I am a beginner in PyTorch and I’m working on training a cnn in the fourier domain completely to exploit advantages of the fourier domain, like the convolution theorem etc. Though PyTorch has an rfft function to get fourier representation of real images and weights etc., after taking the rfft, I would need to take the dot product of the complex tensor outputs of rfft. Similarly many other operations on complex tensors might be needed in the cnn’s functioning.
Given the lack of complex data type in PyTorch currently, would it be better to shift to Tensorflow which does support a complex data type? Also, what are the advantages and drawbacks of tensorflow over PyTorch?
Or can the complex data type supported by NumPy be used in PyTorch?