You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
303 lines
16 KiB
303 lines
16 KiB
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_path 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 ${data_path}
|
|
--int8_model_save_path int8_models/${target}
|
|
--warmup_batch_size ${WARMUP_BATCH_SIZE}
|
|
--batch_size 50)
|
|
endfunction()
|
|
|
|
function(inference_analysis_python_api_int8_test target model_dir data_path filename)
|
|
_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path} ${filename} False)
|
|
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} ${filename})
|
|
endfunction()
|
|
|
|
function(inference_analysis_python_api_int8_test_mkldnn target model_dir data_path filename)
|
|
_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path} ${filename} True)
|
|
endfunction()
|
|
|
|
function(download_qat_data install_dir data_file)
|
|
if (NOT EXISTS ${install_dir}/${data_file})
|
|
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file})
|
|
endif()
|
|
endfunction()
|
|
|
|
function(download_qat_model install_dir data_file)
|
|
if (NOT EXISTS ${install_dir}/${data_file})
|
|
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file})
|
|
endif()
|
|
endfunction()
|
|
|
|
function(download_qat_fp32_model install_dir data_file)
|
|
if (NOT EXISTS ${install_dir}/${data_file})
|
|
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8/QAT_models/fp32 ${data_file})
|
|
endif()
|
|
endfunction()
|
|
|
|
function(inference_qat_int8_image_classification_test target qat_model_dir dataset_path)
|
|
py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant_int8_image_classification_comparison.py"
|
|
ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
|
|
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
|
|
FLAGS_use_mkldnn=true
|
|
ARGS --qat_model ${qat_model_dir}
|
|
--infer_data ${dataset_path}
|
|
--batch_size 25
|
|
--batch_num 2
|
|
--acc_diff_threshold 0.1)
|
|
endfunction()
|
|
|
|
|
|
# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 25
|
|
function(inference_qat2_int8_image_classification_test target qat_model_dir fp32_model_dir dataset_path ops_to_quantize)
|
|
py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_image_classification_comparison.py"
|
|
ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
|
|
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
|
|
FLAGS_use_mkldnn=true
|
|
ARGS --qat_model ${qat_model_dir}
|
|
--fp32_model ${fp32_model_dir}
|
|
--infer_data ${dataset_path}
|
|
--batch_size 10
|
|
--batch_num 2
|
|
--acc_diff_threshold 0.1
|
|
--ops_to_quantize ${ops_to_quantize})
|
|
endfunction()
|
|
|
|
# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 20
|
|
function(inference_qat2_int8_nlp_test target qat_model_dir fp32_model_dir dataset_path labels_path)
|
|
py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_nlp_comparison.py"
|
|
ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
|
|
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
|
|
FLAGS_use_mkldnn=true
|
|
ARGS --qat_model ${qat_model_dir}
|
|
--fp32_model ${fp32_model_dir}
|
|
--infer_data ${dataset_path}
|
|
--labels ${labels_path}
|
|
--batch_size 10
|
|
