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Python-SLAM Test Suite

This directory contains a comprehensive test suite for the Python-SLAM project.

Test Structure

Test Categories

  1. Comprehensive Tests (test_comprehensive.py)

    • Core SLAM functionality
    • System integration tests
    • Basic smoke tests

  2. GPU Acceleration Tests (test_gpu_acceleration.py)

    • GPU detection and management
    • CUDA, ROCm, and Metal backend testing
    • Accelerated operations validation
    • Performance benchmarking

  3. GUI Component Tests (test_gui_components.py)

    • PyQt6/PySide6 interface testing
    • Material Design styling
    • 3D visualization components
    • Control panels and metrics dashboard

  4. Benchmarking Tests (test_benchmarking.py)

    • Trajectory evaluation metrics (ATE, RPE)
    • Processing performance metrics
    • Dataset loading and validation
    • Benchmark report generation

  5. Integration Tests (test_integration.py)

    • Component interaction testing
    • Data pipeline validation
    • Performance monitoring
    • Error handling and recovery
    • Scalability testing

Running Tests

Quick Start

# Run all tests
python tests/run_tests.py

# Run specific test categories
python tests/run_tests.py --categories gpu benchmarking

# Run with coverage analysis
python tests/run_tests.py --coverage

# Check dependencies
python tests/run_tests.py --check-deps

Test Runner Options

python tests/run_tests.py [OPTIONS]

Options:
  --categories CATEGORIES   Test categories to run (comprehensive, gpu, gui, benchmarking, integration, all)
  --verbosity VERBOSITY     Test output verbosity (0, 1, 2)
  --coverage               Run with coverage analysis
  --performance            Run performance benchmarks
  --output OUTPUT          Output file for test report
  --check-deps             Only check dependencies and exit

Examples

# Run only GPU and benchmarking tests with high verbosity
python tests/run_tests.py --categories gpu benchmarking --verbosity 2

# Run all tests with coverage and save report
python tests/run_tests.py --coverage --output test_results.json

# Run performance benchmarks
python tests/run_tests.py --performance

# Check if all dependencies are available
python tests/run_tests.py --check-deps

Test Requirements

Core Dependencies

  • Python 3.8+
  • NumPy
  • PyTorch (for GPU acceleration tests)

Optional Dependencies

  • PyQt6 or PySide6 (for GUI tests)
  • Matplotlib (for visualization tests)
  • OpenCV (for computer vision tests)
  • psutil (for system monitoring tests)
  • coverage.py (for coverage analysis)

GPU Testing

  • NVIDIA GPU + CUDA (for CUDA tests)
  • AMD GPU + ROCm (for ROCm tests)
  • Apple Silicon (for Metal tests)

Note: GPU tests will automatically skip if the corresponding hardware/software is not available.

Test Output

Console Output

The test runner provides detailed console output including:

  • Dependency checking results
  • Test execution progress
  • Individual test results
  • Summary statistics
  • Performance metrics

Test Reports

  • JSON report with detailed results (test_report.json)
  • Coverage reports (if --coverage used)
  • Performance benchmark results

Example Output

==================== COMPREHENSIVE TESTS ====================
test_basic_slam_pipeline (__main__.TestPythonSLAMCore) ... ok
test_feature_extraction (__main__.TestPythonSLAMCore) ... ok
...

============================================================
TEST SUITE SUMMARY
============================================================
Total execution time: 45.23s
Total tests run: 89
Passed: 85
Failed: 2
Errors: 0
Skipped: 2
Success rate: 95.5%

Category breakdown:
  ✓ comprehensive    25 tests,  96.0% success,   8.45s
  ✓ gpu             18 tests, 100.0% success,  12.34s
  ✗ gui             15 tests,  86.7% success,   5.67s
  ✓ benchmarking    21 tests, 100.0% success,  11.23s
  ✓ integration     10 tests, 100.0% success,   7.54s

Continuous Integration

GitHub Actions

The test suite is designed to work with GitHub Actions. Example workflow:

name: Tests
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [3.8, 3.9, '3.10', 3.11]
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
        pip install -r requirements.txt
        pip install -r requirements-dev.txt
    - name: Run tests
      run: python tests/run_tests.py --coverage

Local Development

For local development, you can run tests automatically on file changes using tools like pytest-watch or watchdog.

Writing New Tests

Test Structure

Follow the existing test structure:

import unittest
import sys
import os

# Add src directory to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))

class TestNewComponent(unittest.TestCase):
    """Test new component functionality."""

    def setUp(self):
        """Set up test environment."""
        pass

    def tearDown(self):
        """Clean up test environment."""
        pass

    def test_component_functionality(self):
        """Test specific functionality."""
        try:
            from python_slam.new_component import NewComponent
            component = NewComponent()
            self.assertIsNotNone(component)
        except ImportError:
            self.skipTest("New component not available")

if __name__ == "__main__":
    unittest.main(verbosity=2)

Best Practices

  1. Use skipTest() for optional dependencies
  2. Clean up resources in tearDown()
  3. Use meaningful test names
  4. Test both success and failure cases
  5. Mock external dependencies when possible
  6. Include performance tests for critical paths

Adding Tests to Runner

To add new test categories, update the test_categories dictionary in run_tests.py:

self.test_categories = {
    "new_category": "test_new_category.py",
    # ... existing categories
}

Troubleshooting

Common Issues

  1. Import Errors

    • Ensure all dependencies are installed
    • Check Python path configuration
    • Verify src directory structure

  2. GPU Test Failures

    • Check GPU drivers and libraries
    • Verify CUDA/ROCm installation
    • Tests should skip gracefully if GPU unavailable

  3. GUI Test Failures

    • May require display server (use Xvfb on headless systems)
    • Check PyQt6/PySide6 installation
    • Some tests may need to be skipped in CI environments

  4. Memory Issues

    • Large datasets may cause memory issues
    • Reduce test data size if needed
    • Ensure proper cleanup in tearDown methods

Debug Mode

For debugging test failures, increase verbosity and run specific test files:

python -m unittest tests.test_gpu_acceleration.TestGPUDetector.test_cuda_detection -v

Contributing

When contributing new features:

  1. Write corresponding tests
  2. Ensure all existing tests pass
  3. Add integration tests for component interactions
  4. Update documentation if test structure changes
  5. Consider performance implications

Performance Monitoring

The test suite includes performance monitoring:

  • Execution time tracking
  • Memory usage monitoring
  • GPU utilization metrics
  • Benchmark comparisons

Use the --performance flag to run additional performance tests that measure system capabilities and identify potential bottlenecks.