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How to convert model

This tutorial briefly introduces how to export an OpenMMlab model to a specific backend using MMDeploy tools. Notes:

How to convert models from Pytorch to other backends

Prerequisite

  1. Install and build your target backend. You could refer to ONNXRuntime-install, TensorRT-install, ncnn-install, PPLNN-install, OpenVINO-install for more information.
  2. Install and build your target codebase. You could refer to MMClassification-install, MMDetection-install, MMSegmentation-install, MMOCR-install, MMEditing-install.

Usage

python ./tools/deploy.py \
    ${DEPLOY_CFG_PATH} \
    ${MODEL_CFG_PATH} \
    ${MODEL_CHECKPOINT_PATH} \
    ${INPUT_IMG} \
    --test-img ${TEST_IMG} \
    --work-dir ${WORK_DIR} \
    --calib-dataset-cfg ${CALIB_DATA_CFG} \
    --device ${DEVICE} \
    --log-level INFO \
    --show \
    --dump-info

Description of all arguments

  • deploy_cfg : The deployment configuration of mmdeploy for the model, including the type of inference framework, whether quantize, whether the input shape is dynamic, etc. There may be a reference relationship between configuration files, mmdeploy/mmcls/classification_ncnn_static.py is an example.
  • model_cfg : Model configuration for algorithm library, e.g. mmclassification/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py, regardless of the path to mmdeploy.
  • checkpoint : torch model path. It can start with http/https, see the implementation of mmcv.FileClient for details.
  • img : The path to the image or point cloud file used for testing during model conversion.
  • --test-img : The path of image file that used to test model. If not specified, it will be set to None.
  • --work-dir : The path of work directory that used to save logs and models.
  • --calib-dataset-cfg : Only valid in int8 mode. Config used for calibration. If not specified, it will be set to None and use "val" dataset in model config for calibration.
  • --device : The device used for model conversion. If not specified, it will be set to cpu, for trt use cuda:0 format.
  • --log-level : To set log level which in 'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'. If not specified, it will be set to INFO.
  • --show : Whether to show detection outputs.
  • --dump-info : Whether to output information for SDK.

How to find the corresponding deployment config of a PyTorch model

  1. Find model's codebase folder in configs/. Example, convert a yolov3 model you need to find configs/mmdet folder.
  2. Find model's task folder in configs/codebase_folder/. Just like yolov3 model, you need to find configs/mmdet/detection folder.
  3. Find deployment config file in configs/codebase_folder/task_folder/. Just like deploy yolov3 model you can use configs/mmdet/detection/detection_onnxruntime_dynamic.py.

Example

python ./tools/deploy.py \
    configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
    $PATH_TO_MMDET/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
    $PATH_TO_MMDET/checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.pth \
    $PATH_TO_MMDET/demo/demo.jpg \
    --work-dir work_dir \
    --show \
    --device cuda:0

How to evaluate the exported models

You can try to evaluate model, referring to how_to_evaluate_a_model.

List of supported models exportable to other backends

Refer to Support model list