--batch_num 2
|
|
--acc_diff_threshold 0.1)
|
|
endfunction()
|
|
|
|
function(download_qat_data install_dir data_file)
|
|
if (NOT EXISTS ${install_dir}/${data_file})
|
|
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file})
|
|
endif()
|
|
endfunction()
|
|
|
|
function(download_qat_model install_dir data_file)
|
|
if (NOT EXISTS ${install_dir}/${data_file})
|
|
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file})
|
|
endif()
|
|
endfunction()
|
|
|
|
function(save_qat_ic_model_test target qat_model_dir fp32_model_save_path int8_model_save_path ops_to_quantize)
|
|
py_test(${target} SRCS ${CMAKE_CURRENT_SOURCE_DIR}/save_quant_model.py
|
|
ARGS --qat_model_path ${qat_model_dir}
|
|
--fp32_model_save_path ${fp32_model_save_path}
|
|
--int8_model_save_path ${int8_model_save_path}
|
|
--ops_to_quantize ${ops_to_quantize})
|
|
endfunction()
|
|
|
|
function(save_qat_nlp_model_test target qat_model_dir fp32_model_save_path int8_model_save_path)
|
|
py_test(${target} SRCS ${CMAKE_CURRENT_SOURCE_DIR}/save_quant_model.py
|
|
ARGS --qat_model_path ${qat_model_dir}
|
|
--fp32_model_save_path ${fp32_model_save_path}
|
|
--int8_model_save_path ${int8_model_save_path})
|
|
endfunction()
|
|
|
|
function(convert_model2dot_test target model_path save_graph_dir save_graph_name)
|
|
py_test(${target} SRCS ${CMAKE_CURRENT_SOURCE_DIR}/convert_model2dot.py
|
|
ARGS --model_path ${model_path}
|
|
--save_graph_dir ${save_graph_dir}
|
|
--save_graph_name ${save_graph_name})
|
|
endfunction()
|
|
|
|
if(WIN32)
|
|
list(REMOVE_ITEM TEST_OPS test_light_nas)
|
|
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
|
|
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50)
|
|
list(REMOVE_ITEM TEST_OPS test_weight_quantization_mobilenetv1)
|
|
endif()
|
|
|
|
if(LINUX AND WITH_MKLDNN)
|
|
|
|
#### Image classification dataset: ImageNet (small)
|
|
# The dataset should already be downloaded for INT8v2 unit tests
|
|
set(IMAGENET_DATA_PATH "${INFERENCE_DEMO_INSTALL_DIR}/imagenet/data.bin")
|
|
|
|
#### INT8 image classification python api test
|
|
# Models should be already downloaded for INT8v2 unit tests
|
|
|
|
set(INT8_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2")
|
|
set(INT8_IC_TEST_FILE "test_mkldnn_int8_quantization_strategy.py")
|
|
set(INT8_IC_TEST_FILE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/${INT8_IC_TEST_FILE}")
|
|
|
|
# googlenet int8
|
|
set(INT8_GOOGLENET_MODEL_DIR "${INT8_INSTALL_DIR}/googlenet")
|
|
inference_analysis_python_api_int8_test_custom_warmup_batch_size(test_slim_int8_googlenet ${INT8_GOOGLENET_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH} 10)
|
|
|
|
# mobilenet int8
|
|
set(INT8_MOBILENET_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv1")
|
|
inference_analysis_python_api_int8_test(test_slim_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
|
|
inference_analysis_python_api_int8_test_mkldnn(test_slim_int8_mobilenet_mkldnn ${INT8_MOBILENET_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
|
|
|
|
# temporarily adding WITH_SLIM_MKLDNN_FULL_TEST FLAG for QA testing the following UTs locally,
|
|
# since the following UTs cost too much time on CI test.
|
