def __init__(self, data_path, **kwargs):
ds_kwargs = {
'num_classes': 10,
'mean': ch.tensor([0.5, 0.5, 0.5]),
'std': ch.tensor([0.5, 0.5, 0.5]),
'custom_class': torchvision.datasets.SVHN,
'label_mapping': None,
'transform_train': da.TRAIN_TRANSFORMS_DEFAULT(32),
'transform_test': da.TEST_TRANSFORMS_DEFAULT(32)
}
super(SVHN, self).__init__('svhn', data_path, **ds_kwargs)
def __init__(self, data_path, **kwargs):
"""
"""
ds_kwargs = {
'num_classes': 10,
'mean': ch.tensor([0.47889522, 0.47227842, 0.43047404]),
'std': ch.tensor([0.24205776, 0.23828046, 0.25874835]),
'custom_class': None,
'label_mapping': None,
'transform_train': da.TRAIN_TRANSFORMS_DEFAULT(32),
'transform_test': da.TEST_TRANSFORMS_DEFAULT(32)
}
super(CINIC, self).__init__('cinic', data_path, **ds_kwargs)
def __init__(self, data_path='/tmp/', **kwargs):
"""
"""
ds_kwargs = {
'num_classes': 10,
'mean': ch.tensor([0.4914, 0.4822, 0.4465]),
'std': ch.tensor([0.2023, 0.1994, 0.2010]),
'custom_class': datasets.CIFAR10,
'label_mapping': None,
'transform_train': da.TRAIN_TRANSFORMS_DEFAULT(32),
'transform_test': da.TEST_TRANSFORMS_DEFAULT(32)
}
super(CIFAR, self).__init__('cifar', data_path, **ds_kwargs)
def __init__(
self,
data_path,
corruption_type: str = 'gaussian_noise',
severity: int = 1,
**kwargs,
):
class CustomCIFAR10(CIFAR10):
def __init__(self, root, train=True, transform=None,
target_transform=None, download=False):
VisionDataset.__init__(self, root, transform=transform,
target_transform=target_transform)
if train:
raise NotImplementedError(
'No train dataset for CIFAR-10-C')
if download and not os.path.exists(root):
raise NotImplementedError(
'Downloading CIFAR-10-C has not been implemented')
all_data = np.load(
os.path.join(root, f'{corruption_type}.npy'))
all_labels = np.load(os.path.join(root, f'labels.npy'))
severity_slice = slice(
(severity - 1) * 10000,
severity * 10000,
)
self.data = all_data[severity_slice]
self.targets = all_labels[severity_slice]
DataSet.__init__(
self,
'cifar10c',
data_path,
num_classes=10,
mean=torch.tensor([0.4914, 0.4822, 0.4465]),
std=torch.tensor([0.2023, 0.1994, 0.2010]),
custom_class=CustomCIFAR10,
label_mapping=None,
transform_train=data_augmentation.TRAIN_TRANSFORMS_DEFAULT(32),
transform_test=data_augmentation.TEST_TRANSFORMS_DEFAULT(32)
)