From 44c9eda346c53b106a76668d033db7fc83cc05ff Mon Sep 17 00:00:00 2001 From: JingyunLiang Date: Wed, 15 Jun 2022 14:37:35 +0200 Subject: [PATCH] Support VFI and STVSR for VRT --- README.md | 16 +- data/dataset_video_test.py | 142 +- data/dataset_video_train.py | 73 +- .../meta_info_Vimeo90K_test_fast_GT.txt | 1225 ++++ .../meta_info_Vimeo90K_test_medium_GT.txt | 4977 +++++++++++++++++ .../meta_info_Vimeo90K_test_slow_GT.txt | 1613 ++++++ data/select_dataset.py | 9 + docs/README_RVRT.md | 4 +- docs/README_VRT.md | 45 +- main_download_pretrained_models.py | 25 +- main_test_rvrt.py | 5 +- main_test_vrt.py | 55 +- models/network_rvrt.py | 28 +- models/network_vrt.py | 179 +- ...03_train_vrt_videosr_bi_vimeo_7frames.json | 2 +- ...04_train_vrt_videosr_bd_vimeo_7frames.json | 2 +- .../009_train_vrt_videofi_vimeo_4frames.json | 116 + 17 files changed, 8371 insertions(+), 145 deletions(-) create mode 100755 data/meta_info/meta_info_Vimeo90K_test_fast_GT.txt create mode 100755 data/meta_info/meta_info_Vimeo90K_test_medium_GT.txt create mode 100755 data/meta_info/meta_info_Vimeo90K_test_slow_GT.txt create mode 100644 options/vrt/009_train_vrt_videofi_vimeo_4frames.json diff --git a/README.md b/README.md index 7894aa7f..b3e2c2c7 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -## Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR, VRT +## Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR, VRT, RVRT [![download](https://img.shields.io/github/downloads/cszn/KAIR/total.svg)](https://github.com/cszn/KAIR/releases) ![visitors](https://visitor-badge.glitch.me/badge?page_id=cszn/KAIR) [Kai Zhang](https://cszn.github.io/) @@ -6,7 +6,7 @@ *[Computer Vision Lab](https://vision.ee.ethz.ch/the-institute.html), ETH Zurich, Switzerland* _______ -- **_News (2022-06-01)_**: We release [the training codes](https://github.com/cszn/KAIR/blob/master/docs/README_RVRT.md) of [RVRT ![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/RVRT?style=social)](https://github.com/JingyunLiang/RVRT) for video SR, deblurring and denoising. +- **_News (2022-10-04)_**: We release [the training codes](https://github.com/cszn/KAIR/blob/master/docs/README_RVRT.md) of [RVRT, NeurlPS2022 ![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/RVRT?style=social)](https://github.com/JingyunLiang/RVRT) for video SR, deblurring and denoising. - **_News (2022-05-05)_**: Try the [online demo](https://replicate.com/cszn/scunet) of [SCUNet ![GitHub Stars](https://img.shields.io/github/stars/cszn/SCUNet?style=social)](https://github.com/cszn/SCUNet) for blind real image denoising. @@ -23,13 +23,11 @@ We did not use the paired noisy/clean data by DND and SIDD during training!*__ - **_News (2022-02-15)_**: We release [the training codes](https://github.com/cszn/KAIR/blob/master/docs/README_VRT.md) of [VRT ![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/VRT?style=social)](https://github.com/JingyunLiang/VRT) for video SR, deblurring and denoising. -

- - - - - -

+![Eg1](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vsr.gif) +![Eg2](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vdb.gif) +![Eg3](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vdn.gif) +![Eg4](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vfi.gif) +![Eg5](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_stvsr.gif) - **_News (2021-12-23)_**: Our techniques are adopted in [https://www.amemori.ai/](https://www.amemori.ai/). - **_News (2021-12-23)_**: Our new work for practical image denoising. diff --git a/data/dataset_video_test.py b/data/dataset_video_test.py index 512bd55c..62f4c402 100755 --- a/data/dataset_video_test.py +++ b/data/dataset_video_test.py @@ -2,6 +2,8 @@ import torch from os import path as osp import torch.utils.data as data +from torchvision import transforms +from PIL import Image import utils.utils_video as utils_video @@ -245,12 +247,12 @@ def __init__(self, opt): super(VideoTestVimeo90KDataset, self).__init__() self.opt = opt self.cache_data = opt['cache_data'] - temporal_scale = opt.get('temporal_scale', 1) + self.temporal_scale = opt.get('temporal_scale', 1) if self.cache_data: raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.') self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} - neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])][:: temporal_scale] + neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])][:: self.temporal_scale] with open(opt['meta_info_file'], 'r') as fin: subfolders = [line.split(' ')[0] for line in fin] @@ -259,7 +261,7 @@ def __init__(self, opt): self.data_info['gt_path'].append(gt_path) lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list] self.data_info['lq_path'].append(lq_paths) - self.data_info['folder'].append('vimeo90k') + self.data_info['folder'].append(subfolder) self.data_info['idx'].append(f'{idx}/{len(subfolders)}') self.data_info['border'].append(0) @@ -271,7 +273,6 @@ def __getitem__(self, index): gt_path = self.data_info['gt_path'][index] imgs_lq = utils_video.read_img_seq(lq_path) img_gt = utils_video.read_img_seq([gt_path]) - img_gt.squeeze_(0) if self.pad_sequence: # pad the sequence: 7 frames to 8 frames imgs_lq = torch.cat([imgs_lq, imgs_lq[-1:,...]], dim=0) @@ -285,9 +286,140 @@ def __getitem__(self, index): 'folder': self.data_info['folder'][index], # folder name 'idx': self.data_info['idx'][index], # e.g., 0/843 'border': self.data_info['border'][index], # 0 for non-border - 'lq_path': lq_path[self.opt['num_frame'] // 2] # center frame + 'lq_path': lq_path, + 'gt_path': [gt_path] } def __len__(self): return len(self.data_info['gt_path']) + +class VFI_DAVIS(data.Dataset): + """Video test dataset for DAVIS dataset in video frame interpolation. + Modified from https://github.com/tarun005/FLAVR/blob/main/dataset/Davis_test.py + """ + + def __init__(self, data_root, ext="png"): + + super().__init__() + + self.data_root = data_root + self.images_sets = [] + + for label_id in os.listdir(self.data_root): + ctg_imgs_ = sorted(os.listdir(os.path.join(self.data_root , label_id))) + ctg_imgs_ = [os.path.join(self.data_root , label_id , img_id) for img_id in ctg_imgs_] + for start_idx in range(0,len(ctg_imgs_)-6,2): + add_files = ctg_imgs_[start_idx : start_idx+7 : 2] + add_files = add_files[:2] + [ctg_imgs_[start_idx+3]] + add_files[2:] + self.images_sets.append(add_files) + + self.transforms = transforms.Compose([ + transforms.CenterCrop((480, 840)), + transforms.ToTensor() + ]) + + def __getitem__(self, idx): + + imgpaths = self.images_sets[idx] + images = [Image.open(img) for img in imgpaths] + images = [self.transforms(img) for img in images] + + return { + 'L': torch.stack(images[:2] + images[3:], 0), + 'H': images[2].unsqueeze(0), + 'folder': str(idx), + 'gt_path': ['vfi_result.png'], + } + + def __len__(self): + return len(self.images_sets) + + +class VFI_UCF101(data.Dataset): + """Video test dataset for UCF101 dataset in video frame interpolation. + Modified from https://github.com/tarun005/FLAVR/blob/main/dataset/ucf101_test.py + """ + + def __init__(self, data_root, ext="png"): + super().