DAN-Basd-on-Openmmlab
DAN: Unfolding the Alternating Optimization for Blind Super Resolution
We reproduce DAN via mmediting based on open-sourced code.
Requirements
- PyTorch >= 1.3
- mmediting >= 0.9
DataSets
We use DIV2K and Flickr2K as our training datasets. For evaluation of Setting 2, we use DIV2KRK datasets,
Usages
How to run this repo: copy the file to the mmediting workspace and run the program directly based on the commands in mmediting
- Copy files to MMEditing workspace.
cd DAN-Basd-on-Openmmlab/
mv ./mmedit/models/restorers/dan.py ${mmediting_workspace}/mmedit/models/restorers/
mv ./mmedit/models/backbones/sr_backbones/dan_net.py ${mmediting_workspace}/mmedit/models/backbones/sr_backbones/
mv ./mmedit/models/common/DANpreprocess.py ${mmediting_workspace}/mmedit/models/common
mv ./configs/restorers/dan ${mmediting_workspace}/configs/restorers/
mv ./tools/data/super-resolution/dan_datasets ${mmediting_workspace}/tools/data/super-resolution/
- Modify the configuration file as follows:
pca_matrix_path='${mmediting_workspace}/tools/data/super-resolution/div2k/pca_matrix/pca_aniso_matrix_x4.pth' # your pca_matrix path
# Training
gt_folder='${dataset_workspace}/dataset/DF2K_train_HR_sub' # your train data path
# Testing
lq_folder='${dataset_workspace}/dataset/DIV2KRK/lr_x4' # your test data LR path
gt_folder='${dataset_workspace}/dataset/DIV2KRK/gt' # your test data HR path
- Add script to init file, as follows:
- modify the
mmedit/models/backbones/sr_backbones/__init__.py:
from .dan_net import DAN
# add DAN into __all__ list.
- modify the
mmedit/models/commons/__init__.py:
from .dan_preprocess import SRMDPreprocessing
# add SRMDreprocessing into __all__ list.
- modify the
mmedit/models/restorers/__init__.py:
from .dan import DAN
# add DAN into __all__ list.
- Training/Test
Before using it, please download and process the dataset and set the path in the configuration file.
- Train
# Single GPU
python tools/train.py configs/restorers/dan/dan_setting2.py --work_dir ${YOUR_WORK_DIR}
# Multiple GPUs
./tools/dist_train.sh configs/restorers/dan/dan_setting2.py ${GPU_NUM} --work_dir ${YOUR_WORK_DIR}
- Test
# Single GPU
python tools/test.py configs/restorers/dan/dan_setting2.py ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}]
# Multiple GPUs
./tools/dist_test.sh configs/restorers/dan/dan_setting2.py ${CHECKPOINT_FILE} ${GPU_NUM} [--metrics ${METRICS}] [--out ${RESULT_FILE}]
Result
DIV2KRK
The passwds of download links are all 'ta2o'
| Method | scale | Datasets | PSNR | Download |
|---|---|---|---|---|
| DAN | x4 | DIV2KRK | 27.41 | model / test_pkl |
503 Jan 04, 2023
1.4k Jan 07, 2023
15 Dec 12, 2022
5 Nov 07, 2022
10 Apr 06, 2022
1.2k Jan 09, 2023
28 Sep 16, 2022
114 Nov 30, 2022
13.6k Jan 04, 2023
1 Jan 16, 2022
5 Oct 31, 2022
151 Dec 14, 2022
1 Dec 31, 2021
42 Dec 23, 2022
107 Dec 27, 2022
9 Apr 29, 2022
107 Jan 02, 2023
115 Jan 05, 2023
29 Nov 09, 2022
4 Jun 17, 2021