def get_cifar(cfg):
"""Gets training and validation data loaders for the CIFAR datasets
Args:
cfg: A YACS config object.
"""
logging.info("==> Preparing to load data " + cfg.DATASET.NAME + " at " + cfg.DATASET.ROOT)
cifar_train_transform = get_transform("cifar", augment=True)
cifar_test_transform = get_transform("cifar", augment=False)
# transform = {
# 'cifar_train': transforms.Compose([
# # Data augmentation
# transforms.RandomCrop(32, padding=4),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
# ]),
# 'cifar_test': transforms.Compose([
# transforms.ToTensor(),
# transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
# ])
# }
if cfg.DATASET.NAME == "CIFAR10":
train_set = torchvision.datasets.CIFAR10(
cfg.DATASET.ROOT, train=True, download=True, transform=cifar_train_transform
)
val_set = torchvision.datasets.CIFAR10(
cfg.DATASET.ROOT, train=False, download=True, transform=cifar_test_transform
)
elif cfg.DATASET.NAME == "CIFAR100":
train_set = torchvision.datasets.CIFAR100(
cfg.DATASET.ROOT, train=True, download=True, transform=cifar_train_transform
)
val_set = torchvision.datasets.CIFAR100(
cfg.DATASET.ROOT, train=False, download=True, transform=cifar_test_transform
)
else:
raise NotImplementedError
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=cfg.SOLVER.TRAIN_BATCH_SIZE,
shuffle=True,
num_workers=cfg.DATASET.NUM_WORKERS,
pin_memory=True,
drop_last=True,
)
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=cfg.SOLVER.TEST_BATCH_SIZE,
shuffle=False,
num_workers=cfg.DATASET.NUM_WORKERS,
pin_memory=True,
)
return train_loader, val_loader
def get_cifar(cfg):
"""Gets training and validation data loaders for the CIFAR datasets
Args:
cfg: A YACS config object.
"""
logging.info("==> Preparing to load data " + cfg.DATASET.NAME + " at " +
cfg.DATASET.ROOT)
cifar_train_transform = get_transform("cifar", augment=True)
cifar_test_transform = get_transform("cifar", augment=False)
if cfg.DATASET.NAME == "CIFAR10":
train_set = torchvision.datasets.CIFAR10(
cfg.DATASET.ROOT,
train=True,
download=True,
transform=cifar_train_transform)
val_set = torchvision.datasets.CIFAR10(cfg.DATASET.ROOT,
train=False,
download=True,
transform=cifar_test_transform)
elif cfg.DATASET.NAME == "CIFAR100":
train_set = torchvision.datasets.CIFAR100(
cfg.DATASET.ROOT,
train=True,
download=True,
transform=cifar_train_transform)
val_set = torchvision.datasets.CIFAR100(cfg.DATASET.ROOT,
train=False,
download=True,
transform=cifar_test_transform)
else:
raise NotImplementedError
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=cfg.SOLVER.TRAIN_BATCH_SIZE,
shuffle=True,
num_workers=cfg.DATASET.NUM_WORKERS,
pin_memory=True,
drop_last=True,
)
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=cfg.SOLVER.TEST_BATCH_SIZE,
shuffle=False,
num_workers=cfg.DATASET.NUM_WORKERS,
pin_memory=True,
)
return train_loader, val_loader
def __init__(self, data_path, transform_kind):
super().__init__(n_classes=10)
self._data_path = data_path
self._transform = image_transform.get_transform(transform_kind)
"""
def get_train(self):
return datasets.SVHN(self._data_path,
split="train",
transform=self._transform,
download=True)
def get_test(self):
return datasets.SVHN(self._data_path,
split="test",
transform=self._transform,
download=True)
OFFICE_DOMAINS = ["amazon", "caltech", "dslr", "webcam"]
office_transform = get_transform("office")
class OfficeAccess(MultiDomainImageFolder, DatasetAccess):
"""Common API for office dataset access
Args:
root (string): root directory of dataset
transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
version. Defaults to office_transform.
download (bool, optional): Whether to allow downloading the data if not found on disk. Defaults to False.
References:
[1] Saenko, K., Kulis, B., Fritz, M. and Darrell, T., 2010, September. Adapting visual category models to
new domains. In European Conference on Computer Vision (pp. 213-226). Springer, Berlin, Heidelberg.
[2] Griffin, Gregory and Holub, Alex and Perona, Pietro, 2007. Caltech-256 Object Category Dataset.
