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Nanodet Retraining

  • To learn more about NanoDet 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/nanodet
docker build -t nanodet: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  nanodet:v0
    • docker run create a new docker container.
    • --name <your_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.
    • nanodet:v0 the name of the docker image.

Training and exporting to ONNX

  1. Prepare your data:
    Data is expected to be in coco format. More information can be found here
  2. Training:
    Configure your model in a .yaml file. We'll use /workspace/nanodet/config/legacy_v0.x_configs/RepVGG/nanodet-RepVGG-A0_416.yml in this guide.
    Modify the path for the dataset in the .yaml configuration file:
    data:
  train:
    name: CocoDataset
    img_path: <path-to-train-dir>
    ann_path: <path-to-annotations-file>
    ...
  val:
    name: CocoDataset
    img_path: <path-to-validation-dir>
    ann_path: <path-to-annotations-file>
    ...
    Start training with the following commands:
    cd /workspace/nanodet
ln -s /workspace/data/coco/ /coco
python tools/train.py ./config/legacy_v0.x_configs/RepVGG/nanodet-RepVGG-A0_416.yml
    In case you want to use the pretrained nanodet-RepVGG-A0_416.ckpt, which was predownloaded into your docker modify your configurationf file:
    schedule:
  load_model: ./pretrained/nanodet-RepVGG-A0_416.ckpt
    Modifying the batch size and the number of GPUs used for training can be done also in the configuration file:
    device:
  gpu_ids: [0]
  workers_per_gpu: 1
  batchsize_per_gpu: 128
  3. Exporting to onnx
    After training, install the ONNX and ONNXruntime packages, then export the ONNX model:
    python tools/export_onnx.py --cfg_path ./config/legacy_v0.x_configs/RepVGG/nanodet-RepVGG-A0_416.yml --model_path /workspace/nanodet/workspace/RepVGG-A0-416/model_last.ckpt

NOTE: Your trained model will be found under the following path: /workspace/nanodet/workspace/<backbone-name> /model_last.ckpt, and exported onnx will be written to /workspace/nanodet/nanodet.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/nanodet_repvgg.yaml, and run compilation using the model zoo:
hailomz compile --ckpt nanodet.onnx --calib-path /path/to/calibration/imgs/dir/ --yaml path/to/nanodet_repvgg.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

  • On your desired YAML file, change preprocessing.input_shape if changed on retraining.

More details about YAML files are presented here.