Awesome
ARM: Any-Time Super-Resolution Method (Paper Link)
Pytorch implementation of ARM-Net in ECCV 2022.
Dependencies
- Python 3.6
- Pytorch 1.7
Results
<img src="img\compare.png" alt="compare" width="50%;" />Run the command python img/plot_psnr_flops.py
to get the above figure.
Train
Data preprocessing
cd data_scripts
python extract_subimages_test.py
python data_augmentation.py
python generate_mod_LR_bic.py
python extract_subimages_train.py
Modify the configuration file (options/*.yml)
# train_us_fsrcnn.yml
is_train: True
# train_us_carn.yml
is_train: True
# train_us_srresnet.yml
is_train: True
Run training scripts
python main.py -opt options/train_us_fsrcnn.yml
python main.py -opt options/train_us_carn.yml
python main.py -opt options/train_us_srresnet.yml
Test
Modify the configuration file (options/*.yml)
# train_us_fsrcnn.yml
path:
pretrain_model_G: ckpt/arm-fsrcnn.pth
resume_state: ckpt/arm-fsrcnn.state
is_train: False
is_test: True
# train_us_carn.yml
path:
pretrain_model_G: ckpt/arm-carn.pth
resume_state: ckpt/arm-carn.state
is_train: False
is_test: True
# train_us_srresnet.yml
path:
pretrain_model_G: ckpt/arm-srresnet.pth
resume_state: ckpt/arm-srresnet.state
is_train: False
is_test: True
Run test scripts
python main.py -opt options/train_us_fsrcnn.yml
python main.py -opt options/train_us_carn.yml
python main.py -opt options/train_us_srresnet.yml