create_model_name}, + {is_ignored_name}, + ::{generated_cts_namespace}::get_{examples_name}(){test_dynamic_output_shape}); +}} +""" + + testTemplate += """ +TEST_F(ValidationTest, {test_name}) {{ + const Model model = {create_model_name}(); + const std::vector<Request> requests = createRequests(::{generated_cts_namespace}::get_{examples_name}()); + validateEverything(model, requests); +}} + +}} // namespace {generated_vts_namespace} +""" + + print(testTemplate.format( + test_case_name="DynamicOutputShapeTest" if example.model.hasDynamicOutputShape \ + else "NeuralnetworksHidlTest", + test_name=str(example.testName), + generated_vts_namespace=generated_vts_namespace, + generated_cts_namespace=generated_cts_namespace, + hal_version=target_hal_version, + create_model_name=str(example.model.createTestFunctionName), + is_ignored_name=str(example.model.isIgnoredFunctionName), + examples_name=str(example.examplesName), + test_dynamic_output_shape=", true" if example.model.hasDynamicOutputShape else "", + validation_method="validateFailure" if example.expectFailure else "validateEverything", + ), file=test_fd) + +def InitializeFiles(model_fd, test_fd): + specFileBase = os.path.basename(tg.FileNames.specFile) + fileHeader = """\ +// Generated from {spec_file} +// DO NOT EDIT +// clang-format off +#include "GeneratedTests.h" +""".format(spec_file=specFileBase) + print(fileHeader, file=model_fd) + print(fileHeader, file=test_fd) + +if __name__ == "__main__": + ParseCmdLine() + while tg.FileNames.NextFile(): + print("Generating VTS tests from %s" % tg.FileNames.specFile) + exec (open(tg.FileNames.specFile, "r").read()) + with SmartOpen(tg.FileNames.modelFile) as model_fd, \ + SmartOpen(tg.FileNames.testFile) as test_fd: + InitializeFiles(model_fd, test_fd) + Example.DumpAllExamples( + DumpModel=generate_vts, model_fd=model_fd, + DumpTest=generate_vts_test, test_fd=test_fd) diff --git a/tests/nnapi/specs/Ex/argmax_ex_float_1.mod.py b/tests/nnapi/specs/Ex/argmax_ex_float_1.mod.py index e2255e004..dc29fb358 100644 --- a/tests/nnapi/specs/Ex/argmax_ex_float_1.mod.py +++ b/tests/nnapi/specs/Ex/argmax_ex_float_1.mod.py @@ -1,7 +1,7 @@ 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}") +output = Output("output", "TENSOR_INT32", "{1, 2, 1}") model = model.Operation("ARGMAX_EX", i1, axis).To(output) diff --git a/tests/nnapi/specs/Ex/argmax_ex_float_2.mod.py b/tests/nnapi/specs/Ex/argmax_ex_float_2.mod.py index 6f06bfd0b..8b6ed565f 100644 --- a/tests/nnapi/specs/Ex/argmax_ex_float_2.mod.py +++ b/tests/nnapi/specs/Ex/argmax_ex_float_2.mod.py @@ -1,7 +1,7 @@ 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}") +output = Output("output", "TENSOR_INT32", "{1, 2, 2}") model = model.Operation("ARGMAX_EX", i1, axis).To(output) diff --git a/tests/nnapi/specs/Ex/argmax_ex_int32.mod.py b/tests/nnapi/specs/Ex/argmax_ex_int32.mod.py index f7f98afe2..b5d01dd85 100644 --- a/tests/nnapi/specs/Ex/argmax_ex_int32.mod.py +++ b/tests/nnapi/specs/Ex/argmax_ex_int32.mod.py @@ -1,7 +1,7 @@ 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}") +output = Output("output", "TENSOR_INT32", "{1, 2, 1}") model = model.Operation("ARGMAX_EX", i1, axis).To(output) 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 index b29cf1eb6..977cac061 100644 --- a/tests/nnapi/specs/Ex/argmax_ex_neg_axis_float.mod.py +++ b/tests/nnapi/specs/Ex/argmax_ex_neg_axis_float.mod.py @@ -1,7 +1,7 @@ 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}") +output = Output("output", "TENSOR_INT32", "{1, 4, 1}") model = model.Operation("ARGMAX_EX", i1, axis).To(output) 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 index ddc27b35a..9f448e08d 100644 --- a/tests/nnapi/specs/Ex/argmax_ex_neg_axis_int32.mod.py +++ b/tests/nnapi/specs/Ex/argmax_ex_neg_axis_int32.mod.py @@ -1,7 +1,7 @@ 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}") +output = Output("output", "TENSOR_INT32", "{1, 4, 1}") model = model.Operation("ARGMAX_EX", i1, axis).To(output) diff --git a/tests/nnapi/specs/Ex/argmax_ex_quant8.mod.py b/tests/nnapi/specs/Ex/argmax_ex_quant8.mod.py index fd3c5230b..c3131ef91 100644 --- a/tests/nnapi/specs/Ex/argmax_ex_quant8.mod.py +++ b/tests/nnapi/specs/Ex/argmax_ex_quant8.mod.py @@ -1,7 +1,7 @@ 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}") +output = Output("output", "TENSOR_INT32", "{1, 2, 1}") model = model.Operation("ARGMAX_EX", i1, axis).To(output) 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 index c7ddc0e5d..9146d8f14 100644 --- a/tests/nnapi/specs/Ex/argmax_ex_quant8_neg_axis.mod.py +++ b/tests/nnapi/specs/Ex/argmax_ex_quant8_neg_axis.mod.py @@ -1,7 +1,7 @@ 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}") +output = Output("output", "TENSOR_INT32", "{1, 4, 1}") model = model.Operation("ARGMAX_EX", i1, axis).To(output) diff --git a/tests/nnapi/specs/Ex/gather_ex_2D_2D_float_1.mod.py b/tests/nnapi/specs/Ex/gather_ex_2D_2D_float_1.mod.py new file mode 100644 index 000000000..9edc80deb --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_2D_2D_float_1.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", "{1,2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_FLOAT32", "{1,2,4}") # 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 + [1, 0]} + +output0 = {i3: # output 0 + [7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_2D_2D_float_2.mod.py b/tests/nnapi/specs/Ex/gather_ex_2D_2D_float_2.mod.py new file mode 100644 index 000000000..85fd43466 --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_2D_2D_float_2.mod.py @@ -0,0 +1,23 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3,4}") # a vector of 12 float32s +i2 = Input("op2", "TENSOR_INT32", "{1,2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 1) +i3 = Output("op3", "TENSOR_FLOAT32", "{3,1,2}") # a vector of 6 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 + [1, 0]} + +output0 = {i3: # output 0 + [4.123456789123456789, 3.123456789123456789, + 8.123456789123456789, 7.123456789123456789, + 18.123456789123456789, 2.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_2D_3D_float_1.mod.py b/tests/nnapi/specs/Ex/gather_ex_2D_3D_float_1.mod.py new file mode 100644 index 000000000..652016af5 --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_2D_3D_float_1.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", "{1,1,2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_FLOAT32", "{1,1,2,4}") # 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 + [1, 0]} + +output0 = {i3: # output 0 + [7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_2D_3D_float_2.mod.py b/tests/nnapi/specs/Ex/gather_ex_2D_3D_float_2.mod.py new file mode 100644 index 000000000..850d330b9 --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_2D_3D_float_2.mod.py @@ -0,0 +1,23 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3,4}") # a vector of 12 float32s +i2 = Input("op2", "TENSOR_INT32", "{1,2,1}") # another vector of 2 int32s +axis = Int32Scalar("axis", 1) +i3 = Output("op3", "TENSOR_FLOAT32", "{3,1,2,1}") # a vector of 6 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 + [1, 0]} + +output0 = {i3: # output 0 + [4.123456789123456789, 3.123456789123456789, + 8.123456789123456789, 7.123456789123456789, + 18.123456789123456789, 2.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_3D_2D_float_1.mod.py b/tests/nnapi/specs/Ex/gather_ex_3D_2D_float_1.mod.py new file mode 100644 index 000000000..09db60294 --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_3D_2D_float_1.mod.py @@ -0,0 +1,29 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,3,4}") # a vector of 24 float32s +i2 = Input("op2", "TENSOR_INT32", "{1,2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_FLOAT32", "{1,2,3,4}") # a vector of 24 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, + 13.123456789123456789, 14.123456789123456789, 15.123456789123456789, 16.123456789123456789, + 17.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 12.123456789123456789, 28.123456789123456789, 29.123456789123456789, 21.123456789123456789], + i2: # input 1 + [1, 0]} + +output0 = {i3: # output 0 + [13.123456789123456789, 14.123456789123456789, 15.123456789123456789, 16.123456789123456789, + 17.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 12.123456789123456789, 28.123456789123456789, 29.123456789123456789, 21.123456789123456789, + 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/gather_ex_3D_2D_float_2.mod.py b/tests/nnapi/specs/Ex/gather_ex_3D_2D_float_2.mod.py new file mode 100644 index 000000000..bfdc83c58 --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_3D_2D_float_2.mod.py @@ -0,0 +1,29 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,3,4}") # a vector of 24 float32s +i2 = Input("op2", "TENSOR_INT32", "{2,1}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_FLOAT32", "{2,1,3,4}") # a vector of 24 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, + 13.123456789123456789, 14.123456789123456789, 15.123456789123456789, 16.123456789123456789, + 17.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 12.123456789123456789, 28.123456789123456789, 29.123456789123456789, 21.123456789123456789], + i2: # input 1 + [0, 0]} + +output0 = {i3: # 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, + 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/gather_ex_3D_2D_float_3.mod.py b/tests/nnapi/specs/Ex/gather_ex_3D_2D_float_3.mod.py new file mode 100644 index 000000000..cfcd1b479 --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_3D_2D_float_3.mod.py @@ -0,0 +1,26 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,3,4}") # a vector of 24 float32s +i2 = Input("op2", "TENSOR_INT32", "{2,1}") # another vector of 2 int32s +axis = Int32Scalar("axis", 2) +i3 = Output("op3", "TENSOR_FLOAT32", "{2,3,2,1}") # a vector of 12 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, + 13.123456789123456789, 14.123456789123456789, 15.123456789123456789, 16.123456789123456789, + 17.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 12.123456789123456789, 28.123456789123456789, 29.123456789123456789, 21.123456789123456789], + i2: # input 1 + [3, 1]} + +output0 = {i3: # output 0 + [6.123456789123456789, 4.123456789123456789, 1.123456789123456789, 8.123456789123456789, + 11.123456789123456789, 18.123456789123456789, 16.123456789123456789, 14.123456789123456789, + 11.123456789123456789, 18.123456789123456789, 21.123456789123456789, 28.