def get_data_loaders(self):
transform = self.TRANSFORM
test_transform = transforms.Compose(
[transforms.ToTensor(), self.get_normalization_transform()])
train_dataset = MyTinyImagenet(base_path() + 'TINYIMG',
train=True, download=True, transform=transform)
if self.args.validation:
train_dataset, test_dataset = get_train_val(train_dataset,
test_transform, self.NAME)
else:
test_dataset = TinyImagenet(base_path() + 'TINYIMG',
train=False, download=True, transform=test_transform)
train, test = store_masked_loaders(train_dataset, test_dataset, self)
return train, test
def get_data_loaders(self, nomask=False):
transform = transforms.ToTensor()
train_dataset = MyFMNIST(base_path() + 'FMNIST',
train=True,
download=True,
transform=transform)
if self.args.validation:
train_dataset, test_dataset = get_train_val(
train_dataset, transform, self.NAME)
else:
test_dataset = FMNIST(base_path() + 'FMNIST',
train=False,
download=True,
transform=transform)
if not nomask:
train, test = store_masked_loaders(train_dataset, test_dataset,
self)
return train, test
else:
return train_dataset, test_dataset
def get_data_loaders(self, nomask=False):
transform = self.TRANSFORM
test_transform = transforms.Compose(
[transforms.ToTensor(),
self.get_normalization_transform()])
train_dataset = MyCIFAR10(base_path() + 'CIFAR10',
train=True,
download=True,
transform=transform)
if self.args.validation:
train_dataset, test_dataset = get_train_val(
train_dataset, test_transform, self.NAME)
else:
test_dataset = CIFAR10(base_path() + 'CIFAR10',
train=False,
download=True,
transform=test_transform)
if not nomask:
if isinstance(train_dataset.targets, list):
train_dataset.targets = torch.tensor(train_dataset.targets,
dtype=torch.long)
if isinstance(test_dataset.targets, list):
test_dataset.targets = torch.tensor(test_dataset.targets,
dtype=torch.long)
train, test = store_masked_loaders(train_dataset, test_dataset,
self)
return train, test
else:
train_loader = DataLoader(train_dataset,
batch_size=32,
shuffle=True,
num_workers=2)
test_loader = DataLoader(test_dataset,
batch_size=32,
shuffle=False,
num_workers=2)
return train_loader, test_loader