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YOLOX Retraining

  • To learn more about yolox look here

Prerequisites

Environment Preparations

  1. Build the docker image:
    cd hailo_model_zoo/training/yolox
docker build --build-arg timezone=`cat /etc/timezone` -t yolox: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.
  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 yolox: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.
    • yolox:v0 the name of the docker image.

Training and exporting to ONNX

  1. Prepare your data:
    You can use coco format, which is already supported for training on your own custom dataset. More information can be found here
  2. Training:
    Start training with the following command:
    python tools/train.py -f exps/default/yolox_s_leaky.py -d 8 -b 64 -c yolox_s.pth

                        exps/default/yolox_m_leaky.py
                        exps/default/yolox_l_leaky.py
                        exps/default/yolox_x_leaky.py
                        exps/default/yolox_s_wide_leaky.py

    • -f: experiment description file

    • -d: number of gpu devices

    • -b: total batch size, the recommended number for -b is num-gpu * 8

    • -c: path to pretrained weights which can be found in your working directory

      |_ yolox_s.pth
|_ yolox_m.pth
|_ yolox_l.pth
|_ yolox_x.pth
  3. Exporting to onnx:
    After finishing training run the following command:
    python tools/export_onnx.py --output-name yolox_s_leaky.onnx -f ./exps/default/yolox_s_leaky.py -c YOLOX_outputs/yolox_s_leaky/best_ckpt.pth
NOTE: Your trained model will be found under the following path: /workspace/YOLOX/YOLOX_outputs/yolox_s_leaky/, and the exported onnx will be written to /workspace/YOLOX/yolox_s_leaky.onnx

Compile the Model using Hailo Model Zoo

You can generate an HEF file for inference on Hailo-8 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/yolox_s_leaky.yaml, and run compilation using the model zoo:

hailomz compile --ckpt yolox_s_leaky.onnx --calib-path /path/to/calibration/imgs/dir/ --yaml path/to/yolox_s_leaky.yaml --start-node-names name1 name2 --end-node-names name1 --classes 80
  • --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).
  • --classes - adjusting the number of classes in post-processing configuration (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.