CONTRIBUTING.md

Contributing to awesome-depth-anything-3

Thank you for your interest in contributing! This document provides guidelines for contributing to this project.

Important Note

This is an optimized fork of Depth Anything 3 by ByteDance.

• Model/architecture changes should be proposed to the upstream repository
• Optimization/deployment improvements are welcome here

Development Setup

# Clone the repository
git clone https://github.com/Aedelon/awesome-depth-anything-3.git
cd awesome-depth-anything-3

# Install with development dependencies (using uv)
uv sync --extra dev

# Or with pip
pip install -e ".[dev]"

Running Tests

# Run all tests
uv run pytest tests/ -v

# Run specific test file
uv run pytest tests/test_adaptive_batching.py -v

# Run with coverage
uv run pytest tests/ --cov=src/depth_anything_3

Code Style

We use ruff for linting and formatting:

# Check for issues
uv run ruff check src/

# Auto-fix issues
uv run ruff check src/ --fix

# Format code
uv run ruff format src/

Pre-commit Hooks

We recommend using pre-commit hooks:

uv run pre-commit install
uv run pre-commit run --all-files

Pull Request Process

1. Fork the repository
2. Create a branch for your feature (git checkout -b feature/amazing-feature)
3. Make your changes with clear, descriptive commits
4. Run tests and linting
5. Update documentation if needed
6. Push to your fork and open a Pull Request

PR Guidelines

• Keep PRs focused on a single change
• Include tests for new functionality
• Update CHANGELOG.md for user-facing changes
• Ensure CI passes before requesting review

Types of Contributions Welcome

Highly Welcome

• Performance optimizations
• Bug fixes
• Documentation improvements
• Test coverage improvements
• CI/CD improvements
• Device compatibility (CUDA, MPS, CPU)

Discuss First

• New CLI commands
• API changes
• New dependencies

Redirect to Upstream

• Model architecture changes
• Training code changes
• New model variants

Reporting Issues

When reporting bugs, please include:

• Python version
• PyTorch version
• Device type (CUDA/MPS/CPU)
• Minimal reproduction code
• Full error traceback

License

By contributing, you agree that your contributions will be licensed under the Apache License 2.0.