Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

Overview

ARM-Net

Dependencies

  • Python 3.6
  • Pytorch 1.7

Results

compare

tab1

tab2

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
Issues
  • What does ARM-L/ARM-M/ARM-S mean?

    What does ARM-L/ARM-M/ARM-S mean?

    It is a interesting work. I have some questions about your paper.

    1. In your work. When inference, my understanding is to build edge table at first, then use this table when inference. But How to decide which width network a patch choose? The others methods in Figure 7 is clear. But I can't know how ARM-FSRCNN choose width. It is confuse me (
    2. In Table 1&2, ARM-L/ARM-M/ARM-S means: fixed width no matter what the input patch is, right?
    3. About ARM-L/ARM-M/ARM-S, If just fix width. May be you should compare with ClassSR use your width choose policy insted of fixing width?
    4. By the way. In table 1 (row: Module FSRCNN, column: FLOPS) is 0%, may be error. :)

    Many questions from me. Thanks!

    opened by wangqiim 3
Owner
Bohong Chen
Bohong Chen
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