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The Aggregated COntextual-Transformation GAN (AOT-GAN) is for high-resolution image inpainting.The AOT blocks aggregate contextual transformations from various receptive fields, allowing to capture both informative distant image contexts and rich patterns of interest for context reasoning.
Paper: Aggregated Contextual Transformations for High-Resolution Image Inpainting
Official Repo: https://github.com/megvii-research/NAFNet
Download pretrained generator weights from: (https://paddlegan.bj.bcebos.com/models/AotGan_g.pdparams)
python applications/tools/aotgan.py \
--input_image_path data/aotgan/armani1.jpg \
--input_mask_path data/aotgan/armani1.png \
--weight_path test/aotgan/g.pdparams \
--output_path output_dir/armani_pred.jpg \
--config-file configs/aotgan.yaml
Parameters:
AI Studio Project:(https://aistudio.baidu.com/aistudio/datasetdetail/165081)
Data Preparation:
The pretained model uses 'Place365Standard' and 'NVIDIA Irregular Mask' as its training datasets. You can download then from (Place365Standard) and (NVIDIA Irregular Mask Dataset).
└─data
└─aotgan
├─train_img
├─train_mask
├─val_img
└─val_mask
Train(Single Card):
python -u tools/main.py --config-file configs/aotgan.yaml
Train(Mult-Card):
!python -m paddle.distributed.launch \
tools/main.py \
--config-file configs/photopen.yaml \
-o dataset.train.batch_size=6
Train(continue):
python -u tools/main.py \
--config-file configs/aotgan.yaml \
--resume output_dir/[path_to_checkpoint]/iter_[iternumber]_checkpoint.pdparams
On Places365-Val Dataset
mask | PSNR | SSIM | download |
---|---|---|---|
20-30% | 26.04001 | 0.89011 | download |
@inproceedings{yan2021agg, author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining}, title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting}, booktitle = {Arxiv}, pages={-}, year = {2020} }
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