This repository provides the official PyTorch implementation of our paper "TransCNN-HAE: Transformer-CNN Hybrid AutoEncoder for Blind Image Inpainting".
Our paper can be found in https://dl.acm.org/doi/pdf/10.1145/3503161.3547848
- Linux
- Python 3.7
- NVIDIA GPU + CUDA CuDNN
- Clone this repo:
git clone https://github.com/zhenglab/TransCNN-HAE.git
cd TransCNN-HAE- Install PyTorch and 1.7 and other dependencies (e.g., torchvision).
- For Conda users, you can create a new Conda environment using
conda create --name <env> --file requirements.txt.
- For Conda users, you can create a new Conda environment using
Please change the pathes to your dataset path in datasets folder.
python train.py --path=$configpath$
For example: python train.py --path=./checkpoints/FFHQ/
The model is automatically saved every 10,000 iterations, please rename the file g.pth_$iter_number$ to g.pth and then run testing command.
python test.py --path=$configpath$
For example: python test.py --path=./checkpoints/FFHQ/
@inproceedings{10.1145/3503161.3547848,
author = {Zhao, Haoru and Gu, Zhaorui and Zheng, Bing and Zheng, Haiyong},
title = {TransCNN-HAE: Transformer-CNN Hybrid AutoEncoder for Blind Image Inpainting},
booktitle = {ACM MM},
pages={6813--6821},
year = {2022}
}