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Options for padding

See original GitHub issue

Working with scattering for weather forecasts image analysis, I was wondering what influence the type of padding did have on results.

The reflection padding implemented by default seems to produce high gradients on the boundaries. After a few subsamplings, the large values on the boundary gain a certain weight in the spatial average, that could lead to estimation biases.

This is visible on orientation averaged second-order coefficients.

Question 1 : have the topic already been discussed ?

Question 2 : what about providing numpy padding modes as an option to scattering ?

Now for a bit of demo, if it adds to the discussion. After a fork and 1 or 2 adds on numpy frontend/backend, one comes to the following code :

from kymatio import Scattering2D
import numpy as np
import matplotlib.pyplot as plt



# defining indexing of order 2 coefficients

                           +l2 \
                            for l2 in range(L)]\
                            for l1 in range(L)]\
                            for j2 in range(j1+1,J)] \
                            for j1 in range(J-1)]

def order2(data, pad_type):
    compute orientation averaged l2 coefficients
             out_type='array', pad_type=pad_type)
    ################### order 2 coefficients

    ################### averaging over l2 orientations

    for j1 in range(J-1):
        for j2 in range(J-j1-1):
            for l1 in range(L):

    return S2_j1j2l1

S2_symm=order2(wind_norm, 'symmetric')
S2_reflect=order2(wind_norm, 'reflect')

ratio=100*(S2_symm/S2_reflect -1.0)

print(ratio.min(), ratio.max(), ratio.mean())


The data is downloadable in the zip attached.

The result of this yields:

-19.169381905671035 13.210792343005728 -0.07116768707598231 ratio_plot

Here both transforms coincide at the center, but not on the border. Overestimation by reflection padding is manifest here.

Issue Analytics

  • State:open
  • Created a year ago
  • Comments:5

github_iconTop GitHub Comments

flyIchtuscommented, Jun 21, 2022

OK, sorry for the long delay. Thank you both for pointing me to useful discussions.

Great proposal @lostanlen , having flexible padding options for the same scattering object really more elegant (and lean). Looking at #727, the main issue for implementation seems to be the diversity of backends.

Agree that pad_mode seems better than pad_type.

As for validation and correctness issue @MuawizChaudhary :

A starting point could be something like and showing what bias the different padding modes introduce, especially on intermediate activations (my modest plot upwards was a push in this direction).

Hoping for “perfect” 2D padding, that would allow to forget boundary effects, seems unreasonable however.

Anyway, on texture datasets, it would be interesting to (statistically) compare

  • the effect of (M,N)-padding on (H,W) images
  • the effect of no padding on (H+M, W+N) images

I might be able to have a try on my weather dataset (which, strictly speaking, is not a texture but incorporate some). I keep you updated on this issue 1) if you are interested and 2) if i can get it done in the coming weeks.

lostanlencommented, Jun 10, 2022

Hello @flyIchtus ,

Right now, what you’re doing is indeed the only way to have custom padding/unpadding in Kymatio 2D: pass pre_pad=False and roll up your own implementation, order2_ind in your case.

But i agree that this is not satisfactory and ideally you should be able to pass a single keyword argument at runtime. Not:

scattering=Scattering2D(4,data.shape,8, pad_type=pad_type)


results_order2=scattering(data, pad_type=pad_type)

(the keyword pad_type is up for debate. It’s not a “type” in the CS sense of the word)

In other words, the Scattering2D object shouldn’t need to know about what type of padding you’ll want to apply later. From a user’s perspective, this will allow you to have a single object computing scattering transforms with several kinds of padding. When that plan is ready, all your code will rewrite as

scattering = Scattering2D(J=4, shape=data.shape, L=8)
S2_symm = scattering(data, pad_type='symmetric')
S2_reflect = scattering(data, pad_type='reflect')


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