__init__() + + self.data_root = data_root + self.file_list = sorted(os.listdir(self.data_root)) + + self.transforms = transforms.Compose([ + transforms.CenterCrop((224,224)), + transforms.ToTensor(), + ]) + + def __getitem__(self, idx): + + imgpath = os.path.join(self.data_root , self.file_list[idx]) + imgpaths = [os.path.join(imgpath , "frame0.png") , os.path.join(imgpath , "frame1.png") ,os.path.join(imgpath , "frame2.png") ,os.path.join(imgpath , "frame3.png") ,os.path.join(imgpath , "framet.png")] + + images = [Image.open(img) for img in imgpaths] + images = [self.transforms(img) for img in images] + + return { + 'L': torch.stack(images[:-1], 0), + 'H': images[-1].unsqueeze(0), + 'folder': self.file_list[idx], + 'gt_path': ['vfi_result.png'], + } + + def __len__(self): + return len(self.file_list) + + +class VFI_Vid4(data.Dataset): + """Video test dataset for Vid4 dataset in video frame interpolation. + Modified from https://github.com/tarun005/FLAVR/blob/main/dataset/Davis_test.py + """ + + def __init__(self, data_root, ext="png"): + + super().__init__() + + self.data_root = data_root + self.images_sets = [] + self.data_info = {'lq_path': [], 'gt_path': [], 'folder': []} + self.lq_path = [] + self.folder = [] + + for label_id in os.listdir(self.data_root): + ctg_imgs_ = sorted(os.listdir(os.path.join(self.data_root, label_id))) + ctg_imgs_ = [os.path.join(self.data_root , label_id , img_id) for img_id in ctg_imgs_] + if len(ctg_imgs_) % 2 == 0: + ctg_imgs_.append(ctg_imgs_[-1]) + ctg_imgs_.insert(0, None) + ctg_imgs_.insert(0, ctg_imgs_[1]) + ctg_imgs_.append(None) + ctg_imgs_.append(ctg_imgs_[-2]) + + for start_idx in range(0,len(ctg_imgs_)-6,2): + add_files = ctg_imgs_[start_idx : start_idx+7 : 2] + self.data_info['lq_path'].append([os.path.basename(path) for path in add_files]) + self.data_info['gt_path'].append(os.path.basename(ctg_imgs_[start_idx + 3])) + self.data_info['folder'].append(label_id) + add_files = add_files[:2] + [ctg_imgs_[start_idx+3]] + add_files[2:] + self.images_sets.append(add_files) + + self.transforms = transforms.Compose([ + transforms.ToTensor() + ]) + + def __getitem__(self, idx): + imgpaths = self.images_sets[idx] + images = [Image.open(img) for img in imgpaths] + images = [self.transforms(img) for img in images] + + return { + 'L': torch.stack(images[:2] + images[3:], 0), + 'H': images[2].unsqueeze(0), + 'folder': self.data_info['folder'][idx], + 'lq_path': self.data_info['lq_path'][idx], + 'gt_path': [self.data_info['gt_path'][idx]] + } + + def __len__(self): + return len(self.images_sets) diff --git a/data/dataset_video_train.py b/data/dataset_video_train.py index b596ed1a..0813a9f6 100755 --- a/data/dataset_video_train.py +++ b/data/dataset_video_train.py @@ -3,6 +3,7 @@ import torch from pathlib import Path import torch.utils.data as data +from torchvision import transforms import utils.utils_video as utils_video @@ -302,6 +303,7 @@ def __init__(self, opt): super(VideoRecurrentTrainVimeoDataset, self).__init__() self.opt = opt self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) + self.temporal_scale = opt.get('temporal_scale', 1) with open(opt['meta_info_file'], 'r') as fin: self.keys = [line.split(' ')[0] for line in fin] @@ -316,7 +318,7 @@ def __init__(self, opt): self.io_backend_opt['client_keys'] = ['lq', 'gt'] # indices of input images - self.neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] + self.neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])][::self.temporal_scale] # temporal augmentation configs self.random_reverse = opt['random_reverse'] @@ -324,7 +326,6 @@ def __init__(self, opt): self.mirror_sequence = opt.get('mirror_sequence', False) self.pad_sequence = opt.get('pad_sequence', False) - self.neighbor_list = [1, 2, 3, 4, 5, 6, 7] def __getitem__(self, index): if self.file_client is None: @@ -378,9 +379,75 @@ def __getitem__(self, index): img_gts = torch.cat([img_gts, img_gts[-1:,...]], dim=0) # img_lqs: (t, c, h, w) - # img_gt: (c, h, w) + # img_gt: (t, c, h, w) # key: str return {'L': img_lqs, 'H': img_gts, 'key': key} def __len__(self): return len(self.keys) + +class VideoRecurrentTrainVimeoVFIDataset(VideoRecurrentTrainVimeoDataset): + + def __init__(self, opt): + super(VideoRecurrentTrainVimeoVFIDataset, self).__init__(opt) + self.color_jitter = self.opt.get('color_jitter', False) + + if self.color_jitter: + self.transforms_color_jitter = transforms.ColorJitter(0.05, 0.05, 0.05, 0.05) + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = utils_video.FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # random reverse + if self.random_reverse and random.random() < 0.5: + self.neighbor_list.reverse() + + scale = self.opt['scale'] + gt_size = self.opt['gt_size'] + key = self.keys[index] + clip, seq = key.split('/') # key example: 00001/0001 + + # get the neighboring LQ and GT frames + img_lqs = [] + img_gts = [] + for neighbor in self.neighbor_list: + if self.is_lmdb: + img_lq_path = f'{clip}/{seq}/im{neighbor}' + else: + img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png' + # LQ + img_bytes = self.file_client.get(img_lq_path, 'lq') + img_lq = utils_video.imfrombytes(img_bytes, float32=True) + img_lqs.append(img_lq) + + # GT + if self.is_lmdb: + img_gt_path = f'{clip}/{seq}/im4' + else: + img_gt_path = self.gt_root / clip / seq / 'im4.png' + + img_bytes = self.file_client.get(img_gt_path, 'gt') + img_gt = utils_video.imfrombytes(img_bytes, float32=True) + img_gts.append(img_gt) + + # randomly crop + img_gts, img_lqs = utils_video.paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) + + # augmentation - flip, rotate + img_lqs.extend([img_gts]) + img_results = utils_video.augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) + + img_results = utils_video.img2tensor(img_results) + img_results = torch.stack(img_results, dim=0) + + if self.color_jitter: # same color_jitter for img_lqs and img_gts + img_results = self.transforms_color_jitter(img_results) + + img_lqs = img_results[:-1, ...] + img_gts = img_results[-1:, ...] + + # img_lqs: (t, c, h, w) + # img_gt: (t, c, h, w) + # key: str + return {'L': img_lqs, 'H': img_gts, 'key': key} diff --git a/data/meta_info/meta_info_Vimeo90K_test_fast_GT.txt b/data/meta_info/meta_info_Vimeo90K_test_fast_GT.txt new file mode 100755 index 00000000..79e7915d --- /dev/null +++ b/data/meta_info/meta_info_Vimeo90K_test_fast_GT.txt @@ 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(256,448,3) +00096/0347 7 (256,448,3) +00096/0348 7 (256,448,3) +00096/0359 7 (256,448,3) +00096/0363 7 (256,448,3) +00096/0373 7 (256,448,3) +00096/0378 7 (256,448,3) +00096/0387 7 (256,448,3) +00096/0395 7 (256,448,3) +00096/0396 7 (256,448,3) +00096/0404 7 (256,448,3) +00096/0653 7 (256,448,3) +00096/0668 7 (256,448,3) +00096/0679 7 (256,448,3) +00096/0729 7 (256,448,3) +00096/0736 7 (256,448,3) +00096/0823 7 (256,448,3) diff --git a/data/select_dataset.py b/data/select_dataset.py index bbe9986c..b639bbca 100644 --- a/data/select_dataset.py +++ b/data/select_dataset.py @@ -62,12 +62,21 @@ def define_Dataset(dataset_opt): from data.dataset_video_train import VideoRecurrentTrainNonblindDenoisingDataset as D