def get_data_loaders(self, nomask=False):
transform = self.TRANSFORM
test_transform = transforms.Compose(
[transforms.ToTensor(), self.get_normalization_transform()])
train_dataset = MyCIFAR100(base_path() + 'CIFAR100', 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 = CIFAR100(base_path() + 'CIFAR100', 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
def get_joint_loaders(self, nomask=False):
transform = self.TRANSFORM
test_transform = transforms.Compose(
[transforms.ToTensor(),
self.get_normalization_transform()])
train_dataset = MyCore50(base_path() + 'CORE50',
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 = Core50(base_path() + 'CORE50',
train=False,
download=True,
transform=test_transform)
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
def init_train_loaders(self) -> None:
"""
Initializes the test loader.
"""
train_dataset = MyMNIST(base_path() + 'MNIST',
train=True,
download=True)
if self.args.validation:
test_transform = transforms.ToTensor()
train_dataset, self.val_dataset = get_train_val(
train_dataset, test_transform, self.NAME)
for j in range(self.N_CLASSES):
self.train_loaders.append([])
self.remaining_training_items.append([])
train_mask = np.isin(np.array(train_dataset.targets), [j])
train_rotation = IncrementalRotation(init_deg=(j - 1) * 60,
increase_per_iteration=360.0 /
train_mask.sum())
for k in range(self.num_rounds * 2):
tmp_train_dataset = deepcopy(train_dataset)
numbers_per_batch = train_mask.sum() // (self.num_rounds *
2) + 1
tmp_train_dataset.data = tmp_train_dataset.data[train_mask][
k * numbers_per_batch:(k + 1) * numbers_per_batch]
tmp_train_dataset.targets = tmp_train_dataset.targets[
train_mask][k * numbers_per_batch:(k + 1) *
numbers_per_batch]
tmp_train_dataset.transform = transforms.Compose(
[train_rotation, transforms.ToTensor()])
self.train_loaders[-1].append(
DataLoader(tmp_train_dataset, batch_size=1, shuffle=True))
self.remaining_training_items[-1].append(
tmp_train_dataset.data.shape[0])
def get_data_loaders(self):
transform = transforms.ToTensor()
train_dataset = MyMNIST(base_path() + 'MNIST',
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 = MNIST(base_path() + 'MNIST',
train=False, download=True, transform=transform)
train, test = store_masked_loaders(train_dataset, test_dataset, self)
return train, test
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 store_mnist_loaders(transform, setting):
train_dataset = MyMNIST(base_path() + 'MNIST',
train=True,
download=True,
transform=transform)
if setting.args.validation:
train_dataset, test_dataset = get_train_val(train_dataset, transform,
setting.NAME)
else:
test_dataset = MNIST(base_path() + 'MNIST',
train=False,
download=True,
transform=transform)
train_loader = DataLoader(train_dataset,
batch_size=setting.args.batch_size,
shuffle=True)
test_loader = DataLoader(test_dataset,
batch_size=setting.args.batch_size,
shuffle=False)
setting.test_loaders.append(test_loader)
setting.train_loader = train_loader
return train_loader, test_loader