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_4D_float.mod.py b/tests/nnapi/specs/Ex/gather_ex_4D_float.mod.py new file mode 100644 index 000000000..38fa8d015 --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_4D_float.mod.py @@ -0,0 +1,41 @@ +# This test case is for 4d input gather operator. +# The input shape is [1,2,3,4] and this test produces output +# by referencing the data on axis 0 from the input to the indices value [0,0]. +# In this case, the output shape changeds to [2,2,3,4] because it uses [0,0] indices on the 0 axis, +# and the data is configured as filling the 0th data of the input axis 0 twice. + +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1,2,3,4}") # a vector of 24 float32s +i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_FLOAT32", "{2,2,3,4}") # a vector of 48 float32s +model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.123456789123456789, 2.123456789123456789, 3.123456789123456789, 4.123456789123456789, + 5.123456789123456789, 6.123456789123456789, 7.123456789123456789, 8.123456789123456789, + 9.123456789123456789, 10.123456789123456789, 11.123456789123456789, 12.123456789123456789, + 13.123456789123456789, 14.123456789123456789, 15.123456789123456789, 16.123456789123456789, + 17.123456789123456789, 18.123456789123456789, 19.123456789123456789, 20.123456789123456789, + 21.123456789123456789, 22.123456789123456789, 23.123456789123456789, 24.123456789123456789], + i2: # input 1 + [0, 0]} + +output0 = {i3: # output 0 + [1.123456789123456789, 2.123456789123456789, 3.123456789123456789, 4.123456789123456789, + 5.123456789123456789, 6.123456789123456789, 7.123456789123456789, 8.123456789123456789, + 9.123456789123456789, 10.123456789123456789, 11.123456789123456789, 12.123456789123456789, + 13.123456789123456789, 14.123456789123456789, 15.123456789123456789, 16.123456789123456789, + 17.123456789123456789, 18.123456789123456789, 19.123456789123456789, 20.123456789123456789, + 21.123456789123456789, 22.123456789123456789, 23.123456789123456789, 24.123456789123456789, + 1.123456789123456789, 2.123456789123456789, 3.123456789123456789, 4.123456789123456789, + 5.123456789123456789, 6.123456789123456789, 7.123456789123456789, 8.123456789123456789, + 9.123456789123456789, 10.123456789123456789, 11.123456789123456789, 12.123456789123456789, + 13.123456789123456789, 14.123456789123456789, 15.123456789123456789, 16.123456789123456789, + 17.123456789123456789, 18.123456789123456789, 19.123456789123456789, 20.123456789123456789, + 21.123456789123456789, 22.123456789123456789, 23.123456789123456789, 24.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/greater_equal_ex.mod.py b/tests/nnapi/specs/Ex/greater_equal_ex.mod.py new file mode 100644 index 000000000..7c62d568b --- /dev/null +++ b/tests/nnapi/specs/Ex/greater_equal_ex.mod.py @@ -0,0 +1,35 @@ +# +# Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved +# Copyright (C) 2018 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. +# + +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 1}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2}") +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.0, 0") +model = model.Operation("GREATER_EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [5, 10], + i2: # input 1 + [10, 5]} + +output0 = {i3: # output 0 + [0, 255, 255, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/less_ex.mod.py b/tests/nnapi/specs/Ex/less_ex.mod.py new file mode 100644 index 000000000..3ae15b62f --- /dev/null +++ b/tests/nnapi/specs/Ex/less_ex.mod.py @@ -0,0 +1,35 @@ +# +# Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved +# Copyright (C) 2018 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. +# + +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 1}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2}") +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.0, 0") +model = model.Operation("LESS_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [5, 10], + i2: # input 1 + [10, 5]} + +output0 = {i3: # output 0 + [255, 0, 0, 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_2D_float_1.mod.py index 3f3ea683f..06f6e6a7b 100644 --- a/tests/nnapi/specs/Ex/pack_ex_3D_float_1.mod.py +++ b/tests/nnapi/specs/Ex/pack_ex_2D_float_1.mod.py @@ -1,11 +1,11 @@ # 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}") +i1 = Input("input1", "TENSOR_FLOAT32", "{6, 4}") +i2 = Input("input2", "TENSOR_FLOAT32", "{6, 4}") +i3 = Input("input3", "TENSOR_FLOAT32", "{6, 4}") num = Int32Scalar("num_tensors", 3) axis = Int32Scalar("axis", 0) -out = Output("output", "TENSOR_FLOAT32", "{3, 2, 3, 4}") +out = Output("output", "TENSOR_FLOAT32", "{3, 6, 4}") model = model.Operation("PACK_EX", i1, i2, i3, num, axis).To(out) input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/pack_ex_3D_float_2.mod.py b/tests/nnapi/specs/Ex/pack_ex_2D_float_2.mod.py index d0caa02cc..acc8c891f 100644 --- a/tests/nnapi/specs/Ex/pack_ex_3D_float_2.mod.py +++ b/tests/nnapi/specs/Ex/pack_ex_2D_float_2.mod.py @@ -1,11 +1,11 @@ # 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}") +i1 = Input("input1", "TENSOR_FLOAT32", "{6, 4}") +i2 = Input("input2", "TENSOR_FLOAT32", "{6, 4}") +i3 = Input("input3", "TENSOR_FLOAT32", "{6, 4}") num = Int32Scalar("num_tensors", 3) -axis = Int32Scalar("axis", 2) -out = Output("output", "TENSOR_FLOAT32", "{2, 3, 3, 4}") +axis = Int32Scalar("axis", 1) +out = Output("output", "TENSOR_FLOAT32", "{6, 3, 4}") model = model.Operation("PACK_EX", i1, i2, i3, num, axis).To(out) input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/pack_ex_3D_int_1.mod.py b/tests/nnapi/specs/Ex/pack_ex_2D_int_1.mod.py index 23b365957..0ffd6cf9d 100644 --- a/tests/nnapi/specs/Ex/pack_ex_3D_int_1.mod.py +++ b/tests/nnapi/specs/Ex/pack_ex_2D_int_1.mod.py @@ -1,11 +1,11 @@ # 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}") +i1 = Input("input1", "TENSOR_INT32", "{6, 4}") +i2 = Input("input2", "TENSOR_INT32", "{6, 4}") +i3 = Input("input3", "TENSOR_INT32", "{6, 4}") num = Int32Scalar("num_tensors", 3) axis = Int32Scalar("axis", 0) -out = Output("output", "TENSOR_INT32", "{3, 2, 3, 4}") +out = Output("output", "TENSOR_INT32", "{3, 6, 4}") model = model.Operation("PACK_EX", i1, i2, i3, num, axis).To(out) input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/pack_ex_3D_int_2.mod.py b/tests/nnapi/specs/Ex/pack_ex_2D_int_2.mod.py index 747fcc628..c9bdc419b 100644 --- a/tests/nnapi/specs/Ex/pack_ex_3D_int_2.mod.py +++ b/tests/nnapi/specs/Ex/pack_ex_2D_int_2.mod.py @@ -1,11 +1,11 @@ # 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}") +i1 = Input("input1", "TENSOR_INT32", "{6, 4}") +i2 = Input("input2", "TENSOR_INT32", "{6, 4}") +i3 = Input("input3", "TENSOR_INT32", "{6, 4}") num = Int32Scalar("num_tensors", 3) -axis = Int32Scalar("axis", 2) -out = Output("output", "TENSOR_INT32", "{2, 3, 3, 4}") +axis = Int32Scalar("axis", 1) +out = Output("output", "TENSOR_INT32", "{6, 3, 4}") model = model.Operation("PACK_EX", i1, i2, i3, num, axis).To(out) input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/prelu_ex_broadcast_float_1.mod.py b/tests/nnapi/specs/Ex/prelu_ex_broadcast_float_1.mod.py new file mode 100644 index 000000000..23e363f5c --- /dev/null +++ b/tests/nnapi/specs/Ex/prelu_ex_broadcast_float_1.mod.py @@ -0,0 +1,23 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 3}") # a vector of input +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 1, 1, 3}") # a vector of alpha +i3 = Output("op3", "TENSOR_FLOAT32", "{1, 2, 2, 3}") # a vector of output +model = model.Operation("PRELU_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0.0, 0.0, 0.0, + 1.0, 1.0, 1.0, + -1.0, -1.0, -1.0, + -2.0, -2.0, -2.0], + i2: # input 1 + [0.0, 1.0, 2.0]} + +output0 = {i3: # output 0 + [0.0, 0.0, 0.0, + 1.0, 1.0, 1.0, + 0.0, -1.0, -2.0, + 0.0, -2.0, -4.0]} +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/prelu_ex_broadcast_quant8_1.mod.py b/tests/nnapi/specs/Ex/prelu_ex_broadcast_quant8_1.mod.py new file mode 100644 index 000000000..5fc6884bc --- /dev/null +++ b/tests/nnapi/specs/Ex/prelu_ex_broadcast_quant8_1.mod.py @@ -0,0 +1,24 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 3}, 1.0f, 2") # a vector of input +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 1, 3}, 1.0f, 1") # a vector of alpha +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 3}, 0.5f, 3") # a vector of output +model = model.Operation("PRELU_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 1, 1, + 2, 2, 2, + 3, 3, 3, + 1, 2, 3], + i2: # input 1 + [0, 1, 2]} + +output0 = {i3: # output 0 + [5, 3, 1, + 3, 3, 3, + 5, 5, 5, + 5, 3, 5]} +# 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 deleted file mode 100644 index d2ebd2a0b..000000000 --- a/tests/nnapi/specs/Ex/prelu_ex_float_2.mod.py +++ /dev/null @@ -1,22 +0,0 @@ -# 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_quant8_1.mod.py b/tests/nnapi/specs/Ex/prelu_ex_quant8_1.mod.py new file mode 100644 index 000000000..9548f8255 --- /dev/null +++ b/tests/nnapi/specs/Ex/prelu_ex_quant8_1.mod.py @@ -0,0 +1,23 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 0.5f, 5") # a vector of input +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 0.5f, 1") # a vector of alpha +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 0.1f, 3") # a vector of output +model = model.Operation("PRELU_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3, 1, + 7, 11 + ], + i2: # input 1 + [0, 1, + 2, 2]} + +output0 = {i3: # output 0 + [8, 3, + 13, 33 + ]} +# Instantiate an example +Example((input0, output0)) + diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_2D_float.mod.py index 6cf8d83e7..5df76cbdb 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_2D_float.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_2D_float.mod.py @@ -3,7 +3,7 @@ model = Model() i1 = Input("input", "TENSOR_FLOAT32", "{3, 4}") axis = Int32Scalar("axis", 1) out1 = Output("output", "TENSOR_FLOAT32", "{3}") -model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(out1) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(out1) # Example 1. Input in operand 0, 1 input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_2D_int32.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_2D_int32.mod.py index 940dab3c3..94e952646 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_2D_int32.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_2D_int32.mod.py @@ -3,7 +3,7 @@ model = Model() i1 = Input("input", "TENSOR_INT32", "{3, 4}") axis = Int32Scalar("axis", 1) out1 = Output("output", "TENSOR_INT32", "{3}") -model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(out1) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(out1) # Example 1. Input in operand 0, 1 input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_C.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_4D_float_reducing_C.mod.py index 82a5dbb93..4143b4b4c 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_C.