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# 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](https://github.com/ByteDance-Seed/Depth-Anything-3) by ByteDance.
- **Model/architecture changes** should be proposed to the [upstream repository](https://github.com/ByteDance-Seed/Depth-Anything-3)
- **Optimization/deployment improvements** are welcome here
## Development Setup
```bash
# 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
```bash
# 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:
```bash
# 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:
```bash
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.
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