import random import torch from pathlib import Path from torch.utils import data as data from basicsr.data.transforms import augment, paired_random_crop from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from 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)