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_data install_dir url data_file check_sum)
  if(NOT EXISTS ${install_dir}/${data_file})
    inference_download_and_uncompress(${install_dir} ${url} ${data_file}
                                      ${check_sum})
  endif()
endfunction()

function(download_quant_data install_dir data_file check_sum)
  if(NOT EXISTS ${install_dir}/${data_file})
    inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8
                                      ${data_file} ${check_sum})
  endif()
endfunction()

function(download_quant_model install_dir data_file check_sum)
  if(NOT EXISTS ${install_dir}/${data_file})
    inference_download_and_uncompress(
      ${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file} ${check_sum})
  endif()
endfunction()

function(download_quant_fp32_model install_dir data_file check_sum)
  if(NOT EXISTS ${install_dir}/${data_file})
    inference_download_and_uncompress(
      ${install_dir} ${INFERENCE_URL}/int8/QAT_models/fp32 ${data_file}
      ${check_sum})
  endif()
endfunction()

function(download_lstm_model install_dir data_file check_sum)
  if(NOT EXISTS ${install_dir}/${data_file})
    inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/lstm
                                      ${data_file} ${check_sum})
  endif()
endfunction()

function(inference_quant_int8_image_classification_test target quant_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
         --quant_model
         ${quant_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_quant2_int8_image_classification_test target quant_model_dir
         fp32_model_dir dataset_path)
  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
         --quant_model
         ${quant_model_dir}
         --fp32_model
         ${fp32_model_dir}
         --infer_data
         ${dataset_path}
         --batch_size
         50
         --batch_num
         2
         --acc_diff_threshold
         0.1)
endfunction()

# set batch_size 10 for UT only (avoid OOM).
# For whole dataset, use batch_size 20
function(
  inference_quant2_int8_nlp_test
  target
  quant_model_dir
  fp32_model_dir
  dataset_path
  labels_path
  ops_to_quantize)
  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
         --quant_model
         ${quant_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
         --ops_to_quantize
         ${ops_to_quantize})
endfunction()

function(inference_quant2_int8_lstm_model_test target fp32_model quant_model
         dataset_path)
  py_test(
    ${target}
    SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_lstm_model.py"
         ARGS
         --fp32_model
         ${fp32_model}
         --quant_model
         ${quant_model}
         --infer_data
         ${dataset_path}
         --num_threads
         1
         --mkldnn_cache_capacity
         100
         --warmup_iter
         100
         --acc_diff_threshold
         0.11)
endfunction()

function(download_quant_data install_dir data_file check_sum)
  if(NOT EXISTS ${install_dir}/${data_file})
    inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8
                                      ${data_file} ${check_sum})
  endif()
endfunction()

function(download_quant_model install_dir data_file check_sum)
  if(NOT EXISTS ${install_dir}/${data_file})
    inference_download_and_uncompress(
      ${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file} ${check_sum})
  endif()
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_mnist)
  list(REMOVE_ITEM TEST_OPS test_post_training_quantization_while)
  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_post_training_quantization_program_resnet50)
  list(REMOVE_ITEM TEST_OPS test_post_training_quantization_lstm_model)
  list(REMOVE_ITEM TEST_OPS test_imperative_ptq)
  list(REMOVE_ITEM TEST_OPS test_weight_quantization_mobilenetv1)
  list(REMOVE_ITEM TEST_OPS test_imperative_qat_amp)
  list(REMOVE_ITEM TEST_OPS test_imperative_qat_lsq)
  list(REMOVE_ITEM TEST_OPS test_imperative_qat_matmul)

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")

  #### QUANT & INT8 comparison python api tests

  set(QUANT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/quant")

  ### Quant1 for image classification

  # Quant ResNet50
  set(QUANT_RESNET50_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet50_quant")
  set(QUANT_RESNET50_MODEL_ARCHIVE "ResNet50_qat_model.tar.gz")
  download_quant_model(
    ${QUANT_RESNET50_MODEL_DIR} ${QUANT_RESNET50_MODEL_ARCHIVE}
    ff89b934ab961c3a4a844193ece2e8a7)
  inference_quant_int8_image_classification_test(
    test_quant_int8_resnet50_mkldnn ${QUANT_RESNET50_MODEL_DIR}/model
    ${IMAGENET_DATA_PATH})

  # Quant ResNet101
  set(QUANT_RESNET101_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet101_quant")
  set(QUANT_RESNET101_MODEL_ARCHIVE "ResNet101_qat_model.tar.gz")
  download_quant_model(
    ${QUANT_RESNET101_MODEL_DIR} ${QUANT_RESNET101_MODEL_ARCHIVE}
    95c6d01e3aeba31c13efb2ba8057d558)
  # inference_quant_int8_image_classification_test( \
  #   test_quant_int8_resnet101_mkldnn \
  #   ${QUANT_RESNET101_MODEL_DIR}/model \
  #   ${IMAGENET_DATA_PATH})

  # Quant GoogleNet
  set(QUANT_GOOGLENET_MODEL_DIR "${QUANT_INSTALL_DIR}/GoogleNet_quant")
  set(QUANT_GOOGLENET_MODEL_ARCHIVE "GoogleNet_qat_model.tar.gz")
  download_quant_model(
    ${QUANT_GOOGLENET_MODEL_DIR} ${QUANT_GOOGLENET_MODEL_ARCHIVE}
    1d4a7383baa63e7d1c423e8db2b791d5)
  inference_quant_int8_image_classification_test(
    test_quant_int8_googlenet_mkldnn ${QUANT_GOOGLENET_MODEL_DIR}/model
    ${IMAGENET_DATA_PATH})

  # Quant MobileNetV1
  set(QUANT_MOBILENETV1_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV1_quant")
  set(QUANT_MOBILENETV1_MODEL_ARCHIVE "MobileNetV1_qat_model.tar.gz")
  download_quant_model(
    ${QUANT_MOBILENETV1_MODEL_DIR} ${QUANT_MOBILENETV1_MODEL_ARCHIVE}
    3b774d94a9fcbb604d09bdb731fc1162)
  inference_quant_int8_image_classification_test(
    test_quant_int8_mobilenetv1_mkldnn ${QUANT_MOBILENETV1_MODEL_DIR}/model
    ${IMAGENET_DATA_PATH})

  # Quant MobileNetV2
  set(QUANT_MOBILENETV2_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV2_quant")
  set(QUANT_MOBILENETV2_MODEL_ARCHIVE "MobileNetV2_qat_model.tar.gz")
  download_quant_model(
    ${QUANT_MOBILENETV2_MODEL_DIR} ${QUANT_MOBILENETV2_MODEL_ARCHIVE}
    758a99d9225d8b73e1a8765883f96cdd)
  inference_quant_int8_image_classification_test(
    test_quant_int8_mobilenetv2_mkldnn ${QUANT_MOBILENETV2_MODEL_DIR}/model
    ${IMAGENET_DATA_PATH})

  # Quant VGG16
  set(QUANT_VGG16_MODEL_DIR "${QUANT_INSTALL_DIR}/VGG16_quant")
  set(QUANT_VGG16_MODEL_ARCHIVE "VGG16_qat_model.tar.gz")
  download_quant_model(${QUANT_VGG16_MODEL_DIR} ${QUANT_VGG16_MODEL_ARCHIVE}
                       c37e63ca82a102f47be266f8068b0b55)
  # inference_quant_int8_image_classification_test( \
  #   test_quant_int8_vgg16_mkldnn \
  #   ${QUANT_VGG16_MODEL_DIR}/model \
  #   ${IMAGENET_DATA_PATH})

