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DAMO-YOLO 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/damoyolo
docker build --build-arg timezone=`cat /etc/timezone` -t damoyolo:v0 .
    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.
    * This command will build the docker image with the necessary requirements using the Dockerfile exists in damoyolo directory.
  2. Start your docker:
    docker run --name "your_docker_name" -it --gpus all --ipc=host -v  /path/to/local/data/dir:/path/to/docker/data/dir damoyolo: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.
    • damoyolo:v0 the name of the docker image.

Training and exporting to ONNX

  1. Train your model:
    Once the docker is started, you can start training your model.
    • Prepare your custom dataset (must be coco format) - Follow the steps described here.
    • Modify num_classes and class_names in the configuration file, for example damoyolo_tinynasL20_T.py
    • Use self.train.batch_size / self.train.total_epochs in the configuration file to modify the batch_size and number of epochs
    • Update the symbolic link to your dataset: ln -sfn /your/coco/like/dataset/path datasets/coco
    • Start training - The following command is an example for training a damoyolo_tinynasL20_T model.
      python tools/train.py -f configs/damoyolo_tinynasL20_T.py

                        configs/damoyolo_tinynasL25_S.py
                        configs/damoyolo_tinynasL35_M.py
      • configs/damoyolo_tinynasL20_T.py - configuration file of the DAMO-YOLO variant you would like to train. In order to change the number of classes make sure you update num_classes and class_names in this file.
  2. Export to ONNX:
    In order to export your trained DAMO-YOLO model to ONNX run the following script:
    python tools/converter.py -f configs/damoyolo_tinynasL20_T.py -c /path/to/trained/model.pth --batch_size 1 --img_size 640 # export at 640x640 with batch size 1

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/damoyolo_tinynasL20_T.yaml, and run compilation using the model zoo:
hailomz compile --ckpt damoyolo_tinynasL20_T.onnx --calib-path /path/to/calibration/imgs/dir/ --yaml path/to/damoyolo/variant.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.

    More details about YAML files are presented here.
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