Image inpainting with edge focused WGAN-GP adversarial loss.

This is a school project from course Convolutional Neural Networks (CNN) at FIT BUT.

Authors: Bc. Tomáš Beránek (xberan46), Bc. Richard Klem (xklemr00)
Date: 10.5.2022

Authors mentioned above are referenced by we, us, our etc. furthermore and in project report (article).

Full project repository URL: https://github.com/RichardKlem/KNN_image_inpainting.

Pretrained models on GDrive: https://drive.google.com/drive/folders/1HzGg9QCF1qiQhGBcK8nmX742tOEnFetf?usp=sharing.
beranek_klem.zip is ours, yu_et_al.zip is Yu's model trained by us.

This project should work on Ubuntu and Windows 10. (has been tested)
Submitted zip file has limited content due to the file size restriction to 2 MB.
We have placed the full version of this project in the public git repository on GitHub.

This reduced project contains:

• full resource codes,
• README files etc.,
• very limited example data,
• reduced graphics quality (and potentially content) final report (article)
• url for full version and
• urls to pre-traned models etc.

The full version of project contains additionally:

• full content and full resolution final report,
• more example data

We made two branches to compare our changes to original project.
Branch with our code is named beranek_klem and the original one is named yu_et_al.
Note that we have came out from a branch named v1.0.0, which is the correct one connected to the cited paper.
The neuralgym repository was taken also from TFv2 PR 15 and has not been changed.

How to ... common stuff

1. Install requirements: pip install -r requirements.txt.
2. Install any other required library, which will be missing in your particular case.
3. (optional) Install sufficient tools like cuDNN etc. to run code on GPU.

How to TEST

1. Download pre-trained model from GDrive and place it in model_logs folder.
2. Continue as described in README.md in generative_inpainting subfolder.

How to TRAIN

1. Download dataset and insert it in data folder.
2. Create a .flist file where are paths to the dataset files. (each file one line)
   It can be relative or absolute path.
3. Continue as described in README.md in generative_inpainting subfolder.

Licensing + Credits

Part of the generative_inpainting directory is also a neuralgym folder. Both folders have origin in repositories of Jiahui Yu.

The difference between ours and theirs work can be seen on branches beranek_klem and yu_et_al.
We take credits only on these particular changes. The rest goes to Yu and others.

Please follow the LICENCE files in all sub-folders.

We were able to train original model from scratch and our enhanced model each on single 3060 Ti/3070 Ti. That means 8 GB of VRAM is sufficient for batch size 8 witch consistent results.