  # Quant VGG19
  set(QUANT_VGG19_MODEL_DIR "${QUANT_INSTALL_DIR}/VGG19_quant")
  set(QUANT_VGG19_MODEL_ARCHIVE "VGG19_qat_model.tar.gz")
  download_quant_model(${QUANT_VGG19_MODEL_DIR} ${QUANT_VGG19_MODEL_ARCHIVE}
                       62bcd4b6c3ca2af67e8251d1c96ea18f)
  # inference_quant_int8_image_classification_test( \
  #   test_quant_int8_vgg19_mkldnn ${QUANT_VGG19_MODEL_DIR}/model \
  #   ${IMAGENET_DATA_PATH})

  ### Quant2 for image classification

  # Quant2 ResNet50 with input/output scales in
  # `fake_quantize_moving_average_abs_max` operators,
  # with weight scales in `fake_dequantize_max_abs` operators
  set(QUANT2_RESNET50_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet50_quant2")
  set(QUANT2_RESNET50_MODEL_ARCHIVE "ResNet50_qat_perf.tar.gz")
  download_quant_model(
    ${QUANT2_RESNET50_MODEL_DIR} ${QUANT2_RESNET50_MODEL_ARCHIVE}
    e87309457e8c462a579340607f064d66)
  set(FP32_RESNET50_MODEL_DIR "${INT8_INSTALL_DIR}/resnet50")
  inference_quant2_int8_image_classification_test(
    test_quant2_int8_resnet50_mkldnn
    ${QUANT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float
    ${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})

  # Quant2 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(QUANT2_RESNET50_RANGE_MODEL_DIR
      "${QUANT_INSTALL_DIR}/ResNet50_quant2_range")
  set(QUANT2_RESNET50_RANGE_MODEL_ARCHIVE "ResNet50_qat_range.tar.gz")
  download_quant_model(
    ${QUANT2_RESNET50_RANGE_MODEL_DIR} ${QUANT2_RESNET50_RANGE_MODEL_ARCHIVE}
    2fdc8a139f041c0d270abec826b2d304)
  inference_quant2_int8_image_classification_test(
    test_quant2_int8_resnet50_range_mkldnn
    ${QUANT2_RESNET50_RANGE_MODEL_DIR}/ResNet50_qat_range
    ${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})

  # Quant2 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(QUANT2_RESNET50_CHANNELWISE_MODEL_DIR
      "${QUANT_INSTALL_DIR}/ResNet50_quant2_channelwise")
  set(QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE
      "ResNet50_qat_channelwise.tar.gz")
  download_quant_model(
    ${QUANT2_RESNET50_CHANNELWISE_MODEL_DIR}
    ${QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE}
    887a1b1b0e9a4efd10f263a43764db26)
  inference_quant2_int8_image_classification_test(
    test_quant2_int8_resnet50_channelwise_mkldnn
    ${QUANT2_RESNET50_CHANNELWISE_MODEL_DIR}/ResNet50_qat_channelwise
    ${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})

  # Quant2 MobileNetV1
  set(QUANT2_MOBILENETV1_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV1_quant2")
  set(QUANT2_MOBILENETV1_MODEL_ARCHIVE "MobileNet_qat_perf.tar.gz")
  download_quant_model(
    ${QUANT2_MOBILENETV1_MODEL_DIR} ${QUANT2_MOBILENETV1_MODEL_ARCHIVE}
    7f626e453db2d56fed6c2538621ffacf)
  set(FP32_MOBILENETV1_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv1")
  inference_quant2_int8_image_classification_test(
    test_quant2_int8_mobilenetv1_mkldnn
    ${QUANT2_MOBILENETV1_MODEL_DIR}/MobileNet_qat_perf/float
    ${FP32_MOBILENETV1_MODEL_DIR}/model ${IMAGENET_DATA_PATH})

  ### Quant2 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 "${NLP_DATA_DIR}/Ernie_dataset/label.xnli.dev")
  download_quant_data(${NLP_DATA_DIR} ${NLP_DATA_ARCHIVE}
                      e650ce0cbc1fadbed5cc2c01d4e734dc)

