MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)

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Deep LearningMASA-SR
Overview

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
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DV Lab
Deep Vision Lab
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