Padding for powers of 2 about pytorch_wavelets HOT 1 CLOSED

Hi @andreasfloros! Sorry for the late reply, I don't check this as much these days. This is intended, and matches the behaviour of PyWavelets.

For the odd signal length, see my answer in #39 .

For this padding, this is because when you convolve a wavelet of length M with a signal of length N, the output will be N+M-1. This happens before the downsampling, and we want to keep this information on the edges so we can reconstruct the signal later. If it's a problem for you (and I know sometimes it's nice to have everything a power of 2 in deep networks), you could use the db1 wavelet (as that only has length 2), or use the periodization padding mode. Neither are ideal though, db1 wavelets aren't too great, and periodization pads the image on the right with the first column on the left (like what you see in fourier transforms)

from pytorch_wavelets.