Pytorch Implementation of Paper [Arxiv]
conda create -n sr-nas python=3.8
conda activate sr-nas
conda install -y pytorch==1.9.1 torchvision==0.10.1 cudatoolkit=10.2 tensorboard h5py scikit-image -c pytorch
- Configuration
- dataset (default: div2k): train dataset
- eval_datasets (set5/set14/urban100/bsds100...): evaluation dataset
- scale (4): scale factor
- num_blocks (default: 16): number of blocks in wdsr
- num_residual_units (default 24): number of residual units in wdsr
- Download the dataset, put them in folder
data
- Prepare dataset using
prepare_dataset.py
before distributed training - Training
- Speed model
- Here are several trained speed models provided here for different feature size and platform
Benchmarks (Set5, BSDS100, Urban100)
Download and organize data like:
./data/DIV2K/
├── DIV2K_train_HR
├── DIV2K_train_LR_bicubic
│ └── X2
│ └── X3
│ └── X4
├── DIV2K_valid_HR
└── DIV2K_valid_LR_bicubic
└── X2
└── X3
└── X4
./data/Set5/*.png
./data/BSDS100/*.png
./data/Urban100/*.png
https://github.com/ychfan/wdsr
If you find this code useful for your research, please cite our paper
@inproceedings{wu2022compiler,
title={Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution},
author={Wu, Yushu and Gong, Yifan and Zhao, Pu and Li, Yanyu and Zhan, Zheng and Niu, Wei and Tang, Hao and Qin, Minghai and Ren, Bin and Wang, Yanzhi},
booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XIX},
pages={92--111},
year={2022}
}