|
if (WITH_SLIM_MKLDNN_FULL_TEST)
|
|
# resnet50 int8
|
|
set(INT8_RESNET50_MODEL_DIR "${INT8_INSTALL_DIR}/resnet50")
|
|
inference_analysis_python_api_int8_test(test_slim_int8_resnet50 ${INT8_RESNET50_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
|
|
|
|
# mobilenetv2 int8
|
|
set(INT8_MOBILENETV2_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv2")
|
|
inference_analysis_python_api_int8_test(test_slim_int8_mobilenetv2 ${INT8_MOBILENETV2_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
|
|
|
|
# resnet101 int8
|
|
set(INT8_RESNET101_MODEL_DIR "${INT8_INSTALL_DIR}/resnet101")
|
|
inference_analysis_python_api_int8_test(test_slim_int8_resnet101 ${INT8_RESNET101_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
|
|
|
|
# vgg16 int8
|
|
set(INT8_VGG16_MODEL_DIR "${INT8_INSTALL_DIR}/vgg16")
|
|
inference_analysis_python_api_int8_test(test_slim_int8_vgg16 ${INT8_VGG16_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
|
|
|
|
# vgg19 int8
|
|
set(INT8_VGG19_MODEL_DIR "${INT8_INSTALL_DIR}/vgg19")
|
|
inference_analysis_python_api_int8_test(test_slim_int8_vgg19 ${INT8_VGG19_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
|
|
endif()
|
|
|
|
#### QAT FP32 & INT8 comparison python api tests
|
|
|
|
set(QAT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/qat")
|
|
|
|
### QATv1 for image classification
|
|
|
|
# QAT ResNet50
|
|
set(QAT_RESNET50_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet50_QAT")
|
|
set(QAT_RESNET50_MODEL_ARCHIVE "ResNet50_qat_model.tar.gz")
|
|
download_qat_model(${QAT_RESNET50_MODEL_DIR} ${QAT_RESNET50_MODEL_ARCHIVE})
|
|
inference_qat_int8_image_classification_test(test_qat_int8_resnet50_mkldnn ${QAT_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
|
|
|
|
# QAT ResNet101
|
|
set(QAT_RESNET101_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet101_QAT")
|
|
set(QAT_RESNET101_MODEL_ARCHIVE "ResNet101_qat_model.tar.gz")
|
|
download_qat_model(${QAT_RESNET101_MODEL_DIR} ${QAT_RESNET101_MODEL_ARCHIVE})
|
|
# inference_qat_int8_image_classification_test(test_qat_int8_resnet101_mkldnn ${QAT_RESNET101_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
|
|
|
|
# QAT GoogleNet
|
|
set(QAT_GOOGLENET_MODEL_DIR "${QAT_INSTALL_DIR}/GoogleNet_QAT")
|
|
set(QAT_GOOGLENET_MODEL_ARCHIVE "GoogleNet_qat_model.tar.gz")
|
|
download_qat_model(${QAT_GOOGLENET_MODEL_DIR} ${QAT_GOOGLENET_MODEL_ARCHIVE})
|
|
inference_qat_int8_image_classification_test(test_qat_int8_googlenet_mkldnn ${QAT_GOOGLENET_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
|
|
|
|
# QAT MobileNetV1
|
|
set(QAT_MOBILENETV1_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNetV1_QAT")
|
|
set(QAT_MOBILENETV1_MODEL_ARCHIVE "MobileNetV1_qat_model.tar.gz")
|
|
download_qat_model(${QAT_MOBILENETV1_MODEL_DIR} ${QAT_MOBILENETV1_MODEL_ARCHIVE})
|
|
inference_qat_int8_image_classification_test(test_qat_int8_mobilenetv1_mkldnn ${QAT_MOBILENETV1_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
|
|
|
|
# QAT MobileNetV2
|
|
set(QAT_MOBILENETV2_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNetV2_QAT")
|
|
set(QAT_MOBILENETV2_MODEL_ARCHIVE "MobileNetV2_qat_model.tar.gz")
|
|
