Paired Transforms in torchvision

Extension of torchvision transforms to handle simultaneous transformation of input and ground-truth when the latter is an image.

Note: Extensions for PyTorch 0.3 and 0.4 are provided in separate files.

When performing data augmentation in dense pixel-wise prediction tasks we typically want to transform in exactly the same way the input image and the ground-truth. The recommended way for dealing with this requires to handle these paired transforms in the training part of your code.

The files paired_transforms_pt03.py and paired_transforms_pt04.py in this repo contain suitably modified classes so that the user does not need to take care of this:

• If a transform is called with two inputs it will transform both in the same way automatically.
• If a transform is called with only one input, the behavior of the several classes will be preserved wrt to the original implementation.

This means that you can use this as a plug-and-play extension, e.g. replacing:

import torchvision.transforms as tr

by:

import paired_ransforms_pt04 as tr

Please see the notebooks paired_transforms_pytorch0.3.ipynb and paired_transforms_pytorch0.4.ipynb for an example of how the original implementation was modified, and paired_transforms_examples_pytorch0.3.ipynb, paired_transforms_examples_pytorch0.4.ipynb for examples of all the transforms that were modified. I am also including an example of how to build a PyTorch dataset and dataloader using paired transforms.

Below you can find a visual example, while also meeting Luppo and More, who are part of my family.

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An image of Luppo and More happily sitting in their basket, useful for the task of segmenting charismatic dogs and charismatic cats from the background:

Result of transforming them with standard torchvision code:

from torchvision import transforms as tr
degrees=(0,180)
rotate = tr.RandomRotation(degrees)
rotated_im = rotate(image)
rotated_gt = rotate(gdt)
imshow_pair(rotated_im, rotated_gt)

Result of transforming them with extended transforms:

import paired_ransforms_pt04 as tr
rotate = tr.RandomRotation(degrees)
rotated_pair = rotate(image, gdt)
imshow_pair(*rotated_pair)

Have fun!