PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Related tags

Deep LearningEMSRDPN
Overview

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning

This repository is for EMSRDPN introduced in the following paper

Bin-Cheng Yang and Gangshan Wu, "Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning", [arxiv]

It's an extension to a conference paper

Bin-Cheng Yang. 2019. Super Resolution Using Dual Path Connections. In Proceedings of the 27th ACM International Conference on Multimedia (MM ’19), October 21–25, 2019, Nice, France. ACM, NewYork, NY, USA, 9 pages. https://doi.org/10.1145/3343031.3350878

The code is built on EDSR (PyTorch) and tested on Ubuntu 16.04 environment (Python3.7, PyTorch_1.1.0, CUDA9.0) with Titan X/Xp/V100 GPUs.

Contents

  1. Introduction
  2. Train
  3. Test
  4. Results
  5. Citation
  6. Acknowledgements

Introduction

Deep convolutional neural networks have been demonstrated to be effective for SISR in recent years. On the one hand, residual connections and dense connections have been used widely to ease forward information and backward gradient flows to boost performance. However, current methods use residual connections and dense connections separately in most network layers in a sub-optimal way. On the other hand, although various networks and methods have been designed to improve computation efficiency, save parameters, or utilize training data of multiple scale factors for each other to boost performance, it either do super-resolution in HR space to have a high computation cost or can not share parameters between models of different scale factors to save parameters and inference time. To tackle these challenges, we propose an efficient single image super-resolution network using dual path connections with multiple scale learning named as EMSRDPN. By introducing dual path connections inspired by Dual Path Networks into EMSRDPN, it uses residual connections and dense connections in an integrated way in most network layers. Dual path connections have the benefits of both reusing common features of residual connections and exploring new features of dense connections to learn a good representation for SISR. To utilize the feature correlation of multiple scale factors, EMSRDPN shares all network units in LR space between different scale factors to learn shared features and only uses a separate reconstruction unit for each scale factor, which can utilize training data of multiple scale factors to help each other to boost performance, meanwhile which can save parameters and support shared inference for multiple scale factors to improve efficiency. Experiments show EMSRDPN achieves better performance and comparable or even better parameter and inference efficiency over SOTA methods.

Train

Prepare training data

  1. Download DIV2K training data (800 training images for x2, x3, x4 and x8) from DIV2K dataset and Flickr2K training data (2650 training images) from Flickr2K dataset.

  2. Untar the download files.

  3. Using src/generate_LR_x8.m to generate x8 LR data for Flickr2K dataset, you need to modify 'folder' in src/generate_LR_x8.m to your directory to place Flickr2K dataset.

  4. Specify '--dir_data' in src/option.py to your directory to place DIV2K and Flickr2K datasets.

For more informaiton, please refer to EDSR(PyTorch).

Begin to train

  1. Cd to 'src', run the following scripts to train models.

    You can use scripts in file 'demo.sh' to train models for our paper.

    To train a fresh model using DIV2K dataset

    CUDA_VISIBLE_DEVICES=0,1 python3.7 main.py --scale 2+3+4+8 --test_scale 2+3+4+8 --save EMSRDPN_BIx2348 --model EMSRDPN --epochs 5000 --batch_size 16 --patch_size 48 --n_GPUs 2 --n_threads 16 --SRDPNconfig A --ext sep --data_test Set5 --reset --decay 1000-2000-3000-4000-5000 --lr_patch_size --data_range 1-3450 --data_train DIV2K

    To train a fresh model using Flickr2K dataset

    CUDA_VISIBLE_DEVICES=0,1 python3.7 main.py --scale 2+3+4+8 --test_scale 2+3+4+8 --save EMSRDPN_BIx2348 --model EMSRDPN --epochs 5000 --batch_size 16 --patch_size 48 --n_GPUs 2 --n_threads 16 --SRDPNconfig A --ext sep --data_test Set5 --reset --decay 1000-2000-3000-4000-5000 --lr_patch_size --data_range 1-3450 --data_train Flickr2K

    To train a fresh model using both DIV2K and Flickr2K datasets to reproduce results in the paper, you need copy all the files in DIV2K_HR/ to Flickr2K_HR/, copy all the directories in DIV2K_LR_bicubic/ to Flickr2K_LR_bicubic/, then using the following script

