PyTorch-Implementations

Introduction

PyTorch-Implementations features implementations of foundational deep learning architectures. Ranging from classical computer vision and sequence-to-sequence NLP models to fundamental generative AI models, this repository serves as a reference for understanding the mechanisms of these architectures. All models are implemented natively in PyTorch and trained on standard benchmarking datasets.

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📁 File Structure

PyTorch-Implementations/
├── DCGAN/                       # Deep Convolutional Generative Adversarial Network
│   ├── models/                  # Saved model (pth)
│   ├── results/                 # Training loss plot
│   └── dcgan.ipynb              # Implementation and training notebook
├── DDPM/                        # Denoising Diffusion Probabilistic Model
│   ├── models/                  
│   ├── results/                 
│   └── ddpm.ipynb               
├── Transformer/                 # Sequence-to-Sequence Transformer
│   ├── models/                  
│   ├── results/                 
│   └── transformer.ipynb        
├── UNet/                        # UNet for Segmentation
│   ├── models/                  
│   ├── results/                 
│   └── unet.ipynb               
├── Variational-Autoencoder/     # Variational Autoencoder
│   ├── models/                  
│   ├── results/                 
│   └── vae.ipynb                
├── Vision-Transformer/          # Vision Transformer for Classification
│   ├── models/                  
│   ├── results/                 
│   └── vit.ipynb                
├── .gitignore                   
├── LICENSE                      # MIT License
└── README.md                    # Project Documentation

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⚙️ Models & Datasets

Domain	Specific Architecture	Dataset	Objective
Generative AI	Denoising Diffusion Probabilistic Model (DDPM)	CelebA	Unconditional Image Generation
	Deep Convolutional GAN (DCGAN)	CelebA	Adversarial Image Generation
	Variational Autoencoder (VAE)	CelebA	Latent Space Reconstruction & Generation
Computer Vision	Vision Transformer (ViT)	CIFAR-10	Multi-class Image Classification
	U-Net	ISBI 2016	Biomedical Image Segmentation
Natural Language	Transformer (Standard Seq2Seq)	Multi30k	English to German Translation

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🎥 Results Showcase

Architecture	Dataset	Task	Demonstration / Loss Curve
DDPM	CelebA	Image Generation
VAE	CelebA	Latent Generation
DCGAN	CelebA	Image Generation
U-Net	ISBI 2016	Segmentation
Vision Transformer	CIFAR-10	Classification
Transformer	Multi30k	Translation

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🚀 Getting Started

Follow these steps to set up the environment and run any of the models locally.

1. Clone the repository and install dependencies:

Ensure you have Python 3.10+ installed. Install the required libraries, including PyTorch and Torchvision:

git clone [https://github.com/amimayo/PyTorch-Implementations.git](https://github.com/amimayo/PyTorch-Implementations.git)
cd PyTorch-Implementations
pip install torch torchvision numpy matplotlib jupyter

2. Navigate to the desired model:

Move into the directory of the specific architecture you want to explore.

cd DDPM

3. Launch the Jupyter Notebook:

Open the notebook to view the architecture code, training loop, and inference results.

jupyter notebook ddpm.ipynb

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🛠️ To-Do List

• [🟩] Core Architectures: Complete training and verification for UNet, Transformer, ViT, VAE, DCGAN, and DDPM.
• [🟨] BERT (Bidirectional Encoder Representations from Transformers): Implement masked language modeling for sequence representation.
• [🟨] GPT (Generative Pre-trained Transformer): Implement an autoregressive decoder-only architecture for text generation.

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📜 License

Distributed under the MIT License.

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