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- from torch.utils import data as data
- from torchvision.transforms.functional import normalize
- from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb, paired_paths_from_meta_info_file
- from basicsr.data.transforms import augment, paired_random_crop
- from basicsr.utils import FileClient, imfrombytes, img2tensor
- from basicsr.utils.registry import DATASET_REGISTRY
- @DATASET_REGISTRY.register()
- class PairedImageDataset(data.Dataset):
- """Paired image dataset for image restoration.
- Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
- GT image pairs.
- There are three modes:
- 1. 'lmdb': Use lmdb files.
- If opt['io_backend'] == lmdb.
- 2. 'meta_info_file': Use meta information file to generate paths.
- If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
- 3. 'folder': Scan folders to generate paths.
- The rest.
- Args:
- opt (dict): Config for train datasets. 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.
- filename_tmpl (str): Template for each filename. Note that the
- template excludes the file extension. Default: '{}'.
- gt_size (int): Cropped patched size for gt patches.
- use_flip (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.
- phase (str): 'train' or 'val'.
- """
- def __init__(self, opt):
- super(PairedImageDataset, self).__init__()
- self.opt = opt
- # file client (io backend)
- self.file_client = None
- self.io_backend_opt = opt['io_backend']
- self.mean = opt['mean'] if 'mean' in opt else None
- self.std = opt['std'] if 'std' in opt else None
- self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
- if 'filename_tmpl' in opt:
- self.filename_tmpl = opt['filename_tmpl']
- else:
- self.filename_tmpl = '{}'
- if self.io_backend_opt['type'] == 'lmdb':
- self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
- self.io_backend_opt['client_keys'] = ['lq', 'gt']
- self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
- elif 'meta_info_file' in self.opt and self.opt['meta_info_file'] is not None:
- self.paths = paired_paths_from_meta_info_file([self.lq_folder, self.gt_folder], ['lq', 'gt'],
- self.opt['meta_info_file'], self.filename_tmpl)
- else:
- self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
- def __getitem__(self, index):
- if self.file_client is None:
- self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
- scale = self.opt['scale']
- # Load gt and lq images. Dimension order: HWC; channel order: BGR;
- # image range: [0, 1], float32.
- gt_path = self.paths[index]['gt_path']
- img_bytes = self.file_client.get(gt_path, 'gt')
- img_gt = imfrombytes(img_bytes, float32=True)
- lq_path = self.paths[index]['lq_path']
- img_bytes = self.file_client.get(lq_path, 'lq')
- img_lq = imfrombytes(img_bytes, float32=True)
- # augmentation for training
- if self.opt['phase'] == 'train':
- gt_size = self.opt['gt_size']
- # random crop
- img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
- # flip, rotation
- img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'], self.opt['use_rot'])
- # TODO: color space transform
- # BGR to RGB, HWC to CHW, numpy to tensor
- img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
- # normalize
- if self.mean is not None or self.std is not None:
- normalize(img_lq, self.mean, self.std, inplace=True)
- normalize(img_gt, self.mean, self.std, inplace=True)
- return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
- def __len__(self):
- return len(self.paths)
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