pyautoencoder 1.0.8

A Python package offering implementations of state-of-the-art autoencoder architectures in PyTorch.

PyAutoencoder

A clean, modular PyTorch library for building and training autoencoders.

---

Highlights

PyAutoencoder is designed to offer simple and easy access to autoencoder frameworks. Here's what it offers:

• Minimal, composable API
  You don't have to inherit from complicated base classes or learn a new training loop. Simply provide your own PyTorch nn.Module encoder and decoder, and plug them into the ready‑to‑use autoencoder wrappers. This makes it easy to experiment with different architectures (e.g. MLPs, CNNs) while reusing the same training pipeline.

• Ready‑to‑use autoencoders The library ships with working implementations of autoencoders, each paired with their respective loss functions. You can start training in a few lines, without re‑implementing reconstruction likelihoods, KL divergence, or other boilerplate.

• PyTorch compatibility
  The library is fully compatible with the PyTorch ecosystem, so models integrate naturally with modules, tensors, optimizers, and schedulers.

• Lightweight, research‑oriented
  The library is intentionally minimal: no training loop frameworks, no heavy abstractions. This makes it well suited for research prototypes where you want control and transparency.

Status: The project is in an early but usable stage. Contributions, issues, and feedback are highly encouraged!

Currently implemented:

• Autoencoder (AE)
• Variational Autoencoder (VAE)

---

Installation

pip install pyautoencoder

Or install from source for development:

git clone https://github.com/andrea-pollastro/pyautoencoder.git
cd pyautoencoder
pip install -e .

Quick start

import torch
import torch.nn as nn
from pyautoencoder.variational import VAE
from pyautoencoder.loss import VAELoss

# Define encoder/decoder
encoder = nn.Sequential(
    nn.Linear(784, 512), 
    nn.ReLU(),
    nn.Linear(512, 256)
)

decoder = nn.Sequential(
    nn.Linear(256, 512), 
    nn.ReLU(),
    nn.Linear(512, 784)
)

# Model
vae = VAE(encoder=encoder, decoder=decoder, latent_dim=32)

# Loss
criterion = VAELoss(beta=1.0, likelihood="gaussian")
optimizer = torch.optim.Adam(vae.parameters())
for x in dataloader:
    optimizer.zero_grad()
    out = vae(x)
    losses = criterion(out, x)
    losses.total.backward() # negative ELBO
    optimizer.step()

    # optional: log components
    log_likelihood = losses.components["log_likelihood"]
    kl_divergence = losses.components["kl_divergence"]

Built‑in models

• AE — standard Autoencoder

  from pyautoencoder.vanilla import AE
  from pyautoencoder.loss import AELoss
  ae = AE(encoder=encoder, decoder=decoder)
  criterion = AELoss(likelihood="gaussian") # or bernoulli
  

• VAE — Variational Autoencoder

  from pyautoencoder.variational import VAE, VAELoss
  from pyautoencoder.loss import VAELoss
  vae = VAE(encoder=encoder, decoder=decoder, latent_dim=32)
  criterion = VAELoss(beta=1.0, likelihood="gaussian") # or bernoulli
  

Examples

See the examples/ folder for runnable scripts showing example of usage.

License

This project is released under the MIT License. See LICENSE.

Citation

If you use this package in academic work, please cite:

@misc{pollastro2025pyautoencoder,
  author       = {Andrea Pollastro},
  title        = {pyautoencoder},
  year         = {2025},
  howpublished = {GitHub repository},
  url          = {https://github.com/andrea-pollastro/pyautoencoder}
}