elif dataset_type in ['videorecurrenttrainvimeodataset']: from data.dataset_video_train import VideoRecurrentTrainVimeoDataset as D + elif dataset_type in ['videorecurrenttrainvimeovfidataset']: + from data.dataset_video_train import VideoRecurrentTrainVimeoVFIDataset as D elif dataset_type in ['videorecurrenttestdataset']: from data.dataset_video_test import VideoRecurrentTestDataset as D elif dataset_type in ['singlevideorecurrenttestdataset']: from data.dataset_video_test import SingleVideoRecurrentTestDataset as D elif dataset_type in ['videotestvimeo90kdataset']: from data.dataset_video_test import VideoTestVimeo90KDataset as D + elif dataset_type in ['vfi_davis']: + from data.dataset_video_test import VFI_DAVIS as D + elif dataset_type in ['vfi_ucf101']: + from data.dataset_video_test import VFI_UCF101 as D + elif dataset_type in ['vfi_vid4']: + from data.dataset_video_test import VFI_Vid4 as D + # ----------------------------------------- # common diff --git a/docs/README_RVRT.md b/docs/README_RVRT.md index 5c70d168..f2839979 100644 --- a/docs/README_RVRT.md +++ b/docs/README_RVRT.md @@ -1,4 +1,4 @@ -# [Recurrent Video Restoration Transformer with Guided Deformable Attention (RVRT)](https://github.com/JingyunLiang/RVRT) +# [Recurrent Video Restoration Transformer with Guided Deformable Attention (RVRT, NeurlPS2022)](https://github.com/JingyunLiang/RVRT) [arxiv](https://arxiv.org/abs/2206.02146) **|** [supplementary](https://github.com/JingyunLiang/RVRT/releases/download/v0.0/RVRT_supplementary.pdf) @@ -16,7 +16,7 @@ [ google colab logo](https://colab.research.google.com/gist/JingyunLiang/23502e2c65d82144219fa3e3322e4fc3/rvrt-demo-on-video-restoration.ipynb) This is the readme of "Recurrent Video Restoration Transformer with Guided Deformable Attention" -([arxiv](https://arxiv.org/pdf/2206.02146.pdf), [supp](https://github.com/JingyunLiang/RVRT/releases/download/v0.0/RVRT_supplementary.pdf), [pretrained models](https://github.com/JingyunLiang/RVRT/releases), [visual results](https://github.com/JingyunLiang/RVRT/releases)). RVRT ahcieves state-of-the-art performance with balanced model size, testing memory and runtime in +([arxiv](https://arxiv.org/pdf/2206.02146.pdf), [supp](https://github.com/JingyunLiang/RVRT/releases/download/v0.0/RVRT_supplementary.pdf), [pretrained models](https://github.com/JingyunLiang/RVRT/releases), [visual results](https://github.com/JingyunLiang/RVRT/releases)). RVRT achieves state-of-the-art performance with balanced model size, testing memory and runtime in - video SR (REDS, Vimeo90K, Vid4, UDM10) - video deblurring (GoPro, DVD) - video denoising (DAVIS, Set8) diff --git a/docs/README_VRT.md b/docs/README_VRT.md index bb4e0d28..7dc4b5b9 100644 --- a/docs/README_VRT.md +++ b/docs/README_VRT.md @@ -16,11 +16,14 @@ [ google colab logo](https://colab.research.google.com/gist/JingyunLiang/deb335792768ad9eb73854a8efca4fe0#file-vrt-demo-on-video-restoration-ipynb) This is the readme of "VRT: A Video Restoration Transformer" -([arxiv](https://arxiv.org/pdf/2201.12288.pdf), [supp](https://github.com/JingyunLiang/VRT/releases/download/v0.0/VRT_supplementary.pdf), [pretrained models](https://github.com/JingyunLiang/VRT/releases), [visual results](https://github.com/JingyunLiang/VRT/releases)). VRT ahcieves state-of-the-art performance **(up to 2.16dB)** in +([arxiv](https://arxiv.org/pdf/2201.12288.pdf), [supp](https://github.com/JingyunLiang/VRT/releases/download/v0.0/VRT_supplementary.pdf), [pretrained models](https://github.com/JingyunLiang/VRT/releases), [visual results](https://github.com/JingyunLiang/VRT/releases)). VRT achieves state-of-the-art performance **(up to 2.16dB)** in - video SR (REDS, Vimeo90K, Vid4 and UDM10) - video deblurring (GoPro, DVD and REDS) - video denoising (DAVIS and Set8) +- video frame interpolation (Vimeo90K, UCF101, DAVIS) +- space-time video SR (Vimeo90K, Vid4) + + +![Eg1](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vsr.gif) +![Eg2](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vdb.gif) +![Eg3](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vdn.gif) +![Eg4](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_vfi.gif) +![Eg5](https://raw.githubusercontent.com/JingyunLiang/VRT/main/assets/teaser_stvsr.gif) --- -> Video restoration (e.g., video super-resolution) aims to restore high-quality frames from low-quality frames. Different from single image restoration, video restoration generally requires to utilize temporal information from multiple adjacent but usually misaligned video frames. Existing deep methods generally tackle with this by exploiting a sliding window strategy or a recurrent architecture, which either is restricted by frame-by-frame restoration or lacks long-range modelling ability. In this paper, we propose a Video Restoration Transformer (VRT) with parallel frame prediction and long-range temporal dependency modelling abilities. More specifically, VRT is composed of multiple scales, each of which consists of two kinds of modules: temporal mutual self attention (TMSA) and parallel warping. TMSA divides the video into small clips, on which mutual attention is applied for joint motion estimation, feature alignment and feature fusion, while self-attention is used for feature extraction. To enable cross-clip interactions, the video sequence is shifted for every other layer. Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping. Experimental results on three tasks, including video super-resolution, video deblurring and video denoising, demonstrate that VRT outperforms the state-of-the-art methods by large margins (**up to 2.16 dB**) on nine benchmark datasets. +> Video restoration (e.g., video super-resolution) aims to restore high-quality frames from low-quality frames. Different from single image restoration, video restoration generally requires to utilize temporal information from multiple adjacent but usually misaligned video frames. Existing deep methods generally tackle with this by exploiting a sliding window strategy or a recurrent architecture, which either is restricted by frame-by-frame restoration or lacks long-range modelling ability. In this paper, we propose a Video Restoration Transformer (VRT) with parallel frame prediction and long-range temporal dependency modelling abilities. More specifically, VRT is composed of multiple scales, each of which consists of two kinds of modules: temporal mutual self attention (TMSA) and parallel warping. TMSA divides the video into small clips, on which mutual attention is applied for joint motion estimation, feature alignment and feature fusion, while self-attention is used for feature extraction. To enable cross-clip interactions, the video sequence is shifted for every other layer. Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping. Experimental results on three tasks, including video super-resolution, video deblurring, video denoising, video frame interpolation and space-time video super-resolution, demonstrate that VRT outperforms the state-of-the-art methods by large margins (**up to 2.16 dB**) on nine benchmark datasets.