    CUDA_VISIBLE_DEVICES=0,1 python3.7 main.py --scale 2+3+4+8 --test_scale 2+3+4+8 --save EMSRDPN_BIx2348 --model EMSRDPN --epochs 5000 --batch_size 16 --patch_size 48 --n_GPUs 2 --n_threads 16 --SRDPNconfig A --ext sep --data_test Set5 --reset --decay 1000-2000-3000-4000-5000 --lr_patch_size --data_range 1-3450 --data_train Flickr2K

    To continue a unfinished model using DIV2K dataset, the processes for other datasets are similiar

    CUDA_VISIBLE_DEVICES=0,1 python3.7 main.py --scale 2+3+4+8 --test_scale 2+3+4+8 --save EMSRDPN_BIx2348 --model EMSRDPN --epochs 5000 --batch_size 16 --patch_size 48 --n_GPUs 2 --n_threads 16 --SRDPNconfig A --ext sep --data_test Set5 --resume -1 --decay 1000-2000-3000-4000-5000 --lr_patch_size --data_range 1-3450 --data_train DIV2K --load EMSRDPN_BIx2348

Test

Quick start

  1. Download benchmark dataset from BaiduYun (access code: 20v5), place them in directory specified by '--dir_data' in src/option.py, untar it.

  2. Download EMSRDPN model for our paper from BaiduYun (access code: d2ov) and place them in 'experiment/'. Other multiple scale models can be downloaded from BaiduYun (access code: z5ey).

  3. Cd to 'src', run the following scripts to test downloaded EMSRDPN model.

    You can use scripts in file 'demo.sh' to produce results for our paper.

    To test a trained model

    CUDA_VISIBLE_DEVICES=0 python3.7 main.py --scale 2+3+4+8 --test_scale 2+3+4+8 --save EMSRDPN_BIx2348_test --model EMSRDPN --epochs 5000 --batch_size 16 --patch_size 48 --n_GPUs 1 --n_threads 16 --SRDPNconfig A --ext sep --data_test Set5+Set14+B100+Urban100+Manga109 --reset --decay 1000-2000-3000-4000-5000 --lr_patch_size --data_range 1-3450 --data_train DIV2K --pre_train ../experiment/EMSRDPN_BIx2348.pt --test_only --save_results

    To test a trained model using self ensemble

    CUDA_VISIBLE_DEVICES=0 python3.7 main.py --scale 2+3+4+8 --test_scale 2+3+4+8 --save EMSRDPN_BIx2348_test+ --model EMSRDPN --epochs 5000 --batch_size 16 --patch_size 48 --n_GPUs 1 --n_threads 16 --SRDPNconfig A --ext sep --data_test Set5+Set14+B100+Urban100+Manga109 --reset --decay 1000-2000-3000-4000-5000 --lr_patch_size --data_range 1-3450 --data_train DIV2K --pre_train ../experiment/EMSRDPN_BIx2348.pt --test_only --save_results --self_ensemble

    To test a trained model using multiple scale infer

    CUDA_VISIBLE_DEVICES=0 python3.7 main.py --scale 2+3+4+8 --test_scale 2+3+4+8 --save EMSRDPN_BIx2348_test_multi_scale_infer --model EMSRDPN --epochs 5000 --batch_size 16 --patch_size 48 --n_GPUs 1 --n_threads 16 --SRDPNconfig A --ext sep --data_test Set5 --reset --decay 1000-2000-3000-4000-5000 --lr_patch_size --data_range 1-3450 --data_train DIV2K --pre_train ../experiment/EMSRDPN_BIx2348.pt --test_only --save_results --multi_scale_infer

Results

All the test results can be download from BaiduYun (access code: oawz).

Citation

If you find the code helpful in your resarch or work, please cite the following papers.

@InProceedings{Lim_2017_CVPR_Workshops,
  author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
  title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {July},
  year = {2017}
}

@inproceedings{2019Super,
  title={Super Resolution Using Dual Path Connections},
  author={ Yang, Bin Cheng },
  booktitle={the 27th ACM International Conference},
  year={2019},
}

@misc{yang2021efficient,
      title={Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning}, 
      author={Bin-Cheng Yang and Gangshan Wu},
      year={2021},
      eprint={2112.15386},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Acknowledgements

This code is built on EDSR (PyTorch). We thank the authors for sharing their code.