  # Quant2 Ernie
  set(QUANT2_ERNIE_MODEL_ARCHIVE "ernie_qat.tar.gz")
  set(QUANT2_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_quant2")
  download_quant_model(${QUANT2_ERNIE_MODEL_DIR} ${QUANT2_ERNIE_MODEL_ARCHIVE}
                       f7cdf4720755ecf66efbc8044e9922d9)
  set(FP32_ERNIE_MODEL_ARCHIVE "ernie_fp32_model.tar.gz")
  set(FP32_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_float")
  download_quant_fp32_model(${FP32_ERNIE_MODEL_DIR} ${FP32_ERNIE_MODEL_ARCHIVE}
                            114f38804a3ef8c45e7259e68bbd838b)
  set(QUANT2_ERNIE_OPS_TO_QUANTIZE
      "fc,reshape2,transpose2,matmul,elementwise_add,slice")
  inference_quant2_int8_nlp_test(
    test_quant2_int8_ernie_mkldnn ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float
    ${FP32_ERNIE_MODEL_DIR}/ernie_fp32_model ${NLP_DATA_PATH}
    ${NLP_LABLES_PATH} ${QUANT2_ERNIE_OPS_TO_QUANTIZE})

  # Quant2 GRU
  set(QUANT2_GRU_MODEL_DIR "${QUANT_INSTALL_DIR}/GRU_quant2")
  set(QUANT2_GRU_OPS_TO_QUANTIZE "multi_gru")

  # Quant2 LSTM
  set(QUANT2_LSTM_MODEL_ARCHIVE "lstm_quant.tar.gz")
  set(QUANT2_LSTM_MODEL_DIR "${QUANT_INSTALL_DIR}/lstm_quant_test")
  download_quant_model(${QUANT2_LSTM_MODEL_DIR} ${QUANT2_LSTM_MODEL_ARCHIVE}
                       40a693803b12ee9e251258f32559abcb)

  # Convert Quant2 model to dot and pdf files
  set(QUANT2_INT8_ERNIE_DOT_SAVE_PATH
      "${QUANT_INSTALL_DIR}/Ernie_quant2_int8_dot_file")
  convert_model2dot_test(
    convert_model2dot_ernie ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float
    ${QUANT2_INT8_ERNIE_DOT_SAVE_PATH} "Ernie_quant2_int8")

  ### PTQ INT8

  # PTQ int8 lstm model
  set(QUANT2_INT8_LSTM_SAVE_PATH "${QUANT_INSTALL_DIR}/lstm_quant2_int8")
  set(LSTM_DATA_FILE "quant_lstm_input_data.tar.gz")
  set(LSTM_URL "${INFERENCE_URL}/int8/unittest_model_data")
  download_data(${QUANT2_INT8_LSTM_SAVE_PATH} ${LSTM_URL} ${LSTM_DATA_FILE}
                add84c754e9b792fea1fbd728d134ab7)
  set(QUANT2_FP32_LSTM_MODEL_ARCHIVE "lstm_fp32_model.tar.gz")
  download_lstm_model(
    ${QUANT2_INT8_LSTM_SAVE_PATH} ${QUANT2_FP32_LSTM_MODEL_ARCHIVE}
    eecd9f44d69a84acc1cf2235c4b8b743)
  inference_quant2_int8_lstm_model_test(
    test_quant2_int8_lstm_mkldnn ${QUANT2_INT8_LSTM_SAVE_PATH}/lstm_fp32_model
    ${QUANT2_LSTM_MODEL_DIR}/lstm_quant
    ${QUANT2_INT8_LSTM_SAVE_PATH}/quant_lstm_input_data)

endif()

# Since the tests for Quant & 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
     quant_int8_image_classification_comparison quant_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)

