def download_CIFAR10(train_transforms, test_transforms, batch_size=128, isCuda=Utility.isCuda()):
"""
Load CIFAR10 dataset. Uses the provided train_transforms and the test_transforms and create a object of Data.
:param train_transforms: Transfomrations for train
:param test_transforms: Transformations for test
:param batch_size: Default value is 128
:param isCuda: Default value is True
:return: Data
"""
dataloader_args = dict(shuffle=True, batch_size=batch_size, num_workers=4, pin_memory=True) if isCuda else dict(
shuffle=True, batch_size=batch_size)
train_data = datasets.CIFAR10("../data", train=True, transform=train_transforms, download=True)
train_loader = torch.utils.data.DataLoader(train_data, **dataloader_args)
test_data = datasets.CIFAR10("../data", train=False, transform=test_transforms, download=True)
test_loader = torch.utils.data.DataLoader(test_data, **dataloader_args)
print(f'Shape of a train data batch: {shape(train_loader)}')
print(f'Shape of a test data batch: {shape(test_loader)}')
print(f'Number of train images: {len(train_data.data)}')
print(f'Number of test images: {len(test_data.data)}')
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
return Data(train_loader, test_loader, classes)
def loadTinyImagenet(data_folder, train_transforms, test_transforms, batch_size=128, isCuda=Utility.isCuda()):
dataloader_args = dict(shuffle=True, batch_size=batch_size, num_workers=4, pin_memory=True) if isCuda else dict(
shuffle=True, batch_size=batch_size)
train_data = ImageNet.TinyImageNet(data_folder, train=True, transform=train_transforms)
train_loader = torch.utils.data.DataLoader(train_data, **dataloader_args)
test_data = ImageNet.TinyImageNet(data_folder, train=False, transform=test_transforms)
test_loader = torch.utils.data.DataLoader(test_data, **dataloader_args)
print(f'Shape of a train data batch: {shape(train_loader)}')
print(f'Shape of a test data batch: {shape(test_loader)}')
print(f'Number of train images: {len(train_data)}')
print(f'Number of test images: {len(test_data)}')
return Data(train_loader, test_loader, train_data.idx_class)