download_qat_model(${QAT_MOBILENETV2_MODEL_DIR} ${QAT_MOBILENETV2_MODEL_ARCHIVE})
|
|
inference_qat_int8_image_classification_test(test_qat_int8_mobilenetv2_mkldnn ${QAT_MOBILENETV2_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
|
|
|
|
# QAT VGG16
|
|
set(QAT_VGG16_MODEL_DIR "${QAT_INSTALL_DIR}/VGG16_QAT")
|
|
set(QAT_VGG16_MODEL_ARCHIVE "VGG16_qat_model.tar.gz")
|
|
download_qat_model(${QAT_VGG16_MODEL_DIR} ${QAT_VGG16_MODEL_ARCHIVE})
|
|
# inference_qat_int8_image_classification_test(test_qat_int8_vgg16_mkldnn ${QAT_VGG16_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
|
|
|
|
# QAT VGG19
|
|
set(QAT_VGG19_MODEL_DIR "${QAT_INSTALL_DIR}/VGG19_QAT")
|
|
set(QAT_VGG19_MODEL_ARCHIVE "VGG19_qat_model.tar.gz")
|
|
download_qat_model(${QAT_VGG19_MODEL_DIR} ${QAT_VGG19_MODEL_ARCHIVE})
|
|
# inference_qat_int8_image_classification_test(test_qat_int8_vgg19_mkldnn ${QAT_VGG19_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
|
|
|
|
### QATv2 for image classification
|
|
|
|
set(QAT2_IC_OPS_TO_QUANTIZE "conv2d,pool2d")
|
|
|
|
# QAT2 ResNet50 with input/output scales in `fake_quantize_moving_average_abs_max` operators,
|
|
# with weight scales in `fake_dequantize_max_abs` operators
|
|
set(QAT2_RESNET50_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet50_qat_perf")
|
|
set(FP32_RESNET50_MODEL_DIR "${INT8_INSTALL_DIR}/resnet50")
|
|
set(QAT2_RESNET50_MODEL_ARCHIVE "ResNet50_qat_perf.tar.gz")
|
|
download_qat_model(${QAT2_RESNET50_MODEL_DIR} ${QAT2_RESNET50_MODEL_ARCHIVE})
|
|
inference_qat2_int8_image_classification_test(test_qat2_int8_resnet50_mkldnn ${QAT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float ${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH} ${QAT2_IC_OPS_TO_QUANTIZE})
|
|
|
|
# QAT2 ResNet50 with input/output scales in `fake_quantize_range_abs_max` operators and the `out_threshold` attributes,
|
|
# with weight scales in `fake_dequantize_max_abs` operators
|
|
set(QAT2_RESNET50_RANGE_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet50_qat_range")
|
|
set(QAT2_RESNET50_RANGE_MODEL_ARCHIVE "ResNet50_qat_range.tar.gz")
|
|
download_qat_model(${QAT2_RESNET50_RANGE_MODEL_DIR} ${QAT2_RESNET50_RANGE_MODEL_ARCHIVE})
|
|
inference_qat2_int8_image_classification_test(test_qat2_int8_resnet50_range_mkldnn ${QAT2_RESNET50_RANGE_MODEL_DIR}/ResNet50_qat_range ${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH} ${QAT2_IC_OPS_TO_QUANTIZE})
|
|
|
|
# QAT2 ResNet50 with input/output scales in `fake_quantize_range_abs_max` operators and the `out_threshold` attributes,
|
|
# with weight scales in `fake_channel_wise_dequantize_max_abs` operators
|
|
set(QAT2_RESNET50_CHANNELWISE_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet50_qat_channelwise")
|
|
set(QAT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE "ResNet50_qat_channelwise.tar.gz")
|
|
download_qat_model(${QAT2_RESNET50_CHANNELWISE_MODEL_DIR} ${QAT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE})
|
|
inference_qat2_int8_image_classification_test(test_qat2_int8_resnet50_channelwise_mkldnn ${QAT2_RESNET50_CHANNELWISE_MODEL_DIR}/ResNet50_qat_channelwise ${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH} ${QAT2_IC_OPS_TO_QUANTIZE})
