Pytorch transformation on MNIST dataset
I currently have a project with Weak Supervision where I need to put a "masking" in front of a dataset. My issue right now is that I don't exactly know how to do it. Let me explain further with some code and images. 

I am using the MNIST dataset that I have to edit in this way. As you can see a middle square is cut out. The code below is used to edit the MNIST using a for loop.

for i in range(int(image_size/2-5),int(image_size/2+3)):
   for j in range(int(image_size/2-5),int(image_size/2+3)):
      image[i][j] = 0


However, I am currently not sure how I should use this in a dataloader transform. The code for the dataloader and transform is shown here:

transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])

train_dataset = torchvision.datasets.MNIST(
    root="~/torch_datasets", train=True, transform=transform, download=True
)

test_dataset = torchvision.datasets.MNIST(
    root="~/torch_datasets", train=False, transform=transform, download=True
)

train_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=128, shuffle=True, num_workers=4, pin_memory=True
)

test_loader = torch.utils.data.DataLoader(
    test_dataset, batch_size=32, shuffle=False, num_workers=4
)

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

dataiter = iter(train_loader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))


So is there a straightforward way to apply the transform to the full dataset in the torchvision.transforms.Compose?

You can define any custom transformation and as a function and use torchvision.transforms.Lambda in the transformation pipeline.

def erase_middle(image: torch.Tensor) -&gt; torch.Tensor:
    for i in range(int(image_size/2-5),int(image_size/2+3)):
        for j in range(int(image_size/2-5),int(image_size/2+3)):
            image[:, i, j] = 0
    return image

transform = torchvision.transforms.Compose(
    [
        # First transform it to a tensor
        torchvision.transforms.ToTensor(),
        # Then erase the middle
        torchvision.transforms.Lambda(erase_middle),
    ]
)




erase_middle can be made more generic, such that it works for images with varying sizes and that aren't necessarily square.

def erase_middle(image: torch.Tensor) -&gt; torch.Tensor:
    _, height, width = image.size()
    x_start = width // 2 - 5
    x_end = width // 2 + 3
    y_start = height // 2 - 5
    y_end = height // 2 + 3
    # Using slices achieves the same as the for loops
    image[:, y_start:y_end, x_start:x_end] = 0
    return image