Re-implememtation of MAE (Masked Autoencoders Are Scalable Vision Learners) using PyTorch.

mae-repo PyTorch re-implememtation of "masked autoencoders are scalable vision learners". In this repo, it heavily borrows codes from codebase https:/

Peng Qiao 1 Dec 14, 2021
BboxToolkit is a tiny library of special bounding boxes.

BboxToolkit is a light codebase collecting some practical functions for the special-shape detection, such as oriented detection

jbwang1997 73 Jan 01, 2023
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19)

Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19) Tianyu Wang*, Xin Yang*, Ke Xu, Shaozhe Chen, Qiang Zhang, Ry

Steve Wong 177 Dec 01, 2022
Can we visualize a large scientific data set with a surrogate model? We're building a GAN for the Earth's Mantle Convection data set to see if we can!

EarthGAN - Earth Mantle Surrogate Modeling Can a surrogate model of the Earth’s Mantle Convection data set be built such that it can be readily run in

Tim 0 Dec 09, 2021
A simple, fast, and efficient object detector without FPN

You Only Look One-level Feature (YOLOF), CVPR2021 A simple, fast, and efficient object detector without FPN. This repo provides an implementation for

789 Jan 09, 2023
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

Nonuniform-to-Uniform Quantization This repository contains the training code of N2UQ introduced in our CVPR 2022 paper: "Nonuniform-to-Uniform Quanti

Zechun Liu 60 Dec 28, 2022
Repository containing detailed experiments related to the paper "Memotion Analysis through the Lens of Joint Embedding".

Memotion Analysis Through The Lens Of Joint Embedding This repository contains the experiments conducted as described in the paper 'Memotion Analysis

Nethra Gunti 1 Mar 16, 2022
In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021

In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021. Balestriero et

Sean M. Hendryx 1 Jan 27, 2022
Repo for the paper Extrapolating from a Single Image to a Thousand Classes using Distillation

Extrapolating from a Single Image to a Thousand Classes using Distillation by Yuki M. Asano* and Aaqib Saeed* (*Equal Contribution) Extrapolating from

Yuki M. Asano 16 Nov 04, 2022
An excellent hash algorithm combining classical sponge structure and RNN.

SHA-RNN Recurrent Neural Network with Chaotic System for Hash Functions Anonymous Authors [摘要] 在这次作业中我们提出了一种新的 Hash Function —— SHA-RNN。其以海绵结构为基础,融合了混

Houde Qian 5 May 15, 2022
Script utilizando OpenCV e modelo Machine Learning para detectar o uso de máscaras.

Reconhecendo máscaras Este repositório contém um script em Python3 que reconhece se um rosto está ou não portando uma máscara! O código utiliza da bib

Maria Eduarda de Azevedo Silva 168 Oct 20, 2022
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers Authors: Jaemin Cho, Abhay Zala, and Mohit Bansal (

Jaemin Cho 98 Dec 15, 2022
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption, CVPR 2021 (Oral)

TAP: Text-Aware Pre-training TAP: Text-Aware Pre-training for Text-VQA and Text-Caption by Zhengyuan Yang, Yijuan Lu, Jianfeng Wang, Xi Yin, Dinei Flo

Microsoft 61 Nov 14, 2022
tsflex - feature-extraction benchmarking

tsflex - feature-extraction benchmarking This repository withholds the benchmark results and visualization code of the tsflex paper and toolkit. Flow

PreDiCT.IDLab 5 Mar 25, 2022
Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at [email protected]

TableParser Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at DS3 Lab 11 Dec 13, 2022

LoFTR:Detector-Free Local Feature Matching with Transformers CVPR 2021

LoFTR-with-train-script LoFTR:Detector-Free Local Feature Matching with Transformers CVPR 2021 (with train script --- unofficial ---). About Megadepth

Nan Xiaohu 15 Nov 04, 2022
Repositório da disciplina de APC, no segundo semestre de 2021

NOTAS FINAIS: https://github.com/fabiommendes/apc2018/blob/master/nota-final.pdf Algoritmos e Programação de Computadores Este é o Git da disciplina A

16 Dec 16, 2022
How to Leverage Multimodal EHR Data for Better Medical Predictions?

How to Leverage Multimodal EHR Data for Better Medical Predictions? This repository contains the code of the paper: How to Leverage Multimodal EHR Dat

13 Dec 13, 2022
PyTorch implementation of the TTC algorithm

Trust-the-Critics This repository is a PyTorch implementation of the TTC algorithm and the WGAN misalignment experiments presented in Trust the Critic

0 Nov 29, 2021