[solved] Discrete cosine transform implementation in pytorch?

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UPDATE: I figured it out. This link explains how to incorporate scipy functions into the computational graph. You can use scipy.fftpack.dct http://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html

Hi everyone,
So I want to backprop the error gradient through the inverse discrete cosine transform.

I tried implementing a naive versi
on of the DCT but it was reallyyyyy slow.

Does anyone have any suggestions for writing a fast, backpropable implementation of the inverse discrete cosine transform?

I can paste my code here. The only reason I didn’t is because it is very messy and probably would not help clarify the situation.

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For anyone coming here from Google search: I have implemented DCT for pytorch in terms of the built-in FFT, so that it works on CPU and GPU, through back propagation:

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I just found this on Github, many thanks :+1:t2: