Dear pytorchers,
Is it possible to compute a Circular convolution in pytorch?
My initial thought was to use Circular padding followed by regular convolution with no padding but it seems that Circular padding does not exist.
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You probably have to use a regular square convolution with a constraint on the weights to remove the corners of the convolution.
Could you elaborate on what do you mean by:
“constrain on the weights to remove the corners”
For example, a 2d convolution with kernel size 4 would have a 4x4 matrix of weights for each channel. Forcing the corners of this 4x4 matrix to be zero would give your convolution a nearly circular receptive field.
But maybe I have completely misunderstood what you mean by “circular convolution”.
Maybe you can draw some ideas from here: https://github.com/DmitryUlyanov/texture_nets/blob/master/src/SpatialCircularPadding.lua . It is easy to read.
You can use F.conv2d(F.pad(input, pad=(5,5,5,5), mode='circular'), kernel, padding=0) for circular convolution. Btw, you sometimes cannot find the mode circular for F.pad if you directly search in the help doc, but your can go to https://pytorch.org/docs/stable/nn.functional.html?highlight=pad#torch.nn.functional.pad
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