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FCN Retraining

  • To learn more about FCN 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/fcn
docker build -t fcn: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  fcn:v0
    • docker run create a new docker container.
    • --name <docker-name> name for your container.
    • -u <username> same username as used for building the image.
    • -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.
    • fcn: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
|-- mmsegmentation
`-- |-- data
        `-- cityscapes
            |-- gtFine
            |   | -- train
            |   |    | -- aachem
            |   |    | -- | -- *.png
            |   |    ` -- ...
            |   ` -- test
            |        | -- berlin
            |        | -- | -- *.png
            |        ` -- ...
            `-- leftImg8bit
                | -- train
                | -- | -- aachem
                | -- | -- | -- *.png
                | -- ` -- ...
                ` -- test
                     | -- berlin
                     | -- | -- *.png
                     ` -- ...
    more information can be found here
  2. Training:
    Configure your model in a .py file. We'll use /workspace/mmsegmentation/configs/fcn/fcn8_r18_hailo.py in this guide.
    start training with the following command:
    cd /workspace/mmsegmentation
./tools/dist_train.sh configs/fcn/fcn8_r18_hailo.py 2
    Where 2 is the number of GPUs used for training.
  3. Exporting to onnx
    After training, run the following command:
    cd /workspace/mmsegmentation
python ./tools/pytorch2onnx.py configs/fcn/fcn8_r18_hailo.py --checkpoint ./work_dirs/fcn8_r18_hailo/iter_59520.pth --shape 1024 1920 --out_name fcn.onnx

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/fcn8_resnet_v1_18.yaml, and run compilation using the model zoo:
hailomz compile --ckpt fcn.onnx --calib-path /path/to/calibration/imgs/dir/ --yaml path/to/fcn8_resnet_v1_18.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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