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_4D_float_reducing_C.mod.py @@ -21,7 +21,7 @@ i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, d 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) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(output) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_HW.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_4D_float_reducing_HW.mod.py index a12762055..e220df7a8 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_HW.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_4D_float_reducing_HW.mod.py @@ -21,7 +21,7 @@ i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, d 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) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(output) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_float.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_float.mod.py index 6de220721..c6387ae3d 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_float.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_float.mod.py @@ -3,7 +3,7 @@ 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) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(output) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_float_1.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_float_1.mod.py index 42dc9d94b..7871c6123 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_float_1.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_float_1.mod.py @@ -3,7 +3,7 @@ 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) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(output) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_float_2.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_float_2.mod.py index 98c0d9d03..fb523b294 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_float_2.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_float_2.mod.py @@ -3,7 +3,7 @@ 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) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(output) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_1.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_quant8_1.mod.py index 8e85633b3..9cef1def4 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_1.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_quant8_1.mod.py @@ -3,7 +3,7 @@ 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) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(output) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_2.mod.py b/tests/nnapi/specs/Ex/reduce_max_ex_quant8_2.mod.py index 75138b14a..d59c11b0e 100644 --- a/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_2.mod.py +++ b/tests/nnapi/specs/Ex/reduce_max_ex_quant8_2.mod.py @@ -3,7 +3,7 @@ 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) +model = model.Operation("REDUCE_MAX_EX", i1, axis).To(output) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/split_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/split_ex_1D_float.mod.py index 97b61462e..137ece828 100644 --- a/tests/nnapi/specs/Ex/split_ex_1D_float.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_1D_float.mod.py @@ -2,6 +2,7 @@ model = Model() i1 = Input("op1", "TENSOR_FLOAT32", "{8}") axis = Int32Scalar("axis", 0) +num_out = Int32Scalar("num_out", 8) i2 = Output("op2", "TENSOR_FLOAT32", "{1}") i3 = Output("op3", "TENSOR_FLOAT32", "{1}") i4 = Output("op4", "TENSOR_FLOAT32", "{1}") @@ -11,7 +12,7 @@ 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3, i4, i5, i6, i7, i8, i9]) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/split_ex_1D_int32.mod.py b/tests/nnapi/specs/Ex/split_ex_1D_int32.mod.py index 378b37707..9bfc215f6 100644 --- a/tests/nnapi/specs/Ex/split_ex_1D_int32.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_1D_int32.mod.py @@ -2,6 +2,7 @@ model = Model() i1 = Input("op1", "TENSOR_INT32", "{8}") axis = Int32Scalar("axis", 0) +num_out = Int32Scalar("num_out", 8) i2 = Output("op2", "TENSOR_INT32", "{1}") i3 = Output("op3", "TENSOR_INT32", "{1}") i4 = Output("op4", "TENSOR_INT32", "{1}") @@ -11,7 +12,7 @@ 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3, i4, i5, i6, i7, i8, i9]) # Example 1. Input in operand 0, input0 = {i1: # input 0 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 index 3059fae7c..2353a8a0f 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_float_1.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_float_1.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_FLOAT32", "{2,2,2,2}") axis = Int32Scalar("axis", 0) +num_out = Int32Scalar("num_out", 2) 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 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 index faa59bc3c..30ecf2416 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_float_2.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_float_2.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_FLOAT32", "{2,2,2,2}") axis = Int32Scalar("axis", 3) +num_out = Int32Scalar("num_out", 2) 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 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 index 2091e4b4a..1f7a07880 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_float_3.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_float_3.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_FLOAT32", "{2,2,2,2}") axis = Int32Scalar("axis", -4) # Negative axis +num_out = Int32Scalar("num_out", 2) 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 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 index 3e9438f50..b1c6692ee 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_int32_1.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_1.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") axis = Int32Scalar("axis", 0) +num_out = Int32Scalar("num_out", 2) 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 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 index 83253cb14..88a7cca48 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_int32_2.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_2.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") axis = Int32Scalar("axis", 1) +num_out = Int32Scalar("num_out", 2) 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 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 index b20b2053b..6f5cc621b 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_int32_3.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_3.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") axis = Int32Scalar("axis", 2) +num_out = Int32Scalar("num_out", 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 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 index 3c255cc48..fb6710171 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_int32_4.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_4.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") axis = Int32Scalar("axis", 3) +num_out = Int32Scalar("num_out", 2) 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 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 index 30e5e245d..338794855 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_int32_5.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_5.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") axis = Int32Scalar("axis", -4) # Negative axis +num_out = Int32Scalar("num_out", 2) 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/split_ex_4D_quant8.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_quant8.mod.py index 46cb3ab78..a3dbffaa9 100644 --- a/tests/nnapi/specs/Ex/split_ex_4D_quant8.mod.py +++ b/tests/nnapi/specs/Ex/split_ex_4D_quant8.mod.py @@ -2,9 +2,10 @@ model = Model() i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2,2,2,2}, 0.5f, 1") axis = Int32Scalar("axis", 0) +num_out = Int32Scalar("num_out", 2) 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]) +model = model.Operation("SPLIT_EX", i1, axis, num_out).To([i2, i3]) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/Ex/transpose_conv_ex_float_4.mod.py b/tests/nnapi/specs/Ex/transpose_conv_ex_float_4.mod.py new file mode 100644 index 000000000..71383b4c1 --- /dev/null +++ b/tests/nnapi/specs/Ex/transpose_conv_ex_float_4.mod.py @@ -0,0 +1,56 @@ +# 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", 2) +s_y = Int32Scalar("stride_y", 2) +i2 = Output("op", "TENSOR_FLOAT32", "{1, 8, 8, 1}") +model = model.Operation("TRANSPOSE_CONV_EX", i0, weights, i1, pad, s_x, s_y).To(i2) + +batch = 1 +in_chans = 1 +out_chans = 1 +in_rows = 4 +in_cols = 4 +out_rows = 8 +out_cols = 8 +ker_rows = 3 +ker_cols = 3 +stride = 2 +# pad is 0 (left: 0 right: 1 top: 0 bottom: 1) +input_table = [x for x in range(batch * in_rows * in_cols * in_chans)] +kernel_table = [x for x in range(out_chans * ker_rows * ker_cols * in_chans)] +out_table = [0 for x in range(batch * out_rows * out_cols * out_chans)] + +for i in range(batch): + for j in range(in_rows): + for k in range(in_cols): + for l in range(in_chans): + out_row_origin = j * stride + out_col_origin = k * stride + input_value = input_table[((i * in_rows + j) * in_cols + k) * in_chans + l] + + for m in range(ker_rows): + for n in range(ker_cols): + for o in range(out_chans): + out_row = out_row_origin + m + out_col = out_col_origin + n + if (out_row < out_rows) and (out_col < out_cols) and (out_row >= 0) and (out_col >= 0): + kernel_value = kernel_table[((o * ker_rows + m) * ker_cols + n) * in_chans + l] + out_table[((i * out_rows + out_row) * out_cols + out_col) * out_chans + o] += (input_value * kernel_value) + +# Example 1. Input in operand 0, +input0 = {i0: # output shape + [1, 8, 8, 1], + i1: # input 0 + input_table, + weights: # input 1 + kernel_table} + +output0 = {i2: # output 0 + out_table} + +# 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 deleted file mode 100644 index fb8eb72be..000000000 --- a/tests/nnapi/specs/Ex/transpose_conv_ex_int_1.mod.py +++ /dev/null @@ -1,24 +0,0 @@ -# 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 deleted file mode 100644 index 9bf4b113d..000000000 --- a/tests/nnapi/specs/Ex/transpose_conv_ex_int_2.mod.py +++ /dev/null @@ -1,25 +0,0 @@ -# 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 deleted file mode 100644 index e44480968..000000000 --- a/tests/nnapi/specs/Ex/transpose_conv_ex_int_3.mod.py +++ /dev/null @@ -1,27 +0,0 @@ -# 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 index e5cb38e4b..7e8ef6086 100644 --- a/tests/nnapi/specs/Ex/unpack_ex_3D_float_1.mod.py +++ b/tests/nnapi/specs/Ex/unpack_ex_3D_float_1.mod.py @@ -1,11 +1,11 @@ # Sample UnPack model, axis = 0 model = Model() -input = Input("input", "TENSOR_FLOAT32", "{3, 2, 3, 4}") +input = Input("input", "TENSOR_FLOAT32", "{3, 6, 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}") +out1 = Output("output1", "TENSOR_FLOAT32", "{6, 4}") +out2 = Output("output2", "TENSOR_FLOAT32", "{6, 4}") +out3 = Output("output3", "TENSOR_FLOAT32", "{6, 4}") model = model.Operation("UNPACK_EX", input, num_splits, axis).To([out1, out2, out3]) input0 = {input: # input 0 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 index 1d1045ae9..ed7800ad4 100644 --- a/tests/nnapi/specs/Ex/unpack_ex_3D_float_2.mod.py +++ b/tests/nnapi/specs/Ex/unpack_ex_3D_float_2.mod.py @@ -1,11 +1,11 @@ -# Sample UnPack model, axis = 2 +# Sample UnPack model, axis = 1 model = Model() -input = Input("input", "TENSOR_FLOAT32", "{3, 2, 3, 4}") -axis = Int32Scalar("axis", 2) +input = Input("input", "TENSOR_FLOAT32", "{6, 3, 4}") +axis = Int32Scalar("axis", 1) 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}") +out1 = Output("output1", "TENSOR_FLOAT32", "{6, 4}") +out2 = Output("output2", "TENSOR_FLOAT32", "{6, 4}") +out3 = Output("output3", "TENSOR_FLOAT32", "{6, 4}") model = model.Operation("UNPACK_EX", input, num_splits, axis).To([out1, out2, out3]) input0 = {input: # input 0 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 index 2a668cf7e..34e153bfc 100644 --- a/tests/nnapi/specs/Ex/unpack_ex_3D_int_1.mod.py +++ b/tests/nnapi/specs/Ex/unpack_ex_3D_int_1.mod.py @@ -1,11 +1,11 @@ # Sample UnPack model, axis = 0 model = Model() -input = Input("input", "TENSOR_INT32", "{3, 2, 3, 4}") +input = Input("input", "TENSOR_INT32", "{3, 6, 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}") +out1 = Output("output1", "TENSOR_INT32", "{6, 4}") +out2 = Output("output2", "TENSOR_INT32", "{6, 4}") +out3 = Output("output3", "TENSOR_INT32", "{6, 4}") model = model.Operation("UNPACK_EX", input, num_splits, axis).To([out1, out2, out3]) input0 = {input: # input 0 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 index 115954972..db51351c5 100644 --- a/tests/nnapi/specs/Ex/unpack_ex_3D_int_2.mod.py +++ b/tests/nnapi/specs/Ex/unpack_ex_3D_int_2.mod.py @@ -1,11 +1,11 @@ -# Sample UnPack model, axis = 2 +# Sample UnPack model, axis = 1 model = Model() -input = Input("input", "TENSOR_INT32", "{3, 2, 3, 4}") -axis = Int32Scalar("axis", 2) +input = Input("input", "TENSOR_INT32", "{6, 3, 4}") +axis = Int32Scalar("axis", 1) 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}") +out1 = Output("output1", "TENSOR_INT32", "{6, 4}") +out2 = Output("output2", "TENSOR_INT32", "{6, 4}") +out3 = Output("output3", "TENSOR_INT32", "{6, 4}") model = model.Operation("UNPACK_EX", input, num_splits, axis).To([out1, out2, out3]) input0 = {input: # input 0 diff --git a/tests/nnapi/specs/V1_0/conv_1_h3_w2_SAME.mod.py b/tests/nnapi/specs/V1_0/conv_1_h3_w2_SAME.mod.py index 34f2c49f9..45a356927 100644 --- a/tests/nnapi/specs/V1_0/conv_1_h3_w2_SAME.mod.py +++ b/tests/nnapi/specs/V1_0/conv_1_h3_w2_SAME.mod.py @@ -7,7 +7,7 @@ i2 = Input("op2", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # input 0 i3 = Output("op3", "TENSOR_FLOAT32", "{1, 8, 8, 1}") # output 0 i0 = Parameter("op0", "TENSOR_FLOAT32", "{1, 3, 2, 3}", [-0.966213, -0.467474, -0.82203, -0.579455, 0.0278809, -0.79946, -0.684259, 0.563238, 0.37289, 0.738216, 0.386045, -0.917775, 0.184325, -0.270568, 0.82236, 0.0973683, -0.941308, -0.144706]) # parameters i1 = Parameter("op1", "TENSOR_FLOAT32", "{1}", [0]) # parameters -model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) +model = model.Operation("CONV_2D", i2, i0, i1, i4, i5, i6, i7).To(i3) input0 = {i2: [-0.869931, 0.644628, -0.918393, 0.153672, 0.868562, -0.358177, -0.134931, -0.247565, 0.22174, -0.259157, -0.284296, -0.538065, 0.765559, 0.41986, -0.556241, 0.658494, 0.214355, -0.850169, -0.252893, -0.478935, 0.530526, -0.0700663, -0.988729, -0.303061, 0.150845, 0.829915, 0.476349, 0.406537, -0.355343, 0.757145, -0.356362, 0.800482, -0.713861, 0.210483, -0.634303, 0.718236, -0.752038, 0.457547, -0.550769, -0.551178, 0.446766, -0.227462, 0.216348, -0.852806, -0.351486, 0.55906, -0.668493, -0.303493, -0.363763, -0.162837, 0.0701012, 0.756097, -0.142269, 0.329724, -0.656317, -0.998086, -0.652949, -0.40316, -0.893682, 0.432744, 0.612362, -0.869588, -0.71327, -0.398092, -0.0423559, 0.436576, -0.925272, 0.176549, 0.822904, 0.096833, -0.296802, -0.427195, 0.031654, -0.254479, 0.244905, 0.0948254, 0.643769, -0.90391, 0.352665, -0.901179, 0.266159, -0.968068, -0.615401, -0.388975, 0.939052, -0.116289, 0.107523, -0.0582711, 0.435172, 0.334675, 0.459711, 0.717436, 0.496627, -0.680175, -0.415066, 0.339848, 0.506004, -0.337808, -0.107218, -0.172496, 0.870638, 0.931872, -0.953884, 0.903042, 0.760078, 0.209727, -0.285384, -0.45514, 0.113194, 0.0756611, 0.0924435, -0.472863, 0.960609, -0.160385, -0.839445, 0.457097, 0.163348, 0.344867, -0.131619, 0.688715, -0.540827, 0.571259, -0.95587, 0.506164, -0.155839, 0.0789621, 0.756772, -0.662069, 0.242908, 0.460821, 0.177872, -0.289839, -0.640603, 0.702598, -0.506406, -0.568262, -0.0713716, 0.413792, 0.159673, -0.305208, 0.133816, -0.160254, 0.787323, -0.753244, 0.600721, 0.263186, -0.162387, 0.477962, -0.702951, -0.731036, -0.939481, -0.524519, 0.934072, -0.511637, -0.503499, 0.106236, -0.323684, 0.534444, -0.843745, 0.364171, 0.0370358, -0.168801, -0.404559, -0.814178, 0.91745, -0.334276, 0.66925, -0.801201, 0.156511, -0.427949, 0.379153, 0.818597, -0.649902, 0.427087, -0.586015, -0.559789, -0.833923, 0.0892409, -0.621251, 0.213826, 0.465509, 0.4704, 0.380261, 0.413067, 0.180822, 0.172866, 0.59614, 0.825575, 0.662916, -0.704381, -0.297631, 0.697778]} diff --git a/tests/nnapi/specs/V1_0/conv_1_h3_w2_VALID.mod.py b/tests/nnapi/specs/V1_0/conv_1_h3_w2_VALID.mod.py index 3a4a2a1aa..0e0974371 100644 --- a/tests/nnapi/specs/V1_0/conv_1_h3_w2_VALID.mod.py +++ b/tests/nnapi/specs/V1_0/conv_1_h3_w2_VALID.mod.py @@ -7,7 +7,7 @@ i2 = Input("op2", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # input 0 i3 = Output("op3", "TENSOR_FLOAT32", "{1, 6, 7, 1}") # output 0 i0 = Parameter("op0", "TENSOR_FLOAT32", "{1, 3, 2, 3}", [-0.966213, -0.467474, -0.82203, -0.579455, 0.0278809, -0.79946, -0.684259, 0.563238, 0.37289, 0.738216, 0.386045, -0.917775, 0.184325, -0.270568, 0.82236, 0.0973683, -0.941308, -0.144706]) # parameters i1 = Parameter("op1", "TENSOR_FLOAT32", "{1}", [0]) # parameters -model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) +model = model.Operation("CONV_2D", i2, i0, i1, i4, i5, i6, i7).To(i3) input0 = {i2: [-0.869931, 0.644628, -0.918393, 0.153672, 0.868562, -0.358177, -0.134931, -0.247565, 0.22174, -0.259157, -0.284296, -0.538065, 0.765559, 0.41986, -0.556241, 0.658494, 0.214355, -0.850169, -0.252893, -0.478935, 0.530526, -0.0700663, -0.988729, -0.303061, 0.150845, 0.829915, 0.476349, 0.406537, -0.355343, 0.757145, -0.356362, 0.800482, -0.713861, 0.210483, -0.634303, 0.718236, -0.752038, 0.457547, -0.550769, -0.551178, 0.446766, -0.227462, 0.216348, -0.852806, -0.351486, 0.55906, -0.668493, -0.303493, -0.363763, -0.162837, 0.0701012, 0.756097, -0.142269, 0.329724, -0.656317, -0.998086, -0.652949, -0.40316, -0.893682, 0.432744, 0.612362, -0.869588, -0.71327, -0.398092, -0.0423559, 0.436576, -0.925272, 0.176549, 0.822904, 0.096833, -0.296802, -0.427195, 0.031654, -0.254479, 0.244905, 0.0948254, 0.643769, -0.90391, 0.352665, -0.901179, 0.266159, -0.968068, -0.615401, -0.388975, 0.939052, -0.116289, 0.107523, -0.0582711, 0.435172, 0.334675, 0.459711, 0.717436, 0.496627, -0.680175, -0.415066, 0.339848, 0.506004, -0.337808, -0.107218, -0.172496, 0.870638, 0.931872, -0.953884, 0.903042, 0.760078, 0.209727, -0.285384, -0.45514, 0.113194, 0.0756611, 0.0924435, -0.472863, 0.960609, -0.160385, -0.839445, 0.457097, 0.163348, 0.344867, -0.131619, 0.688715, -0.540827, 0.571259, -0.95587, 0.506164, -0.155839, 0.0789621, 0.756772, -0.662069, 0.242908, 0.460821, 0.177872, -0.289839, -0.640603, 0.702598, -0.506406, -0.568262, -0.0713716, 0.413792, 0.159673, -0.305208, 0.133816, -0.160254, 0.787323, -0.753244, 0.600721, 0.263186, -0.162387, 0.477962, -0.702951, -0.731036, -0.939481, -0.524519, 0.934072, -0.511637, -0.503499, 0.106236, -0.323684, 0.534444, -0.843745, 0.364171, 0.0370358, -0.168801, -0.404559, -0.814178, 0.91745, -0.334276, 0.66925, -0.801201, 0.156511, -0.427949, 0.379153, 0.818597, -0.649902, 0.427087, -0.586015, -0.559789, -0.833923, 0.0892409, -0.621251, 0.213826, 0.465509, 0.4704, 0.380261, 0.413067, 0.180822, 0.172866, 0.59614, 0.825575, 0.662916, -0.704381, -0.297631, 0.697778]} diff --git a/tests/nnapi/specs/V1_0/conv_3_h3_w2_SAME.mod.py b/tests/nnapi/specs/V1_0/conv_3_h3_w2_SAME.mod.py index d7df3a34f..3ba4bad52 100644 --- a/tests/nnapi/specs/V1_0/conv_3_h3_w2_SAME.mod.py +++ b/tests/nnapi/specs/V1_0/conv_3_h3_w2_SAME.mod.py @@ -7,7 +7,7 @@ i2 = Input("op2", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # input 0 i3 = Output("op3", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # output 0 i0 = Parameter("op0", "TENSOR_FLOAT32", "{3, 3, 2, 3}", [-0.966213, -0.579455, -0.684259, 0.738216, 0.184325, 0.0973683, -0.176863, -0.23936, -0.000233404, 0.055546, -0.232658, -0.316404, -0.012904, 0.320705, -0.326657, -0.919674, 0.868081, -0.824608, -0.467474, 0.0278809, 0.563238, 0.386045, -0.270568, -0.941308, -0.779227, -0.261492, -0.774804, -0.79665, 0.22473, -0.414312, 0.685897, -0.327792, 0.77395, -0.714578, -0.972365, 0.0696099, -0.82203, -0.79946, 0.37289, -0.917775, 0.82236, -0.144706, -0.167188, 0.268062, 0.702641, -0.412223, 0.755759, 0.721547, -0.43637, -0.274905, -0.269165, 0.16102, 0.819857, -0.312008]) # parameters i1 = Parameter("op1", "TENSOR_FLOAT32", "{3}", [0, 0, 0]) # parameters -model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) +model = model.Operation("CONV_2D", i2, i0, i1, i4, i5, i6, i7).To(i3) input0 = {i2: [-0.869931, 0.644628, -0.918393, 0.153672, 0.868562, -0.358177, -0.134931, -0.247565, 0.22174, -0.259157, -0.284296, -0.538065, 0.765559, 0.41986, -0.556241, 0.658494, 0.214355, -0.850169, -0.252893, -0.478935, 0.530526, -0.0700663, -0.988729, -0.303061, 0.150845, 0.829915, 0.476349, 0.406537, -0.355343, 0.757145, -0.356362, 0.800482, -0.713861, 0.210483, -0.634303, 0.718236, -0.752038, 0.457547, -0.550769, -0.551178, 0.446766, -0.227462, 0.216348, -0.852806, -0.351486, 0.55906, -0.668493, -0.303493, -0.363763, -0.162837, 0.0701012, 0.756097, -0.142269, 0.329724, -0.656317, -0.998086, -0.652949, -0.40316, -0.893682, 0.432744, 0.612362, -0.869588, -0.71327, -0.398092, -0.0423559, 0.436576, -0.925272, 0.176549, 0.822904, 0.096833, -0.296802, -0.427195, 0.031654, -0.254479, 0.244905, 0.0948254, 0.643769, -0.90391, 0.352665, -0.901179, 0.266159, -0.968068, -0.615401, -0.388975, 0.939052, -0.116289, 0.107523, -0.0582711, 0.435172, 0.334675, 0.459711, 0.717436, 0.496627, -0.680175, -0.415066, 0.339848, 0.506004, -0.337808, -0.107218, -0.172496, 0.870638, 0.931872, -0.953884, 0.903042, 0.760078, 0.209727, -0.285384, -0.45514, 0.113194, 0.0756611, 0.0924435, -0.472863, 0.960609, -0.160385, -0.839445, 0.457097, 0.163348, 