gcpds-cv-pykit 0.1.0.30

A comprehensive toolkit for computer vision and segmentation tasks

GCPDS Computer Vision Python Kit

A comprehensive toolkit for computer vision and segmentation tasks, developed by the GCPDS Team. This package provides state-of-the-art tools for training, evaluating, and deploying segmentation models with support for various architectures, loss functions, and performance metrics.

🚀 Features

• Segmentation Models: Support for UNet and other popular architectures
• Multiple Loss Functions: DICE, Cross Entropy, Focal Loss, and Tversky Loss
• Performance Evaluation: Comprehensive metrics including Dice, Jaccard, Sensitivity, and Specificity
• Training Pipeline: Complete training workflow with validation and monitoring
• Experiment Tracking: Integration with Weights & Biases (wandb)
• Mixed Precision Training: Automatic Mixed Precision (AMP) support for faster training
• Flexible Configuration: YAML/JSON-based configuration system
• Visualization Tools: Built-in visualization utilities for model predictions
• Memory Management: Efficient memory handling and cleanup utilities

📋 Requirements

• Python >= 3.8
• PyTorch >= 2.0.0
• CUDA-compatible GPU (recommended)

🔧 Installation

From PyPI (when available)

pip install gcpds-cv-pykit

From Source

git clone https://github.com/UN-GCPDS/gcpds-cv-pykit.git
cd gcpds-cv-pykit
pip install -e .

Development Installation

git clone https://github.com/UN-GCPDS/gcpds-cv-pykit.git
cd gcpds-cv-pykit
pip install -e ".[dev,docs]"

📦 Dependencies

Core Dependencies

• torch>=2.0.0 - Deep learning framework
• torchvision>=0.15.0 - Computer vision utilities
• numpy>=1.21.0 - Numerical computing
• opencv-python>=4.6.0 - Image processing
• matplotlib>=3.5.0 - Plotting and visualization
• wandb>=0.15.0 - Experiment tracking
• tqdm>=4.64.0 - Progress bars
• Pillow>=9.0.0 - Image handling
• scipy>=1.9.0 - Scientific computing
• pandas>=1.4.0 - Data manipulation

Optional Dependencies

• Development: pytest, black, flake8, isort
• Documentation: sphinx, sphinx-rtd-theme

🏗️ Project Structure

gcpds_cv_pykit/
├── baseline/
│   ├── trainers/           # Training utilities
│   ├── models/             # Model architectures
│   ├── losses/             # Loss functions
│   ├── dataloaders/        # Data loading utilities
│   └── performance_model.py # Model evaluation
├── crowd/                  # Crowd-specific implementations
├── datasets/               # Dataset utilities
└── visuals/               # Visualization tools

🚀 Quick Start

Basic Training Example

from gcpds_cv_pykit.baseline.trainers import SegmentationModel_Trainer
from torch.utils.data import DataLoader

# Define your configuration
config = {
    'Model': 'UNet',
    'Backbone': 'resnet34',
    'Number of classes': 2,
    'Loss Function': 'DICE',
    'Optimizer': 'Adam',
    'Learning Rate': 0.001,
    'Epochs': 100,
    'Batch Size': 8,
    'Input size': [3, 256, 256],
    'AMP': True,
    'Device': 'cuda'
}

# Initialize trainer
trainer = SegmentationModel_Trainer(
    train_loader=train_dataloader,
    valid_loader=valid_dataloader,
    config=config
)

# Start training
trainer.start()

Model Evaluation Example

from gcpds_cv_pykit.baseline import PerformanceModels

# Evaluate trained model
evaluator = PerformanceModels(
    model=trained_model,
    test_dataset=test_dataloader,
    config=config,
    save_results=True
)

Custom Loss Function

from gcpds_cv_pykit.baseline.losses import DICELoss, FocalLoss

# DICE Loss
dice_loss = DICELoss(smooth=1.0, reduction='mean')

# Focal Loss
focal_loss = FocalLoss(alpha=0.25, gamma=2.0, reduction='mean')

📊 Supported Models

• UNet: Classic U-Net architecture with various backbone options
  • Backbones: ResNet, EfficientNet, and more
  • Pretrained weights support
  • Customizable activation functions

🎯 Loss Functions

• DICE Loss: Optimized for segmentation tasks
• Cross Entropy: Standard classification loss
• Focal Loss: Addresses class imbalance
• Tversky Loss: Generalization of Dice loss

📈 Metrics

The toolkit provides comprehensive evaluation metrics:

• Dice Coefficient: Overlap-based similarity measure
• Jaccard Index (IoU): Intersection over Union
• Sensitivity (Recall): True positive rate
• Specificity: True negative rate

All metrics are calculated both globally and per-class.

🔧 Configuration

The toolkit uses dictionary-based configuration. Key parameters include:

config = {
    # Model Configuration
    'Model': 'UNet',
    'Backbone': 'resnet34',
    'Pretrained': True,
    'Number of classes': 2,
    'Input size': [3, 256, 256],
    
    # Training Configuration
    'Loss Function': 'DICE',
    'Optimizer': 'Adam',
    'Learning Rate': 0.001,
    'Epochs': 100,
    'Batch Size': 8,
    
    # Advanced Options
    'AMP': True,  # Automatic Mixed Precision
    'Device': 'cuda',
    'Wandb monitoring': ['api_key', 'project_name', 'run_name']
}

📊 Experiment Tracking

Integration with Weights & Biases for experiment tracking:

config['Wandb monitoring'] = [
    'your_wandb_api_key',
    'your_project_name',
    'experiment_name'
]

🎨 Visualization

Built-in visualization tools for:

• Training/validation curves
• Model predictions vs ground truth
• Metric evolution across epochs
• Sample predictions

🧪 Testing

Run the test suite:

# Run all tests
pytest

# Run with coverage
pytest --cov=gcpds_cv_pykit

# Run specific test file
pytest tests/test_models.py

📚 Documentation

Build documentation locally:

cd docs
make html

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

# Clone the repository
git clone https://github.com/UN-GCPDS/gcpds-cv-pykit.git
cd gcpds-cv-pykit

# Install in development mode
pip install -e ".[dev]"

# Run code formatting
black gcpds_cv_pykit/
isort gcpds_cv_pykit/

# Run linting
flake8 gcpds_cv_pykit/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Authors

• GCPDS Team - Initial work - [e-mail]

🙏 Acknowledgments

• PyTorch team for the excellent deep learning framework
• The computer vision community for inspiration and best practices
• Contributors and users of this toolkit

📞 Support

• Issues: GitHub Issues
• Documentation: Read the Docs
• Email: [e-mail]

🔄 Changelog

Version 0.1.0 (Alpha)

• Initial release
• Basic UNet implementation
• Core loss functions
• Training and evaluation pipeline
• Weights & Biases integration

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Note: This project is in active development. APIs may change between versions. Please check the documentation for the latest updates.