Skip to content

Latest commit

 

History

History
135 lines (91 loc) · 4.8 KB

README.md

File metadata and controls

135 lines (91 loc) · 4.8 KB
Raw

Group Project

Python 3.7 pytorch

Overview

This project allows you to:

  • Run the state-of-art single-reference-based super-resolution model C2-Matching (pretrained weight included), the scripts for it to run on CUFED5 with each individual reference image are included.
  • Run scripts to fuse the outputs into one final result. These scripts implement the methods mentioned in our report.
  • Run scripts to do qualitative and quantitative experiments on the result.

Dependencies and Installation

  • Python == 3.9.7
  • PyTorch == 1.9.0
  • CUDA >= 10.2
  • GCC >= 7.5.0

  1. Clone Repo

    git clone git@github.com:yumingj/C2-Matching.git

  2. Create Conda Environment

    conda create --prefix ./c2_matching python=3.9.7
conda activate ./c2_matching

  3. Install Dependencies

    cd C2-Matching
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv==0.4.4
pip install -r requirements.txt

  4. Install MMSR and DCNv2

    python setup.py develop --user
cd mmsr/models/archs/DCNv2
python setup.py build develop --user

Dataset Preparation

Please refer to Datasets.md for pre-processing and more details.

Get Started

Pretrained Models

Downloading the pretrained models from this link and put them under experiments/pretrained_models folder.

Test

We provide quick test code with the pretrained model.

  1. Modify the paths to dataset and pretrained model in the following yaml files for configuration.

    ./options/test/test_C2_matching_multi.yml

  2. Run test code for models trained using GAN loss.

    python mmsr/test.py -opt "options/test/test_C2_matching_multi.yml"

    Check out the results in ./results.

Train (Note for TA: this part can be skipped)

All logging files in the training process, e.g., log message, checkpoints, and snapshots, will be saved to ./experiments and ./tb_logger directory.

  1. Modify the paths to dataset in the following yaml files for configuration.

    ./options/train/stage1_teacher_contras_network.yml
./options/train/stage2_student_contras_network.yml
./options/train/stage3_restoration_gan.yml

  2. Stage 1: Train teacher contrastive network.

    python mmsr/train.py -opt "options/train/stage1_teacher_contras_network.yml"

  3. Stage 2: Train student contrastive network.

    # add the path to *pretrain_model_teacher* in the following yaml
# the path to *pretrain_model_teacher* is the model obtained in stage1
./options/train/stage2_student_contras_network.yml
python mmsr/train.py -opt "options/train/stage2_student_contras_network.yml"

  4. Stage 3: Train restoration network.

    # add the path to *pretrain_model_feature_extractor* in the following yaml
# the path to *pretrain_model_feature_extractor* is the model obtained in stage2
./options/train/stage3_restoration_gan.yml
python mmsr/train.py -opt "options/train/stage3_restoration_gan.yml"

# if you wish to train the restoration network with only mse loss
# prepare the dataset path and pretrained model path in the following yaml
./options/train/stage3_restoration_mse.yml
python mmsr/train.py -opt "options/train/stage3_restoration_mse.yml"

The Fusion Module

Check out the scripts in ./fusion. The fusion method is implemented in the non_nn_fuse.py, and the script to run the experiments are written in experiments_C2_mathcing.py and experiments_AMSA.py. For a visualization of the quantitative experimental results, check ou the code in ./util/visualize.py

License and Acknowledgement

This project is mainly modified from C2-Matching, please cite their paper if you find the C2-Matching Module of this project useful.

The original project is open sourced under MIT license. The code framework is mainly modified from BasicSR and MMSR (Now reorganized as MMEditing). Please refer to the original repo for more usage and documents.

Contact

If you have any question, please feel free to contact us via kke.zhao@mail.utoronto.ca.