Selective Wavelet Attention Learning for Single Image Deraining

Last update: Jun 17, 2022

Overview

SWAL

Code for Paper "Selective Wavelet Attention Learning for Single Image Deraining"

Prerequisites

Python 3
PyTorch

Models

We provide the models trained on DDN, DID, Rain100H, Rain100L, and AGAN datasets in the following links:

Download them into the model folder before testing.

Dataset

Download the rain datasets.
Arrange the images and generate a list file, just like the rain12 set in the data folder.

You can also modify the data_loader code in your manner.

Run

Train SWAL on a single GPU:

 CUDA_VISIBLE_DEVICES=0 python main.py --ngf=16 --ndf=64  --output_height=320  --trainroot=YOURPATH --trainfiles='YOUR_FILELIST'  --save_iter=1 --batchSize=8 --nrow=8 --lr_d=1e-4 --lr_g=1e-4  --cuda  --nEpochs=500

Train SWAL on multiple GPUs:

 CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --ngf=16 --ndf=64  --output_height=320  --trainroot=YOURPATH --trainfiles='YOUR_FILELIST'  --save_iter=1 --batchSize=32 --nrow=8 --lr_d=1e-4 --lr_g=1e-4  --cuda  --nEpochs=500

Test SWAL:

 CUDA_VISIBLE_DEVICES=0 python test.py --ngf=16  --outf='test' --testroot='data/rain12_test' --testfiles='data/rain12_test.list' --pretrained='model/rain100l_best.pth'  --cuda

Adjust the parameters according to your own settings.

Citation

If you use our codes, please cite the following paper:

 @article{huang2021selective,
   title={Selective Wavelet Attention Learning for Single Image Deraining},
   author={Huang, Huaibo and Yu, Aijing and Chai, Zhenhua and He, Ran and Tan, Tieniu},
   journal={International Journal of Computer Vision},
   volume={129},
   number={4},
   pages={1282--1300},
   year={2021},
  }

The released codes are only allowed for non-commercial use.

Selective Wavelet Attention Learning for Single Image Deraining

Related tags

Overview

SWAL

Prerequisites

Models

Dataset

Run

Citation

Owner

Bobo

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Selective Wavelet Attention Learning for Single Image Deraining

Related tags

Overview

SWAL

Prerequisites

Models

Dataset

Run

Citation

Owner

Bobo

Semantic Segmentation Architectures Implemented in PyTorch

KIDA: Knowledge Inheritance in Data Aggregation

This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

Official codebase for Pretrained Transformers as Universal Computation Engines.

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Generalized Decision Transformer for Offline Hindsight Information Matching

YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

Demonstrational Session git repo for H SAF User Workshop (28/1)

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation

Gym Threat Defense

Dataset and Code for the paper "DepthTrack: Unveiling the Power of RGBD Tracking" (ICCV2021), and "Depth-only Object Tracking" (BMVC2021)

A TikTok-like recommender system for GitHub repositories based on Gorse

Human-Pose-and-Motion History

Spherical Confidence Learning for Face Recognition, accepted to CVPR2021.

GT China coal model

This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI