convolutional-autoencoder-pytorch 1.0.1

A package to simplify the implementing an autoencoder model.

convolutional-autoencoder-pytorch

A minimal, customizable PyTorch package for building and training convolutional autoencoders based on a simplified U-Net architecture (without skip connections). Ideal for representation learning, image compression, and reconstruction tasks.

🔧 Features

• 📦 Modular architecture (Encoder, Decoder, AutoEncoder)
• 🔁 Symmetric U-Net-like design without skip connections
• ⚡ Tanh output activation for stable image reconstruction
• 🧠 Residual blocks with RMS normalization and SiLU activation
• 📱 Designed for image inputs (3×H×W) with configurable channels and latent dim
• 🧪 Works with batched input tensors (e.g., torch.Tensor[B, C, H, W])

📦 Installation

You can access the PyPI page or install the package directly.

pip install convolutional-autoencoder-pytorch

🧩 Package Structure

convolutional-autoencoder-pytorch/
├── convolutional_autoencoder_pytorch/
│   ├── __init__.py
│   └── module.py          # All architecture classes and logic
├── pyproject.toml
├── LICENSE
└── README.md

🚀 Quick Start

1. Import the package and create the model

import torch
from convolutional_autoencoder_pytorch import AutoEncoder

model = AutoEncoder(
    dim=64,
    dim_mults=(1, 2, 4, 8),
    dim_latent=128,
    image_channels=3
)

2. Forward pass and reconstruction

images = torch.randn(8, 3, 128, 128)  # batch of images
reconstructed, latent = model(images)

# Or just get the reconstruction
recon = model.reconstruct(images)

3. Training step (sample loop)

import torch.nn.functional as F
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

def train_step(images):
    model.train()
    optimizer.zero_grad()
    recon, _ = model(images)
    loss = F.mse_loss(recon, images)
    loss.backward()
    optimizer.step()
    return loss.item()

⚙️ Configuration Options

Parameter	Description	Default
dim	Base channel size	64
dim_mults	List of multipliers for down/up blocks	(1, 2, 4, 8)
dim_latent	Latent bottleneck dimension	64
image_channels	Input/output image channels (e.g., 3)	3
dropout	Dropout probability	0.0

🙋‍♂️ Author

Developed by Mehran Bazrafkan This project is an original implementation of a simplified autoencoder architecture. Some ideas and design inspirations were drawn from the open-source denoising-diffusion-pytorch project, but the code and architecture were written independently.

📢 Contributions & Feedback

Contributions, issues, and feedback are welcome via GitHub Issues.

📄 License

This project is licensed under the terms of the MIT LICENSE.