file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py") string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") function(_inference_analysis_python_api_int8_test target model_dir data_dir filename use_mkldnn) py_test(${target} SRCS ${filename} ENVS CPU_NUM_THREADS=${CPU_NUM_THREADS_ON_CI} FLAGS_use_mkldnn=${use_mkldnn} ARGS --infer_model ${model_dir}/model --infer_data ${data_dir}/data.bin --int8_model_save_path int8_models/${target} --warmup_batch_size ${WARMUP_BATCH_SIZE} --batch_size 50) endfunction() function(inference_analysis_python_api_int8_test target model_dir data_dir filename) _inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_dir} ${filename} False) endfunction() function(inference_analysis_python_api_int8_test_mkldnn target model_dir data_dir filename) _inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_dir} ${filename} True) endfunction() function(inference_analysis_python_api_int8_test_custom_warmup_batch_size target model_dir data_dir filename warmup_batch_size) set(WARMUP_BATCH_SIZE ${warmup_batch_size}) inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_dir} ${filename}) endfunction() function(inference_qat_int8_test target model_dir data_dir test_script use_mkldnn) py_test(${target} SRCS ${test_script} ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI} OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI} FLAGS_use_mkldnn=${use_mkldnn} ARGS --qat_model ${model_dir}/model --infer_data ${data_dir}/data.bin --batch_size 25 --batch_num 2 --acc_diff_threshold 0.1) endfunction() # set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 25 function(inference_qat2_int8_test target model_dir data_dir test_script use_mkldnn) py_test(${target} SRCS ${test_script} ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI} OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI} FLAGS_use_mkldnn=${use_mkldnn} ARGS --qat_model ${model_dir}/float --infer_data ${data_dir}/data.bin --batch_size 10 --batch_num 2 --acc_diff_threshold 0.1 --qat2) endfunction() function(save_qat_model_test target qat_model_dir fp32_model_save_path int8_model_save_path test_script) py_test(${target} SRCS ${test_script} ARGS --qat_model_path ${qat_model_dir} --fp32_model_save_path ${fp32_model_save_path} --int8_model_save_path ${int8_model_save_path}) endfunction() if(WIN32) list(REMOVE_ITEM TEST_OPS test_light_nas) list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1) list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50) endif() # int8 image classification python api test if(LINUX AND WITH_MKLDNN) set(INT8_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2") set(MKLDNN_INT8_TEST_FILE "test_mkldnn_int8_quantization_strategy.py") set(MKLDNN_INT8_TEST_FILE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/${MKLDNN_INT8_TEST_FILE}") # googlenet int8 set(INT8_GOOGLENET_MODEL_DIR "${INT8_DATA_DIR}/googlenet") inference_analysis_python_api_int8_test_custom_warmup_batch_size(test_slim_int8_googlenet ${INT8_GOOGLENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE_PATH} 10) # mobilenet int8 set(INT8_MOBILENET_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv1") inference_analysis_python_api_int8_test(test_slim_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE_PATH}) inference_analysis_python_api_int8_test_mkldnn(test_slim_int8_mobilenet_mkldnn ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE_PATH}) # temporarily adding WITH_SLIM_MKLDNN_FULL_TEST FLAG for QA testing the following UTs locally, # since the following UTs cost too much time on CI test. if (WITH_SLIM_MKLDNN_FULL_TEST) # resnet50 int8 set(INT8_RESNET50_MODEL_DIR "${INT8_DATA_DIR}/resnet50") inference_analysis_python_api_int8_test(test_slim_int8_resnet50 ${INT8_RESNET50_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE_PATH}) # mobilenetv2 int8 set(INT8_MOBILENETV2_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv2") inference_analysis_python_api_int8_test(test_slim_int8_mobilenetv2 ${INT8_MOBILENETV2_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE_PATH}) # resnet101 int8 set(INT8_RESNET101_MODEL_DIR "${INT8_DATA_DIR}/resnet101") inference_analysis_python_api_int8_test(test_slim_int8_resnet101 ${INT8_RESNET101_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE_PATH}) # vgg16 int8 set(INT8_VGG16_MODEL_DIR "${INT8_DATA_DIR}/vgg16") inference_analysis_python_api_int8_test(test_slim_int8_vgg16 ${INT8_VGG16_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE_PATH}) # vgg19 int8 set(INT8_VGG19_MODEL_DIR "${INT8_DATA_DIR}/vgg19") inference_analysis_python_api_int8_test(test_slim_int8_vgg19 ${INT8_VGG19_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE_PATH}) endif() endif() # Since test_mkldnn_int8_quantization_strategy only supports testing on Linux # with MKL-DNN, we remove it here for not repeating test, or not testing on other systems. list(REMOVE_ITEM TEST_OPS test_mkldnn_int8_quantization_strategy) # QAT FP32 & INT8 comparison python api tests if(LINUX AND WITH_MKLDNN) set(DATASET_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2") set(QAT_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2") set(QAT_MODELS_BASE_URL "${INFERENCE_URL}/int8/QAT_models") set(MKLDNN_QAT_TEST_FILE "qat_int8_comparison.py") set(MKLDNN_QAT_TEST_FILE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/${MKLDNN_QAT_TEST_FILE}") # ImageNet small dataset # May be already downloaded for INT8v2 unit tests if (NOT EXISTS ${DATASET_DIR}) inference_download_and_uncompress(${DATASET_DIR} "${INFERENCE_URL}/int8" "imagenet_val_100_tail.tar.gz") endif() # QAT ResNet50 set(QAT_RESNET50_MODEL_DIR "${QAT_DATA_DIR}/ResNet50_QAT") if (NOT EXISTS ${QAT_RESNET50_MODEL_DIR}) inference_download_and_uncompress(${QAT_RESNET50_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "ResNet50_qat_model.tar.gz" ) endif() inference_qat_int8_test(test_qat_int8_resnet50_mkldnn ${QAT_RESNET50_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) # QAT ResNet101 set(QAT_RESNET101_MODEL_DIR "${QAT_DATA_DIR}/ResNet101_QAT") if (NOT EXISTS ${QAT_RESNET101_MODEL_DIR}) inference_download_and_uncompress(${QAT_RESNET101_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "ResNet101_qat_model.tar.gz" ) endif() # inference_qat_int8_test(test_qat_int8_resnet101_mkldnn ${QAT_RESNET101_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) # QAT GoogleNet set(QAT_GOOGLENET_MODEL_DIR "${QAT_DATA_DIR}/GoogleNet_QAT") if (NOT EXISTS ${QAT_GOOGLENET_MODEL_DIR}) inference_download_and_uncompress(${QAT_GOOGLENET_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "GoogleNet_qat_model.tar.gz" ) endif() inference_qat_int8_test(test_qat_int8_googlenet_mkldnn ${QAT_GOOGLENET_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) # QAT MobileNetV1 set(QAT_MOBILENETV1_MODEL_DIR "${QAT_DATA_DIR}/MobileNetV1_QAT") if (NOT EXISTS ${QAT_MOBILENETV1_MODEL_DIR}) inference_download_and_uncompress(${QAT_MOBILENETV1_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "MobileNetV1_qat_model.tar.gz" ) endif() inference_qat_int8_test(test_qat_int8_mobilenetv1_mkldnn ${QAT_MOBILENETV1_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) # QAT MobileNetV2 set(QAT_MOBILENETV2_MODEL_DIR "${QAT_DATA_DIR}/MobileNetV2_QAT") if (NOT EXISTS ${QAT_MOBILENETV2_MODEL_DIR}) inference_download_and_uncompress(${QAT_MOBILENETV2_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "MobileNetV2_qat_model.tar.gz" ) endif() inference_qat_int8_test(test_qat_int8_mobilenetv2_mkldnn ${QAT_MOBILENETV2_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) # QAT VGG16 set(QAT_VGG16_MODEL_DIR "${QAT_DATA_DIR}/VGG16_QAT") if (NOT EXISTS ${QAT_VGG16_MODEL_DIR}) inference_download_and_uncompress(${QAT_VGG16_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "VGG16_qat_model.tar.gz" ) endif() # inference_qat_int8_test(test_qat_int8_vgg16_mkldnn ${QAT_VGG16_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) # QAT VGG19 set(QAT_VGG19_MODEL_DIR "${QAT_DATA_DIR}/VGG19_QAT") if (NOT EXISTS ${QAT_VGG19_MODEL_DIR}) inference_download_and_uncompress(${QAT_VGG19_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "VGG19_qat_model.tar.gz" ) endif() # inference_qat_int8_test(test_qat_int8_vgg19_mkldnn ${QAT_VGG19_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) set(QAT2_RESNET50_MODEL_DIR "${QAT_DATA_DIR}/ResNet50_qat_perf") if (NOT EXISTS ${QAT2_RESNET50_MODEL_DIR}) inference_download_and_uncompress(${QAT2_RESNET50_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "ResNet50_qat_perf.tar.gz" ) endif() inference_qat2_int8_test(test_qat2_int8_resnet50_mkldnn ${QAT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) set(QAT2_MOBILENETV1_MODEL_DIR "${QAT_DATA_DIR}/MobileNet_qat_perf") if (NOT EXISTS ${QAT2_MOBILENETV1_MODEL_DIR}) inference_download_and_uncompress(${QAT2_MOBILENETV1_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "MobileNet_qat_perf.tar.gz" ) endif() inference_qat2_int8_test(test_qat2_int8_mobilenetv1_mkldnn ${QAT2_MOBILENETV1_MODEL_DIR}/MobileNet_qat_perf ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) # Save qat2 fp32 model or qat2 int8 model set(QAT2_INT8_SAVE_PATH "${QAT_DATA_DIR}/ResNet50_qat2_int8") set(QAT2_FP32_SAVE_PATH "${QAT_DATA_DIR}/ResNet50_qat2_fp32") set(SAVE_QAT2_MODEL_SCRIPT "${CMAKE_CURRENT_SOURCE_DIR}/save_qat_model.py") save_qat_model_test(save_qat2_model_resnet50 ${QAT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float ${QAT2_FP32_SAVE_PATH} ${QAT2_INT8_SAVE_PATH} ${SAVE_QAT2_MODEL_SCRIPT} true) endif() # Since the test for QAT FP32 & INT8 comparison supports only testing on Linux # with MKL-DNN, we remove it here to not test it on other systems. list(REMOVE_ITEM TEST_OPS qat_int8_comparison.py) foreach(src ${TEST_OPS}) py_test(${src} SRCS ${src}.py) endforeach()