0.344867, -0.131619, 0.688715, -0.540827, 0.571259, -0.95587, 0.506164, -0.155839, 0.0789621, 0.756772, -0.662069, 0.242908, 0.460821, 0.177872, -0.289839, -0.640603, 0.702598, -0.506406, -0.568262, -0.0713716, 0.413792, 0.159673, -0.305208, 0.133816, -0.160254, 0.787323, -0.753244, 0.600721, 0.263186, -0.162387, 0.477962, -0.702951, -0.731036, -0.939481, -0.524519, 0.934072, -0.511637, -0.503499, 0.106236, -0.323684, 0.534444, -0.843745, 0.364171, 0.0370358, -0.168801, -0.404559, -0.814178, 0.91745, -0.334276, 0.66925, -0.801201, 0.156511, -0.427949, 0.379153, 0.818597, -0.649902, 0.427087, -0.586015, -0.559789, -0.833923, 0.0892409, -0.621251, 0.213826, 0.465509, 0.4704, 0.380261, 0.413067, 0.180822, 0.172866, 0.59614, 0.825575, 0.662916, -0.704381, -0.297631, 0.697778]} diff --git a/tests/nnapi/specs/V1_0/conv_3_h3_w2_VALID.mod.py b/tests/nnapi/specs/V1_0/conv_3_h3_w2_VALID.mod.py index a7ab91f33..545e9fdc6 100644 --- a/tests/nnapi/specs/V1_0/conv_3_h3_w2_VALID.mod.py +++ b/tests/nnapi/specs/V1_0/conv_3_h3_w2_VALID.mod.py @@ -7,7 +7,7 @@ i2 = Input("op2", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # input 0 i3 = Output("op3", "TENSOR_FLOAT32", "{1, 6, 7, 3}") # output 0 i0 = Parameter("op0", "TENSOR_FLOAT32", "{3, 3, 2, 3}", [-0.966213, -0.579455, -0.684259, 0.738216, 0.184325, 0.0973683, -0.176863, -0.23936, -0.000233404, 0.055546, -0.232658, -0.316404, -0.012904, 0.320705, -0.326657, -0.919674, 0.868081, -0.824608, -0.467474, 0.0278809, 0.563238, 0.386045, -0.270568, -0.941308, -0.779227, -0.261492, -0.774804, -0.79665, 0.22473, -0.414312, 0.685897, -0.327792, 0.77395, -0.714578, -0.972365, 0.0696099, -0.82203, -0.79946, 0.37289, -0.917775, 0.82236, -0.144706, -0.167188, 0.268062, 0.702641, -0.412223, 0.755759, 0.721547, -0.43637, -0.274905, -0.269165, 0.16102, 0.819857, -0.312008]) # parameters i1 = Parameter("op1", "TENSOR_FLOAT32", "{3}", [0, 0, 0]) # parameters -model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) +model = model.Operation("CONV_2D", i2, i0, i1, i4, i5, i6, i7).To(i3) input0 = {i2: [-0.869931, 0.644628, -0.918393, 0.153672, 0.868562, -0.358177, -0.134931, -0.247565, 0.22174, -0.259157, -0.284296, -0.538065, 0.765559, 0.41986, -0.556241, 0.658494, 0.214355, -0.850169, -0.252893, -0.478935, 0.530526, -0.0700663, -0.988729, -0.303061, 0.150845, 0.829915, 0.476349, 0.406537, -0.355343, 0.757145, -0.356362, 0.800482, -0.713861, 0.210483, -0.634303, 0.718236, -0.752038, 0.457547, -0.550769, -0.551178, 0.446766, -0.227462, 0.216348, -0.852806, -0.351486, 0.55906, -0.668493, -0.303493, -0.363763, -0.162837, 0.0701012, 0.756097, -0.142269, 0.329724, -0.656317, -0.998086, -0.652949, -0.40316, -0.893682, 0.432744, 0.612362, -0.869588, -0.71327, -0.398092, -0.0423559, 0.436576, -0.925272, 0.176549, 0.822904, 0.096833, -0.296802, -0.427195, 0.031654, -0.254479, 0.244905, 0.0948254, 0.643769, -0.90391, 0.352665, -0.901179, 0.266159, -0.968068, -0.615401, -0.388975, 0.939052, -0.116289, 0.107523, -0.0582711, 0.435172, 0.334675, 0.459711, 0.717436, 0.496627, -0.680175, -0.415066, 0.339848, 0.506004, -0.337808, -0.107218, -0.172496, 0.870638, 0.931872, -0.953884, 0.903042, 0.760078, 0.209727, -0.285384, -0.45514, 0.113194, 0.0756611, 0.0924435, -0.472863, 0.960609, -0.160385, -0.839445, 0.457097, 0.163348, 0.344867, -0.131619, 0.688715, -0.540827, 0.571259, -0.95587, 0.506164, -0.155839, 0.0789621, 0.756772, -0.662069, 0.242908, 0.460821, 0.177872, -0.289839, -0.640603, 0.702598, -0.506406, -0.568262, -0.0713716, 0.413792, 0.159673, -0.305208, 0.133816, -0.160254, 0.787323, -0.753244, 0.600721, 0.263186, -0.162387, 0.477962, -0.702951, -0.731036, -0.939481, -0.524519, 0.934072, -0.511637, -0.503499, 0.106236, -0.323684, 0.534444, -0.843745, 0.364171, 0.0370358, -0.168801, -0.404559, -0.814178, 0.91745, -0.334276, 0.66925, -0.801201, 0.156511, -0.427949, 0.379153, 0.818597, -0.649902, 0.427087, -0.586015, -0.559789, -0.833923, 0.0892409, -0.621251, 0.213826, 0.465509, 0.4704, 0.380261, 0.413067, 0.180822, 0.172866, 0.59614, 0.825575, 0.662916, -0.704381, -0.297631, 0.697778]} diff --git a/tests/nnapi/specs/V1_0/depthwise_conv.mod.py b/tests/nnapi/specs/V1_0/depthwise_conv.mod.py index d88206fdd..5e7886d26 100644 --- a/tests/nnapi/specs/V1_0/depthwise_conv.mod.py +++ b/tests/nnapi/specs/V1_0/depthwise_conv.mod.py @@ -8,7 +8,7 @@ i2 = Input("op2", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # input 0 i3 = Output("op3", "TENSOR_FLOAT32", "{1, 8, 8, 3}") # output 0 i0 = Parameter("op0", "TENSOR_FLOAT32", "{1, 1, 1, 3}", [-0.966213, -0.467474, -0.82203]) # parameters i1 = Parameter("op1", "TENSOR_FLOAT32", "{3}", [0, 0, 0]) # parameters -model = model.DepthWiseConv(i2, i0, i1, i4, i5, i6, i7, i8).To(i3) +model = model.Operation("DEPTHWISE_CONV_2D", i2, i0, i1, i4, i5, i6, i7, i8).To(i3) input0 = {i2: [-0.869931, 0.644628, -0.918393, 0.153672, 0.868562, -0.358177, -0.134931, -0.247565, 0.22174, -0.259157, -0.284296, -0.538065, 0.765559, 0.41986, -0.556241, 0.658494, 0.214355, -0.850169, -0.252893, -0.478935, 0.530526, -0.0700663, -0.988729, -0.303061, 0.150845, 0.829915, 0.476349, 0.406537, -0.355343, 0.757145, -0.356362, 0.800482, -0.713861, 0.210483, -0.634303, 0.718236, -0.752038, 0.457547, -0.550769, -0.551178, 0.446766, -0.227462, 0.216348, -0.852806, -0.351486, 0.55906, -0.668493, -0.303493, -0.363763, -0.162837, 0.0701012, 0.756097, -0.142269, 0.329724, -0.656317, -0.998086, -0.652949, -0.40316, -0.893682, 0.432744, 0.612362, -0.869588, -0.71327, -0.398092, -0.0423559, 0.436576, -0.925272, 0.176549, 0.822904, 0.096833, -0.296802, -0.427195, 0.031654, -0.254479, 0.244905, 0.0948254, 0.643769, -0.90391, 0.352665, -0.901179, 0.266159, -0.968068, -0.615401, -0.388975, 0.939052, -0.116289, 0.107523, -0.0582711, 0.435172, 0.334675, 0.459711, 0.717436, 0.496627, -0.680175, -0.415066, 0.339848, 0.506004, -0.337808, -0.107218, -0.172496, 0.870638, 0.931872, -0.953884, 0.903042, 0.760078, 0.209727, -0.285384, -0.45514, 0.113194, 0.0756611, 0.0924435, -0.472863, 0.960609, -0.160385, -0.839445, 0.457097, 0.163348, 0.344867, -0.131619, 0.688715, -0.540827, 0.571259, -0.95587, 0.506164, -0.155839, 0.0789621, 0.756772, -0.662069, 0.242908, 0.460821, 0.177872, -0.289839, -0.640603, 0.702598, -0.506406, -0.568262, -0.0713716, 0.413792, 0.159673, -0.305208, 0.133816, -0.160254, 0.787323, -0.753244, 0.600721, 0.263186, -0.162387, 0.477962, -0.702951, -0.731036, -0.939481, -0.524519, 0.934072, -0.511637, -0.503499, 0.106236, -0.323684, 0.534444, -0.843745, 0.364171, 0.0370358, -0.168801, -0.404559, -0.814178, 0.91745, -0.334276, 0.66925, -0.801201, 0.156511, -0.427949, 0.379153, 0.818597, -0.649902, 0.427087, -0.586015, -0.559789, -0.833923, 0.0892409, -0.621251, 0.213826, 0.465509, 0.4704, 0.380261, 0.413067, 0.180822, 0.172866, 0.59614, 0.825575, 0.662916, -0.704381, -0.297631, 0.697778]} diff --git a/tests/nnapi/specs/V1_0/lstm3.mod.py b/tests/nnapi/specs/V1_0/lstm3.mod.py index a9d2bf884..3abbaa001 100644 --- a/tests/nnapi/specs/V1_0/lstm3.mod.py +++ b/tests/nnapi/specs/V1_0/lstm3.mod.py @@ -60,7 +60,6 @@ output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_ cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) -# TODO: need support for more than one output model = model.Operation("LSTM", input, diff --git a/tests/nnapi/specs/V1_0/lstm3_state.mod.py b/tests/nnapi/specs/V1_0/lstm3_state.mod.py index 1fd91a91e..18a2d11f3 100644 --- a/tests/nnapi/specs/V1_0/lstm3_state.mod.py +++ b/tests/nnapi/specs/V1_0/lstm3_state.mod.py @@ -60,7 +60,6 @@ output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_ cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) -# TODO: need support for more than one output model = model.Operation("LSTM", input, diff --git a/tests/nnapi/specs/V1_0/lstm3_state2.mod.py b/tests/nnapi/specs/V1_0/lstm3_state2.mod.py index bc6ae7e86..38be40645 100644 --- a/tests/nnapi/specs/V1_0/lstm3_state2.mod.py +++ b/tests/nnapi/specs/V1_0/lstm3_state2.mod.py @@ -60,7 +60,6 @@ output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_ cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) -# TODO: need support for more than one output model = model.Operation("LSTM", input, diff --git a/tests/nnapi/specs/V1_0/lstm3_state3.mod.py b/tests/nnapi/specs/V1_0/lstm3_state3.mod.py index 40262c251..a0cd9df58 100644 --- a/tests/nnapi/specs/V1_0/lstm3_state3.mod.py +++ b/tests/nnapi/specs/V1_0/lstm3_state3.mod.py @@ -60,7 +60,6 @@ output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d} cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) -# TODO: need support for more than one output model = model.Operation("LSTM", input, diff --git a/tests/nnapi/specs/V1_0/mobilenet_224_gender_basic_fixed.mod.py b/tests/nnapi/specs/V1_0/mobilenet_224_gender_basic_fixed.mod.py index 390ccc35e..c08bb310b 100644 --- a/tests/nnapi/specs/V1_0/mobilenet_224_gender_basic_fixed.mod.py +++ b/tests/nnapi/specs/V1_0/mobilenet_224_gender_basic_fixed.mod.py @@ -1,3 +1,5 @@ +# Comment out: too large test. we can test mobilenet using frameworktest +""" # Passing weights via shared memory Configuration.use_shm_for_weights = True model = Model() @@ -219,36 +221,36 @@ i81 = Internal("op81", "TENSOR_FLOAT32", "{1, 1, 1, 256}") # intermediate result i82 = Internal("op82", "TENSOR_FLOAT32", "{1, 1, 1, 11}") # intermediate result i83 = Parameter("op83", "TENSOR_FLOAT32", "{11}", [-0.0293549, -0.0835053, -0.228576, -0.0980885, -0.0370638, -0.195879, -0.0452258, 0.0371641, -0.0585841, -0.0984154, -0.141293]) # parameters i84 = Parameter("op84", "TENSOR_FLOAT32", "{11, 1, 1, 256}", [0.0984852, 0.672424, 0.392549, -0.262182, 0.303914, -0.0118188, 0.027943, 0.0164078, 0.200583, -0.808626, 0.234772, -0.0253635, -0.198519, -0.176824, -0.580674, 0.0681573, -0.0134279, 0.172173, -0.284882, -0.0895141, 0.0142356, -0.0479431, 0.0736678, 0.00298977, 0.152355, -0.0370715, 0.463201, 0.0146613, 0.0971624, -0.0791196, 0.556621, -0.00950762, 0.0160531, 0.091037, 0.376353, -0.0996081, -0.0418334, -0.427482, -0.202679, -0.197079, 0.021873, -0.105617, 0.36447, 0.389277, 0.0429815, 0.0480496, -0.170086, -0.191548, -0.237921, 0.155838, -0.100796, 0.0539355, 0.103154, 0.0441985, -0.20672, 0.358565, -0.105794, -0.635905, 0.193301, 0.112419, -0.184668, 0.157954, -0.301095, -0.153072, -0.0535615, -0.0661999, -0.197056, -0.0835003, -0.074932, -0.111766, -0.356266, 0.649165, -0.0527003, -0.0597135, 0.109839, -0.270809, 0.0333183, -0.211454, 0.0594729, -0.166949, 0.21057, 0.224925, -0.222835, -0.0178217, 0.127268, 0.229248, 0.262987, 0.0318244, 0.293201, -0.361254, -0.0624992, -0.0696259, 0.0456531, -0.0287401, 0.0863351, -0.106142, 0.81137, 0.305728, 0.398482, -0.0190696, -0.133965, -0.223757, -0.153242, -0.261303, 0.111363, -0.113733, 0.0028724, -0.0878969, 