# fix
if(WIN32)
  set(SINGLE_CARD_TEST_OPS
      test_user_defined_quantization
      test_quantization_scale_pass
      test_quantization_pass
      test_moving_average_abs_max_scale_op
      test_imperative_qat_channelwise
      test_imperative_qat
      test_imperative_qat_lsq
      test_imperative_qat_matmul
      test_imperative_out_scale
      test_graph)
  list(REMOVE_ITEM TEST_OPS ${SINGLE_CARD_TEST_OPS})
  foreach(src ${SINGLE_CARD_TEST_OPS})
    py_test(${src} SRCS ${src}.py ENVS CUDA_VISIBLE_DEVICES=0)
  endforeach()
endif()

foreach(src ${TEST_OPS})
  py_test(${src} SRCS ${src}.py)
endforeach()

# setting timeout value for old unittests
if(NOT WIN32)
  set_tests_properties(test_post_training_quantization_lstm_model
                       PROPERTIES TIMEOUT 120)
  set_tests_properties(test_post_training_quantization_program_resnet50
                       PROPERTIES TIMEOUT 240)
  set_tests_properties(test_post_training_quantization_mobilenetv1
                       PROPERTIES TIMEOUT 900 LABELS "RUN_TYPE=NIGHTLY")
  set_tests_properties(test_post_training_quantization_resnet50
                       PROPERTIES TIMEOUT 600 LABELS "RUN_TYPE=NIGHTLY")
  set_tests_properties(test_post_training_quantization_mnist PROPERTIES TIMEOUT
                                                                        150)
  set_tests_properties(test_post_training_quantization_while PROPERTIES TIMEOUT
                                                                        120)
  set_tests_properties(test_imperative_ptq PROPERTIES TIMEOUT 120)
  set_tests_properties(test_weight_quantization_mobilenetv1 PROPERTIES TIMEOUT
                                                                       120)
endif()

set_tests_properties(test_graph PROPERTIES TIMEOUT 120)
set_tests_properties(test_quantization_pass PROPERTIES TIMEOUT 120)
set_tests_properties(test_imperative_qat_channelwise PROPERTIES TIMEOUT 200)
set_tests_properties(test_user_defined_quantization PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_qat PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_qat_fuse PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_out_scale PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_qat_user_defined PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_qat_lsq PROPERTIES TIMEOUT 300)
set_tests_properties(test_imperative_qat_matmul PROPERTIES TIMEOUT 300)

if(LINUX AND WITH_MKLDNN)
  set_tests_properties(test_quant2_int8_mobilenetv1_mkldnn PROPERTIES TIMEOUT
                                                                      120)
  set_tests_properties(convert_model2dot_ernie PROPERTIES TIMEOUT 120)
  set_tests_properties(test_quant2_int8_resnet50_channelwise_mkldnn
                       PROPERTIES TIMEOUT 120)
  set_tests_properties(test_quant_int8_mobilenetv2_mkldnn PROPERTIES TIMEOUT
                                                                     120)
  set_tests_properties(test_quant2_int8_resnet50_range_mkldnn PROPERTIES TIMEOUT
                                                                         120)
  set_tests_properties(test_quant_int8_resnet50_mkldnn PROPERTIES TIMEOUT 120)
  set_tests_properties(test_quant_int8_mobilenetv1_mkldnn PROPERTIES TIMEOUT
                                                                     120)
  set_tests_properties(test_quant2_int8_ernie_mkldnn PROPERTIES TIMEOUT 120)
  set_tests_properties(test_quant_int8_googlenet_mkldnn PROPERTIES TIMEOUT 120)
  set_tests_properties(test_quant2_int8_resnet50_mkldnn PROPERTIES TIMEOUT 200)
  set_tests_properties(test_quant2_int8_lstm_mkldnn PROPERTIES TIMEOUT 120)
endif()

if(APPLE)
  set_tests_properties(test_post_training_quantization_mnist PROPERTIES TIMEOUT
                                                                        300)
  set_tests_properties(test_post_training_quantization_while PROPERTIES TIMEOUT
                                                                        300)
  set_tests_properties(test_imperative_ptq PROPERTIES TIMEOUT 300)
  set_tests_properties(test_imperative_skip_op PROPERTIES TIMEOUT 300)
endif()