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How to do regression test

This tutorial describes how to do regression test. The deployment configuration file contains codebase config and inference config.

1. Python Environment

pip install -r requirements/tests.txt

If pip throw an exception, try to upgrade numpy.

pip install -U numpy

2. Usage

python ./tools/regression_test.py \
    --codebase "${CODEBASE_NAME}" \
    --backends "${BACKEND}" \
    [--models "${MODELS}"] \
    --work-dir "${WORK_DIR}" \
    --device "${DEVICE}" \
    --log-level INFO \
    [--performance 或 -p] \
    [--checkpoint-dir "$CHECKPOINT_DIR"]

Description

  • --codebase : The codebase to test, eg.mmdet. If you want to test multiple codebase, use mmpretrain mmdet ...
  • --backends : The backend to test. By default, all backends would be tested. You can use onnxruntime tesensorrtto choose several backends. If you also need to test the SDK, you need to configure the sdk_config in tests/regression/${codebase}.yml.
  • --models : Specify the model to be tested. All models in yml are tested by default. You can also give some model names. For the model name, please refer to the relevant yml configuration file. For example ResNet SE-ResNet "Mask R-CNN". Model name can only contain numbers and letters.
  • --work-dir : The directory of model convert and report, use ../mmdeploy_regression_working_dir by default.
  • --checkpoint-dir: The path of downloaded torch model, use ../mmdeploy_checkpoints by default.
  • --device : device type, use cuda by default
  • --log-level : These options are available:'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'. The default value is INFO.
  • -p or --performance : Test precision or not. If not enabled, only model convert would be tested.

Notes

For Windows user:

  1. To use the && connector in shell commands, you need to download PowerShell 7 Preview 5+.
  2. If you are using conda env, you may need to change python3 to python in regression_test.py because there is python3.exe in %USERPROFILE%\AppData\Local\Microsoft\WindowsApps directory.

Example

  1. Test all backends of mmdet and mmpose for model convert and precision
python ./tools/regression_test.py \
    --codebase mmdet mmpose \
    --work-dir "../mmdeploy_regression_working_dir" \
    --device "cuda" \
    --log-level INFO \
    --performance
  1. Test model convert and precision of some backends of mmdet and mmpose
python ./tools/regression_test.py \
    --codebase mmdet mmpose \
    --backends onnxruntime tensorrt \
    --work-dir "../mmdeploy_regression_working_dir" \
    --device "cuda" \
    --log-level INFO \
    -p
  1. Test some backends of mmdet and mmpose, only test model convert
python ./tools/regression_test.py \
    --codebase mmdet mmpose \
    --backends onnxruntime tensorrt \
    --work-dir "../mmdeploy_regression_working_dir" \
    --device "cuda" \
    --log-level INFO
  1. Test some models of mmdet and mmpretrain, only test model convert
python ./tools/regression_test.py \
    --codebase mmdet mmpose \
    --models ResNet SE-ResNet "Mask R-CNN" \
    --work-dir "../mmdeploy_regression_working_dir" \
    --device "cuda" \
    --log-level INFO

3. Regression Test Tonfiguration

Example and parameter description

globals:
  codebase_dir: ../mmocr # codebase path to test
  checkpoint_force_download: False # whether to redownload the model even if it already exists
  images:
    img_densetext_det: &img_densetext_det ../mmocr/demo/demo_densetext_det.jpg
    img_demo_text_det: &img_demo_text_det ../mmocr/demo/demo_text_det.jpg
    img_demo_text_ocr: &img_demo_text_ocr ../mmocr/demo/demo_text_ocr.jpg
    img_demo_text_recog: &img_demo_text_recog ../mmocr/demo/demo_text_recog.jpg
  metric_info: &metric_info
    hmean-iou: # metafile.Results.Metrics
      eval_name: hmean-iou #  test.py --metrics args
      metric_key: 0_hmean-iou:hmean # the key name of eval log
      tolerance: 0.1 # tolerated threshold interval
      task_name: Text Detection # the name of metafile.Results.Task
      dataset: ICDAR2015 # the name of metafile.Results.Dataset
    word_acc: # same as hmean-iou, also a kind of metric
      eval_name: acc
      metric_key: 0_word_acc_ignore_case
      tolerance: 0.2
      task_name: Text Recognition
      dataset: IIIT5K
  convert_image_det: &convert_image_det # the image that will be used by detection model convert
    input_img: *img_densetext_det
    test_img: *img_demo_text_det
  convert_image_rec: &convert_image_rec
    input_img: *img_demo_text_recog
    test_img: *img_demo_text_recog
  backend_test: &default_backend_test True # whether test model precision for backend
  sdk: # SDK config
    sdk_detection_dynamic: &sdk_detection_dynamic configs/mmocr/text-detection/text-detection_sdk_dynamic.py
    sdk_recognition_dynamic: &sdk_recognition_dynamic configs/mmocr/text-recognition/text-recognition_sdk_dynamic.py

onnxruntime:
  pipeline_ort_recognition_static_fp32: &pipeline_ort_recognition_static_fp32
    convert_image: *convert_image_rec # the image used by model conversion
    backend_test: *default_backend_test # whether inference on the backend
    sdk_config: *sdk_recognition_dynamic # test SDK or not. If it exists, use a specific SDK config for testing
    deploy_config: configs/mmocr/text-recognition/text-recognition_onnxruntime_static.py # the deploy cfg path to use, based on mmdeploy path

