Galaxies99 / VAE-pytorch

Personal Pytorch Implementations of Variational Auto-Encoders

VAE-pytorch

Personal Pytorch Implementations of Variational Auto-Encoders. This repository mainly contains the following VAEs.

• VAE
• CVAE
• Beta-VAE
• Disentangled Beta-VAE
• Beta-TC-VAE
• DFC-VAE
• MSSIM-VAE

Run

Training

To train the models, just execute the following command after setting the configurations correctly.

python run.py --cfg [Configuration File] --mode train

where [Configuration File] is the path to your configuration file, and --mode train can be omitted.

Inference

For inference, just execute the following command after setting the configurations correctly.

python run.py --cfg [Configuration File] --mode inference

where [Configuration File] is the path to your configuration file, and there should be a checkpoint file in the corresponding path in stats folder.

Configurations

The configuration file should be in yaml format. Specifically, the following fields can be specified in the configuration file.

• model, which describes the parameters of the model, including model name and other parameters.
• dataset, which describes the parameter of the dataset, including dataset type and other parameters.
• optimizer, which describes the parameter of the AdamW optimizer.
• scheduler, which describes the parameter of the ExponentialLR learning rate scheduler.
• trainer, which describes the parameters used in the training process, such as max_epoch and multigpu.
• inference, which describes the parameters used in inference stage, such as sample_num.
• stats, which describes the path to the statistics file, such as stats_dir and stats_folder. The checkpoint, reconstructed images and generated images should be stored in stats_dir/stats_folder directory.

You can see the given configuration files for more details.

Performance on CelebA Dataset

The pretrained models and the generated images along with the reconstructed images are released on Baidu Netdisk (Extract code: lt4k).

Models	Generated Images
VAE (Code, Configs) [2]
CVAE (Code, Configs) [3]
Beta-VAE (Code, Configs) [4]
Disentangled Beta-VAE (Code, Configs) [Ref. 5]
Beta-TC-VAE (Code, Configs) [6]
DFC-VAE (Code, Configs) [7]
MSSIM-VAE (Code, Configs) [8]

Performance on CUB200 Dataset

We also provide the CUB200 dataset configurations. But unfortunately, our VAEs performs badly on this generation task. Therefore, the performance is omitted here.

Citations

@misc{fang2021vaepytorch,
  author =       {Hongjie Fang},
  title =        {VAE-pytorch},
  howpublished = {\url{https://github.com/Galaxies99/VAE-pytorch}},
  year =         {2021}
}

References

1. Subramanian, A.K, PyTorch-VAE, GitHub repository, GitHub (2020), online available at https://github.com/AntixK/PyTorch-VAE.
2. Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013).
3. Sohn, Kihyuk, Honglak Lee, and Xinchen Yan. "Learning structured output representation using deep conditional generative models." Advances in neural information processing systems 28 (2015): 3483-3491.
4. Higgins, Irina, et al. "beta-vae: Learning basic visual concepts with a constrained variational framework." (2016).
5. Burgess, Christopher P., et al. "Understanding disentangling in $\beta $-VAE." arXiv preprint arXiv:1804.03599 (2018).
6. Chen, Ricky TQ, et al. "Isolating sources of disentanglement in variational autoencoders." arXiv preprint arXiv:1802.04942 (2018).
7. Hou, Xianxu, et al. "Deep feature consistent variational autoencoder." 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017.
8. Snell, Jake, et al. "Learning to generate images with perceptual similarity metrics." 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017.