USR_DA

Unofficial pytorch implementation of the paper "Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective" (https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Unsupervised_Real-World_Super-Resolution_A_Domain_Adaptation_Perspective_ICCV_2021_paper.pdf)

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

• In the paper, it doesn't provide accurate descriptions of the model (encoder, decoder and discriminator)
• Therefore, I use 5 convolution layers as encoder, RRDB network as decoder, and VGG network as discriminator.

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 the NTIRE dataset.

• Download the dataset from this link: https://competitions.codalab.org/competitions/22221
• In the link, downlod all data related to NTIRE20
• After download the dataset, the 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 NTIRE20 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 --weights ./weights/epoch20.pth --dir_test /mnt/Dataset/NTIRE20 (execution code)

Acknolwdgements

We refer to repos below to implement this code.

• official ESRGAN github (https://github.com/xinntao/ESRGAN)