Comments (4)
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
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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!
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@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
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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!
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