feat: Set up comprehensive Python testing infrastructure

[llbbl]

Set up Python Testing Infrastructure

Summary

This PR establishes a comprehensive testing infrastructure for the Deep Supervision project, providing a ready-to-use testing environment with modern Python tooling and best practices.

Changes Made

Package Management

• Poetry Setup: Added pyproject.toml with Poetry configuration as the primary package manager
• Dependencies: Configured core dependencies (PyTorch, torchvision, numpy, Pillow) and testing dependencies
• Dependency Groups: Organized testing dependencies in a separate [tool.poetry.group.test.dependencies] section

Testing Framework Configuration

• pytest Configuration: Comprehensive pytest settings in pyproject.toml including:
  • Test discovery patterns (test_*.py, *_test.py)
  • Custom markers: unit, integration, slow
  • Strict configuration options for robust testing
  • Verbose output formatting

Coverage Reporting

• Coverage Settings: 80% coverage threshold with multiple report formats
• HTML Reports: Generated in htmlcov/ directory for detailed coverage visualization
• XML Reports: coverage.xml for CI/CD integration
• Source Configuration: Covers cifar/ and fine-grained/ modules
• Exclusions: Properly excludes test files, migrations, and environment directories

Directory Structure

tests/
├── __init__.py
├── conftest.py              # Shared fixtures and test configuration  
├── test_infrastructure.py   # Validation tests
├── unit/
│   └── __init__.py
└── integration/
    └── __init__.py

Shared Test Fixtures (conftest.py)

Comprehensive fixtures tailored for machine learning and computer vision testing:

• File System: temp_dir, temp_file, sample_image_paths
• PyTorch Tensors: sample_tensor, sample_labels, small_image_batch
• Dataset Fixtures: cifar_like_batch, imagenet_like_batch
• Configuration Mocks: mock_dataset_config, mock_model_config, mock_training_config
• Deterministic Testing: Automatic seed setting for reproducible tests
• Training Utilities: mock_checkpoint_data, mock_loss_history

Development Environment

• .gitignore: Comprehensive exclusions for Python, PyTorch, testing artifacts, and development tools
• Deterministic Testing: Configured automatic seeding for PyTorch and NumPy
• IDE Support: Excluded common IDE files and directories

Testing Commands

After this setup, developers can immediately start testing with:

# Install dependencies
poetry install

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov

# Run specific test types  
poetry run pytest -m unit        # Unit tests only
poetry run pytest -m integration # Integration tests only  
poetry run pytest -m slow        # Slow tests only

# Run without coverage (for faster iteration)
poetry run pytest --no-cov

# Generate coverage reports
poetry run pytest --cov --cov-report=html

Validation

• ✅ All dependencies install successfully
• ✅ pytest discovers and runs tests correctly
• ✅ Coverage reporting generates HTML and XML reports
• ✅ Custom markers work for test filtering
• ✅ All shared fixtures are functional
• ✅ Deterministic testing environment verified
• ✅ Testing directory structure follows best practices

Infrastructure Features

Ready for ML/CV Development

• PyTorch and torchvision fixtures for tensor operations
• CIFAR-10 and ImageNet-like batch fixtures
• Deterministic behavior for reproducible tests
• GPU/CPU device handling fixtures

Extensible Architecture

• Modular fixture design in conftest.py
• Separate unit and integration test directories
• Configurable coverage thresholds and reporting
• Custom markers for test categorization

Developer Experience

• Clear separation of test types
• Comprehensive test examples in validation suite
• Mock utilities with pytest-mock integration
• Temporary file/directory management

Notes

• Coverage Threshold: Set to 80% - adjust in pyproject.toml if needed
• Lock File: poetry.lock is tracked for reproducible dependency resolution
• Python Version: Configured for Python ^3.8 compatibility
• Framework Agnostic: Infrastructure supports any Python testing approach

The testing infrastructure is now ready for immediate use. Developers can start writing unit and integration tests using the provided fixtures and configuration.

🤖 Generated with Claude Code