@@ -85,10 +95,18 @@ python main_test_vrt.py --task 006_VRT_videodeblurring_GoPro --folder_lq testset # 007, video deblurring trained on REDS, tested on REDS4 python main_test_vrt.py --task 007_VRT_videodeblurring_REDS --folder_lq testsets/REDS4/blur --folder_gt testsets/REDS4/GT --tile 12 256 256 --tile_overlap 2 20 20 -# 008, video denoising trained on DAVIS (noise level 0-50) and tested on Set8 and DAVIS +# 008, video denoising trained on DAVIS (noise level 0-50), tested on Set8 and DAVIS python main_test_vrt.py --task 008_VRT_videodenoising_DAVIS --sigma 10 --folder_lq testsets/Set8 --folder_gt testsets/Set8 --tile 12 256 256 --tile_overlap 2 20 20 python main_test_vrt.py --task 008_VRT_videodenoising_DAVIS --sigma 10 --folder_lq testsets/DAVIS-test --folder_gt testsets/DAVIS-test --tile 12 256 256 --tile_overlap 2 20 20 +# 009, video frame interpolation trained on Vimeo (single frame interpolation), tested on Viemo, UCF101 and DAVIS-train +python main_test_vrt.py --task 009_VRT_videofi_Vimeo_4frames --folder_lq testsets/vimeo90k/vimeo_septuplet/sequences --folder_gt testsets/vimeo90k/vimeo_septuplet/sequences --tile 0 0 0 --tile_overlap 0 0 0 +python main_test_vrt.py --task 009_VRT_videofi_Vimeo_4frames --folder_lq testsets/UCF101 --folder_gt testsets/UCF101 --tile 0 0 0 --tile_overlap 0 0 0 +python main_test_vrt.py --task 009_VRT_videofi_Vimeo_4frames --folder_lq testsets/DAVIS-train --folder_gt testsets/DAVIS-train --tile 0 256 256 --tile_overlap 0 20 20 + +# 010, space-time video sr, using pretrained models from 003 and 009, tested on Vid4 and Viemo +# Please refer to 003 and 009 + # test on your own datasets (an example) python main_test_vrt.py --task 001_VRT_videosr_bi_REDS_6frames --folder_lq testsets/your/own --tile 40 128 128 --tile_overlap 2 20 20 ``` @@ -110,6 +128,8 @@ Note: You do **NOT need** to prepare the datasets if you just want to test the m | video deblurring (setting 2, motion blur) | [GoPro](http://data.cv.snu.ac.kr:8008/webdav/dataset/GOPRO/GOPRO_Large.zip) (22 videos, 2103 frames)

*Use [prepare_GoPro_as_video.py](https://github.com/cszn/KAIR/tree/master/scripts/data_preparation/prepare_GoPro_as_video.py) to regroup and rename the dataset. | GoPro (11 videos, 1111 frames)

*Use [evaluate_video_deblurring.m](https://github.com/cszn/KAIR/tree/master/scripts/matlab_scripts/evaluate_video_deblurring.m) for final evaluation. | [here](https://github.com/JingyunLiang/VRT/releases) | | video deblurring (setting 3, motion blur) | [REDS sharp & blur](https://seungjunnah.github.io/Datasets/reds.html) (266 videos, 266000 frames: train & val except REDS4)

*Use [regroup_reds_dataset.py](https://github.com/cszn/KAIR/tree/master/scripts/data_preparation/regroup_reds_dataset.py) to regroup and rename REDS val set. Note that it shares the same HQ frames as in VSR. | REDS4 (4 videos, 400 frames: 000, 011, 015, 020 of REDS) | [here](https://github.com/JingyunLiang/VRT/releases) | | video denoising (Gaussian noise) | [DAVIS-2017](https://data.vision.ee.ethz.ch/csergi/share/davis/DAVIS-2017-Unsupervised-trainval-480p.zip) (90 videos, 6208 frames)

*Use all files in DAVIS/JPEGImages/480p | [DAVIS-2017-test](https://github.com/JingyunLiang/VRT/releases) (30 videos) + [Set8](https://www.dropbox.com/sh/20n4cscqkqsfgoj/AABGftyJuJDwuCLGczL-fKvBa/test_sequences?dl=0&subfolder_nav_tracking=1) (8 videos: tractor, touchdown, park_joy and sunflower selected from DERF + hypersmooth, motorbike, rafting and snowboard from GOPRO_540P) | [here](https://github.com/JingyunLiang/VRT/releases) | +| video frame interpolation (single-frame interpolation) | [Vimeo90K](http://data.csail.mit.edu/tofu/dataset/vimeo_septuplet.zip) (64612 seven-frame videos as in `sep_trainlist.txt`) | Vimeo90K-T (the rest 7824 7-frame videos) + [UCF101](https://www.dropbox.com/s/dbihqk5deobn0f7/ucf101_extracted.zip?dl=0) (100 videos, 100 quintuples) + [DAVIS-2017](https://data.vision.ee.ethz.ch/csergi/share/davis/DAVIS-2017-Unsupervised-trainval-480p.zip) (90 videos, 6208 frames, 2849 quintuples)

*For DAVIS-2017, use all files in DAVIS/JPEGImages/480p | [here](https://github.com/JingyunLiang/VRT/releases) | +| space-time video SR | Not trained. Using pretrianed models 003 and 009. | Vimeo90K-T (the rest 7824 7-frame videos) + [Vid4](https://drive.google.com/file/d/1ZuvNNLgR85TV_whJoHM7uVb-XW1y70DW/view) (4 videos)

*Using fast/medium/slow splits in data/meta_info. | [here](https://github.com/JingyunLiang/VRT/releases) | Run following commands for training: ```bash @@ -141,6 +161,12 @@ python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_tr # 008, video denoising trained on DAVIS (noise level 0-50) and tested on Set8 and DAVIS python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_vrt.py --opt options/vrt/008_train_vrt_videodenoising_davis.json --dist True + +# 009, video frame interpolation trained on Vimeo (single frame interpolation), tested on Viemo, UCF101 and DAVIS-train +python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_vrt.py --opt options/vrt/009_train_vrt_videofi_vimeo_4frames.json --dist True + +# 010, space-time video sr, using pretrained models from 003 and 009, tested on Vid4 and Viemo +# Not trained. Directly using 003 and 009. ``` Tip: The training process will terminate automatically at 20000 iteration due to a bug. Just resume training after that.