0.0498853, -0.000613516, -0.0819123, -0.0154599, -0.0938842, -0.108295, 0.340323, -0.139579, -0.117066, 0.145283, -0.106254, 0.201248, -0.152479, 0.162457, -0.0751263, 0.00127508, -0.0218281, 0.126278, -0.100075, 0.426783, -0.108719, 0.207569, -0.327427, 0.277309, 0.0404061, -0.334901, 0.154047, -0.287619, 0.0161922, -0.00054208, -0.233675, 0.564603, 0.201628, 0.0510375, -0.16502, -0.0155493, -0.125359, -0.0996153, 0.0133961, -0.492208, 0.109118, -0.136327, 0.0252329, 0.0556799, -0.196804, -0.0612012, -0.0392273, 0.133385, 0.253763, -0.208136, -0.00507434, -0.0584744, 0.0855089, -0.00321895, -0.209376, 0.0618401, 0.0129248, -0.130721, -0.168413, 0.122652, 0.0927544, -0.180775, -0.0463842, -0.626248, -0.00596579, 0.0822374, -0.254325, -0.361624, 0.778701, -0.0705549, 0.40832, 0.0932269, 0.10348, 0.258843, -0.117135, 0.131713, -0.457018, -0.364692, 0.0741725, 0.168267, 0.0904773, -0.333243, 0.18358, -0.0407786, -0.0115824, 0.304328, 0.177285, 0.206312, -0.503914, 0.310439, 0.533919, 0.0925376, 0.449889, -0.45417, 0.89017, -0.00580558, 0.317744, 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-0.091557, -0.262375, -0.0645785, 0.188566, -0.202261, -0.112712, 0.00631479, 0.0132917, -0.0130675, -0.302285, 0.0556928, -0.0211812, -0.0555546, 0.0291112, 0.168815, 0.143654, -0.00564186, -0.0614248, -0.0939664, 0.0959667, -0.209823, -0.103889, -0.206011, -0.0394793, 0.0545815, -0.0348762, -0.132075, -0.0489917, -0.177563, -0.164591, -0.0174372, -0.276844, -0.132214, -0.236278, -0.0614254, -0.230962, -0.409367, -0.08959, 0.182197, -0.341314, -0.0645579, -0.0161434, -0.166644, -0.0784324, -0.387537, 0.236617, -0.115318, -0.11315, -0.109817, -0.0949309, -0.253715, -0.254404, -0.0876592, -0.243118, -0.219172, 0.0341202, 0.0203343, 0.0435131, -0.0266338, 0.140304, -0.20669, -0.130739, 0.0213059, 0.182793, -0.0711616, -0.165651, -0.212373, -0.0972764, -0.284464, -0.0834676, -0.129573, -0.133945, 0.0684521, -0.133913, 0.165726, -0.176839, -0.0940447, -0.145421, -0.0471074, 0.00950449, 0.0308656, -0.00761046, -0.19397, -0.161623, 0.10975, -0.0398157, 0.00168868, 0.0626417, -0.118388, -0.134741, -0.243707, 0.146451, -0.165854, 0.0585878, 0.0269307, 0.163195, -0.197056, 0.0438799, -0.152668, -0.178631, -0.167278, 0.0258257, -0.22958, -0.101918, 0.0360034, -0.165612, -0.112482, -0.419959, -0.369384, 0.0468117, 0.202511, 0.161559, 0.0360435, -0.211843, 0.0480519, -0.252478, -0.0951382, 0.100791, -0.379245, -0.129869, -0.036501, 0.0685223, 0.0247177, -0.0751386, -0.12451, 0.244585, -0.0103249, -0.346383, -0.300614, 0.230366, -0.187795, -0.0326416, 0.0735751, -0.0136039, -0.0219528, 0.0629145, -0.0308739, -0.101514, -0.169444, 0.058706, -0.133274, -0.200294, -0.372511, -0.214898, -0.184366, 0.253648, -0.0362453, 0.0618937, 0.0838244, -0.0386255, 0.129191, -0.147435, -0.180809, -0.0797491, -0.286544, -0.273005, 0.116222, -0.255255, -0.0504643, -0.0567216, -0.0204081, 0.206331, -0.225266, -0.211665, -0.259216, -0.0676753, -0.176153, 0.285802, -0.00560349, -0.0253936, -0.182537, -0.344487, -0.341246, -0.171879, 0.24462, 0.015354, -0.0255803, -0.0855239, -0.151488, -0.0329621, 0.311794, 0.0889872, -0.142655, -0.00124048, 0.0175189, 0.0459686, 0.279491, -0.237445, 0.0570048, -0.00665275, -0.0558817, 0.0731352, 0.0291331, 0.0918153, 0.0276626, -0.135103, -0.303909, 0.0283329, -0.203482, -0.0849922, -0.284485, -0.214908, 0.0836636, -0.219738, 0.136157, 0.0332432, -0.143305, 0.0283252, -0.178703, -0.0742534, -0.153174, 0.02235, -0.0753622, -0.210102, -0.0915751, -0.0189732, -0.239039, -0.135349, -0.104589, -0.0658414, -0.183206, -0.123006, 0.0835748, -0.0703047, -0.207461, -0.274129, -0.225327, -0.113485, 0.13316, 0.0295303, -0.0958281]) # parameters -model = model.Conv(i86, i2, i1, i87, i88, i89, i90).To(i0) -model = model.DepthWiseConv(i0, i29, i28, i91, i92, i93, i94, i95).To(i27) -model = model.Conv(i27, i32, i31, i96, i97, i98, i99).To(i30) -model = model.DepthWiseConv(i30, i35, i34, i100, i101, i102, i103, i104).To(i33) -model = model.Conv(i33, i38, i37, i105, i106, i107, i108).To(i36) -model = model.DepthWiseConv(i36, i41, i40, i109, i110, i111, i112, i113).To(i39) -model = model.Conv(i39, i44, i43, i114, i115, i116, i117).To(i42) -model = model.DepthWiseConv(i42, i47, i46, i118, i119, i120, i121, i122).To(i45) -model = model.Conv(i45, i50, i49, i123, i124, i125, i126).To(i48) -model = model.DepthWiseConv(i48, i53, i52, i127, i128, i129, i130, i131).To(i51) -model = model.Conv(i51, i56, i55, i132, i133, i134, i135).To(i54) -model = model.DepthWiseConv(i54, i59, i58, i136, i137, i138, i139, i140).To(i57) -model = model.Conv(i57, i62, i61, i141, i142, i143, i144).To(i60) -model = model.DepthWiseConv(i60, i65, i64, i145, i146, i147, i148, i149).To(i63) -model = model.Conv(i63, i68, i67, i150, i151, i152, i153).To(i66) -model = model.DepthWiseConv(i66, i71, i70, i154, i155, i156, i157, i158).To(i69) -model = model.Conv(i69, i74, i73, i159, i160, i161, i162).To(i72) -model = model.DepthWiseConv(i72, i77, i76, i163, i164, i165, i166, i167).To(i75) -model = model.Conv(i75, i80, i79, i168, i169, i170, i171).To(i78) -model = model.DepthWiseConv(i78, i5, i4, i172, i173, i174, i175, i176).To(i3) -model = model.Conv(i3, i8, i7, i177, i178, i179, i180).To(i6) -model = model.DepthWiseConv(i6, i11, i10, i181, i182, i183, i184, i185).To(i9) -model = model.Conv(i9, i14, i13, i186, i187, i188, i189).To(i12) -model = model.DepthWiseConv(i12, i17, i16, i190, i191, i192, i193, i194).To(i15) -model = model.Conv(i15, i20, i19, i195, i196, i197, i198).To(i18) -model = model.DepthWiseConv(i18, i23, i22, i199, i200, i201, i202, i203).To(i21) -model = model.Conv(i21, i26, i25, i204, i205, i206, i207).To(i24) -model = model.AveragePool(i24, i208, i209, i210, i211, i212, i213).To(i81) -model = model.Conv(i81, i84, i83, i214, i215, i216, i217).To(i82) -model = model.Logistic(i82).To(i85) +model = model.Operation("CONV_2D", i86, i2, i1, i87, i88, i89, i90).To(i0) +model = model.Operation("DEPTHWISE_CONV_2D", i0, i29, i28, i91, i92, i93, i94, i95).To(i27) +model = model.Operation("CONV_2D", i27, i32, i31, i96, i97, i98, i99).To(i30) +model = model.Operation("DEPTHWISE_CONV_2D", i30, i35, i34, i100, i101, i102, i103, i104).To(i33) +model = model.Operation("CONV_2D", i33, i38, i37, i105, i106, i107, i108).To(i36) +model = model.Operation("DEPTHWISE_CONV_2D", i36, i41, i40, i109, i110, i111, i112, i113).To(i39) +model = model.Operation("CONV_2D", i39, i44, i43, i114, i115, i116, i117).To(i42) +model = model.Operation("DEPTHWISE_CONV_2D", i42, i47, i46, i118, i119, i120, i121, i122).To(i45) +model = model.Operation("CONV_2D", i45, i50, i49, i123, i124, i125, i126).To(i48) +model = model.Operation("DEPTHWISE_CONV_2D", i48, i53, i52, i127, i128, i129, i130, i131).To(i51) +model = model.Operation("CONV_2D", i51, i56, i55, i132, i133, i134, i135).To(i54) +model = model.Operation("DEPTHWISE_CONV_2D", i54, i59, i58, i136, i137, i138, i139, i140).To(i57) +model = model.Operation("CONV_2D", i57, i62, i61, i141, i142, i143, i144).To(i60) +model = model.Operation("DEPTHWISE_CONV_2D", i60, i65, i64, i145, i146, i147, i148, i149).To(i63) +model = model.Operation("CONV_2D", i63, i68, i67, i150, i151, i152, i153).To(i66) +model = model.Operation("DEPTHWISE_CONV_2D", i66, i71, i70, i154, i155, i156, i157, i158).To(i69) +model = model.Operation("CONV_2D", i69, i74, i73, i159, i160, i161, i162).To(i72) +model = model.Operation("DEPTHWISE_CONV_2D", i72, i77, i76, i163, i164, i165, i166, i167).To(i75) +model = model.Operation("CONV_2D", i75, i80, i79, i168, i169, i170, i171).To(i78) +model = model.Operation("DEPTHWISE_CONV_2D", i78, i5, i4, i172, i173, i174, i175, i176).To(i3) +model = model.Operation("CONV_2D", i3, i8, i7, i177, i178, i179, i180).To(i6) +model = model.Operation("DEPTHWISE_CONV_2D", i6, i11, i10, i181, i182, i183, i184, i185).To(i9) +model = model.Operation("CONV_2D", i9, i14, i13, i186, i187, i188, i189).To(i12) +model = model.Operation("DEPTHWISE_CONV_2D", i12, i17, i16, i190, i191, i192, i193, i194).To(i15) +model = model.Operation("CONV_2D", i15, i20, i19, i195, i196, i197, i198).To(i18) +model = model.Operation("DEPTHWISE_CONV_2D", i18, i23, i22, i199, i200, i201, i202, i203).To(i21) +model = model.Operation("CONV_2D", i21, i26, i25, i204, i205, i206, i207).To(i24) +model = model.Operation("AVERAGE_POOL_2D", i24, i208, i209, i210, i211, i212, i213).To(i81) +model = model.Operation("CONV_2D", i81, i84, i83, i214, i215, i216, i217).To(i82) +model = model.Operation("LOGISTIC", i82).To(i85) # Example 1 input0 = { i86: [0.791841, 0.131271, 0.840444, 0.524133, 0.301398, 0.711596, 8.78908e-05, 0.358888, 0.156309, 0.273055, 0.143706, 0.663516, 0.817875, 0.0178006, 0.0663899, 0.38532, 0.108224, 0.120309, 0.252355, 0.468634, 0.535587, 0.372087, 0.0191815, 0.0750463, 0.896686, 0.564102, 0.781927, 0.138955, 0.876559, 0.672544, 0.47254, 0.685613, 0.864712, 0.678431, 0.681087, 0.954624, 0.707985, 0.0945955, 0.663676, 0.0174337, 0.514265, 0.0993437, 0.837646, 0.16886, 0.0206787, 0.460454, 0.298217, 0.0939906, 0.792089, 0.145739, 0.124991, 0.152928, 0.18507, 0.0910606, 0.886938, 0.703805, 0.738545, 0.0914867, 0.785625, 0.255978, 0.25971, 0.564347, 0.496365, 0.40138, 0.181351, 0.229051, 0.528806, 0.815946, 0.723594, 0.0782805, 0.417488, 0.357831, 0.699987, 0.110877, 0.706929, 0.361355, 0.44735, 0.51417, 0.234213, 0.49157, 0.754239, 0.35671, 0.90015, 0.368686, 0.162982, 0.467298, 0.712059, 0.73199, 0.394652, 0.216439, 0.0325005, 0.675254, 0.0965207, 0.919576, 0.46926, 0.923022, 0.0721544, 0.0620356, 0.570436, 0.418233, 0.734994, 0.626141, 0.189962, 0.680934, 0.576122, 0.377738, 0.927142, 0.31627, 0.0390065, 0.305891, 0.777981, 0.576827, 0.999185, 0.849222, 0.221597, 0.0529092, 0.741046, 0.810611, 0.641734, 0.2737, 0.0306115, 0.382373, 0.117532, 0.690233, 0.20231, 0.599944, 0.464123, 0.79606, 0.171281, 0.0924674, 0.502584, 0.689291, 0.549744, 0.951152, 0.579524, 0.796293, 0.971173, 0.903601, 0.376714, 0.915486, 0.521651, 0.888365, 0.355228, 0.648741, 0.900593, 0.48239, 0.317946, 0.524421, 0.99061, 0.842221, 0.102702, 0.679481, 0.588507, 0.7512, 0.177413, 0.762043, 0.63814, 0.509979, 0.245258, 0.97713, 0.206899, 0.0342631, 0.582589, 0.677466, 0.548678, 0.969459, 0.763207, 0.659911, 0.720006, 0.492729, 0.721137, 0.198181, 0.0767499, 0.279166, 0.563685, 0.578014, 0.284114, 0.0313309, 0.545107, 0.675547, 0.902745, 0.799514, 0.14417, 0.197687, 0.187282, 0.0420023, 0.238308, 0.154671, 0.243883, 0.777129, 0.291907, 0.959133, 0.166203, 0.355963, 0.831646, 0.308086, 0.148097, 0.342163, 0.366069, 0.669976, 0.482005, 0.50455, 0.114772, 0.909615, 0.592529, 0.819863, 0.7448, 0.733599, 0.0818953, 0.333677, 0.436425, 0.640028, 0.128689, 0.802751, 0.053884, 0.0968059, 0.0141547, 