feat: Add comprehensive Python testing infrastructure with Poetry

[llbbl]

Add Python Testing Infrastructure

Summary

This PR sets up a comprehensive testing infrastructure for the TensorFlow-Slim models project, providing developers with all the tools needed to write and run tests effectively.

Changes Made

Package Management

• Poetry Setup: Configured Poetry as the package manager via pyproject.toml
• Dependency Migration: Migrated from traditional setup.py to modern Poetry configuration
• Lock File: Poetry will generate a poetry.lock file for reproducible builds

Testing Dependencies

Added the following development dependencies:

• pytest ^7.4.0 - Modern Python testing framework
• pytest-cov ^4.1.0 - Coverage reporting plugin for pytest
• pytest-mock ^3.11.0 - Mocking utilities for pytest

Testing Configuration

Configured in pyproject.toml:

• Test Discovery: Configured to find test_*.py and *_test.py files
• Coverage Settings:
  • 80% minimum coverage threshold
  • HTML, XML, and terminal coverage reports
  • Excludes test files and setup files from coverage
• Custom Markers: Added unit, integration, and slow markers for test categorization
• Strict Mode: Enabled strict markers and verbose output

Directory Structure

Created organized testing structure:

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

Shared Fixtures (conftest.py)

Comprehensive fixtures for common testing needs:

• temp_dir - Temporary directory management
• mock_image_file - Creates test images
• mock_model_checkpoint - Mock TensorFlow checkpoints
• mock_tfrecord_file - Mock TFRecord files
• mock_labels_file - Label file generation
• mock_config - Configuration dictionaries
• mock_dataset_split - Dataset directory structures
• capture_logs - Log capture utilities
• mock_env_vars - Environment variable mocking
• cleanup_files - File cleanup tracking
• Auto-reset of TensorFlow graphs between tests

Development Commands

Configured Poetry scripts for convenience:

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

Both commands support all standard pytest options (e.g., -v, -k, -m, etc.)

Version Control

Added comprehensive .gitignore covering:

• Python build artifacts and caches
• Testing outputs (coverage reports, pytest cache)
• Virtual environments
• IDE files
• TensorFlow specific files (checkpoints, events, models)
• Claude AI settings

Usage Instructions

Initial Setup

# Install Poetry if not already installed
curl -sSL https://install.python-poetry.org | python3 -

# Install dependencies
poetry install

# Run validation tests
poetry run test tests/test_infrastructure_validation.py -v

Running Tests

# Run all tests
poetry run test

# Run with coverage report
poetry run test --cov-report=html

# Run specific test markers
poetry run test -m unit
poetry run test -m integration
poetry run test -m "not slow"

# Run specific test file
poetry run test tests/unit/test_example.py

Writing New Tests

1. Create test files following the naming convention: test_*.py or *_test.py
2. Place unit tests in tests/unit/ and integration tests in tests/integration/
3. Use the provided fixtures from conftest.py for common testing needs
4. Mark tests appropriately with @pytest.mark.unit, @pytest.mark.integration, or @pytest.mark.slow

Notes

• The infrastructure is ready for immediate use - developers can start writing tests right away
• Coverage reporting is configured but will show 0% until actual tests are written for the codebase
• The validation test file demonstrates all fixture usage and can serve as a reference
• TensorFlow dependencies are included based on the project requirements

Next Steps

1. Developers can now write unit tests for individual components
2. Integration tests can be added for end-to-end workflows
3. Consider adding pre-commit hooks to run tests automatically
4. Set up CI/CD pipeline to run tests on pull requests