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


  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 MMPretrain-install, MMDetection-install, MMSegmentation-install, MMOCR-install, MMagic-install.


python ./tools/ \
    ${INPUT_IMG} \
    --test-img ${TEST_IMG} \
    --work-dir ${WORK_DIR} \
    --calib-dataset-cfg ${CALIB_DATA_CFG} \
    --device ${DEVICE} \
    --log-level INFO \
    --show \

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/mmpretrain/ is an example.

  • model_cfg : Model configuration for algorithm library, e.g. mmpretrain/configs/vision_transformer/, 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 the model conversion.

  • --test-img : The path of the image file that is used to test the model. If not specified, it will be set to None.

  • --work-dir : The path of the work directory that is used to save logs and models.

  • --calib-dataset-cfg : Only valid in int8 mode. The config used for calibration. If not specified, it will be set to None and use the “val” dataset in the 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 the model’s codebase folder in configs/. For converting a yolov3 model, you need to check configs/mmdet folder.

  2. Find the model’s task folder in configs/codebase_folder/. For a yolov3 model, you need to check configs/mmdet/detection folder.

  3. Find the deployment config file in configs/codebase_folder/task_folder/. For deploying a yolov3 model to the onnx backend, you could use configs/mmdet/detection/


python ./tools/ \
    configs/mmdet/detection/ \
    $PATH_TO_MMDET/configs/yolo/ \
    $PATH_TO_MMDET/checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.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

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