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Arface Retraining

  • To learn more about arcface look here

Prerequisites

Environment Preparations

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

Training and exporting to ONNX

  1. Prepare your data:
    For more information on obtraining datasets here
    The repository supports the following formats:
    1. ImageFolder dataset - each class (person) has its own directory
      Validation data is packed .bin files
      data_dir/
├── agedb_30.bin
├── cfp_fp.bin
├── lfw.bin
├── person0/
├── person1/
├── ...
└── personlast/
    2. MxNetRecord - train.rec and train.idx files. This is the format of insightface datasets.
      Validation data is packed .bin files
      data_dir/
├── agedb_30.bin
├── cfp_fp.bin
├── lfw.bin
├── train.idx
└── train.rec
  2. Training:
    Start training with the following command:
    python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12581 train_v2.py /path/to/config
    • nproc_per_node: number of gpu devices
  3. Exporting to onnx:
    After finishing training run the following command:
    python torch2onnx.py /path/to/model.pt --network mbf --output /path/to/model.onnx --simplify true

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/arcface_mobilefacenet.yaml, and run compilation using the model zoo:

hailomz compile --ckpt arcface_s_leaky.onnx --calib-path /path/to/calibration/imgs/dir/ --yaml /path/to/arcface_mobilefacenet.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.