How to write your own TVTensor class

Note

Try on Colab or go to the end to download the full example code.

This guide is intended for advanced users and downstream library maintainers. We explain how to write your own TVTensor class, and how to make it compatible with the built-in Torchvision v2 transforms. Before continuing, make sure you have read TVTensors FAQ.

import torch
from torchvision import tv_tensors
from torchvision.transforms import v2

We will create a very simple class that just inherits from the base TVTensor class. It will be enough to cover what you need to know to implement your more elaborate uses-cases. If you need to create a class that carries meta-data, take a look at how the BoundingBoxes class is implemented.

class MyTVTensor(tv_tensors.TVTensor):
    pass


my_dp = MyTVTensor([1, 2, 3])
my_dp

MyTVTensor([1., 2., 3.])

Now that we have defined our custom TVTensor class, we want it to be compatible with the built-in torchvision transforms, and the functional API. For that, we need to implement a kernel which performs the core of the transformation, and then “hook” it to the functional that we want to support via register_kernel().

We illustrate this process below: we create a kernel for the “horizontal flip” operation of our MyTVTensor class, and register it to the functional API.

from torchvision.transforms.v2 import functional as F


@F.register_kernel(functional="hflip", tv_tensor_cls=MyTVTensor)
def hflip_my_tv_tensor(my_dp, *args, **kwargs):
    print("Flipping!")
    out = my_dp.flip(-1)
    return tv_tensors.wrap(out, like=my_dp)

To understand why wrap() is used, see I had a TVTensor but now I have a Tensor. Help!. Ignore the *args, **kwargs for now, we will explain it below in Parameter forwarding, and ensuring future compatibility of your kernels.

Note

In our call to register_kernel above we used a string functional="hflip" to refer to the functional we want to hook into. We could also have used the functional itself, i.e. @register_kernel(functional=F.hflip, ...).

Now that we have registered our kernel, we can call the functional API on a MyTVTensor instance:

my_dp = MyTVTensor(torch.rand(3, 256, 256))
_ = F.hflip(my_dp)

Flipping!

And we can also use the RandomHorizontalFlip transform, since it relies on hflip() internally:

t = v2.RandomHorizontalFlip(p=1)
_ = t(my_dp)

Flipping!

Note

We cannot register a kernel for a transform class, we can only register a kernel for a functional. The reason we can’t register a transform class is because one transform may internally rely on more than one functional, so in general we can’t register a single kernel for a given class.

Parameter forwarding, and ensuring future compatibility of your kernels

The functional API that you’re hooking into is public and therefore backward compatible: we guarantee that the parameters of these functionals won’t be removed or renamed without a proper deprecation cycle. However, we don’t guarantee forward compatibility, and we may add new parameters in the future.

Imagine that in a future version, Torchvision adds a new inplace parameter to its hflip() functional. If you already defined and registered your own kernel as

def hflip_my_tv_tensor(my_dp):  # noqa
    print("Flipping!")
    out = my_dp.flip(-1)
    return tv_tensors.wrap(out, like=my_dp)

then calling F.hflip(my_dp) will fail, because hflip will try to pass the new inplace parameter to your kernel, but your kernel doesn’t accept it.

For this reason, we recommend to always define your kernels with *args, **kwargs in their signature, as done above. This way, your kernel will be able to accept any new parameter that we may add in the future. (Technically, adding **kwargs only should be enough).