wrapping pytorch operations - grid_sample about e2cnn

[Gabri95]

Hi @ahyunSeo

What do you need grid-sample for?

Wrapping it in an EquivariantModule (as done in r2upsampling) is indeed not a problem from an implementation point of view.
However, grid_sample can perform some arbitrary deformation of the input, which means it is not generally equivariant and, therefore, should not be implemented in a subclass of EquivariantModule.

Are you maybe assuming some specific grid which guarantees equivariance?

Best,
Gabriele

[ahyunSeo]

Hi @Gabri95

You made a fair point.
I was about to sample a circular/polar grid for each pixel, where each pixel becomes its center.
So each pixel will be assigned with # angle of grid points.
Then, I wanted to compute correlation values between the grid points per pixel for the further pipeline.
Maybe I should move the feature tensor to a non-geometric tensor first.

Regards,
Ahyun

[Gabri95]

Hi @ahyunSeo ,

If the operation you are trying to implement is equivariant you can just wrap it in an EquivariantModule as I did for r2upsampling.
In the forward pass, you just extract the 'tensor' from the GeometricTensor input, process this tensor is an equivariant way and finally wrap the output in a new GeometricTensor.

You can precompute the grid in the init of the module such that you can ensure the grid allows equivariance.

Let me know if this helps

Gabriele

[ahyunSeo]

Thanks a lot!