def ld_cifar10():
  """Load training and test data."""
  train_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
  test_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
  train_dataset = torchvision.datasets.CIFAR10(root='/tmp/data', train=True, transform=train_transforms, download=True)
  test_dataset = torchvision.datasets.CIFAR10(root='/tmp/data', train=False, transform=test_transforms, download=True)
  train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)
  test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2)
  return EasyDict(train=train_loader, test=test_loader)
def ld_cifar10 ( ) : train_transforms = torchvision . transforms . Compose ( [ torchvision . transforms . ToTensor ( ) ] ) test_transforms = torchvision . transforms . Compose ( [ torchvision . transforms . ToTensor ( ) ] ) train_dataset = torchvision . datasets . CIFAR10 ( root = '/tmp/data' , train = True , transform = train_transforms , download = True ) test_dataset = torchvision . datasets . CIFAR10 ( root = '/tmp/data' , train = False , transform = test_transforms , download = True ) train_loader = torch . utils . data . DataLoader ( train_dataset , batch_size = 128 , shuffle = True , num_workers = 2 ) test_loader = torch . utils . data . DataLoader ( test_dataset , batch_size = 128 , shuffle = False , num_workers = 2 ) return EasyDict ( train = train_loader , test = test_loader )
Load training and test data.