├── LICENSE ├── README.md ├── demo ├── DatasetRainNet.py └── demo.py ├── docs ├── css │ ├── bootstrap-theme.min.css │ ├── bootstrap.min.css │ ├── ie10-viewport-bug-workaround.css │ └── jumbotron.css ├── favicon.ico ├── fonts │ ├── glyphicons-halflings-regular.eot │ ├── glyphicons-halflings-regular.svg │ ├── glyphicons-halflings-regular.ttf │ ├── glyphicons-halflings-regular.woff │ └── glyphicons-halflings-regular.woff2 ├── img │ ├── 1.png │ ├── 2.png │ ├── HRGT_201009539_201009571.webp │ ├── HRGT_201010607_201010639.webp │ ├── HRGT_201011587_201011619.webp │ ├── HRGT_201108607_201108655.webp │ ├── HRGT_201109091_201109123.webp │ ├── HRGT_201110059_201110091.webp │ ├── LRGT_201009539_201009571.webp │ ├── LRGT_201010607_201010639.webp │ ├── LRGT_201011587_201011619.webp │ ├── LRGT_201108607_201108655.webp │ ├── LRGT_201109091_201109123.webp │ ├── LRGT_201110059_201110091.webp │ ├── girl2-RGB.png │ ├── girl2.gif │ ├── logo.png │ ├── logo1.png │ ├── new.gif │ ├── region.png │ ├── results1.PNG │ ├── title.png │ ├── university.png │ └── vggface2_hq_compare.png ├── index.html └── js │ ├── bootstrap.min.js │ ├── ie-emulation-modes-warning.js │ ├── ie10-viewport-bug-workaround.js │ └── vendor │ └── jquery.min.js └── scripts ├── dataloader_hdf5.py └── dataset_prepare_hdf5.py /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We thank [SocialBook](https://socialbook.io/) for providing us with enough shared storage space to continue making this dataset available. 19 | 20 | **`2024-01-21`**: We provide the [[Supplementary Material]](https://drive.google.com/file/d/1QmLompRY0WPnF90IDqKkKjOif_DLSyJy/view?usp=sharing). 21 | 22 | **`2022-11-16`**: The download links are now avaliable: [[Google Driver] RainNet_HDF5.zip (13.6G)](https://drive.google.com/file/d/1cvzT3n9N7o06YhykdakOsboyqwTavkEP/view?usp=sharing) [[Baidu Driver] RainNet_HDF5.zip (13.6G)](https://pan.baidu.com/s/1hXa6Tr089KvBefCJIVQPuQ) [Password: sjtu]. 23 | 24 | **`2022-11-16`**: We are working for metric tools and annotation of events. 25 | 26 | ## Download RainNet 27 | 28 | [[Download Via Google Drive] RainNet_HDF5.zip (13.6G)](https://drive.google.com/file/d/1YE9l7S6NNTCqwsaqXX607vUaLZeFRx8I/view?usp=sharing) 29 | 30 | [[Download Via Baidu Drive] RainNet_HDF5.zip (13.6G)](https://pan.baidu.com/s/1hXa6Tr089KvBefCJIVQPuQ) 31 | [password: sjtu] 32 | 33 | ## Resources in Zip: 34 | ***RainNet_HDF5.zip*** 35 | 36 |   ├  $year$_07.hdf5 37 | 38 |   ├  $year$_08.hdf5 39 | 40 |   ├  $year$_09.hdf5 41 | 42 |   ├  $year$_10.hdf5 43 | 44 |   └  $year$_11.hdf5 45 | 46 | ***$year$=2002~2018*** 47 | - ***85*** HDF5 files in total; 48 | - ***322GB*** of hard disk space is required to extract the dataset. 49 | 50 | ## Dependencies 51 | - python3.6+ 52 | - pytorch1.5+ 53 | - torchvision 54 | - h5py 55 | - numpy 56 | 57 | ## Usage 58 | - Data preparation. Run the 'dataset_prepare_hdf5.py' to process the dataset into patches. In 'dataset_prepare_hdf5.py', variable 'dataset_path' sets the hdf5 file path of RainNet; 'patch_hdf5_root' sets the target path to save processed dataset: 59 | 60 | - ```python dataset_prepare_hdf5.py``` 61 | 62 | - We provide a example dataloader (pytorch script) to read the processed dataset: 63 | 64 | - ```dataloader_hdf5.py``` 65 | 66 | - ***python scripts are archived in fold 'scripts'*** 67 | 68 | ## Samples in RainNet 69 | 70 | [![sampleregion](/docs/img/region.png)](https://github.com/neuralchen/RainNet) 71 | 72 | 73 | ### High Resolution Precipitation Map: 74 | 75 | ### Low Resolution Precipitation Map: 76 | 77 | ### High Resolution Precipitation Map: 78 | 79 | ### Low Resolution Precipitation Map: 80 | 81 | ### High Resolution Precipitation Map: 82 | 83 | ### Low Resolution Precipitation Map: 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | ## Citation 96 | If you find this Dataset useful in your research, please consider citing: 97 | 98 | ``` 99 | @misc{chen2020rainnet, 100 |   title={RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling}, 101 |   author={Xuanhong Chen and Kairui Feng and Naiyuan Liu and Yifan Lu and Zhengyan Tong and Bingbing Ni and Ziang Liu and Ning Lin}, 102 |   year={2020}, 103 |   eprint={2012.09700}, 104 |   archivePrefix={arXiv}, 105 |   primaryClass={cs.CV} 106 | } 107 | ``` 108 | 109 | ## Contact 110 | Please concat Kairui Feng [email](kairuif@princeton.com), Xuanhong Chen [email](xuanhongchenzju@outlook.com), Naiyuan Liu [email](naiyuan.liu@student.uts.edu.au) and Yifan Lu [email](yifan_lu@sjtu.edu.cn) for questions about the dataset. 111 | 112 | ## Related Projects 113 | 114 | **Please visit ou popular face swapping project** 115 | 116 | [![logo](./docs/img/logo1.png)](https://github.com/neuralchen/SimSwap) 117 | 118 | [![title](/docs/img/results1.PNG)](https://github.com/neuralchen/SimSwap) 119 | 120 | **Please visit our high-quality style transfer project** 121 | 122 | [![logo](./docs/img/logo.png)](https://github.com/neuralchen/ASMAGAN) 123 | 124 | [![title](/docs/img/title.png)](https://github.com/neuralchen/ASMAGAN) 125 | 126 | **Please visit our AAAI2021 sketch based rendering project** 127 | 128 | [![logo](./docs/img/girl2.gif)](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale) 129 | [![title](/docs/img/girl2-RGB.png)](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale) 130 | 131 | **Please visit our high resolution face dataset VGGFace2-HQ** 132 | 133 | [![logo](./docs/img/vggface2_hq_compare.png)](https://github.com/NNNNAI/VGGFace2-HQ) 134 | 135 | Learn about our other projects 136 | 137 | [[VGGFace2-HQ]](https://github.com/NNNNAI/VGGFace2-HQ); 138 | 139 | [[RainNet]](https://neuralchen.github.io/RainNet); 140 | 141 | [[Sketch Generation]](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale); 142 | 143 | [[CooGAN]](https://github.com/neuralchen/CooGAN); 144 | 145 | [[Knowledge Style Transfer]](https://github.com/AceSix/Knowledge_Transfer); 146 | 147 | [[SimSwap]](https://github.com/neuralchen/SimSwap); 148 | 149 | [[ASMA-GAN]](https://github.com/neuralchen/ASMAGAN); 150 | 151 | [[SNGAN-Projection-pytorch]](https://github.com/neuralchen/SNGAN_Projection) 152 | 153 | [[Pretrained_VGG19]](https://github.com/neuralchen/Pretrained_VGG19). 154 | -------------------------------------------------------------------------------- /demo/DatasetRainNet.py: -------------------------------------------------------------------------------- 1 | import h5py 2 | import torch.utils.data as data 3 | import numpy as np 4 | import cv2 5 | import random 6 | import torch 7 | from PIL import Image, ImageOps 8 | class RainNetDataset(data.Dataset): 9 | def __init__(self,data_path, is_training=True, crop_size_gt=192, upscale_factor=3, rotation=True, flip=True): 10 | super(RainNetDataset,self).