pytorch_script_mode_local_model_inference/utils_cifar.py [7:36]:
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classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


def _get_transform():
    return transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


def get_train_data_loader():
    transform = _get_transform()
    trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                            download=True, transform=transform)
    return torch.utils.data.DataLoader(trainset, batch_size=4,
                                       shuffle=True, num_workers=2)


def get_test_data_loader(download):
    transform = _get_transform()
    testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                           download=download, transform=transform)
    return torch.utils.data.DataLoader(testset, batch_size=4,
                                       shuffle=False, num_workers=2)


# function to show an image
def imshow(img):
    img = img / 2 + 0.5  # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
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pytorch_script_mode_local_training_and_serving/utils_cifar.py [7:36]:
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classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


def _get_transform():
    return transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


def get_train_data_loader():
    transform = _get_transform()
    trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                            download=True, transform=transform)
    return torch.utils.data.DataLoader(trainset, batch_size=4,
                                       shuffle=True, num_workers=2)


def get_test_data_loader(download):
    transform = _get_transform()
    testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                           download=download, transform=transform)
    return torch.utils.data.DataLoader(testset, batch_size=4,
                                       shuffle=False, num_workers=2)


# function to show an image
def imshow(img):
    img = img / 2 + 0.5  # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
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