ConvNormActivation block for 3D convolutions

🚀 The feature

I suggest we add a Conv3d equivalent for the ConvNormActivation block

Motivation, pitch

This will be used in the upcoming ResNext3d implementation.

Alternatives

I see two possible alternatives:

1. We can re-purpose the existing method in a BC way by adding an extra named parameter conv layer with default value of Conv2D.

2. We can explicitly add a new class Conv3DNormActivation, which will be very similar to the existing one.

Additional context

No response

[jdsgomes]

@oke-aditya I kicked off by renaming the ConvNormActivation as we all seem to be aligned. Looking forward to your contribution for step 2.

[jdsgomes]

Thanks for the feedback @oke-aditya!

I was thinking and discussing with @datumbox offline, and I’m inclined to the following solution:

1. Create an Alias for the existing ConvNormActivation and call it Conv2dNormActivation. (this will be done in a separate PR. I would also add a deprecation warning for the ConvNormActivation in this case.
2. Create a new method for 3D convolutions named Conv3dNormActivation, which seems to be your preferred method too.

The advantages of this approach is that we have a clear naming convention in line with the nn.Conv2d and nn.Conv3d from pytorch, and we wouldn’t have to validate invalid combinations of norm_layer and the new conv_layer paramter in ConvNormActivation.

The major disadvantage is that the current ConvNormActivation is already used in 9 models inside TorchVision and numerous places externally but I think this can be mitigated with the deprecation warning and fazing out in future releases.