docker-compose.yml

# Docker Compose config for local ONNX -> RKNN conversion
# Uses the same image as the GitHub Actions workflow

services:
  model-converter:
    container_name: rknn-converter

    build:
      context: .
      dockerfile: Dockerfile

    working_dir: /workspace

    # Mount directories for input and output models
    volumes:
      - ./input_models:/workspace/input_models
      - ./output_models:/workspace/output_models

    # Example conversion command
    # Add/replace parameters with your own:
    #  --model_source: model URL or local fiile name (should be in ./input_models/)
    #  --resolutions: with your required resolutions (hardcoded defaults are used if not specified)
    #  --target_platform: platform to convert for (default: rk3566)
    #  --verbose or -v: for detailed logs
    command: >
      --model_source Real-ESRGAN-x2plus.onnx
      -v

# --- How to use ---

# 1. For a local ONNX file:
#    - Place your ONNX model in ./input_models/
#    - Change model.onnx to your model name in command:
#      --model_source my_model.onnx
#    - Run: docker-compose up

# 2. For downloading from URL:
#    - Specify URL in model_source:
#      --model_source https://example.com/model.onnx
#    - Run: docker-compose up
#    (The script will download the model automatically)

# 3. Command examples:
#
#    # Real-ESRGAN x4 (1440p)
#    command: >
#      --model_source https://huggingface.co/qualcomm/Real-ESRGAN-x4plus/resolve/main/Real-ESRGAN-x4plus.onnx
#      --resolutions 1440x320,1440x384,1440x404
#
#    # Real-ESRGAN x2 (1536p)
#    command: >
#      --model_source Real-ESRGAN_x2plus.onnx
#      --resolutions 1536x448,1536x512,1536x576

# 4. Results:
#    - Converted models will appear in ./output_models/
#    - Names will include the resolution: model_dfp_WxH.rknn

# Note:
# If you need to use an image from a private registry, add:
#
#    image: ghcr.io/your-username/rknn-converter:latest
#    credentials:
#      username: ${GITHUB_USER}
#      password: ${GITHUB_TOKEN}