@@ -177,6 +203,19 @@ We achieved state-of-the-art performance on video SR, video deblurring and video

+
+Video Frame Interpolation +

+ +

+
+ +
+Space-Time Video Super-Resolution +

+ +

+
## Citation @article{liang2022vrt, diff --git a/main_download_pretrained_models.py b/main_download_pretrained_models.py index 02a067a1..f119ee85 100644 --- a/main_download_pretrained_models.py +++ b/main_download_pretrained_models.py @@ -30,6 +30,9 @@ download VRT models: python main_download_pretrained_models.py --models "VRT" --model_dir "model_zoo" +download RVRT models: + python main_download_pretrained_models.py --models "RVRT" --model_dir "model_zoo" + download other models: python main_download_pretrained_models.py --models "others" --model_dir "model_zoo" @@ -53,8 +56,10 @@ def download_pretrained_model(model_dir='model_zoo', model_name='dncnn3.pth'): os.makedirs(model_dir, exist_ok=True) if 'SwinIR' in model_name: url = 'https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/{}'.format(model_name) - elif 'VRT' in model_name: + elif '_VRT_' in model_name: url = 'https://github.com/JingyunLiang/VRT/releases/download/v0.0/{}'.format(model_name) + elif '_RVRT_' in model_name: + url = 'https://github.com/JingyunLiang/RVRT/releases/download/v0.0/{}'.format(model_name) else: url = 'https://github.com/cszn/KAIR/releases/download/v1.0/{}'.format(model_name) r = requests.get(url, allow_redirects=True) @@ -95,9 +100,13 @@ def download_pretrained_model(model_dir='model_zoo', model_name='dncnn3.pth'): '006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth', '006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth', '006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth'], 'VRT': ['001_VRT_videosr_bi_REDS_6frames.pth', '002_VRT_videosr_bi_REDS_16frames.pth', - '003_VRT_videosr_bi_Vimeo_7frames.pth', '004_VRT_videosr_bd_Vimeo_7frames.pth', - '005_VRT_videodeblurring_DVD.pth', '006_VRT_videodeblurring_GoPro.pth', - '007_VRT_videodeblurring_REDS.pth', '008_VRT_videodenoising_DAVIS.pth'], + '003_VRT_videosr_bi_Vimeo_7frames.pth', '004_VRT_videosr_bd_Vimeo_7frames.pth', + '005_VRT_videodeblurring_DVD.pth', '006_VRT_videodeblurring_GoPro.pth', + '007_VRT_videodeblurring_REDS.pth', '008_VRT_videodenoising_DAVIS.pth', + '009_VRT_videofi_Vimeo_4frames.pth'], + 'RVRT': ['001_RVRT_videosr_bi_REDS_30frames.pth', '002_RVRT_videosr_bi_Vimeo_14frames.pth', + '003_RVRT_videosr_bd_Vimeo_14frames.pth', '004_RVRT_videodeblurring_DVD_16frames.pth', + '005_RVRT_videodeblurring_GoPro_16frames.pth', '006_RVRT_videodenoising_DAVIS_16frames.pth'], 'others': ['msrresnet_x4_psnr.pth', 'msrresnet_x4_gan.pth', 'imdn_x4.pth', 'RRDB.pth', 'ESRGAN.pth', 'FSSR_DPED.pth', 'FSSR_JPEG.pth', 'RealSR_DPED.pth', 'RealSR_JPEG.pth'] } @@ -117,15 +126,19 @@ def download_pretrained_model(model_dir='model_zoo', model_name='dncnn3.pth'): for model_name in method_model_zoo[method_model]: if 'SwinIR' in model_name: download_pretrained_model(os.path.join(args.model_dir, 'swinir'), model_name) - elif 'VRT' in model_name: + elif '_VRT_' in model_name: download_pretrained_model(os.path.join(args.model_dir, 'vrt'), model_name) + elif '_RVRT_' in model_name: + download_pretrained_model(os.path.join(args.model_dir, 'rvrt'), model_name) else: download_pretrained_model(args.model_dir, model_name) elif method_model in model_zoo: # model, do not need for loop if 'SwinIR' in method_model: download_pretrained_model(os.path.join(args.model_dir, 'swinir'), method_model) - elif 'VRT' in method_model: + elif '_VRT_' in method_model: download_pretrained_model(os.path.join(args.model_dir, 'vrt'), method_model) + elif '_RVRT_' in method_model: + download_pretrained_model(os.path.join(args.model_dir, 'rvrt'), method_model) else: download_pretrained_model(args.model_dir, method_model) else: diff --git a/main_test_rvrt.py b/main_test_rvrt.py index e0d6833f..75032b40 100644 --- a/main_test_rvrt.py +++ b/main_test_rvrt.py @@ -76,8 +76,7 @@ def main(): if 'vimeo' in args.folder_lq.lower(): output = (output[:, 3:4, :, :, :] + output[:, 10:11, :, :, :]) / 2 - gt = gt.unsqueeze(0) - batch['lq_path'] = [['im4.png']] + batch['lq_path'] = batch['gt_path'] test_results_folder = OrderedDict() test_results_folder['psnr'] = [] @@ -205,7 +204,7 @@ def prepare_model_dataset(args): open(model_path, 'wb').write(r.content) pretrained_model = torch.load(model_path) - model.load_state_dict(pretrained_model['params'] if 'params' in pretrained_model.keys() else pretrained_model) + model.load_state_dict(pretrained_model['params'] if 'params' in pretrained_model.keys() else pretrained_model, strict=True) # download datasets if os.path.exists(f'{args.folder_lq}'): diff --git a/main_test_vrt.py b/main_test_vrt.py index 788176cf..ebf7d869 100755 --- a/main_test_vrt.py +++ b/main_test_vrt.py @@ -17,7 +17,8 @@ from models.network_vrt import VRT as net from utils import utils_image as util -from data.dataset_video_test import VideoRecurrentTestDataset, VideoTestVimeo90KDataset, SingleVideoRecurrentTestDataset +from data.dataset_video_test import VideoRecurrentTestDataset, VideoTestVimeo90KDataset, \ + SingleVideoRecurrentTestDataset, VFI_DAVIS, VFI_UCF101, VFI_Vid4 def main(): @@ -32,6 +33,8 @@ def main(): help='Tile size, [0,0,0] for no tile during testing (testing as a whole)') parser.add_argument('--tile_overlap', type=int, nargs='+', default=[2,20,20], help='Overlapping of different tiles') + parser.add_argument('--num_workers', type=int, default=16, help='number of workers in data loading') + parser.add_argument('--save_result', action='store_true', help='save resulting image') args = parser.parse_args() # define model @@ -40,9 +43,22 @@ def main(): model.eval() model = model.to(device) if 'vimeo' in args.folder_lq.lower(): - test_set = VideoTestVimeo90KDataset({'dataroot_gt':args.folder_gt, 'dataroot_lq':args.folder_lq, - 'meta_info_file': "data/meta_info/meta_info_Vimeo90K_test_GT.txt", - 'pad_sequence': True, 'num_frame': 7, 'cache_data': False}) + if 'videofi' in args.task: + test_set = VideoTestVimeo90KDataset({'dataroot_gt':args.folder_gt, 'dataroot_lq':args.folder_gt, + 'meta_info_file': "data/meta_info/meta_info_Vimeo90K_test_GT.txt", + 'pad_sequence': False, 'num_frame': 7, 'temporal_scale': 2, + 'cache_data': False}) + else: + test_set = VideoTestVimeo90KDataset({'dataroot_gt': args.folder_gt, 'dataroot_lq': args.folder_lq, + 'meta_info_file': "data/meta_info/meta_info_Vimeo90K_test_GT.txt", + 'pad_sequence': True, 'num_frame': 7, 'temporal_scale': 1, + 'cache_data': False}) + elif 'davis' in args.folder_lq.lower() and 'videofi' in args.task: + test_set = VFI_DAVIS(data_root=args.folder_gt) + elif 'ucf101' in args.folder_lq.lower() and 'videofi' in args.task: + test_set = VFI_UCF101(data_root=args.folder_gt) + elif 'vid4' in args.folder_lq.lower() and 'videofi' in args.task: + test_set = VFI_Vid4(data_root=args.folder_gt) elif args.folder_gt is not None: test_set = VideoRecurrentTestDataset({'dataroot_gt':args.folder_gt, 'dataroot_lq':args.folder_lq, 'sigma':args.sigma, 'num_frame':-1, 'cache_data': False}) @@ -50,10 +66,11 @@ def main(): test_set = SingleVideoRecurrentTestDataset({'dataroot_gt':args.folder_gt, 'dataroot_lq':args.folder_lq, 'sigma':args.sigma, 'num_frame':-1, 'cache_data': False}) - test_loader = DataLoader(dataset=test_set, num_workers=8, batch_size=1, shuffle=False) + test_loader = DataLoader(dataset=test_set, num_workers=args.num_workers, batch_size=1, shuffle=False) save_dir = f'results/{args.task}' - os.makedirs(save_dir, exist_ok=True) + if args.save_result: + os.makedirs(save_dir, exist_ok=True) test_results = OrderedDict() test_results['psnr'] = [] test_results['ssim'] = [] @@ -71,10 +88,12 @@ def main(): with torch.no_grad(): output = test_video(lq, model, args) - if 'vimeo' in args.folder_lq.lower(): + if 'videofi' in args.task: + output = output[:, :1, ...] + batch['lq_path'] = batch['gt_path'] + elif 'videosr' in args.task and 'vimeo' in args.folder_lq.lower(): output = output[:, 3:4, :, :, :] - gt = gt.unsqueeze(0) - batch['lq_path'] = [['im4.png']] + batch['lq_path'] = batch['gt_path'] test_results_folder = OrderedDict() test_results_folder['psnr'] = [] @@ -88,9 +107,10 @@ def main(): if img.ndim == 3: img = np.transpose(img[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR img = (img * 255.0).round().astype(np.uint8) # float32 to uint8 - seq_ = osp.basename(batch['lq_path'][i][0]).split('.')[0] - os.makedirs(f'{save_dir}/{folder[0]}', exist_ok=True) - cv2.imwrite(f'{save_dir}/{folder[0]}/{seq_}.png', img) + if args.save_result: + seq_ = osp.basename(batch['lq_path'][i][0]).split('.')[0] + os.makedirs(f'{save_dir}/{folder[0]}', exist_ok=True) + cv2.imwrite(f'{save_dir}/{folder[0]}/{seq_}.png', img) # evaluate psnr/ssim if gt is not None: @@ -203,6 +223,15 @@ def prepare_model_dataset(args): args.window_size = [6,8,8] args.nonblind_denoising = True + elif args.task == '009_VRT_videofi_Vimeo_4frames': + model = net(upscale=1, out_chans=3, img_size=[4,192,192], window_size=[4,8,8], depths=[8,8,8,8,8,8,8, 4,4, 4,4], + indep_reconsts=[], embed_dims=[96,96,96,96,96,96,96, 120,120, 120,120], + num_heads=[6,6,6,6,6,6,6, 6,6, 6,6], pa_frames=0) + datasets = ['UCF101', 'DAVIS-train'] # 'Vimeo'. Vimeo dataset is too large. Please refer to #training to download it. + args.scale = 1 + args.window_size = [4,8,8] + args.nonblind_denoising = False + # download model model_path = f'model_zoo/vrt/{args.task}.pth' if os.path.exists(model_path): @@ -215,7 +244,7 @@ def prepare_model_dataset(args): open(model_path, 'wb').write(r.content) pretrained_model = torch.load(model_path) - model.load_state_dict(pretrained_model['params'] if 'params' in pretrained_model.keys() else pretrained_model) + model.load_state_dict(pretrained_model['params'] if 'params' in pretrained_model.keys() else pretrained_model, strict=True) # download datasets if os.path.exists(f'{args.folder_lq}'): diff --git a/models/network_rvrt.py b/models/network_rvrt.py index 69ccab4e..fd499f3b 100755 --- a/models/network_rvrt.py +++ b/models/network_rvrt.py @@ -1169,30 +1169,4 @@ def forward(self, lqs): feats = self.propagate(feats, flows, module_name, updated_flows) # reconstruction - return self.upsample(lqs[:, :, :3, :, :], feats) - - -if __name__ == '__main__': - device = torch.device('cpu') - upscale = 4 - window_size = 8 - height = (256 // upscale // window_size) * window_size - width = (256 // upscale // window_size) * window_size - - model = RVRT(upscale=upscale, - clip_size=2, - img_size=[2, 64, 64], - window_size=[2, 8, 8], - num_blocks=[1, 2, 1], - depths=[2, 2, 2], - embed_dims=[24, 24, 24], - num_heads=[2, 2, 2], - deformable_groups=2, - attention_heads=2, - ).to(device) - print(model) - print('{:>16s} : {:<.4f} [M]'.format('#Params', sum(map(lambda x: x.numel(), model.parameters())) / 10 ** 6)) - - x = torch.randn((1, 30, 3, height, width)).to(device) - x = model(x) - print(x.shape) + return self.upsample(lqs[:, :, :3, :, :], feats) \ No newline at end of file diff --git a/models/network_vrt.py b/models/network_vrt.py index 4419633b..18280b6a 100755 --- a/models/network_vrt.py +++ b/models/network_vrt.py @@ -1087,18 +1087,20 @@ def __init__(self, self.linear2 = nn.Linear(dim, dim) # parallel warping - self.pa_deform = DCNv2PackFlowGuided(dim, dim, 3, padding=1, deformable_groups=deformable_groups, - max_residue_magnitude=max_residue_magnitude, pa_frames=pa_frames) - self.pa_fuse = Mlp_GEGLU(dim * (1 + 2), dim * (1 + 2), dim) + if self.pa_frames: + self.pa_deform = DCNv2PackFlowGuided(dim, dim, 3, padding=1, deformable_groups=deformable_groups, + max_residue_magnitude=max_residue_magnitude, pa_frames=pa_frames) + self.pa_fuse = Mlp_GEGLU(dim * (1 + 2), dim * (1 + 2), dim) def forward(self, x, flows_backward, flows_forward): x = self.reshape(x) x = self.linear1(self.residual_group1(x).transpose(1, 4)).transpose(1, 4) + x x = self.linear2(self.residual_group2(x).transpose(1, 4)).transpose(1, 4) + x - x = x.transpose(1, 2) - x_backward, x_forward = getattr(self, f'get_aligned_feature_{self.pa_frames}frames')(x, flows_backward, flows_forward) - x = self.pa_fuse(torch.cat([x, x_backward, x_forward], 2).permute(0, 1, 3, 4, 2)).permute(0, 4, 1, 2, 3) + if self.pa_frames: + x = x.transpose(1, 2) + x_backward, x_forward = getattr(self, f'get_aligned_feature_{self.pa_frames}frames')(x, flows_backward, flows_forward) + x = self.pa_fuse(torch.cat([x, x_backward, x_forward], 2).permute(0, 1, 3, 4, 2)).permute(0, 4, 1, 2, 3) return x @@ -1234,6 +1236,7 @@ class VRT(nn.Module): Args: upscale (int): Upscaling factor. Set as 1 for video deblurring, etc. Default: 4. in_chans (int): Number of input image channels. Default: 3. + out_chans (int): Number of output image channels. Default: 3. img_size (int | tuple(int)): Size of input image. Default: [6, 64, 64]. window_size (int | tuple(int)): Window size. Default: (6,8,8). depths (list[int]): Depths of each Transformer stage. @@ -1260,6 +1263,7 @@ class VRT(nn.Module): def __init__(self, upscale=4, in_chans=3, + out_chans=3, img_size=[6, 64, 64], window_size=[6, 8, 8], depths=[8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4], @@ -1284,17 +1288,25 @@ def __init__(self, ): super().