Meta-SR
Official implementation of Meta-SR: A Magnification-Arbitrary Network for Super-Resolution(CVPR2019)(PyTorch)
Our code is built on EDSR(PyTorch).
Attention
I find an error in my camera-ready, the PSNR of our Meta-RDN on scale 1.2 is 40.04 not 40.40.
Requirements
- Pytorch 0.4.0
- Python 3.5
- numpy
- skimage
- imageio
- cv2
*note that if you use another version of pytorch (>0.4.0), you can rewrite the dataloader.py
Update notes
-
2019/12/23: fix a bug in https://github.com/XuecaiHu/Meta-SR-Pytorch/blob/f2cf094248defef242973282627ac8ea50d2e806/trainer.py#L107 , since the zeros in the double data type isnot a real zero.
-
2019/12/06: I rewrite the input_matrix_wpn function in trainer.py. Since the offset is repeated, there are many repeated weight prediction. I remove them. In the metardn, we use repeated_weights to extend a small matrix to a matrix with same size of the feature maps. This version use less memory and less inference times.
-
todo provide code for pytorch 1.0, 1.1 and 1.2
Install and run demo
- download the code
git clone https://github.com/XuecaiHu/Meta-SR-Pytorch.git
cd Meta-SR-Pytorch
- run training demo:
python main.py --model metardn --ext sep --save metardn --lr_decay 200 --epochs 1000 --n_GPUs 1 --batch_size 1
- run test demo:
- download the model from the BaiduYun fetch code: btc5.
- put the model_1000.pt under the ./eperiment/metardn/model/
python main.py --model metardn --ext sep --save metardn --n_GPUs 1 --batch_size 1 --test_only --data_test Set5 --pre_train ./experiment/metardn/model/model_1000.pt --save_results --scale 1.5
Train and Test as our paper
- prepare dataset
- download the dataset DIV2K and test dataset fetch code: w3hk GoogleDrive
- change the path_src = DIV2K HR image folder path and run /prepare_dataset/geberate_LR_metasr_w3hk1_X4.m
- upload the dataset
- change the dir_data in option.py： dir_data = "/path to your DIV2K and testing dataset'(keep the training and test dataset in the same folder: test dataset under the benchmark folder and training dataset rename to DIV2K, or change the data_train to your folder name)
- pre_train model for test
BaiduYun fetch code: btc5
GoogleDrive
train
cd /Meta-SR-Pytorch
python main.py --model metardn --save metardn --ext sep --lr_decay 200 --epochs 1000 --n_GPUs 4 --batch_size 16
test
python main.py --model metardn --save metardn --ext sep --pre_train ./experiment/metardn/model/model_1000.pt --test_only --data_test Set5 --scale 1.5 --n_GPUs 1
Citation
@article{hu2019meta,
title={Meta-SR: A Magnification-Arbitrary Network for Super-Resolution},
author={Hu, Xuecai and Mu, Haoyuan and Zhang, Xiangyu and Wang, Zilei and Tan, Tieniu and Sun, Jian},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}
}
Contact
Xuecai Hu (xuecai.hu@cripac.ia.ac.cn or huxc@mail.ustc.edu.cn)