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on
T4
Running
on
T4
| 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 | |
| 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) | |