__init__() self.in_chans = in_chans + self.out_chans = out_chans self.upscale = upscale self.pa_frames = pa_frames self.recal_all_flows = recal_all_flows self.nonblind_denoising = nonblind_denoising # conv_first - self.conv_first = nn.Conv3d(in_chans*(1+2*4)+1 if self.nonblind_denoising else in_chans*(1+2*4), - embed_dims[0], kernel_size=(1, 3, 3), padding=(0, 1, 1)) + if self.pa_frames: + if self.nonblind_denoising: + conv_first_in_chans = in_chans*(1+2*4)+1 + else: + conv_first_in_chans = in_chans*(1+2*4) + else: + conv_first_in_chans = in_chans + self.conv_first = nn.Conv3d(conv_first_in_chans, embed_dims[0], kernel_size=(1, 3, 3), padding=(0, 1, 1)) # main body - self.spynet = SpyNet(spynet_path, [2, 3, 4, 5]) + if self.pa_frames: + self.spynet = SpyNet(spynet_path, [2, 3, 4, 5]) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule reshapes = ['none', 'down', 'down', 'down', 'up', 'up', 'up'] scales = [1, 2, 4, 8, 4, 2, 1] @@ -1357,54 +1369,103 @@ def __init__(self, self.conv_after_body = nn.Linear(embed_dims[-1], embed_dims[0]) # reconstruction - num_feat = 64 - if self.upscale == 1: - # for video deblurring, etc. - self.conv_last = nn.Conv3d(embed_dims[0], in_chans, kernel_size=(1, 3, 3), padding=(0, 1, 1)) + if self.pa_frames: + if self.upscale == 1: + # for video deblurring, etc. + self.conv_last = nn.Conv3d(embed_dims[0], out_chans, kernel_size=(1, 3, 3), padding=(0, 1, 1)) + else: + # for video sr + num_feat = 64 + self.conv_before_upsample = nn.Sequential( + nn.Conv3d(embed_dims[0], num_feat, kernel_size=(1, 3, 3), padding=(0, 1, 1)), + nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv3d(num_feat, out_chans, kernel_size=(1, 3, 3), padding=(0, 1, 1)) else: - # for video sr - self.conv_before_upsample = nn.Sequential( - nn.Conv3d(embed_dims[0], num_feat, kernel_size=(1, 3, 3), padding=(0, 1, 1)), - nn.LeakyReLU(inplace=True)) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv3d(num_feat, in_chans, kernel_size=(1, 3, 3), padding=(0, 1, 1)) + num_feat = 64 + self.linear_fuse = nn.Conv2d(embed_dims[0]*img_size[0], num_feat, kernel_size=1 , stride=1) + self.conv_last = nn.Conv2d(num_feat, out_chans , kernel_size=7 , stride=1, padding=0) - def forward(self, x): - # x: (N, D, C, H, W) + def init_weights(self, pretrained=None, strict=True): + """Init weights for models. - # obtain noise level map - if self.nonblind_denoising: - x, noise_level_map = x[:, :, :self.in_chans, :, :], x[:, :, self.in_chans:, :, :] + Args: + pretrained (str, optional): Path for pretrained weights. If given + None, pretrained weights will not be loaded. Defaults: None. + strict (boo, optional): Whether strictly load the pretrained model. + Defaults to True. + """ + if isinstance(pretrained, str): + logger = get_root_logger() + load_checkpoint(self, pretrained, strict=strict, logger=logger) + elif pretrained is not None: + raise TypeError(f'"pretrained" must be a str or None. ' + f'But received {type(pretrained)}.') - x_lq = x.clone() + def reflection_pad2d(self, x, pad=1): + """ Reflection padding for any dtypes (torch.bfloat16. - # calculate flows - flows_backward, flows_forward = self.get_flows(x) + Args: + x: (tensor): BxCxHxW + pad: (int): Default: 1. + """ - # warp input - x_backward, x_forward = self.get_aligned_image_2frames(x, flows_backward[0], flows_forward[0]) - x = torch.cat([x, x_backward, x_forward], 2) + x = torch.cat([torch.flip(x[:, :, 1:pad+1, :], [2]), x, torch.flip(x[:, :, -pad-1:-1, :], [2])], 2) + x = torch.cat([torch.flip(x[:, :, :, 1:pad+1], [3]), x, torch.flip(x[:, :, :, -pad-1:-1], [3])], 3) + return x - # concatenate noise level map - if self.nonblind_denoising: - x = torch.cat([x, noise_level_map], 2) + def forward(self, x): + # x: (N, D, C, H, W) # main network - if self.upscale == 1: - # video deblurring, etc. - x = self.conv_first(x.transpose(1, 2)) - x = x + self.conv_after_body( - self.forward_features(x, flows_backward, flows_forward).transpose(1, 4)).transpose(1, 4) - x = self.conv_last(x).transpose(1, 2) - return x + x_lq + if self.pa_frames: + # obtain noise level map + if self.nonblind_denoising: + x, noise_level_map = x[:, :, :self.in_chans, :, :], x[:, :, self.in_chans:, :, :] + + x_lq = x.clone() + + # calculate flows + flows_backward, flows_forward = self.get_flows(x) + + # warp input + x_backward, x_forward = self.get_aligned_image_2frames(x, flows_backward[0], flows_forward[0]) + x = torch.cat([x, x_backward, x_forward], 2) + + # concatenate noise level map + if self.nonblind_denoising: + x = torch.cat([x, noise_level_map], 2) + + if self.upscale == 1: + # video deblurring, etc. + x = self.conv_first(x.transpose(1, 2)) + x = x + self.conv_after_body( + self.forward_features(x, flows_backward, flows_forward).transpose(1, 4)).transpose(1, 4) + x = self.conv_last(x).transpose(1, 2) + return x + x_lq + else: + # video sr + x = self.conv_first(x.transpose(1, 2)) + x = x + self.conv_after_body( + self.forward_features(x, flows_backward, flows_forward).transpose(1, 4)).transpose(1, 4) + x = self.conv_last(self.upsample(self.conv_before_upsample(x))).transpose(1, 2) + _, _, C, H, W = x.shape + return x + torch.nn.functional.interpolate(x_lq, size=(C, H, W), mode='trilinear', align_corners=False) else: - # video sr + # video fi + x_mean = x.mean([1,3,4], keepdim=True) + x = x - x_mean + x = self.conv_first(x.transpose(1, 2)) x = x + self.conv_after_body( - self.forward_features(x, flows_backward, flows_forward).transpose(1, 4)).transpose(1, 4) - x = self.conv_last(self.upsample(self.conv_before_upsample(x))).transpose(1, 2) - _, _, C, H, W = x.shape - return x + torch.nn.functional.interpolate(x_lq, size=(C, H, W), mode='trilinear', align_corners=False) + self.forward_features(x, [], []).transpose(1, 4)).transpose(1, 4) + + x = torch.cat(torch.unbind(x , 2) , 1) + x = self.conv_last(self.reflection_pad2d(F.leaky_relu(self.linear_fuse(x), 0.2), pad=3)) + x = torch.stack(torch.split(x, dim=1, split_size_or_sections=3), 1) + + return x + x_mean + def get_flows(self, x): ''' Get flows for 2 frames, 4 frames or 6 frames.''' @@ -1535,30 +1596,4 @@ def forward_features(self, x, flows_backward, flows_forward): x = self.norm(x) x = rearrange(x, 'n d h w c -> n c d h w') - return x - - -if __name__ == '__main__': - device = torch.device('cpu') - upscale = 4 - window_size = 8 - height = (256 // upscale // window_size) * window_size - width = (256 // upscale // window_size) * window_size - - model = VRT(upscale=4, - img_size=[6, 64, 64], - window_size=[6, 8, 8], - depths=[8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4], - indep_reconsts=[11, 12], - embed_dims=[120, 120, 120, 120, 120, 120, 120, 180, 180, 180, 180, 180, 180], - num_heads=[6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], - spynet_path=None, - pa_frames=2, - deformable_groups=12 - ).to(device) - print(model) - print('{:>16s} : {:<.4f} [M]'.format('#Params', sum(map(lambda x: x.numel(), model.parameters())) / 10 ** 6)) - - x = torch.randn((2, 12, 3, height, width)).to(device) - x = model(x) - print(x.shape) + return x \ No newline at end of file diff --git a/options/vrt/003_train_vrt_videosr_bi_vimeo_7frames.json b/options/vrt/003_train_vrt_videosr_bi_vimeo_7frames.json index 6a27fcf7..8e8db269 100644 --- a/options/vrt/003_train_vrt_videosr_bi_vimeo_7frames.json +++ b/options/vrt/003_train_vrt_videosr_bi_vimeo_7frames.json @@ -23,7 +23,7 @@ , "dataroot_lq": "trainsets/vimeo90k/vimeo90k_train_LR7frames.lmdb" , "meta_info_file": "data/meta_info/meta_info_Vimeo90K_train_GT.txt" , "io_backend": {"type": "lmdb"} - , "num_frame": -1 + , "num_frame": 7 , "gt_size": 256 , "interval_list": [1] , "random_reverse": true diff --git a/options/vrt/004_train_vrt_videosr_bd_vimeo_7frames.json b/options/vrt/004_train_vrt_videosr_bd_vimeo_7frames.json index a4419982..cdb66c44 100644 --- a/options/vrt/004_train_vrt_videosr_bd_vimeo_7frames.json +++ b/options/vrt/004_train_vrt_videosr_bd_vimeo_7frames.json @@ -23,7 +23,7 @@ , "dataroot_lq": "trainsets/vimeo90k/vimeo90k_train_BDLR7frames.lmdb" , "meta_info_file": "data/meta_info/meta_info_Vimeo90K_train_GT.txt" , "io_backend": {"type": "lmdb"} - , "num_frame": -1 + , "num_frame": 7 , "gt_size": 256 , "interval_list": [1] , "random_reverse": true diff --git a/options/vrt/009_train_vrt_videofi_vimeo_4frames.json b/options/vrt/009_train_vrt_videofi_vimeo_4frames.json new file mode 100644 index 00000000..2f5c5534 --- /dev/null +++ b/options/vrt/009_train_vrt_videofi_vimeo_4frames.json @@ -0,0 +1,116 @@ +{ + "task": "009_train_vrt_videofi_vimeo_4frames" + , "model": "vrt" + , "gpu_ids": [0,1,2,3,4,5,6,7] + , "dist": true + , "find_unused_parameters": false + , "use_static_graph": true + + ,"scale": 1 + , "n_channels": 3 + + , "path": { + "root": "experiments" + , "pretrained_netG": null + , "pretrained_netE": null + } + + , "datasets": { + "train": { + "name": "train_dataset" + , "dataset_type": "VideoRecurrentTrainVimeoVFIDataset" + , "dataroot_gt": "trainsets/vimeo90k/vimeo90k_train_GT_all.lmdb" + , "dataroot_lq": "trainsets/vimeo90k/vimeo90k_train_GT_all.lmdb" + , "meta_info_file": "data/meta_info/meta_info_Vimeo90K_train_GT.txt" + , "io_backend": {"type": "lmdb"} + , "num_frame": 7 + , "temporal_scale": 2 + , "gt_size": 192 + , "interval_list": [1] + , "random_reverse": true + , "use_hflip": true + , "use_rot": true + , "color_jitter": true + + , "dataloader_shuffle": true + , "dataloader_num_workers": 32 + , "dataloader_batch_size": 8 + } + , "test": { + "name": "test_dataset" + , "dataset_type": "VideoTestVimeo90KDataset" + , "dataroot_gt": "testsets/vimeo90k/vimeo_septuplet/sequences" + , "dataroot_lq": "testsets/vimeo90k/vimeo_septuplet/sequences" + , "meta_info_file": "data/meta_info/meta_info_Vimeo90K_test_GT.txt" + , "pad_sequence": false + , "num_frame": 7 + , "temporal_scale": 2 + , "cache_data": false + } + } + + , "netG": { + "net_type": "vrt" + , "upscale": 1 + , "img_size": [4,192,192] + , "window_size": [4,8,8] + , "depths": [8,8,8,8,8,8,8, 4,4, 4,4] + , "indep_reconsts": [] + , "embed_dims": [96,96,96,96,96,96,96, 120,120, 120,120] + , "num_heads": [6,6,6,6,6,6,6, 6,6, 6,6] + , "pa_frames": 0 + + , "use_checkpoint_attn": true + , "use_checkpoint_ffn": false + , "no_checkpoint_attn_blocks": [1,2,3,4,5] + , "no_checkpoint_ffn_blocks": [] + + , "init_type": "default" + } + + + , "train": { + "G_lossfn_type": "charbonnier" + , "G_lossfn_weight": 1.0 + , "G_charbonnier_eps": 1e-9 + + , "E_decay": 0 // Exponential Moving Average for netG: set 0 to disable; default setting 0.999 + + , "G_optimizer_type": "adam" // fixed, adam is enough + , "G_optimizer_lr": 4e-4 // learning rate + , "G_optimizer_betas": [0.9,0.99] + , "G_optimizer_wd": 0 // weight decay, default 0 + , "G_optimizer_clipgrad": null // unused + , "G_optimizer_reuse": true // + + , "fix_iter": -1 + , "fix_lr_mul": 0 + , "fix_keys": [] + + , "total_iter": 300000 + , "G_scheduler_type": "CosineAnnealingWarmRestarts" + , "G_scheduler_periods": 300000 + , "G_scheduler_eta_min": 1e-7 + + , "G_regularizer_orthstep": null // unused + , "G_regularizer_clipstep": null // unused + + , "G_param_strict": false + , "E_param_strict": false + + , "checkpoint_test": 5000 // for testing + , "checkpoint_save": 5000 // for saving model + , "checkpoint_print": 200 // for print + } + + , "val": { + "save_img": false + , "pad_seq": false + , "flip_seq": false + , "center_frame_only": false + , "num_frame_testing": 0 + , "num_frame_overlapping": 0 + , "size_patch_testing": 0 + } + +}