import random import time from os import path as osp from torch.utils import data as data from torchvision.transforms.functional import normalize from basicsr.data.transforms import augment from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() class FFHQDataset(data.Dataset): """FFHQ dataset for StyleGAN. Args: opt (dict): Config for train datasets. It contains the following keys: dataroot_gt (str): Data root path for gt. io_backend (dict): IO backend type and other kwarg. mean (list | tuple): Image mean. std (list | tuple): Image std. use_hflip (bool): Whether to horizontally flip. """ def __init__(self, opt): super(FFHQDataset, self).__init__() self.opt = opt # file client (io backend) self.file_client = None self.io_backend_opt = opt['io_backend'] self.gt_folder = opt['dataroot_gt'] self.mean = opt['mean'] self.std = opt['std'] if self.io_backend_opt['type'] == 'lmdb': self.io_backend_opt['db_paths'] = self.gt_folder if not self.gt_folder.endswith('.lmdb'): raise ValueError("'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: self.paths = [line.split('.')[0] for line in fin] else: # FFHQ has 70000 images in total self.paths = [osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)] def __getitem__(self, index): if self.file_client is None: self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) # load gt image gt_path = self.paths[index] # avoid errors caused by high latency in reading files retry = 3 while retry > 0: try: img_bytes = self.file_client.get(gt_path) except Exception as e: logger = get_root_logger() logger.warning(f'File client error: {e}, remaining retry times: {retry - 1}') # change another file to read index = random.randint(0, self.__len__()) gt_path = self.paths[index] time.sleep(1) # sleep 1s for occasional server congestion else: break finally: retry -= 1 img_gt = imfrombytes(img_bytes, float32=True) # random horizontal flip img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False) # BGR to RGB, HWC to CHW, numpy to tensor img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) # normalize normalize(img_gt, self.mean, self.std, inplace=True) return {'gt': img_gt, 'gt_path': gt_path} def __len__(self): return len(self.paths)