__init__() 11 | (self.highres,self.lowres) = RainNet_from_folder(data_path) 12 | self.len = self.highres.shape[2] 13 | self.is_training = is_training 14 | self.crop_size_gt = crop_size_gt 15 | self.upscale_factor = upscale_factor 16 | self.rotation = rotation 17 | self.flip = flip 18 | 19 | def __getitem__(self,index): 20 | img_hr = self.highres[:,:,index] 21 | img_lr = self.lowres[:,:,index] 22 | img_hr = np.expand_dims(img_hr, -1) 23 | img_lr = np.expand_dims(img_lr, -1) 24 | 25 | # augmentation for training 26 | if self.is_training: 27 | # random crop 28 | img_hr, img_lr = random_crop(img_hr, img_lr, self.crop_size_gt, self.upscale_factor) 29 | # flip, rotation 30 | img_hr, img_lr = augment([img_hr, img_lr], self.flip, self.rotation) 31 | else : 32 | pass 33 | img_hr, img_lr = toTensor([img_hr, img_lr]) 34 | 35 | return img_hr, img_lr 36 | 37 | def __len__(self): 38 | return self.len 39 | 40 | def RainNet_from_folder(folder): 41 | h5_path = folder 42 | f = h5py.File(h5_path,'r') 43 | highres = f['highres'] 44 | lowres = f['lowres'] 45 | return [highres,lowres] 46 | 47 | def random_crop(GT_img, LR_img, GT_crop_size,upscale_factor): 48 | 49 | LR_h, LR_w = LR_img.shape[0],LR_img.shape[1] 50 | LR_crop_size = GT_crop_size//upscale_factor 51 | 52 | ix = np.random.randint(0,LR_h-LR_crop_size-1) 53 | iy = np.random.randint(0,LR_w-LR_crop_size-1) 54 | 55 | GT_img_crop = GT_img[ix:ix+GT_crop_size, iy:iy+GT_crop_size,:] 56 | LR_img_crop = LR_img[ix:ix+LR_crop_size, iy:iy+LR_crop_size,:] 57 | 58 | return GT_img_crop, LR_img_crop 59 | 60 | def augment(imgs, hflip=True, rotation=True): 61 | #print(imgs.shape) 62 | hflip = hflip and random.random() < 0.5 63 | vflip = rotation and random.random() < 0.5 64 | rot = rotation and random.random() < 0.5 65 | 66 | def _augument(img): 67 | if hflip: 68 | cv2.flip(img, 1, img) 69 | if vflip: 70 | cv2.flip(img, 0, img) 71 | if rot: 72 | img = img.transpose(1, 0, 2) 73 | 74 | return img 75 | imgs = [_augument(img) for img in imgs] 76 | 77 | return imgs 78 | 79 | def toTensor(imgs): 80 | 81 | def _totensor(img): 82 | img = torch.from_numpy(img.transpose(2, 0, 1)) 83 | 84 | img = img.float() 85 | return img 86 | if isinstance(imgs, list): 87 | return [_totensor(img) for img in imgs] 88 | else: 89 | return _totensor(imgs) 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | -------------------------------------------------------------------------------- /demo/demo.py: -------------------------------------------------------------------------------- 1 | 2 | import torch 3 | from torch.utils.data import DataLoader 4 | import argparse 5 | from DatasetRainNet import RainNetDataset 6 | 7 | parser = argparse.ArgumentParser(description='Demo scipt for loading RainNet dataset') 8 | parser.add_argument('--dataroot', type=str, default='dataset/2018_11.hdf5') 9 | parser.add_argument('--batchsize', type=int, default=4) 10 | parser.add_argument('--upscale_factor', type=int, default=3) 11 | parser.add_argument('--crop_size_gt', type=int, default=192) 12 | parser.add_argument('--threads', type=int, default=8, help='number of threads for data loader to use') 13 | 14 | arg = parser.parse_args() 15 | 16 | train_set = RainNetDataset(arg.dataroot,is_training=True, crop_size_gt=arg.crop_size_gt, upscale_factor=arg.upscale_factor, rotation=True, flip=True) 17 | #val_set = RainNetDataset(arg.dataroot,is_training=False, crop_size_gt=192, upscale_factor=3) 18 | 19 | training_data_loader = DataLoader(dataset=train_set, num_workers=arg.threads, batch_size=arg.batchsize, shuffle=True) 20 | # testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False) 21 | 22 | dataset_len = len(training_data_loader.dataset) 23 | print(dataset_len) 24 | 25 | for idx,batch in enumerate(training_data_loader): 26 | img_gt, img_lr = batch[0], batch[1] 27 | print('The shape of HighRes Images :{}'.format(img_gt.shape)) 28 | print('The shape of LowRes Images :{}'.format(img_lr.shape)) 29 | break 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RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling

