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Diffstat (limited to 'r_basicsr/data/vimeo90k_dataset.py')
| -rw-r--r-- | r_basicsr/data/vimeo90k_dataset.py | 192 |
1 files changed, 192 insertions, 0 deletions
diff --git a/r_basicsr/data/vimeo90k_dataset.py b/r_basicsr/data/vimeo90k_dataset.py new file mode 100644 index 0000000..f20a4fd --- /dev/null +++ b/r_basicsr/data/vimeo90k_dataset.py @@ -0,0 +1,192 @@ +import random
+import torch
+from pathlib import Path
+from torch.utils import data as data
+
+from r_basicsr.data.transforms import augment, paired_random_crop
+from r_basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
+from r_basicsr.utils.registry import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class Vimeo90KDataset(data.Dataset):
+ """Vimeo90K dataset for training.
+
+ The keys are generated from a meta info txt file.
+ basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
+
+ Each line contains:
+ 1. clip name; 2. frame number; 3. image shape, separated by a white space.
+ Examples:
+ 00001/0001 7 (256,448,3)
+ 00001/0002 7 (256,448,3)
+
+ Key examples: "00001/0001"
+ GT (gt): Ground-Truth;
+ LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.
+
+ The neighboring frame list for different num_frame:
+ num_frame | frame list
+ 1 | 4
+ 3 | 3,4,5
+ 5 | 2,3,4,5,6
+ 7 | 1,2,3,4,5,6,7
+
+ 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.
+ meta_info_file (str): Path for meta information file.
+ io_backend (dict): IO backend type and other kwarg.
+
+ num_frame (int): Window size for input frames.
+ gt_size (int): Cropped patched size for gt patches.
+ random_reverse (bool): Random reverse input frames.
+ use_hflip (bool): Use horizontal flips.
+ use_rot (bool): Use rotation (use vertical flip and transposing h
+ and w for implementation).
+
+ scale (bool): Scale, which will be added automatically.
+ """
+
+ def __init__(self, opt):
+ super(Vimeo90KDataset, self).__init__()
+ self.opt = opt
+ self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq'])
+
+ with open(opt['meta_info_file'], 'r') as fin:
+ self.keys = [line.split(' ')[0] for line in fin]
+
+ # file client (io backend)
+ self.file_client = None
+ self.io_backend_opt = opt['io_backend']
+ self.is_lmdb = False
+ if self.io_backend_opt['type'] == 'lmdb':
+ self.is_lmdb = True
+ self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
+ 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'])]
+
+ # temporal augmentation configs
+ self.random_reverse = opt['random_reverse']
+ logger = get_root_logger()
+ logger.info(f'Random reverse is {self.random_reverse}.')
+
+ def __getitem__(self, index):
+ if self.file_client is None:
+ self.file_client = 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 GT frame (im4.png)
+ if self.is_lmdb:
+ img_gt_path = f'{key}/im4'
+ else:
+ img_gt_path = self.gt_root / clip / seq / 'im4.png'
+ img_bytes = self.file_client.get(img_gt_path, 'gt')
+ img_gt = imfrombytes(img_bytes, float32=True)
+
+ # get the neighboring LQ frames
+ img_lqs = []
+ 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'
+ img_bytes = self.file_client.get(img_lq_path, 'lq')
+ img_lq = imfrombytes(img_bytes, float32=True)
+ img_lqs.append(img_lq)
+
+ # randomly crop
+ img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path)
+
+ # augmentation - flip, rotate
+ img_lqs.append(img_gt)
+ img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot'])
+
+ img_results = img2tensor(img_results)
+ img_lqs = torch.stack(img_results[0:-1], dim=0)
+ img_gt = img_results[-1]
+
+ # img_lqs: (t, c, h, w)
+ # img_gt: (c, h, w)
+ # key: str
+ return {'lq': img_lqs, 'gt': img_gt, 'key': key}
+
+ def __len__(self):
+ return len(self.keys)
+
+
+@DATASET_REGISTRY.register()
+class Vimeo90KRecurrentDataset(Vimeo90KDataset):
+
+ def __init__(self, opt):
+ super(Vimeo90KRecurrentDataset, self).__init__(opt)
+
+ self.flip_sequence = opt['flip_sequence']
+ self.neighbor_list = [1, 2, 3, 4, 5, 6, 7]
+
+ def __getitem__(self, index):
+ if self.file_client is None:
+ self.file_client = 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}'
+ img_gt_path = f'{clip}/{seq}/im{neighbor}'
+ else:
+ img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
+ img_gt_path = self.gt_root / clip / seq / f'im{neighbor}.png'
+ # LQ
+ img_bytes = self.file_client.get(img_lq_path, 'lq')
+ img_lq = imfrombytes(img_bytes, float32=True)
+ # GT
+ img_bytes = self.file_client.get(img_gt_path, 'gt')
+ img_gt = imfrombytes(img_bytes, float32=True)
+
+ img_lqs.append(img_lq)
+ img_gts.append(img_gt)
+
+ # randomly crop
+ img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path)
+
+ # augmentation - flip, rotate
+ img_lqs.extend(img_gts)
+ img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot'])
+
+ img_results = img2tensor(img_results)
+ 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
+ img_lqs = torch.cat([img_lqs, img_lqs.flip(0)], dim=0)
+ img_gts = torch.cat([img_gts, img_gts.flip(0)], dim=0)
+
+ # img_lqs: (t, c, h, w)
+ # img_gt: (c, h, w)
+ # key: str
+ return {'lq': img_lqs, 'gt': img_gts, 'key': key}
+
+ def __len__(self):
+ return len(self.keys)
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