README.md

UGATIT - Model Training Tutorial

Selfie2Anime was a successful use of Image-to-Image translation using UGATIT. In this post we learn how to setup a development environment capable of running UGATIT, then train out own variation of a model.

Detailed tutorial for training and inference are outlined in the blog

Setup

Pip

pip3 install --upgrade pip
pip3 install -r requirements.txt

Conda

conda env create -f environment.yml
conda activate UGATIT

Usage

├── dataset
   └── YOUR_DATASET_NAME
       ├── trainA
           ├── xxx.jpg (name, format does not matter)
           ├── yyy.png
           └── ...
       ├── trainB
           ├── zzz.jpg
           ├── www.png
           └── ...
       ├── testA
           ├── aaa.jpg
           ├── bbb.png
           └── ...
       └── testB
           ├── ccc.jpg
           ├── ddd.png
           └── ...

Train

python main.py --dataset YOUR_DATASET_NAME

If the memory of gpu is not sufficient, set --light to True

• But it may not perform well
• paper version is --light to False

Test

python main.py --dataset YOUR_DATASET_NAME --phase test

Test Video

Make sure to set your video device in the State class of main.py if you have a unique setup.

By default it'll use the first video device attached

python main.py --dataset YOUR_DATASET_NAME --phase video

Test Process Endpoint [WIP]

python main.py --dataset YOUR_DATASET_NAME --phase web

POST to http://0.0.0.0:5000/process with the following data format

{
  "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD...."
}

Common Errors

No module named 'tensorflow.contrib'

Version 2.0 of Tensorflow isn't supported, you might need to change the environment.yaml to the following

name: UGATIT
dependencies:
  - python=3.5
  - matplotlib
  - numpy
  - pip
  - pip:
    - opencv-python
    - Pillow
#    - tensorflow==1.15.0 # CPU support
    - tensorflow-gpu==1.15.0

Citation

If you find the code useful for your research, please cite their paper:

@article{kim2019u,
  title={U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation},
  author={Kim, Junho and Kim, Minjae and Kang, Hyeonwoo and Lee, Kwanghee},
  journal={arXiv preprint arXiv:1907.10830},
  year={2019}
}

Attribution

• Junho Kim, Minjae Kim, Hyeonwoo Kang, Kwanghee Lee
• U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation