CRAN

Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)"

This code doesn't exactly match what the paper describes.

• I delete the module "CDRR" (Section 3.2) in the code because of low training speed and performance drop (maybe I wrongly make the code for "CDRR")
• If you want to use "CDRR", just uncomment the remarks in "common.py"

The environmental settings are described below. (I cannot gaurantee if it works on other environments)

• Pytorch=1.7.1+cu110
• numpy=1.18.3
• cv2=4.2.0
• tqdm=4.45.0

Train

First, you need to download weights of ResNet50 pretrained on ImageNet database.

• Downlod the weights from this website (https://download.pytorch.org/models/resnet50-0676ba61.pth)
• rename the .pth file as "resnet50.pth" and put it in the "model" folder

Second, you need to download the DF2K dataset.

• DF2K is a merged training dataset consisting of 800 DIV2K training images and 2560 Flickr2K training images
• DIV2K download link: https://data.vision.ee.ethz.ch/cvl/DIV2K/
• Flickr2K download link: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
• After merging the datasets (DIV2K & Flickr2K), the DF2K dataset should be composed as below

• Set the database path in "./opt/option.py" (It is represented as "dir_data")

After those settings, you can run the train code by running "train.py"

• python3 train.py --gpu_id 0 (execution code)
• This code works on single GPU. If you want to train this code in muti-gpu, you need to change this code
• Options are all included in "./opt/option.py". So you should change the variable in "./opt/option.py"

Inference

First, you need to specify variables in "./opt/option.py"

• dir_test: root folder of test images
• weights: checkpoint file (trained on DF2K dataset)
• results: inference results will be saved on this folder

After those settings, you can run the inference code by running "inference.py"

• python3 inference.py --gpu_id 0 (execution code)

Acknolwdgements

We refer to repos below to implement this code.

• official RCAN github (https://github.com/yulunzhang/RCAN)
• official Context-Gated Convolution github (https://github.com/XudongLinthu/context-gated-convolution)