0.189447, 0.458282, 0.301719, 0.332669, 0.697625, 0.607359, 0.96581, 0.0482129, 0.658574, 0.283779, 0.428031, 0.120848, 0.387377, 0.169832, 0.0381746, 0.549549, 0.721105, 0.281996, 0.282577, 0.183188, 0.237784, 0.590087, 0.289398, 0.540784, 0.301099, 0.396178, 0.855404, 0.460563, 0.125632, 0.150601, 0.271058, 0.696146, 0.947483, 0.735739, 0.425141, 0.977515, 0.3452, 0.143326, 0.274614, 0.925826, 0.81308, 0.535078, 0.998599, 0.427464, 0.260845, 0.137486, 0.837797, 0.0631066, 0.0876196, 0.588056, 0.40014, 0.815027, 0.494389, 0.355774, 0.450119, 0.717888, 0.423054, 0.20979, 0.842124, 0.399906, 0.410846, 0.559933, 0.451017, 0.558568, 0.944169, 0.881834, 0.00384158, 0.103625, 0.309752, 0.40491, 0.98423, 0.303001, 0.486862, 0.394447, 0.108623, 0.258184, 0.172573, 0.0685831, 0.358462, 0.272746, 0.875298, 0.690985, 0.800497, 0.512517, 0.692277, 0.0706645, 0.259856, 0.37559, 0.213658, 0.53025, 0.405643, 0.656994, 0.228048, 0.247219, 0.300241, 0.700641, 0.0177141, 0.466215, 0.200764, 0.857363, 0.483106, 0.522657, 0.144218, 0.267424, 0.291684, 0.67167, 0.0357309, 0.265176, 0.520329, 0.556993, 0.984931, 0.183227, 0.335942, 0.992855, 0.175479, 0.425192, 0.826319, 0.433081, 0.727791, 0.245524, 0.0597154, 0.156634, 0.64591, 0.958063, 0.537418, 0.295089, 0.0732913, 0.742363, 0.642074, 0.826581, 0.682193, 0.852718, 0.999771, 0.315818, 0.571465, 0.491486, 0.235984, 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0.858442, 0.493233, 0.151025, 0.16046, 0.180032, 0.441936, 0.211194, 0.7617, 0.867114, 0.199935, 0.743809, 0.637003, 0.808767, 0.561247, 0.776601, 0.43555, 0.188716]} @@ -256,4 +258,4 @@ input0 = { i86: [0.791841, 0.131271, 0.840444, 0.524133, 0.301398, 0.711596, 8.7 output0 = {i85: [0.605085, 0.0733608, 0.0200205, 0.114568, 0.178815, 0.0580576, 0.123144, 0.232838, 0.0916902, 0.111395, 0.138349]} Example((input0, output0)) - +""" diff --git a/tests/nnapi/specs/V1_0/mobilenet_quantized.mod.py b/tests/nnapi/specs/V1_0/mobilenet_quantized.mod.py index 3f9684950..3a49f0719 100644 --- a/tests/nnapi/specs/V1_0/mobilenet_quantized.mod.py +++ b/tests/nnapi/specs/V1_0/mobilenet_quantized.mod.py @@ -1,3 +1,5 @@ +# Comment out: too large test. we can test mobilenet using frameworktest +""" # Passing weights via shared memory Configuration.use_shm_for_weights = True model = Model() @@ -222,40 +224,41 @@ i83 = Parameter("op83", "TENSOR_INT32", "{1001}, 0.000139550233143, 0", [-4628, i84 = 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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]) # parameters i86 = Internal("op86", "TENSOR_QUANT8_ASYMM", "{1, 1001}, 0.14927123487, 69") # intermediate result i87 = Parameter("op87", "TENSOR_INT32", "{2}, 0, 0", [1, 1001]) # parameters -model = model.Conv(i88, i2, i1, i104, i105, i106, i107).To(i0) -model = model.DepthWiseConv(i0, i29, i28, i108, i109, i110, i111, i112).To(i27) -model = model.Conv(i27, i32, i31, i113, i114, i115, i116).To(i30) -model = model.DepthWiseConv(i30, i35, i34, i117, i118, i119, i120, i121).To(i33) -model = model.Conv(i33, i38, i37, i122, i123, i124, i125).To(i36) -model = model.DepthWiseConv(i36, i41, i40, i126, i127, i128, i129, i130).To(i39) -model = model.Conv(i39, i44, i43, i131, i132, i133, i134).To(i42) -model = model.DepthWiseConv(i42, i47, i46, i135, i136, i137, i138, i139).To(i45) -model = model.Conv(i45, i50, i49, i140, i141, i142, i143).To(i48) -model = model.DepthWiseConv(i48, i53, i52, i144, i145, i146, i147, i148).To(i51) -model = model.Conv(i51, i56, i55, i149, i150, i151, i152).To(i54) -model = model.DepthWiseConv(i54, i59, i58, i153, i154, i155, i156, i157).To(i57) -model = model.Conv(i57, i62, i61, i158, i159, i160, i161).To(i60) -model = model.DepthWiseConv(i60, i65, i64, i162, i163, i164, i165, i166).To(i63) -model = model.Conv(i63, i68, i67, i167, i168, i169, i170).To(i66) -model = model.DepthWiseConv(i66, i71, i70, i171, i172, i173, i174, i175).To(i69) -model = model.Conv(i69, i74, i73, i176, i177, i178, i179).To(i72) -model = model.DepthWiseConv(i72, i77, i76, i180, i181, i182, i183, i184).To(i75) -model = model.Conv(i75, i80, i79, i185, i186, i187, i188).To(i78) -model = model.DepthWiseConv(i78, i5, i4, i189, i190, i191, i192, i193).To(i3) -model = model.Conv(i3, i8, i7, i194, i195, i196, i197).To(i6) -model = model.DepthWiseConv(i6, i11, i10, i198, i199, i200, i201, i202).To(i9) -model = model.Conv(i9, i14, i13, i203, i204, i205, i206).To(i12) -model = model.DepthWiseConv(i12, i17, i16, i207, i208, i209, i210, i211).To(i15) -model = model.Conv(i15, i20, i19, i212, i213, i214, i215).To(i18) -model = model.DepthWiseConv(i18, i23, i22, i216, i217, i218, i219, i220).To(i21) -model = model.Conv(i21, i26, i25, i221, i222, i223, i224).To(i24) -model = model.AveragePool(i24, i225, i226, i227, i228, i229, i230).To(i81) -model = model.Conv(i81, i84, i83, i231, i232, i233, i234).To(i82) -model = model.Reshape(i82, i87).To(i86) -model = model.SoftMax(i86, i235).To(i85) +model = model.Operation("CONV_2D", i88, i2, i1, i104, i105, i106, i107).To(i0) +model = model.Operation("DEPTHWISE_CONV_2D", i0, i29, i28, i108, i109, i110, i111, i112).To(i27) +model = model.Operation("CONV_2D", i27, i32, i31, i113, i114, i115, i116).To(i30) +model = model.Operation("DEPTHWISE_CONV_2D", i30, i35, i34, i117, i118, i119, i120, i121).To(i33) +model = model.Operation("CONV_2D", i33, i38, i37, i122, i123, i124, i125).To(i36) +model = model.Operation("DEPTHWISE_CONV_2D", i36, i41, i40, i126, i127, i128, i129, i130).To(i39) +model = model.Operation("CONV_2D", i39, i44, i43, i131, i132, i133, i134).To(i42) +model = model.Operation("DEPTHWISE_CONV_2D", i42, i47, i46, i135, i136, i137, i138, i139).To(i45) +model = model.Operation("CONV_2D", i45, i50, i49, i140, i141, i142, i143).To(i48) +model = model.Operation("DEPTHWISE_CONV_2D", i48, i53, i52, i144, i145, i146, i147, i148).To(i51) +model = model.Operation("CONV_2D", i51, i56, i55, i149, i150, i151, i152).To(i54) +model = model.Operation("DEPTHWISE_CONV_2D", i54, i59, i58, i153, i154, i155, i156, i157).To(i57) +model = model.Operation("CONV_2D", i57, i62, i61, i158, i159, i160, i161).To(i60) +model = model.Operation("DEPTHWISE_CONV_2D", i60, i65, i64, i162, i163, i164, i165, i166).To(i63) +model = model.Operation("CONV_2D", i63, i68, i67, i167, i168, i169, i170).To(i66) +model = model.Operation("DEPTHWISE_CONV_2D", i66, i71, i70, i171, i172, i173, i174, i175).To(i69) +model = model.Operation("CONV_2D", i69, i74, i73, i176, i177, i178, i179).To(i72) +model = model.Operation("DEPTHWISE_CONV_2D", i72, i77, i76, i180, i181, i182, i183, i184).To(i75) +model = model.Operation("CONV_2D", i75, i80, i79, i185, i186, i187, i188).To(i78) +model = model.Operation("DEPTHWISE_CONV_2D", i78, i5, i4, i189, i190, i191, i192, i193).To(i3) +model = model.Operation("CONV_2D", i3, i8, i7, i194, i195, i196, i197).To(i6) +model = model.Operation("DEPTHWISE_CONV_2D", i6, i11, i10, i198, i199, i200, i201, i202).To(i9) +model = model.Operation("CONV_2D", i9, i14, i13, i203, i204, i205, i206).To(i12) +model = model.Operation("DEPTHWISE_CONV_2D", i12, i17, i16, i207, i208, i209, i210, i211).To(i15) +model = model.Operation("CONV_2D", i15, i20, i19, i212, i213, i214, i215).To(i18) +model = model.Operation("DEPTHWISE_CONV_2D", i18, i23, i22, i216, i217, i218, i219, i220).To(i21) +model = model.Operation("CONV_2D", i21, i26, i25, i221, i222, i223, i224).To(i24) +model = model.Operation("AVERAGE_POOL_2D", i24, i225, i226, i227, i228, i229, i230).To(i81) +model = model.Operation("CONV_2D", i81, i84, i83, i231, i232, i233, i234).To(i82) +model = model.Operation("RESHAPE", i82, i87).To(i86) +model = model.Operation("SOFTMAX", i86, i235).To(i85) input0 = {i88: [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, 161, 151, 148, 138, 167, 164, 154, 153, 152, 141, 148, 141, 132, 162, 157, 147, 153, 148, 136, 161, 156, 144, 156, 153, 140, 138, 136, 123, 131, 135, 123, 115, 112, 100, 123, 128, 112, 118, 117, 106, 104, 106, 94, 120, 121, 112, 112, 112, 100, 120, 122, 110, 112, 114, 100, 102, 105, 90, 109, 112, 97, 106, 107, 91, 127, 129, 115, 130, 128, 113, 110, 108, 93, 121, 119, 103, 99, 96, 81, 87, 85, 70, 112, 113, 95, 118, 119, 100, 106, 107, 89, 131, 129, 114, 113, 111, 93, 129, 120, 107, 122, 120, 105, 111, 111, 95, 96, 95, 84, 90, 90, 76, 96, 98, 72, 85, 87, 75, 80, 82, 72, 13, 13, 0, 24, 23, 12, 6, 6, 0, 52, 51, 40, 104, 103, 94, 118, 120, 107, 125, 124, 115, 133, 135, 123, 120, 121, 114, 136, 139, 131, 150, 153, 145, 144, 145, 138, 148, 151, 143, 154, 155, 148, 130, 133, 126, 138, 142, 134, 149, 152, 144, 150, 153, 145, 156, 159, 151, 159, 165, 156, 151, 157, 148, 145, 151, 142, 149, 155, 147, 161, 167, 158, 159, 165, 156, 146, 152, 143, 150, 153, 147, 166, 165, 154, 227, 223, 206, 249, 245, 226, 255, 251, 234, 255, 253, 236, 255, 255, 239, 255, 255, 250, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 253, 240, 238, 236, 221, 206, 207, 191, 114, 111, 98, 143, 140, 129, 230, 228, 215, 245, 243, 230, 216, 216, 202, 158, 158, 144, 103, 103, 89, 115, 118, 103, 222, 221, 208, 255, 255, 247, 255, 255, 255, 255, 255, 254, 255, 255, 254, 255, 255, 254, 255, 255, 251, 250, 248, 235, 240, 237, 222, 251, 249, 234, 255, 255, 243, 255, 255, 246, 255, 255, 247, 255, 255, 246, 245, 241, 224, 49, 45, 28, 12, 7, 0, 227, 223, 206, 255, 255, 243, 255, 251, 236, 236, 232, 218, 235, 231, 216, 239, 234, 220, 243, 239, 224, 255, 255, 242, 255, 255, 246, 255, 255, 245, 255, 255, 245, 255, 255, 245, 255, 255, 245, 255, 255, 245, 255, 255, 245, 255, 255, 244, 255, 255, 245, 255, 255, 245, 255, 255, 244, 255, 255, 247, 255, 255, 248, 255, 255, 247, 255, 255, 247, 255, 255, 247, 255, 255, 247, 255, 255, 245, 255, 255, 244, 255, 255, 244, 255, 255, 245, 255, 255, 244, 255, 255, 244, 255, 255, 244, 255, 255, 247, 255, 255, 245, 255, 255, 244, 255, 255, 244, 255, 255, 244, 255, 255, 241, 255, 255, 241, 255, 255, 241, 255, 255, 240, 255, 255, 241, 255, 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255, 255, 255, 255, 255, 255, 255, 255, 255, 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, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 90, 84, 68, 87, 85, 72, 82, 80, 67, 78, 78, 65, 89, 87, 72, 80, 78, 63, 82, 80, 67, 67, 67, 51, 84, 83, 75, 76, 76, 60, 68, 68, 52, 16, 18, 6, 74, 76, 66, 35, 37, 23, 38, 42, 29, 36, 38, 26, 56, 55, 47, 54, 52, 41, 41, 41, 29, 55, 54, 46, 31, 30, 19, 19, 24, 11, 29, 31, 19, 30, 29, 18, 35, 37, 25, 34, 34, 22, 19, 21, 8, 38, 37, 26, 22, 26, 13, 30, 32, 20, 29, 31, 21, 54, 51, 43, 28, 35, 21, 57, 57, 46, 35, 31, 23, 33, 30, 22, 35, 31, 23, 31, 30, 21, 32, 29, 19, 38, 35, 27, 35, 29, 22, 21, 23, 13, 24, 26, 16, 35, 38, 28, 25, 24, 15, 18, 22, 10, 35, 32, 21, 29, 31, 17, 22, 30, 13, 35, 32, 19, 12, 14, 2, 36, 36, 25, 19, 21, 