torch-archs 0.1.2

PyTorch vision and language architectures.

torch-archs

PyTorch vision and language architectures.

Overview

torch-archs is a collection of modern neural network architectures implemented in PyTorch, focusing on both computer vision and natural language processing models. This library provides clean, efficient implementations of state-of-the-art architectures that can be easily integrated into your projects.

Features

• Vision Architectures: Vision Transformer (ViT) components and related architectures
• Language Architectures: Mixture of Experts (MoE) and other language model components
• Clean Implementation: Well-documented, modular code following PyTorch best practices
• Easy Integration: Simple APIs for incorporating architectures into your projects

Installation

From source

git clone https://github.com/your-username/torch-archs.git
cd torch-archs
pip install -e .

Using uv (recommended)

uv add torch-archs

Requirements

• Python >= 3.11
• PyTorch >= 2.4

Usage

Vision Architectures

from torch_archs.vision.vit import Patchify2D
import torch

# Create a patch extraction layer
patchify = Patchify2D(patch_size=16, flatten_sequence=True)

# Input image tensor (batch_size=1, channels=3, height=224, width=224)
x = torch.randn(1, 3, 224, 224)
patches = patchify(x)
print(patches.shape)  # Output shape will depend on implementation

Language Architectures

from torch_archs.language import moe
# Implementation coming soon

Architecture Components

Vision (torch_archs.vision)

• Patchify2D: Extracts non-overlapping patches from 2D images for Vision Transformer models
  • Configurable patch size
  • Optional sequence flattening
  • Efficient tensor operations

Language (torch_archs.language)

• Mixture of Experts (MoE): Coming soon
• Additional language model components in development

Project Structure

torch-archs/
├── torch_archs/
│   ├── vision/
│   │   └── vit.py          # Vision Transformer components
│   └── language/
│       └── moe.py          # Mixture of Experts components
├── main.py                 # Example usage and demos
├── pyproject.toml          # Project configuration
└── README.md              # This file

Development

Setting up development environment

# Clone the repository
git clone https://github.com/your-username/torch-archs.git
cd torch-archs

# Install in development mode
pip install -e .

# Or using uv
uv sync

Running examples

python main.py

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Guidelines

• Follow PyTorch coding conventions
• Add comprehensive docstrings to all public methods
• Include type hints
• Write tests for new architectures
• Update documentation as needed

License

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

Roadmap

• Complete Vision Transformer implementation
• Implement Mixture of Experts architectures
• Add more vision architectures (ResNet, EfficientNet, etc.)
• Add more language architectures (Transformer, BERT variants, etc.)
• Comprehensive test suite
• Performance benchmarks
• Pre-trained model weights

Citation

If you use this library in your research, please cite:

@software{torch_archs,
  title = {torch-archs: PyTorch Vision and Language Architectures},
  author = {Javier Cervera Cordero},
  year = {2025},
  url = {https://github.com/javi22020/torch-archs}
}

Acknowledgments

• PyTorch team for the excellent deep learning framework
• The research community for developing these architectures
• Contributors to this project