|
|
|
|
# QAT2 MobileNetV1
|
|
set(QAT2_MOBILENETV1_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNet_qat_perf")
|
|
set(FP32_MOBILENETV1_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv1")
|
|
set(QAT2_MOBILENETV1_MODEL_ARCHIVE "MobileNet_qat_perf.tar.gz")
|
|
download_qat_model(${QAT2_MOBILENETV1_MODEL_DIR} ${QAT2_MOBILENETV1_MODEL_ARCHIVE})
|
|
inference_qat2_int8_image_classification_test(test_qat2_int8_mobilenetv1_mkldnn ${QAT2_MOBILENETV1_MODEL_DIR}/MobileNet_qat_perf/float ${FP32_MOBILENETV1_MODEL_DIR}/model ${IMAGENET_DATA_PATH} ${QAT2_IC_OPS_TO_QUANTIZE})
|
|
|
|
### QATv2 for NLP
|
|
|
|
set(NLP_DATA_ARCHIVE "Ernie_dataset.tar.gz")
|
|
set(NLP_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/Ernie_dataset")
|
|
set(NLP_DATA_PATH "${NLP_DATA_DIR}/Ernie_dataset/1.8w.bs1")
|
|
set(NLP_LABLES_PATH "${NLP_DATA_DIR}/Ernie_dataset/label.xnli.dev")
|
|
download_qat_data(${NLP_DATA_DIR} ${NLP_DATA_ARCHIVE})
|
|
|
|
# QAT2 Ernie
|
|
set(QAT2_ERNIE_MODEL_ARCHIVE "ernie_qat.tar.gz")
|
|
set(QAT2_ERNIE_MODEL_DIR "${QAT_INSTALL_DIR}/Ernie_qat")
|
|
download_qat_model(${QAT2_ERNIE_MODEL_DIR} ${QAT2_ERNIE_MODEL_ARCHIVE})
|
|
set(FP32_ERNIE_MODEL_ARCHIVE "ernie_fp32_model.tar.gz")
|
|
set(FP32_ERNIE_MODEL_DIR "${QAT_INSTALL_DIR}/Ernie_float")
|
|
download_qat_fp32_model(${FP32_ERNIE_MODEL_DIR} ${FP32_ERNIE_MODEL_ARCHIVE})
|
|
inference_qat2_int8_nlp_test(test_qat2_int8_ernie_mkldnn ${QAT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${FP32_ERNIE_MODEL_DIR}/ernie_fp32_model ${NLP_DATA_PATH} ${NLP_LABLES_PATH})
|
|
|
|
### Save QAT2 FP32 model or QAT2 INT8 model
|
|
|
|
set(QAT2_INT8_RESNET50_SAVE_PATH "${QAT_INSTALL_DIR}/ResNet50_qat2_int8")
|
|
set(QAT2_FP32_RESNET50_SAVE_PATH "${QAT_INSTALL_DIR}/ResNet50_qat2_fp32")
|
|
save_qat_ic_model_test(save_qat2_model_resnet50 ${QAT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float ${QAT2_FP32_RESNET50_SAVE_PATH} ${QAT2_INT8_RESNET50_SAVE_PATH} ${QAT2_IC_OPS_TO_QUANTIZE})
|
|
|
|
set(QAT2_INT8_ERNIE_SAVE_PATH "${QAT_INSTALL_DIR}/Ernie_qat2_int8")
|
|
set(QAT2_FP32_ERNIE_SAVE_PATH "${QAT_INSTALL_DIR}/Ernie_qat2_fp32")
|
|
save_qat_nlp_model_test(save_qat2_model_ernie ${QAT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${QAT2_FP32_ERNIE_SAVE_PATH} ${QAT2_INT8_ERNIE_SAVE_PATH})
|
|
|
|
# Convert QAT2 model to dot and pdf files
|
|
set(QAT2_INT8_ERNIE_DOT_SAVE_PATH "${QAT_INSTALL_DIR}/Ernie_qat2_int8_dot_file")
|
|
convert_model2dot_test(convert_model2dot_ernie ${QAT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${QAT2_INT8_ERNIE_DOT_SAVE_PATH} "Ernie_qat2_int8")
|
|
|
|
endif()
|
|
|
|
# Since the tests for QAT FP32 & INT8 comparison support only testing on Linux
|
|
# with MKL-DNN, we remove it here to not test it on other systems.
|
|
list(REMOVE_ITEM TEST_OPS
|
|
test_mkldnn_int8_quantization_strategy
|
|
qat_int8_image_classification_comparison
|
|
qat_int8_nlp_comparison)
|
|
|
|
#TODO(wanghaoshuang): Fix this unitest failed on GCC8.
|
|
LIST(REMOVE_ITEM TEST_OPS test_auto_pruning)
|
|
LIST(REMOVE_ITEM TEST_OPS test_filter_pruning)
|
|
foreach(src ${TEST_OPS})
|
|
py_test(${src} SRCS ${src}.py)
|
|
endforeach()
|