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Centerpose Retraining

  • To learn more about CenterPose look here

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/centerpose
docker build -t centerpose: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  centerpose: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
    `-- |-- images
        |---|-- train2017
        |---|---|-- *.jpg
        |---|-- val2017
        |---|---|-- *.jpg
        |---|-- test2017
        `---|---|-- *.jpg
    The path for the dataset can be configured in the .yaml file, e.g. centerpose/experiments/regnet_fpn.yaml
  2. Training:
    Configure your model in a .yaml file. We'll use /workspace/centerpose/experiments/regnet_fpn.yaml in this guide.
    start training with the following command:
    cd /workspace/centerpose/tools
python -m torch.distributed.launch --nproc_per_node 4 train.py --cfg ../experiments/regnet_fpn.yaml
    Where 4 is the number of GPUs used for training.
    If using a different number, update both this and the used gpus in the .yaml configuration.
  3. Exporting to onnx After training, run the following command:
    cd /workspace/centerpose/tools
python export.py --cfg ../experiments/regnet_fpn.yaml --TESTMODEL /workspace/out/regnet1_6/model_best.pth

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/centerpose_regnetx_1.6gf_fpn.yaml, and run compilation using the model zoo:

hailomz compile --ckpt coco_pose_regnet1.6_fpn.onnx --calib-path /path/to/calibration/imgs/dir/ --yaml path/to/centerpose_regnetx_1.6gf_fpn.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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