xavierww317 / rn

Region Normalization for Image Inpainting, accepted by AAAI-2020

Region Normalization for Image Inpainting

The paper can be found here. If you have any question about the paper/codes, you can contact me through Email([email protected]).

Please run the codes where the python is Version 3.x and pytorch>=0.4.

PS: The results of this version codes are better than those in the paper. The original base inpainting model that RN uses is not very stable (the result variance is a bit large) and we only reported conservative results. However, we optimized the base model and improved its robustness after the pulication so that the results now are better.

Preparation

Before running the codes, you should prepare training/evaluation image file list (flist) and mask file list (flist). You can refer to the folowing command to generate .flist file:

python flist.py --path your_dataset_folder --output xxx.flist

Training

There are some hyperparameters that you can adjust in the main.py. To train the model, you can run:

python main.py --bs 14 --gpus 2 --prefix rn --img_flist your_training_images.flist --mask_flist your_training_masks.flist

PS: You can set the "--bs" and "--gpus" to any number as you like. The above is just an example.

Evaluation

To evaluate the model, you can use GPU or CPU to run.

For GPU:

python eval.py --bs your_batch_size --model your_checkpoint_path --img_flist your_eval_images.flist --mask_flist your_eval_masks.flist

For CPU:

python eval.py --cpu --bs your_batch_size --model your_checkpoint_path --img_flist your_eval_images.flist --mask_flist your_eval_masks.flist

PS: The pretrained model under folder './pretrained_model/' is trained from Places2 dataset with Irregular Mask dataset. Please train RN from scratch if you test data not from Places2 or using regular mask.

Cite Us

Please cite us if you find this work helps.

@inproceedings{yu2020region,
  title={Region Normalization for Image Inpainting.},
  author={Yu, Tao and Guo, Zongyu and Jin, Xin and Wu, Shilin and Chen, Zhibo and Li, Weiping and Zhang, Zhizheng and Liu, Sen},
  booktitle={AAAI},
  pages={12733--12740},
  year={2020}
}

Appreciation

The codes refer to EdgeConnect. Thanks for the authors of it！