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Investigate why there's a transpose operation that cannot be removed. #411
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I did find the cause, there is another place, here, where it's flipped. And both places have to be fixed to make it work. I ran into it during further pytorch stuff and removing both flips fixed the issue. |
Thanks @matham, I think I'm missing something though, where is the second flip? |
Right, because in some places, the assumption seems to be that the order is But, if it used a consistent order with arbitrary dim1/dim2, it should not need flipping anywhere. And in my pytorch code that seemed to work ok. |
Thanks for clarifying, I saw your previous link and it looked correct, because it was normal numpy axis ordering. So it seems like there's a mix of numpy and cartesian axis ordering, and we should decide on one. Everywhere else in BrainGlobe where we've made this decision, we've gone with numpy, so I propose we do the same here. Any issue @brainglobe/active-devs? |
I agree! |
This is more complicated than I thought, just because there are just so many references to axes in cellfinder. We can do three things:
Any thoughts? |
My initial instinct would be to implement 2. soon (it's a concrete, scoped step that will avoid unnecessary computations) and leave 3. for a later time point, as we have more pressing issues in the roadmap? |
Correcting these two flips doesn't seem to fix anything. Tests fail, and detection seems worse. I've opened #413 to track this properly. |
This comment from @matham highlights that there is a transpose that, if removed, causes errors.
In theory cellfinder shouldn't require a specific axis order, so it would be good to know why this tranpose is there.
cc @alessandrofelder
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