make core algorithm a self.variable.
See original GitHub issueCurrently in each specific deep learning framework’s frontend we import from core the ScatteringnD
algorithm and call it.
It would be a nice change for power users if we instead imported the algorithm in the base frontend and defined a variable to contain the call i.e. self.scatteringalgo = scatteringnd
. then in each specific frontend, the call to the algorithm would be given by self.scatteringalgo
. This would allow power users to define a breadth first search algorithm #798 , or define their own scattering implementation where they can retrieve coefficients #445 (tho, these futures should be one day in our code base, please see #735).
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Are you saying we store a function as an attribute (a sort of callback)? I’m not convinced this is a good idea. The way we currently extend the classes is using inheritance. Adding another way of modifying the core behavior of the classes would just cause confusion, I think.
Hm, i don’t know. Then yes, a field for the algorithm is needed: (just like for
out_type
)Maybe refactor
scattering
this way?in this way, we’d be exposing new utility functions (check_input, pad, format …) to potential future core modules (wavelets, phase harmonics …)