feat: Set up complete Python testing infrastructure with Poetry

[llbbl]

Add Python Testing Infrastructure

Summary

This PR sets up a complete testing infrastructure for the PyTorch Nested U-Net project using Poetry as the package manager and pytest as the testing framework.

Changes Made

Package Management

• Poetry Setup: Configured Poetry as the project's package manager via pyproject.toml
• Dependency Migration: Migrated all identified dependencies from the codebase analysis (torch, numpy, opencv-python, pandas, PyYAML, scikit-learn, albumentations, tqdm)
• Development Dependencies: Added pytest, pytest-cov, and pytest-mock as development dependencies

Testing Configuration

• pytest Configuration: Set up comprehensive pytest configuration in pyproject.toml including:

  • Test discovery patterns for test_*.py and *_test.py files
  • Coverage reporting with HTML and XML output formats
  • Custom markers for unit, integration, and slow tests
  • Strict marker enforcement and verbose output

• Coverage Configuration: Configured coverage.py with:

  • Source directory tracking
  • Exclusion patterns for test files, virtual environments, and build artifacts
  • Detailed reporting with line-level precision

Directory Structure

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

Shared Fixtures (conftest.py)

Created comprehensive fixtures for common testing needs:

• temp_dir: Temporary directory management
• sample_config: Configuration dictionary for testing
• config_file: YAML configuration file fixture
• sample_image / sample_mask: NumPy array fixtures
• sample_tensor_image / sample_tensor_mask: PyTorch tensor fixtures
• sample_batch: Batch data fixture
• mock_model: Simple neural network for testing
• device: GPU/CPU device detection
• reset_random_seeds: Reproducible test runs
• sample_dataset_dir: Mock dataset directory structure
• Additional utility fixtures for stdout capture and mock dataloaders

Development Scripts

Added Poetry scripts for test execution:

• poetry run test - Run all tests
• poetry run tests - Alternative command (both work)

Additional Setup

• Updated .gitignore with testing-related patterns and Claude settings
• Created validation tests to verify the infrastructure works correctly

Instructions for Running Tests

1. Install Poetry (if not already installed):

   curl -sSL https://install.python-poetry.org | python3 -

2. Install dependencies:

   poetry install

3. Run tests:

   poetry run test
   # or
   poetry run tests

4. Run specific test files:

   poetry run pytest tests/test_infrastructure.py

5. Run tests with specific markers:

   poetry run pytest -m unit        # Run only unit tests
   poetry run pytest -m integration # Run only integration tests
   poetry run pytest -m "not slow"  # Skip slow tests

6. View coverage report:

   • HTML report: Open htmlcov/index.html in a browser
   • XML report: Available at coverage.xml for CI integration

Notes

• The infrastructure is set up but does not include actual unit tests for the codebase
• Coverage threshold is currently set to 0% to allow the infrastructure validation tests to pass
• The project dependencies were inferred from import statements as the original requirements.txt only listed albumentations
• One validation test may fail due to missing system library (libGL.so.1) which is an environment issue, not a testing infrastructure issue

Next Steps

With this infrastructure in place, developers can now:

1. Write unit tests in the tests/unit/ directory
2. Write integration tests in the tests/integration/ directory
3. Use the provided fixtures for common testing scenarios
4. Adjust the coverage threshold in pyproject.toml as more tests are added