Resample2D and ChannelNorm packages

[ClementPinard]

Hello,

When reading your packages, It seemed to me that two of your packages were already implemented in the standard pytoch library specifically, is channelnorm different than computing torch.norm(input, p=2, dim=1, keepdim=True) ? (maybe it's faster ?)

And is resample2Ddifferent from grid_sample ? I think this one is a recent feature though (from 0.2.0). It obviously doesn't take flow as input, but it could be used with a mixture of affine_grid (just put a zero affine_matrix) and easy normalization. It might be a little slower though.

Thanks !

[fitsumreda]

Hi there,

For some reason torch.norm() leads to NaNs during backpass. So, I had to implement a cuda kernel for channelnorm.

For resample2d, it is possible to use grid_sample if you are learning absolute sampling indices. Since optical flows learn pixel displacements, you'll need to create a sampling grid with the same size as the flow map, and add this to the flow-map before using grid_sample. Also, PyTorch currently doesn't have a direct way of creating a grid. Several functions need to be used to create the grid, and this grid tensor need to be kept around, for every flow-map spatial size, which makes the whole operation suboptimal.

So, I implemented a resample2d custom layer that takes care of these.

Thanks!

[sniklaus]

I have ported "Optical Flow Estimation using a Spatial Pyramid Network" without a custom resample2d despite learning pixel displacements. If you are curious as to how I used grid_sample, feel free to have a look: https://github.com/sniklaus/pytorch-spynet

Specifically: https://github.com/sniklaus/pytorch-spynet/blob/master/run.py#L125

[fitsumreda]

@sniklaus part of the reason we used the resample2d is that it was implemented before pytorch 0.2 was released. It also makes code a little clearer as it can be used just like any other pytorch layer

[sniklaus]

I initially wrote my own implementation as well. My reason for switching is that the official implementation is tested more thoroughly. Anyways, huge thanks for putting this out there!