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123 changes: 116 additions & 7 deletions README.md
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# Adaptive Token Dictionary

This repository is an official implementation of the paper "Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary" ([Arxiv](https://arxiv.org/abs/2401.08209)).
This repository is an official implementation of the paper "Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary", CVPR, 2024.

[[Arxiv](https://arxiv.org/abs/2401.08209)] [[visual results](https://drive.google.com/drive/folders/1HwEbAGU6WEw9ZGbFdt__BOJo_5DKflEb?usp=sharing)] [[pretrained models](https://drive.google.com/drive/folders/1D3BvTS1xBcaU1mp50k3pBzUWb7qjRvmB?usp=sharing)]

By Leheng Zhang, Yawei Li, Xingyu Zhou, Xiaorui Zhao, and Shuhang Gu.

Codes will be coming soon!
> **Abstract:** Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks (DNNs), especially Transformers for super-resolution, have seen significant advancements in recent years, challenges still remain, particularly in limited receptive field caused by window-based self-attention. To address these issues, we introduce a group of auxiliary **A**daptive **T**oken **D**ictionary to SR Transformer and establish an **ATD**-SR method. The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step. The refinement strategy could not only provide global information to all input tokens but also group image tokens into categories. Based on category partitions, we further propose a category-based self-attention mechanism designed to leverage distant but similar tokens for enhancing input features. The experimental results show that our method achieves the best performance on various single image super-resolution benchmarks.
>
> <img width="800" src="figures/tdca-acmsa.png">
> <img width="800" src="figures/arch.png">
> <br/><br/>
> <img width="800" src="figures/viscomp_076.png">


## Contents
1. [Enviroment](#environment)
1. [Training](#training)
1. [Testing](#testing)
1. [Results](#results)
1. [Visual Results](#visual-results)
1. [Citation](#citation)
1. [Acknowledgements](#acknowledgements)


## Environment
- Python 3.9
- PyTorch 2.0.1

### Installation
```bash
git clone https://github.com/LabShuHangGU/Adaptive-Token-Dictionary.git

conda create -n ATD python=3.9
conda activate ATD

pip install -r requirements.txt
python setup.py develop
```


## Training
### Data Preparation
- Download the training dataset DF2K ([DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) + [Flickr2K](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar)) and put them in the folder `./datasets`.

## Overview
### Training Commands
- Refer to the training configuration files in `./options/train` folder for detailed settings.
- ATD (Classical Image Super-Resolution)
```bash
# batch size = 8 (GPUs) × 4 (per GPU)
# training dataset: DF2K

Classical Image Super-Resolution
![classical image sr](/figures/classical.png)
# ×2 scratch, input size = 64×64, 300k iterations
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --use-env --nproc_per_node=8 --master_port=1145 basicsr/train.py -opt options/train/000_ATD_SRx2_scratch.yml --launcher pytorch
# ×2 finetune, input size = 96×96, 250k iterations
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --use-env --nproc_per_node=8 --master_port=1145 basicsr/train.py -opt options/train/001_ATD_SRx2_finetune.yml --launcher pytorch

Lightweight Image Super-Resolution
![lightweight image sr](/figures/lightweight.png)
# ×3 finetune, input size = 96×96, 250k iterations
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --use-env --nproc_per_node=8 --master_port=1145 basicsr/train.py -opt options/train/002_ATD_SRx3_finetune.yml --launcher pytorch

# ×4 finetune, input size = 96×96, 250k iterations
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --use-env --nproc_per_node=8 --master_port=1145 basicsr/train.py -opt options/train/003_ATD_SRx4_finetune.yml --launcher pytorch
```

- ATD-light (Lightweight Image Super-Resolution)
```bash
# batch size = 2 (GPUs) × 16 (per GPU)
# training dataset: DIV2K

# ×2 scratch, input size = 64×64, 500k iterations
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --use-env --nproc_per_node=2 --master_port=1145 basicsr/train.py -opt options/train/101_ATD_light_SRx2_scratch.yml --launcher pytorch

# ×3 finetune, input size = 64×64, 250k iterations
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --use-env --nproc_per_node=2 --master_port=1145 basicsr/train.py -opt options/train/102_ATD_light_SRx3_finetune.yml --launcher pytorch

# ×4 finetune, input size = 64×64, 250k iterations
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --use-env --nproc_per_node=2 --master_port=1145 basicsr/train.py -opt options/train/103_ATD_light_SRx4_finetune.yml --launcher pytorch
```


## Testing
### Data Preparation
- Download the testing data (Set5 + Set14 + BSD100 + Urban100 + Manga109 [[download](https://drive.google.com/file/d/1_FvS_bnSZvJWx9q4fNZTR8aS15Rb0Kc6/view?usp=sharing)]) and put them in the folder `./datasets`.

### Pretrained Models
- Download the [pretrained models](https://drive.google.com/drive/folders/1D3BvTS1xBcaU1mp50k3pBzUWb7qjRvmB?usp=sharing) and put them in the folder `./experiments/pretrained_models`.

### Testing Commands
- Refer to the testing configuration files in `./options/test` folder for detailed settings.
- ATD (Classical Image Super-Resolution)
```bash
python basicsr/test.py -opt options/test/001_ATD_SRx2_finetune.yml
python basicsr/test.py -opt options/test/002_ATD_SRx3_finetune.yml
python basicsr/test.py -opt options/test/003_ATD_SRx4_finetune.yml
```

- ATD-light (Lightweight Image Super-Resolution)
```bash
python basicsr/test.py -opt options/test/101_ATD_light_SRx2_scratch.yml
python basicsr/test.py -opt options/test/102_ATD_light_SRx3_finetune.yml
python basicsr/test.py -opt options/test/103_ATD_light_SRx4_finetune.yml
```


## Results
- Classical Image Super-Resolution

<img width="800" src="figures/classical.png">

- Lightweight Image Super-Resolution

<img width="800" src="figures/lightweight.png">

## Visual Results

- Complete visual results can be downloaded in [link](https://drive.google.com/drive/folders/1HwEbAGU6WEw9ZGbFdt__BOJo_5DKflEb?usp=sharing)

<img width="800" src="figures/viscomp_027.png">
<img width="800" src="figures/viscomp_momo.png">


## Citation
Expand All @@ -28,3 +134,6 @@ Lightweight Image Super-Resolution
}
```

## Acknowledgements
This code is built on [BasicSR](https://github.com/XPixelGroup/BasicSR).

1 change: 1 addition & 0 deletions VERSION
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1.4.2
11 changes: 11 additions & 0 deletions basicsr/__init__.py
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# https://github.com/xinntao/BasicSR
# flake8: noqa
from .archs import *
from .data import *
from .losses import *
from .metrics import *
from .models import *
from .test import *
from .train import *
from .utils import *
# from .version import __gitsha__, __version__
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import importlib
from copy import deepcopy
from os import path as osp

from basicsr.utils import get_root_logger, scandir
from basicsr.utils.registry import ARCH_REGISTRY

__all__ = ['build_network']

# automatically scan and import arch modules for registry
# scan all the files under the 'archs' folder and collect files ending with '_arch.py'
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
# import all the arch modules
_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames]


def build_network(opt):
opt = deepcopy(opt)
network_type = opt.pop('type')
net = ARCH_REGISTRY.get(network_type)(**opt)
logger = get_root_logger()
logger.info(f'Network [{net.__class__.__name__}] is created.')
return net
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