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Running
on
Zero
| import os | |
| from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb | |
| from basicsr.data.transforms import augment, paired_random_crop | |
| from basicsr.utils import FileClient, imfrombytes, img2tensor | |
| from basicsr.utils.registry import DATASET_REGISTRY | |
| from torch.utils import data as data | |
| from torchvision.transforms.functional import normalize | |
| class RealESRGANPairedDataset(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': Use meta information file to generate paths. | |
| If opt['io_backend'] != lmdb and opt['meta_info'] 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 (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_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. | |
| phase (str): 'train' or 'val'. | |
| """ | |
| def __init__(self, opt): | |
| super(RealESRGANPairedDataset, self).__init__() | |
| self.opt = opt | |
| self.file_client = None | |
| self.io_backend_opt = opt["io_backend"] | |
| # mean and std for normalizing the input images | |
| 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"] | |
| self.filename_tmpl = opt["filename_tmpl"] if "filename_tmpl" in opt else "{}" | |
| # file client (lmdb io backend) | |
| 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" in self.opt and self.opt["meta_info"] is not None: | |
| # disk backend with meta_info | |
| # Each line in the meta_info describes the relative path to an image | |
| with open(self.opt["meta_info"]) as fin: | |
| paths = [line.strip() for line in fin] | |
| self.paths = [] | |
| for path in paths: | |
| gt_path, lq_path = path.split(", ") | |
| gt_path = os.path.join(self.gt_folder, gt_path) | |
| lq_path = os.path.join(self.lq_folder, lq_path) | |
| self.paths.append(dict([("gt_path", gt_path), ("lq_path", lq_path)])) | |
| else: | |
| # disk backend | |
| # it will scan the whole folder to get meta info | |
| # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file | |
| 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_hflip"], self.opt["use_rot"] | |
| ) | |
| # 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) | |