https://github.com/divelab/msvae
https://github.com/divelab/msvae
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/divelab/msvae
- Owner: divelab
- Created: 2018-07-02T17:24:45.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-07-23T19:01:43.000Z (almost 8 years ago)
- Last Synced: 2025-04-05T02:21:49.143Z (about 1 year ago)
- Language: Python
- Size: 973 KB
- Stars: 8
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation
## Introduction
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation (MSVAE) is a way to generate high quality images.
Detailed information about MSVAE is provided in [arXiv tech report] (https://arxiv.org/pdf/1705.07202.pdf).
## Citation
If using this code, please cite our paper.
```
@article{cai2017multi,
title={Multi-stage variational auto-encoders for coarse-to-fine image generation},
author={Cai, Lei and Gao, Hongyang and Ji, Shuiwang},
journal={arXiv preprint arXiv:1705.07202},
year={2017}
}
```
## Results
Comparison of reconstructed images from the CelebA dataset. The first row is the input
images in the CelebA training set. The second row is the reconstructed images generated by the
original VAE. The third and fourth rows are the results of deep residual VAE and multi-stage VAE,
respectively.
## System requirement
#### Programming language
Python
#### Python Packages
tensorflow (CPU) or tensorflow-gpu (GPU), numpy, h5py, progressbar, PIL, scipy
## Prepare data
We use celebA dataset in this work. CelebA is a large scale face dataset that contains 202599 face images. The size of each face
image is 178×218. We resize the images to 128×128. Please save the dataset into h5 data format.
## Configure the network
All network hyperparameters are configured in main.py.
#### Training
max_step: how many iterations or steps to train
test_step: how many steps to perform a mini test or validation
save_step: how many steps to save the model
summary_step: how many steps to save the summary
#### Data
data_dir: data directory
train_data: h5 file for training
valid_data: h5 file for validation
test_data: h5 file for testing
batch: batch size
channel: input image channel number
height, width: height and width of input image
#### Debug
logdir: where to store log
modeldir: where to store saved models
sampledir: where to store predicted samples, please add a / at the end for convinience
model_name: the name prefix of saved models
reload_step: where to return training
test_step: which step to test or predict
random_seed: random seed for tensorflow
#### Network architecture
network_depth: how deep of the U-Net including the bottom layer
start_channel_num: the number of channel for the first conv layer
sigma: sigma value for reconstruction loss
## Training and Testing
#### Start training
After configure the network, we can start to train. Run
```
python main.py
```
The training of MSVAE will start.
#### Testing
Run
```
python main.py --action=predict
```