MASA-SR

Official PyTorch implementation of our CVPR2021 paper MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution

Dependencies

• python 3
• pytorch >= 1.1.0
• torchvision >= 0.4.0

Prepare Dataset

1. Download CUFED train set and CUFED test set
2. Place the datasets in this structure:

   CUFED
   ├── train
   │   ├── input
   │   └── ref 
   └── test
       └── CUFED5  
   

Get Started

1. Clone this repo

   git clone https://github.com/Jia-Research-Lab/MASA-SR.git
   cd MASA-SR
   

2. Download the dataset. Modify the argument --data_root in test.py and train.py according to your data path.

Evaluation

1. Download the pre-trained models and place them into the pretrained_weights/ folder

   • Pre-trained models can be downloaded from Google Drive
     • masa_rec.pth: trained with only reconstruction loss
     • masa.pth: trained with all losses

2. Run test.sh. See more details in test.sh (if you are using cpu, please add --gpu_ids -1 in the command)

   sh test.sh
   

3. The testing results are in the test_results/ folder

Training

1. First train masa-rec only with the reconstruction loss.

   python train.py --use_tb_logger --data_augmentation --max_iter 160 --loss_l1 --name train_masa_rec
   

2. After getting masa-rec, train masa with all losses, which is based on the pretrained masa-rec.

   python train.py --use_tb_logger --max_iter 50 --loss_l1 --loss_adv --loss_perceptual --name train_masa_gan --resume ./weights/train_masa_rec/snapshot/net_best.pth --resume_optim ./weights/train_masa_rec/snapshot/optimizer_G_best.pth --resume_scheduler ./weights/train_masa_rec/snapshot/scheduler_best.pth
   

3. The training results are in the weights/ folder