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MSPN Retraining

Prerequisites

NOTE: In case you are using the Hailo Software Suite docker, make sure to run all of the following instructions outside of that docker.

Environment Preparations

  1. Build the docker image:

    cd hailo_model_zoo/training/mspn
docker build -t mspn:v0 --build-arg timezone=`cat /etc/timezone` .
    the following optional arguments can be passed via --build-arg:
    • timezone - a string for setting up timezone. E.g. "Asia/Jerusalem"
    • user - username for a local non-root user. Defaults to 'hailo'.
    • group - default group for a local non-root user. Defaults to 'hailo'.
    • uid - user id for a local non-root user.
    • gid - group id for a local non-root user.

  2. Start your docker:

    docker run --name "your_docker_name" -it --gpus all -u "username" --ipc=host -v /path/to/local/data/dir:/path/to/docker/data/dir  mspn:v0
    • docker run create a new docker container.
    • --name <your_docker_name> name for your container.
    • -it runs the command interactively.
    • --gpus all allows access to all GPUs.
    • --ipc=host sets the IPC mode for the container.
    • -v /path/to/local/data/dir:/path/to/docker/data/dir maps /path/to/local/data/dir from the host to the container. You can use this command multiple times to mount multiple directories.
    • centerpose:v0 the name of the docker image.

Training and exporting to ONNX

  1. Prepare your data:
    Data is expected to be in coco format, and by default should be in /workspace/data/<dataset_name>.
    The expected structure is as follows:
    /workspace
|-- data
`-- |-- coco
    `-- |-- annotations
        |   |-- instances_train2017.json
        |   |-- instances_val2017.json
        |   |-- person_keypoints_train2017.json
        |   |-- person_keypoints_val2017.json
        |   |-- image_info_test-dev2017.json

    `---|-- train2017
    |---|---|-- *.jpg
    `---|-- val2017
    |---|---|-- *.jpg
    `---|-- test2017
    `---|---|-- *.jpg

    The path for the dataset can be configured in the .py config file, e.g. configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/regnetx_800mf_256x192.py

  2. Training:

    Configure your model in a .py config file. We will use /workspace/mmpose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/regnetx_800mf_256x192.py in this guide. Start training with the following command:

    cd /workspace/mmpose
./tools/dist_train.sh ./configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/regnetx_800mf_256x192.py 4 --work-dir exp0

    Where 4 is the number of GPUs used for training. In this example, the trained model will be saved under exp0 directory.

  3. Export to onnx

    In order to export your trained model to ONNX run the following script:

    cd /workspace/mmpose
python tools/deployment/pytorch2onnx.py ./configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/regnetx_800mf_256x192.py exp0/best_AP_epoch_310.pth --output-file mspn_regnetx_800mf.onnx

    where exp0/best_AP_epoch_310.pth should be replaced by the trained model file path.

Compile the Model using Hailo Model Zoo

You can generate an HEF file for inference on Hailo-10H from your trained ONNX model.
In order to do so you need a working model-zoo environment.
Choose the corresponding YAML from our networks configuration directory, i.e. hailo_model_zoo/cfg/networks/mspn_regnetx_800mf.yaml, and run compilation using the model zoo:
hailomz compile --ckpt mspn_regnetx_800mf.onnx --calib-path /path/to/calibration/imgs/dir/ --yaml path/to/mspn_regnetx_800mf.yaml --start-node-names name1 name2 --end-node-names name1
  • --ckpt - path to your ONNX file.
  • --calib-path - path to a directory with your calibration images in JPEG/png format
  • --yaml - path to your configuration YAML file.
  • --start-node-names and --end-node-names - node names for customizing parsing behavior (optional).
  • The model zoo will take care of adding the input normalization to be part of the model.

Note

More details about YAML files are presented here.
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