from typing import Optional, List, Dict
from torchvision import datasets, transforms
from extorch.vision.dataset import CVClassificationDataset
[docs]class SVHN(CVClassificationDataset):
def __init__(self, data_dir: str,
train_transform: Optional[transforms.Compose] = None,
test_transform: Optional[transforms.Compose] = None,
cutout_length: Optional[int] = None,
cutout_n_holes: Optional[int] = 1) -> None:
super(SVHN, self).__init__(data_dir, train_transform, test_transform, cutout_length, cutout_n_holes)
self.datasets["train"] = datasets.SVHN(root = self.data_dir, split = "train",
download = True, transform = self.transforms["train"])
self.datasets["test"] = datasets.SVHN(root = self.data_dir, split = "test",
download = True, transform = self.transforms["test"])
self.datasets["extra"] = datasets.SVHN(root = self.data_dir, split = "extra",
download = True, transform = self.transforms["test"])
@classmethod
def num_classes(cls) -> int:
return 10
@classmethod
def mean(cls) -> List[float]:
return [0.4377, 0.4438, 0.4728]
@classmethod
def std(cls) -> List[float]:
return [0.1980, 0.2010, 0.1970]
@property
def default_transform(self) -> Dict[str, transforms.Compose]:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(self.mean(), self.std())
])
default_transforms = {
"train": transform,
"test": transform
}
return default_transforms