def __init__(self, root, transform=None, target_transform=None, download=False):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
# Set up both the background and eval dataset
omni_background = Omniglot(self.root, background=True, download=download)
# Eval labels also start from 0.
# It's important to add 964 to label values in eval so they don't overwrite background dataset.
omni_evaluation = Omniglot(self.root,
background=False,
download=download,
target_transform=lambda x: x + len(omni_background._characters))
self.dataset = ConcatDataset((omni_background, omni_evaluation))
self._bookkeeping_path = os.path.join(self.root, 'omniglot-bookkeeping.pkl')
from torchvision.datasets.omniglot import Omniglot Omniglot(root='./data', download=True) Omniglot(root='./data', download=True, background=False)
def omniglot(train=True):
dataset = Omniglot(root=root, background=train)
image_name, character_class = list(zip(*dataset._flat_character_images))
return image_name, character_class