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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)
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