8, 39, 43, 31, 30, 34, 21, 29, 31, 17, 39, 43, 31, 42, 44, 32, 32, 31, 20, 23, 27, 14, 33, 35, 22, 37, 39, 27, 36, 40, 29, 33, 37, 29, 43, 51, 42, 33, 39, 30, 14, 32, 20, 20, 28, 18, 41, 47, 40, 40, 43, 38, 42, 43, 36, 42, 45, 39, 41, 42, 37, 38, 41, 34, 44, 45, 42, 54, 52, 48, 44, 45, 40, 56, 57, 52, 47, 45, 41, 55, 54, 48, 69, 70, 63, 80, 79, 70, 66, 65, 56, 109, 108, 99, 73, 72, 63, 84, 83, 75, 103, 100, 92, 95, 94, 83, 105, 105, 94, 96, 95, 84, 97, 95, 84, 152, 151, 140, 223, 220, 210, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 89, 84, 68, 76, 76, 62, 76, 76, 62, 78, 80, 66, 79, 76, 64, 77, 74, 59, 62, 62, 46, 77, 74, 61, 76, 76, 64, 74, 72, 57, 80, 80, 64, 9, 12, 0, 59, 68, 54, 46, 46, 32, 54, 41, 34, 19, 21, 8, 40, 42, 27, 48, 48, 36, 33, 35, 22, 28, 31, 21, 17, 21, 11, 24, 28, 15, 28, 28, 17, 39, 38, 27, 38, 37, 26, 31, 28, 18, 27, 27, 15, 39, 36, 26, 42, 39, 28, 21, 23, 11, 24, 26, 14, 17, 19, 7, 25, 29, 17, 20, 22, 10, 30, 29, 20, 36, 36, 27, 32, 31, 22, 28, 27, 19, 19, 18, 9, 31, 30, 21, 24, 23, 14, 28, 30, 18, 23, 25, 15, 31, 30, 21, 26, 26, 14, 24, 28, 15, 12, 21, 6, 25, 30, 15, 25, 35, 18, 38, 41, 26, 37, 40, 25, 38, 37, 26, 30, 32, 20, 17, 27, 10, 34, 41, 25, 38, 41, 26, 20, 29, 12, 33, 40, 24, 31, 33, 21, 35, 39, 24, 30, 32, 20, 33, 35, 22, 32, 36, 24, 35, 36, 27, 50, 47, 39, 39, 36, 28, 46, 45, 38, 39, 38, 31, 50, 51, 44, 42, 45, 37, 53, 55, 48, 40, 41, 34, 56, 57, 50, 70, 69, 61, 67, 64, 58, 87, 84, 76, 83, 80, 72, 88, 85, 75, 100, 97, 89, 105, 104, 96, 103, 102, 93, 96, 93, 83, 106, 103, 92, 121, 118, 107, 138, 135, 125, 222, 219, 209, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, ]} output0 = {i85: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, ]} # Instantiate an example Example((input0, output0)) +""" diff --git a/tests/nnapi/specs/V1_0/resize_bilinear.mod.py b/tests/nnapi/specs/V1_0/resize_bilinear.mod.py index c596407ac..17fb34007 100644 --- a/tests/nnapi/specs/V1_0/resize_bilinear.mod.py +++ b/tests/nnapi/specs/V1_0/resize_bilinear.mod.py @@ -2,9 +2,9 @@ model = Model() i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") i2 = Output("op2", "TENSOR_FLOAT32", "{1, 3, 3, 1}") -h = Int32Scalar("height", 3) # an int32_t scalar bias w = Int32Scalar("width", 3) -model = model.Operation("RESIZE_BILINEAR", i1, h, w).To(i2) +h = Int32Scalar("height", 3) +model = model.Operation("RESIZE_BILINEAR", i1, w, h).To(i2) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/V1_0/resize_bilinear_2.mod.py b/tests/nnapi/specs/V1_0/resize_bilinear_2.mod.py index 06a33b865..4035b4371 100644 --- a/tests/nnapi/specs/V1_0/resize_bilinear_2.mod.py +++ b/tests/nnapi/specs/V1_0/resize_bilinear_2.mod.py @@ -18,9 +18,9 @@ model = Model() i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 2}") i2 = Output("op2", "TENSOR_FLOAT32", "{1, 3, 3, 2}") -h = Int32Scalar("height", 3) # an int32_t scalar bias w = Int32Scalar("width", 3) -model = model.Operation("RESIZE_BILINEAR", i1, h, w).To(i2) +h = Int32Scalar("height", 3) +model = model.Operation("RESIZE_BILINEAR", i1, w, h).To(i2) # Example 1. Input in operand 0, input0 = {i1: # input 0 diff --git a/tests/nnapi/specs/V1_0/svdf_bias_present.mod.py b/tests/nnapi/specs/V1_0/svdf_bias_present.mod.py new file mode 100644 index 000000000..ae7d1e7b8 --- /dev/null +++ b/tests/nnapi/specs/V1_0/svdf_bias_present.mod.py @@ -0,0 +1,138 @@ +# +# Copyright (C) 2019 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. +# + +batches = 2 +features = 4 +rank = 1 +units = int(features / rank) +input_size = 3 +memory_size = 10 + +model = Model() + +input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size)) +weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size)) +weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) +bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) +state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) +rank_param = Int32Scalar("rank_param", rank) +activation_param = Int32Scalar("activation_param", 0) +state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) + +model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in, + rank_param, activation_param).To([state_out, output]) + +input0 = { + input: [], + weights_feature: [ + -0.31930989, -0.36118156, 0.0079667, 0.37613347, + 0.22197971, 0.12416199, 0.27901134, 0.27557442, + 0.3905206, -0.36137494, -0.06634006, -0.10640851 + ], + weights_time: [ + -0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, + 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, + + 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, + -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, + + -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, + 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, + + -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, + -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657 + ], + bias: [1.0, 2.0, 3.0, 4.0], + state_in: [0 for _ in range(batches * memory_size * features)], +} + +test_inputs = [ + 0.12609188, -0.46347019, -0.89598465, + 0.12609188, -0.46347019, -0.89598465, + + 0.14278367, -1.64410412, -0.75222826, + 0.14278367, -1.64410412, -0.75222826, + + 0.49837467, 0.19278903, 0.26584083, + 0.49837467, 0.19278903, 0.26584083, + + -0.11186574, 0.13164264, -0.05349274, + -0.11186574, 0.13164264, -0.05349274, + + -0.68892461, 0.37783599, 0.18263303, + -0.68892461, 0.37783599, 0.18263303, + + -0.81299269, -0.86831826, 1.43940818, + -0.81299269, -0.86831826, 1.43940818, + + -1.45006323, -0.82251364, -1.69082689, + -1.45006323, -0.82251364, -1.69082689, + + 0.03966608, -0.24936394, -0.77526885, + 0.03966608, -0.24936394, -0.77526885, + + 0.11771342, -0.23761693, -0.65898693, + 0.11771342, -0.23761693, -0.65898693, + + -0.89477462, 1.67204106, -0.53235275, + -0.89477462, 1.67204106, -0.53235275 +] + +golden_outputs = [ + 1.014899, 1.9482339, 2.856275, 3.99728117, + 1.014899, 1.9482339, 2.856275, 3.99728117, + + 1.068281, 1.837783, 2.847732, 4.00323521, + 1.068281, 1.837783, 2.847732, 4.00323521, + + 0.9682179, 1.9666911, 3.0609602, 4.0333759, + 0.9682179, 1.9666911, 3.0609602, 4.0333759, + + 0.99376901, 1.922299, 2.608807, 3.9863309, + 0.99376901, 1.922299, 2.608807, 3.9863309, + + 1.201551, 1.835393, 2.820538, 3.9407261, + 1.201551, 1.835393, 2.820538, 3.9407261, + + 1.0886511, 1.9124599, 2.730717, 4.0281379, + 1.0886511, 1.9124599, 2.730717, 4.0281379, + + 0.798826, 1.413855, 2.371376, 3.9669588, + 0.798826, 1.413855, 2.371376, 3.9669588, + + 0.9160904, 1.700671, 3.108746, 4.109808, + 0.9160904, 1.700671, 3.108746, 4.109808, + + 1.419114, 1.762176, 2.577373, 4.175115, + 1.419114, 1.762176, 2.577373, 4.175115, + + 1.36726, 1.477697, 2.543498, 3.824525, + 1.36726, 1.477697, 2.543498, 3.824525 +] + +output0 = {state_out: [0 for _ in range(batches * memory_size * features)], + output: []} + +# TODO: enable more data points after fixing the reference issue +for i in range(1): + batch_start = i * input_size * batches + batch_end = batch_start + input_size * batches + input0[input] = test_inputs[batch_start:batch_end] + golden_start = i * units * batches + golden_end = golden_start + units * batches + output0[output] = golden_outputs[golden_start:golden_end] + Example((input0, output0)) diff --git a/tests/nnapi/specs/generate_test.sh b/tests/nnapi/specs/generate_test.sh index 2d7e5ccf9..2f1afbc66 100755 --- a/tests/nnapi/specs/generate_test.sh +++ b/tests/nnapi/specs/generate_test.sh @@ -47,7 +47,7 @@ function generate_one_testcase { 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`\ + $NNAPI_BASE/$TEST_DIR/nnapi_test_generator/android-p/test_generator.py ./`basename $1`\ -m $NNAPI_BASE/$TEST_DIR/src/generated/models/$BASENAME.model.cpp $EXAMPLE ret=$? # Paste these lines into TestGenerated.cpp diff --git a/tests/nnapi/src/TestValidation.cpp b/tests/nnapi/src/TestValidation.cpp index 2d605bb7e..19db43800 100644 --- a/tests/nnapi/src/TestValidation.cpp +++ b/tests/nnapi/src/TestValidation.cpp @@ -23,6 +23,9 @@ #include <sys/mman.h> #include <stdio.h> #include <stdlib.h> +// Note: neurun is allow to set activation operand constant only, +// so we change test to set operand #2 to constant. (ANEURALNETWORKS_FUSED_NONE) +// And model's input is changed: [0, 1, 2] -> [0, 1] // This file tests all the validations done by the Neural Networks API. namespace { @@ -84,6 +87,9 @@ class ValidationTestIdentify : public ValidationTestModel { 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); + // neurun is allow to set activation operand constant only + int32_t act = ANEURALNETWORKS_FUSED_NONE; + ASSERT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 2, &act, sizeof(act)), 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, @@ -112,12 +118,15 @@ protected: 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); + // neurun is allow to set activation operand constant only + int32_t act = ANEURALNETWORKS_FUSED_NONE; + ASSERT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 2, &act, sizeof(act)), 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), + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 2, inList, 1, outList), ANEURALNETWORKS_NO_ERROR); ASSERT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_NO_ERROR); @@ -390,44 +399,44 @@ TEST_F(ValidationTestModel, CreateCompilation) { } TEST_F(ValidationTestIdentify, Ok) { - uint32_t inList[3]{0, 1, 2}; + uint32_t inList[2]{0, 1}; uint32_t outList[1]{3}; - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 1, outList), + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 2, 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 inList[2]{0, 1}; uint32_t outList[2]{3, 0}; - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 2, outList), + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 2, inList, 2, outList), ANEURALNETWORKS_BAD_DATA); } TEST_F(ValidationTestIdentify, OutputIsInput) { - uint32_t inList[4]{0, 1, 2, 3}; + uint32_t inList[3]{0, 1, 3}; uint32_t outList[1]{3}; - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 4, inList, 1, outList), + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 1, outList), ANEURALNETWORKS_BAD_DATA); } TEST_F(ValidationTestIdentify, DuplicateInputs) { - uint32_t inList[4]{0, 1, 2, 0}; + uint32_t inList[3]{0, 1, 0}; uint32_t outList[1]{3}; - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 4, inList, 1, outList), + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 1, outList), ANEURALNETWORKS_BAD_DATA); } TEST_F(ValidationTestIdentify, DuplicateOutputs) { - uint32_t inList[3]{0, 1, 2}; + uint32_t inList[2]{0, 1}; uint32_t outList[2]{3, 3}; - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 2, outList), + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 2, inList, 2, outList), ANEURALNETWORKS_BAD_DATA); } |