add RVRT training codes

Author: JingyunLiang

README.md

@@ -6,6 +6,8 @@
 *[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 (2022-05-05)_**: 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.
 
 - **_News (2022-03-23)_**: We release [the testing codes](https://github.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.

data/dataset_video_test.py

@@ -129,7 +129,7 @@ def __len__(self):
 
 
 class SingleVideoRecurrentTestDataset(data.Dataset):
-    """Single ideo test dataset for recurrent architectures, which takes LR video
+    """Single video test dataset for recurrent architectures, which takes LR video
     frames as input and output corresponding HR video frames (only input LQ path).
 
     More generally, it supports testing dataset with following structures:
@@ -245,11 +245,12 @@ def __init__(self, opt):
         super(VideoTestVimeo90KDataset, self).__init__()
         self.opt = opt
         self.cache_data = opt['cache_data']
+        temporal_scale = opt.get('temporal_scale', 1)
         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'])]
+        neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])][:: temporal_scale]
 
         with open(opt['meta_info_file'], 'r') as fin:
             subfolders = [line.split(' ')[0] for line in fin]
@@ -263,6 +264,7 @@ def __init__(self, opt):
             self.data_info['border'].append(0)
 
         self.pad_sequence = opt.get('pad_sequence', False)
+        self.mirror_sequence = opt.get('mirror_sequence', False)
 
     def __getitem__(self, index):
         lq_path = self.data_info['lq_path'][index]
@@ -274,6 +276,9 @@ def __getitem__(self, index):
         if self.pad_sequence:  # pad the sequence: 7 frames to 8 frames
             imgs_lq = torch.cat([imgs_lq, imgs_lq[-1:,...]], dim=0)
 
+        if self.mirror_sequence:  # mirror the sequence: 7 frames to 14 frames
+            imgs_lq = torch.cat([imgs_lq, imgs_lq.flip(0)], dim=0)
+
         return {
             'L': imgs_lq,  # (t, c, h, w)
             'H': img_gt,  # (c, h, w)
@@ -286,97 +291,3 @@ def __getitem__(self, index):
     def __len__(self):
         return len(self.data_info['gt_path'])
 
-
-class SingleVideoRecurrentTestDataset(data.Dataset):
-    """Single Video test dataset (only input LQ path).
-
-    Supported datasets: Vid4, REDS4, REDSofficial.
-    More generally, it supports testing dataset with following structures:
-
-    dataroot
-    ├── subfolder1
-        ├── frame000
-        ├── frame001
-        ├── ...
-    ├── subfolder1
-        ├── frame000
-        ├── frame001
-        ├── ...
-    ├── ...
-
-    For testing datasets, there is no need to prepare LMDB files.
-
-    Args:
-        opt (dict): Config for train dataset. It contains the following keys:
-            dataroot_gt (str): Data root path for gt.
-            dataroot_lq (str): Data root path for lq.
-            io_backend (dict): IO backend type and other kwarg.
-            cache_data (bool): Whether to cache testing datasets.
-            name (str): Dataset name.
-            meta_info_file (str): The path to the file storing the list of test
-                folders. If not provided, all the folders in the dataroot will
-                be used.
-            num_frame (int): Window size for input frames.
-            padding (str): Padding mode.
-    """
-
-    def __init__(self, opt):
-        super(SingleVideoRecurrentTestDataset, self).__init__()
-        self.opt = opt
-        self.cache_data = opt['cache_data']
-        self.lq_root = opt['dataroot_lq']
-        self.data_info = {'lq_path': [], 'folder': [], 'idx': [], 'border': []}
-        # file client (io backend)
-        self.file_client = None
-
-        self.imgs_lq = {}
-        if 'meta_info_file' in opt:
-            with open(opt['meta_info_file'], 'r') as fin:
-                subfolders = [line.split(' ')[0] for line in fin]
-                subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders]
-        else:
-            subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*')))
-
-        for subfolder_lq in subfolders_lq:
-            # get frame list for lq and gt
-            subfolder_name = osp.basename(subfolder_lq)
-            img_paths_lq = sorted(list(utils_video.scandir(subfolder_lq, full_path=True)))
-
-            max_idx = len(img_paths_lq)
-
-            self.data_info['lq_path'].extend(img_paths_lq)
-            self.data_info['folder'].extend([subfolder_name] * max_idx)
-            for i in range(max_idx):
-                self.data_info['idx'].append(f'{i}/{max_idx}')
-            border_l = [0] * max_idx
-            for i in range(self.opt['num_frame'] // 2):
-                border_l[i] = 1
-                border_l[max_idx - i - 1] = 1
-            self.data_info['border'].extend(border_l)
-
-            # cache data or save the frame list
-            if self.cache_data:
-                logger.info(f'Cache {subfolder_name} for VideoTestDataset...')
-                self.imgs_lq[subfolder_name] = utils_video.read_img_seq(img_paths_lq)
-            else:
-                self.imgs_lq[subfolder_name] = img_paths_lq
-
-        # Find unique folder strings
-        self.folders = sorted(list(set(self.data_info['folder'])))
-
-    def __getitem__(self, index):
-        folder = self.folders[index]
-
-        if self.cache_data:
-            imgs_lq = self.imgs_lq[folder]
-        else:
-            imgs_lq = utils_video.read_img_seq(self.imgs_lq[folder])
-
-        return {
-            'L': imgs_lq,
-            'folder': folder,
-            'lq_path': self.imgs_lq[folder],
-        }
-
-    def __len__(self):
-        return len(self.folders)

data/dataset_video_train.py

@@ -322,7 +322,7 @@ def __init__(self, opt):
         self.random_reverse = opt['random_reverse']
         print(f'Random reverse is {self.random_reverse}.')
 
-        self.flip_sequence = opt.get('flip_sequence', False)
+        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]
 
@@ -370,7 +370,7 @@ def __getitem__(self, index):
         img_lqs = torch.stack(img_results[:7], dim=0)
         img_gts = torch.stack(img_results[7:], dim=0)
 
-        if self.flip_sequence:  # flip the sequence: 7 frames to 14 frames
+        if self.mirror_sequence:  # mirror the sequence: 7 frames to 14 frames
             img_lqs = torch.cat([img_lqs, img_lqs.flip(0)], dim=0)
             img_gts = torch.cat([img_gts, img_gts.flip(0)], dim=0)
         elif self.pad_sequence:  # pad the sequence: 7 frames to 8 frames