gcpds-cv-pykit 0.1.0.35

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
• Dataset Management: Built-in Kaggle dataset download and preparation utilities
• Visualization Tools: Random sample visualization utilities for dataset exploration
• Memory Management: Efficient memory handling and cleanup utilities

📋 Requirements

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

🔧 Installation

From PyPI

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
• kagglehub - Kaggle dataset downloads

Optional Dependencies

• Development: pytest>=7.0.0, pytest-cov>=4.0.0, black>=22.0.0, flake8>=5.0.0, isort>=5.10.0
• Documentation: sphinx>=5.0.0, sphinx-rtd-theme>=1.0.0

🏗️ Project Structure

gcpds_cv_pykit/
├── baseline/
│   ├── trainers/           # Training utilities (empty - under development)
│   ├── models/             # Model architectures (empty - under development)
│   ├── losses/             # Loss functions (empty - under development)
│   ├── dataloaders/        # Data loading utilities (empty - under development)
│   └── performance_model.py # Model evaluation and performance metrics
├── crowd/                  # Crowd-specific implementations (under development)
├── datasets/               # Dataset utilities and Kaggle integration
│   ├── datasets.py         # Kaggle dataset download and preparation
│   └── __init__.py
├── visuals/               # Visualization tools
│   ├── random_sample_visualizations.py  # Dataset visualization utilities
│   └── __init__.py
├── _version.py            # Version information
└── __init__.py

🚀 Quick Start

Dataset Download and Preparation

from gcpds_cv_pykit.datasets import download_and_prepare_dataset

# Download a Kaggle dataset
dataset_path = download_and_prepare_dataset('username/dataset-name/versions/1')
print(f"Dataset prepared at: {dataset_path}")

Dataset Visualization

from gcpds_cv_pykit.visuals import random_sample_visualization
from torch.utils.data import DataLoader

# Visualize random samples from your dataset
random_sample_visualization(
    dataset=your_dataloader,
    num_classes=2,
    single_class=None,  # Show all classes
    max_classes_to_show=7,
    type="baseline"
)

Model Performance Evaluation

from gcpds_cv_pykit.baseline import PerformanceModels

# Evaluate trained model
config = {
    'Device': 'cuda',
    'Loss Function': 'DICE',
    'Number of classes': 2,
    # ... other configuration parameters
}

evaluator = PerformanceModels(
    model=trained_model,
    test_dataset=test_dataloader,
    config=config
)

📊 Supported Models

The toolkit provides several state-of-the-art segmentation model architectures, all with ResNet34 backbone support:

UNet

• Architecture: Classic U-Net with ResNet34 encoder
• Features:
  • Skip connections for precise localization
  • Pretrained ResNet34 backbone support
  • Configurable encoder/decoder channels
  • Optional final activation functions (sigmoid, softmax, tanh)
  • Bilinear interpolation for upsampling
• Use Case: General-purpose segmentation tasks

ResUNet

• Architecture: Residual U-Net with ResNet34 backbone
• Features:
  • Enhanced skip connections with residual blocks
  • ResNet34 pretrained encoder
  • Improved gradient flow through residual connections
  • Batch normalization and ReLU activations
• Use Case: Complex segmentation tasks requiring deeper feature learning

DeepLabV3Plus

• Architecture: DeepLab v3+ with Atrous Spatial Pyramid Pooling (ASPP)
• Features:
  • ASPP module for multi-scale feature extraction
  • Separable convolutions for efficiency
  • ResNet34 backbone with dilated convolutions
  • Low-level feature fusion
  • Configurable atrous rates
• Use Case: High-resolution segmentation with multi-scale context

FCN (Fully Convolutional Network)

• Architecture: FCN with ResNet34 backbone
• Features:
  • Multi-scale skip connections (FCN-8s, FCN-16s, FCN-32s style)
  • Transposed convolutions for upsampling
  • ResNet34 pretrained encoder
  • Feature fusion at multiple scales
• Use Case: Semantic segmentation with multi-scale feature integration

Common Model Features

• Backbone: ResNet34 with ImageNet pretrained weights
• Input Channels: Configurable (default: 3 for RGB)
• Output Channels: Configurable number of classes
• Activation Functions: Support for sigmoid, softmax, tanh, or none
• Mixed Precision: Compatible with AMP training
• Memory Efficient: Optimized for GPU memory usage

Model Usage Example

from gcpds_cv_pykit.baseline.models import UNet, ResUNet, DeepLabV3Plus, FCN

# UNet with default settings
model = UNet(
    in_channels=3,
    out_channels=2,  # Binary segmentation
    pretrained=True,
    final_activation='sigmoid'
)

# DeepLabV3Plus for multi-class segmentation
model = DeepLabV3Plus(
    in_channels=3,
    out_channels=5,  # 5-class segmentation
    pretrained=True,
    final_activation='softmax'
)

# ResUNet for complex segmentation
model = ResUNet(
    in_channels=3,
    out_channels=1,
    pretrained=True
)

# FCN for semantic segmentation
model = FCN(
    in_channels=3,
    out_channels=10,
    pretrained=True,
    final_activation='softmax'
)

🎯 Loss Functions

The following loss functions are supported through the baseline.losses module:

• 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 through the PerformanceModels class:

• 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 with detailed statistical analysis.

🔧 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:

• Random dataset sample visualization
• Multi-class segmentation mask display
• Training/validation curves (through wandb integration)
• Model predictions vs ground truth

Example usage:

from gcpds_cv_pykit.visuals import random_sample_visualization

# Visualize a single class
random_sample_visualization(
    dataset=dataloader,
    num_classes=5,
    single_class=1,  # Show only class 1
    type="baseline"
)

# Visualize multiple classes
random_sample_visualization(
    dataset=dataloader,
    num_classes=5,
    max_classes_to_show=3,
    type="baseline"
)

🧪 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
• Kaggle for providing accessible datasets through kagglehub
• Contributors and users of this toolkit

📞 Support

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

🔄 Changelog

Version 0.1.0.34 (Current)

• Dataset download and preparation utilities via Kaggle integration
• Random sample visualization tools for dataset exploration
• Performance evaluation framework with comprehensive metrics
• Loss function implementations (DICE, CrossEntropy, Focal, Tversky)
• Mixed precision training support
• Weights & Biases integration for experiment tracking

Upcoming Features

• Complete trainer implementations
• Additional model architectures
• Enhanced data loading utilities
• Extended visualization capabilities

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Note: This project is in active development. Some modules (trainers, models, dataloaders) are currently under development. APIs may change between versions. Please check the documentation for the latest updates.