  pipeline_ort_recognition_dynamic_fp32: &pipeline_ort_recognition_dynamic_fp32
    convert_image: *convert_image_rec
    backend_test: *default_backend_test
    sdk_config: *sdk_recognition_dynamic
    deploy_config: configs/mmocr/text-recognition/text-recognition_onnxruntime_dynamic.py

  pipeline_ort_detection_dynamic_fp32: &pipeline_ort_detection_dynamic_fp32
    convert_image: *convert_image_det
    deploy_config: configs/mmocr/text-detection/text-detection_onnxruntime_dynamic.py

tensorrt:
  pipeline_trt_recognition_dynamic_fp16: &pipeline_trt_recognition_dynamic_fp16
    convert_image: *convert_image_rec
    backend_test: *default_backend_test
    sdk_config: *sdk_recognition_dynamic
    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt-fp16_dynamic-1x32x32-1x32x640.py

  pipeline_trt_detection_dynamic_fp16: &pipeline_trt_detection_dynamic_fp16
    convert_image: *convert_image_det
    backend_test: *default_backend_test
    sdk_config: *sdk_detection_dynamic
    deploy_config: configs/mmocr/text-detection/text-detection_tensorrt-fp16_dynamic-320x320-2240x2240.py

openvino:
  # same as onnxruntime backend configuration
ncnn:
  # same as onnxruntime backend configuration
pplnn:
  # same as onnxruntime backend configuration
torchscript:
  # same as onnxruntime backend configuration


models:
  - name: crnn # model name
    metafile: configs/textrecog/crnn/metafile.yml # the path of model metafile, based on codebase path
    codebase_model_config_dir: configs/textrecog/crnn # the basepath of `model_configs`, based on codebase path
    model_configs: # the config name to teset
      - crnn_academic_dataset.py
    pipelines: # pipeline name
      - *pipeline_ort_recognition_dynamic_fp32

  - name: dbnet
    metafile: configs/textdet/dbnet/metafile.yml
    codebase_model_config_dir: configs/textdet/dbnet
    model_configs:
      - dbnet_r18_fpnc_1200e_icdar2015.py
    pipelines:
      - *pipeline_ort_detection_dynamic_fp32
      - *pipeline_trt_detection_dynamic_fp16

      # special pipeline can be added like this
      - convert_image: xxx
        backend_test: xxx
        sdk_config: xxx
        deploy_config: configs/mmocr/text-detection/xxx

4. Generated Report

This is an example of mmocr regression test report.

Model Model Config Task Checkpoint Dataset Backend Deploy Config Static or Dynamic Precision Type Conversion Result hmean-iou word_acc Test Pass
0 crnn ../mmocr/configs/textrecog/crnn/crnn_academic_dataset.py Text Recognition ../mmdeploy_checkpoints/mmocr/crnn/crnn_academic-a723a1c5.pth IIIT5K Pytorch - - - - - 80.5 -
1 crnn ../mmocr/configs/textrecog/crnn/crnn_academic_dataset.py Text Recognition ${WORK_DIR}/mmocr/crnn/onnxruntime/static/crnn_academic-a723a1c5/end2end.onnx x onnxruntime configs/mmocr/text-recognition/text-recognition_onnxruntime_dynamic.py static fp32 True - 80.67 True
2 crnn ../mmocr/configs/textrecog/crnn/crnn_academic_dataset.py Text Recognition ${WORK_DIR}/mmocr/crnn/onnxruntime/static/crnn_academic-a723a1c5 x SDK-onnxruntime configs/mmocr/text-recognition/text-recognition_sdk_dynamic.py static fp32 True - x False
3 dbnet ../mmocr/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py Text Detection ../mmdeploy_checkpoints/mmocr/dbnet/dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.pth ICDAR2015 Pytorch - - - - 0.795 - -
4 dbnet ../mmocr/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py Text Detection ../mmdeploy_checkpoints/mmocr/dbnet/dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.pth ICDAR onnxruntime configs/mmocr/text-detection/text-detection_onnxruntime_dynamic.py dynamic fp32 True - - True
5 dbnet ../mmocr/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py Text Detection ${WORK_DIR}/mmocr/dbnet/tensorrt/dynamic/dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597/end2end.engine ICDAR tensorrt configs/mmocr/text-detection/text-detection_tensorrt-fp16_dynamic-320x320-2240x2240.py dynamic fp16 True 0.793302 - True
6 dbnet ../mmocr/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py Text Detection ${WORK_DIR}/mmocr/dbnet/tensorrt/dynamic/dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597 ICDAR SDK-tensorrt configs/mmocr/text-detection/text-detection_sdk_dynamic.py dynamic fp16 True 0.795073 - True

5. Supported Backends

  • ONNX Runtime
  • TensorRT
  • PPLNN
  • ncnn
  • OpenVINO
  • TorchScript
  • SNPE
  • MMDeploy SDK

6. Supported Codebase and Metrics

Codebase Metric Support
mmdet bbox ✔️
segm ✔️
PQ ❌
mmpretrain accuracy ✔️
mmseg mIoU ✔️
mmpose AR ✔️
AP ✔️
mmocr hmean ✔️
acc ✔️
mmagic PSNR ✔️
SSIM ✔️