72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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from os import path as osp
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from torch.utils import data as data
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from torchvision.transforms.functional import normalize
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from basicsr.data.transforms import augment
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from basicsr.utils import FileClient, imfrombytes, img2tensor
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class FFHQDataset(data.Dataset):
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"""FFHQ dataset for StyleGAN.
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Args:
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opt (dict): Config for train datasets. It contains the following keys:
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dataroot_gt (str): Data root path for gt.
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io_backend (dict): IO backend type and other kwarg.
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mean (list | tuple): Image mean.
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std (list | tuple): Image std.
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use_hflip (bool): Whether to horizontally flip.
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"""
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def __init__(self, opt):
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super(FFHQDataset, self).__init__()
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self.opt = opt
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# file client (io backend)
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self.file_client = None
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self.io_backend_opt = opt['io_backend']
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self.gt_folder = opt['dataroot_gt']
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self.mean = opt['mean']
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self.std = opt['std']
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if self.io_backend_opt['type'] == 'lmdb':
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self.io_backend_opt['db_paths'] = self.gt_folder
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if not self.gt_folder.endswith('.lmdb'):
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raise ValueError("'dataroot_gt' should end with '.lmdb', "
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f'but received {self.gt_folder}')
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with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
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self.paths = [line.split('.')[0] for line in fin]
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else:
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# FFHQ has 70000 images in total
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self.paths = [
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osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)
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]
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def __getitem__(self, index):
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if self.file_client is None:
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self.file_client = FileClient(
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self.io_backend_opt.pop('type'), **self.io_backend_opt)
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# load gt image
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gt_path = self.paths[index]
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img_bytes = self.file_client.get(gt_path)
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img_gt = imfrombytes(img_bytes, float32=True)
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# random horizontal flip
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img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False)
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# BGR to RGB, HWC to CHW, numpy to tensor
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img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True)
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# normalize
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normalize(img_gt, self.mean, self.std, inplace=True)
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return {'gt': img_gt, 'gt_path': gt_path}
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def __len__(self):
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return len(self.paths)
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