Awesome
RelGAN (ICCV 2019)
(Official) Keras implementation of RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
The paper is accepted to ICCV 2019. We also have the PyTorch version here.
Preparation
- Prerequisites
- Python 3.5
- Keras 2.2.4
- Dataset
- Celeba-HQ
- Please follow the instructions in celeba-hq-modified to prepare the dataset
- Celeba-HQ
- Pre-trained model
generator519.h5
Get Started
Preprocessing
In this step, we export annotations to a numpy file. You will get anno_dic.npy
and imgIndex.npy
after running the script
-n : number of attributes (5, 9, 17)
-o : target output file
python3 preprocessing.py [--number=17] [--output=anno_dic.npy]
Training
python3 train.py
--path=<path to celeba-256>
--device=<device number>
[--growth=False]
[--step=0]
[--lr=1e-5]
[--beta1=0.5]
[--beta2=0.999]
[--batch_size=4]
[--sample_size=2]
[--epochs=400000]
[--lambda1=10]
[--lambda2=10]
[--lambda4=10]
[--lambda5=10]
[--lambda_gp=150]
[--img_size=256]
[--vec_size=17] #if you change the number of attributes, change this number
Testing
python3 demo_translation.py --device=<device number>
python3 demo_interpolation.py --device=<device number>