feat: Add comprehensive Python testing infrastructure with Poetry

[llbbl]

Add Comprehensive Python Testing Infrastructure

Summary

This PR establishes a modern testing infrastructure for the iTracker PyTorch implementation using Poetry as the package manager and pytest as the testing framework. The setup provides a solid foundation for writing unit and integration tests with comprehensive coverage reporting.

Changes Made

Package Management

• Poetry Setup: Created pyproject.toml with complete Poetry configuration
• Dependency Migration: Migrated all dependencies from requirements.txt to Poetry
• Development Dependencies: Added pytest, pytest-cov, and pytest-mock for testing

Testing Configuration

• pytest Configuration:
  • Configured test discovery patterns
  • Set up coverage reporting with 80% threshold
  • Added HTML and XML coverage report generation
  • Configured strict markers and verbose output
• Coverage Settings:
  • Excluded test files and common patterns from coverage
  • Added branch coverage tracking
  • Configured multiple report formats

Test Structure

• Directory Layout:

  pytorch/
  ├── tests/
  │   ├── __init__.py
  │   ├── conftest.py
  │   ├── test_setup_validation.py
  │   ├── test_infrastructure_validation.py
  │   ├── unit/
  │   │   └── __init__.py
  │   └── integration/
  │       └── __init__.py
  

Shared Fixtures (conftest.py)

Created comprehensive fixtures for testing iTracker components:

• temp_dir: Temporary directory management
• mock_config: Configuration dictionary for testing
• sample_image, sample_face_image, sample_eye_images: Test image generation
• sample_face_grid, sample_gaze_target: Mock tensor data
• mock_dataset_metadata: Dataset metadata for testing
• mock_model_checkpoint: Model checkpoint creation
• device: PyTorch device selection (CPU/CUDA)
• random_seed: Reproducible test execution
• mock_dataloader_batch: Mock DataLoader batch

Test Markers

• @pytest.mark.unit: For unit tests
• @pytest.mark.integration: For integration tests
• @pytest.mark.slow: For slow-running tests

Git Configuration

Updated .gitignore with:

• Poetry artifacts (poetry.lock, dist/, .venv/)
• Testing artifacts (.pytest_cache/, .coverage, htmlcov/, coverage.xml)
• Claude settings (.claude/*)
• Common Python/IDE patterns

How to Use

Running Tests

1. Install Poetry (if not already installed):

   pipx install poetry

2. Install dependencies:

   cd pytorch
   poetry install

3. Run tests:

   # Run all tests
   poetry run test
   # or
   poetry run tests
   
   # Run specific marker
   poetry run pytest -m unit
   poetry run pytest -m integration
   
   # Run with coverage report
   poetry run pytest --cov

Writing New Tests

1. Unit Tests: Place in tests/unit/ directory
2. Integration Tests: Place in tests/integration/ directory
3. Use fixtures from conftest.py for common test data
4. Add markers to categorize tests appropriately

Coverage Reports

• Terminal: Shown after each test run
• HTML: Generated in htmlcov/ directory
• XML: Generated as coverage.xml for CI integration

Notes

• The project uses older versions of PyTorch (1.1.0) and other dependencies as specified in the original requirements.txt
• Poetry is configured to work alongside the existing codebase without disrupting the current structure
• All testing infrastructure is contained within the pytorch/ directory
• The setup is designed to be immediately usable - developers can start writing tests right away

Next Steps

With this infrastructure in place, the team can now:

1. Write unit tests for individual components (ITrackerModel, ITrackerData)
2. Create integration tests for the full training/inference pipeline
3. Add performance benchmarks using the @pytest.mark.slow marker
4. Integrate coverage reporting into CI/CD pipelines