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

Last update: Nov 24, 2022

Related tags

Overview

ARM-Net

Dependencies

Python 3.6
Pytorch 1.7

Results

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

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Comments

What does ARM-L/ARM-M/ARM-S mean?
It is a interesting work. I have some questions about your paper.

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 (

In Table 1&2, ARM-L/ARM-M/ARM-S means: fixed width no matter what the input patch is, right?

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?

By the way. In table 1 (row: Module FSRCNN, column: FLOPS) is 0%, may be error. :)

Many questions from me. Thanks!
opened by wangqiim 2
Where is the defination of "ProgressBar()" in "utils.util" which is imported in extract_subimages_train.py ?

Where is the defination of "ProgressBar()" in "utils.util" ? The "ProgressBar" is imported in extract_subimages_train.py.

opened by huang-hz 1
New Super-Resolution Benchmarks
Hello,

MSU Graphics & Media Lab Video Group has recently launched two new Super-Resolution Benchmarks.

Video Upscalers Benchmark: Quality Enhancement determines the best upscaling methods for increasing video resolution and improving visual quality.

Super-Resolution for Video Compression benchmark aims to test Super-Resolution methods on compressed videos and select the best model for each video codec standard.

If you are interested in participating, you can add your algorithm following the submission steps:

Submit for Video Upscalers Benchmark: Quality Enhancement

Submit for Super-Resolution for Video Compression benchmark

We would be grateful for your feedback on our work!
opened by EvgeneyBogatyrev 0
How to get Fig.4. (a) 0.85 ?

In Fig.4.(a), I find different models will cause different PSNR lookup results. How to get Spearman correlation (0.85) coefficient picture (Fig.4.(a)). What model do you use to generate psnr values. (complex model will cause more pricise PSNR value)

opened by wangqiim 0

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

Related tags

Overview

ARM-Net

Dependencies

Results

Train

Data preprocessing

Modify the configuration file (options/*.yml)

Run training scripts

Test

Modify the configuration file (options/*.yml)

Run test scripts

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Comments

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

Where is the defination of "ProgressBar()" in "utils.util" which is imported in extract_subimages_train.py ?

New Super-Resolution Benchmarks

How to get Fig.4. (a) 0.85 ?

Releases(v1.0.0)

v1.0.0(Sep 28, 2022)

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