Update 22/12/2021: Added support for PyTorch Lightning 1.5.6 version and cleaned up the code.
A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there. All the models are trained on the CelebA dataset for consistency and comparison. The architecture of all the models are kept as similar as possible with the same layers, except for cases where the original paper necessitates a radically different architecture (Ex. VQ VAE uses Residual layers and no Batch-Norm, unlike other models). Here are the results of each model.
- Python >= 3.5
- PyTorch >= 1.3
- Pytorch Lightning >= 0.6.0 (GitHub Repo)
- CUDA enabled computing device
$ git clone https://github.com/AntixK/PyTorch-VAE
$ cd PyTorch-VAE
$ pip install -r requirements.txt
$ cd PyTorch-VAE
$ python run.py -c configs/<config-file-name.yaml>
Config file template
model_params:
name: "<name of VAE model>"
in_channels: 3
latent_dim:
. # Other parameters required by the model
.
.
data_params:
data_path: "<path to the celebA dataset>"
train_batch_size: 64 # Better to have a square number
val_batch_size: 64
patch_size: 64 # Models are designed to work for this size
num_workers: 4
exp_params:
manual_seed: 1265
LR: 0.005
weight_decay:
. # Other arguments required for training, like scheduler etc.
.
.
trainer_params:
gpus: 1
max_epochs: 100
gradient_clip_val: 1.5
.
.
.
logging_params:
save_dir: "logs/"
name: "<experiment name>"View TensorBoard Logs
$ cd logs/<experiment name>/version_<the version you want>
$ tensorboard --logdir .
Note: The default dataset is CelebA. However, there has been many issues with downloading the dataset from google drive (owing to some file structure changes). So, the recommendation is to download the file from google drive directly and extract to the path of your choice. The default path assumed in the config files is `Data/celeba/img_align_celeba'. But you can change it acording to your preference.
| Model | Paper | Reconstruction | Samples |
|---|---|---|---|
| VAE (Code, Config) | Link |  |
 |
| Conditional VAE (Code, Config) | Link |  |
 |
| WAE - MMD (RBF Kernel) (Code, Config) | Link |  |
 |
| WAE - MMD (IMQ Kernel) (Code, Config) | Link |  |
 |
| Beta-VAE (Code, Config) | Link |  |
 |
| Disentangled Beta-VAE (Code, Config) | Link |  |
 |
| Beta-TC-VAE (Code, Config) | Link |  |
 |
| IWAE (K = 5) (Code, Config) | Link |  |
 |
| MIWAE (K = 5, M = 3) (Code, Config) | Link |  |
 |
| DFCVAE (Code, Config) | Link |  |
 |
| MSSIM VAE (Code, Config) | Link |  |
 |
| Categorical VAE (Code, Config) | Link |  |
 |
| Joint VAE (Code, Config) | Link |  |
 |
| Info VAE (Code, Config) | Link |  |
 |
| LogCosh VAE (Code, Config) | Link |  |
 |
| SWAE (200 Projections) (Code, Config) | Link |  |
 |
| VQ-VAE (K = 512, D = 64) (Code, Config) | Link |  |
N/A |
| DIP VAE (Code, Config) | Link |  |
 |
If you have trained a better model, using these implementations, by fine-tuning the hyper-params in the config file, I would be happy to include your result (along with your config file) in this repo, citing your name 😊.
Additionally, if you would like to contribute some models, please submit a PR.
Apache License 2.0
| Permissions | Limitations | Conditions |
|---|---|---|
| ✔️ Commercial use | ❌ Trademark use | ⓘ License and copyright notice |
| ✔️ Modification | ❌ Liability | ⓘ State changes |
| ✔️ Distribution | ❌ Warranty | |
| ✔️ Patent use | ||
| ✔️ Private use |
@misc{Subramanian2020,
author = {Subramanian, A.K},
title = {PyTorch-VAE},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/AntixK/PyTorch-VAE}}
}