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Accepted by NeurIPS 2022
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Xuanhong Chen*, Kairui Feng*, Naiyuan Liu, Bingbing Ni**, Yifan Lu, Zhengyan Tong, Ziang Liu

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Shanghai Jiao Tong University, Princeton University & University of Technology Sydney

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*Equal contribution.
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**Corresponding author.
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Code »

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Download »

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Paper »

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Abstract

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129 | AI-for-science approaches have been applied to solve scientific problems (e.g., nuclear fusion, ecology, genomics, meteorology) and have achieved highly promising results. 130 | Spatial precipitation downscaling is one of the most important meteorological issues and urgently requires the participation of AI. 131 | However, the lack of a well-organized and annotated large-scale dataset hinders the training and verification of more effective and advancing deep-learning models for precipitation downscaling. 132 | To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named RainNet, which contains more than 62,400 pairs of high-quality low/high-resolution precipitation maps for over 17 years, 133 | ready to help the evolution of deep learning models in precipitation downscaling. 134 | Specifically, the precipitation maps carefully collected in RainNet cover various meteorological phenomena (e.g., hurricane, squall), which is of great help to improve the model generalization ability. 135 | In addition, the map pairs in RainNet are organized in the form of image sequences (720 maps per month or 1 map/hour), showing complex physical properties, e.g., temporal misalignment, temporal sparse, and fluid properties. 136 | Two deep-learning-oriented metrics are specifically introduced to evaluate or verify the comprehensive performance of the trained model, (e.g., prediction maps reconstruction accuracy). 137 | To illustrate the applications of RainNet, 14 state-of-the-art models, including deep models and traditional approaches, are evaluated. 138 | To fully explore potential downscaling solutions, we propose an implicit physical estimation benchmark framework to learn the above characteristics. 139 | Extensive experiments demonstrate the value of RainNet in training and evaluating downscaling models. 140 | 141 |

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Samples in RainNet

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Low Resolution Precipitation Maps

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Downloads (RainNet_HDF5.zip (13.6G))

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Donwnload via Google Drive »

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Download via Baidu Drive »

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Citation

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248 | @misc{chen2020rainnet,
249 |   title={RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling},
250 |   author={Xuanhong Chen and Kairui Feng and Naiyuan Liu and Yifan Lu and Zhengyan Tong and Bingbing Ni and Ziang Liu and Ning Lin},
251 |   year={2020},
252 |   eprint={2012.09700},
253 |   archivePrefix={arXiv},
254 |   primaryClass={cs.CV}
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Contact

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Please concat Kairui Feng kairuif@princeton.com, 271 | Xuanhong Chen xuanhongchenzju@outlook.com, 272 | Naiyuan Liu naiyuan.liu@student.uts.edu.au and 273 | Yifan Lu yifan_lu@sjtu.edu.cn 274 | for questions about the dataset.

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DO NOT USE ANY OF THIS JAVASCRIPT 2 | // IT'S JUST JUNK FOR OUR DOCS! 3 | // ++++++++++++++++++++++++++++++++++++++++++ 4 | /*! 5 | * Copyright 2014-2015 Twitter, Inc. 6 | * 7 | * Licensed under the Creative Commons Attribution 3.0 Unported License. For 8 | * details, see https://creativecommons.org/licenses/by/3.0/. 9 | */ 10 | // Intended to prevent false-positive bug reports about Bootstrap not working properly in old versions of IE due to folks testing using IE's unreliable emulation modes. 11 | (function () { 12 | 'use strict'; 13 | 14 | function emulatedIEMajorVersion() { 15 | var groups = /MSIE ([0-9.]+)/.exec(window.navigator.userAgent) 16 | if (groups === null) { 17 | return null 18 | } 19 | var ieVersionNum = parseInt(groups[1], 10) 20 | var ieMajorVersion = Math.floor(ieVersionNum) 21 | return ieMajorVersion 22 | } 23 | 24 | function actualNonEmulatedIEMajorVersion() { 25 | // Detects the actual version of IE in use, even if it's in an older-IE emulation mode. 26 | // IE JavaScript conditional compilation docs: https://msdn.microsoft.com/library/121hztk3%28v=vs.94%29.aspx 27 | // @cc_on docs: https://msdn.microsoft.com/library/8ka90k2e%28v=vs.94%29.aspx 28 | var jscriptVersion = new Function('/*@cc_on return 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