Improve rank filtering performance by removing use of footprint kernel when possible #485
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can avoid one conditional in the inner loop when it is non that there are no non-zero footprint entries set all_weights_nonzero=True for separable min or max filters too omit weights when possible for rank kernels
grlee77
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Here is a benchmark on a 2048x2048 2D image. (not sure why there is a spike specifically at size 13x13)
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This did require one API addition not present in scikit-image. Namely, one can provide an integer size instead of a footprint if a fully populated square footprint is desired. (as available in the API of |
Codecov ReportBase: 92.95% // Head: 92.92% // Decreases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## branch-23.02 #485 +/- ##
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- Coverage 92.95% 92.92% -0.03%
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Files 130 130
Lines 9775 9795 +20
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+ Hits 9086 9102 +16
- Misses 689 693 +4
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closes #520 #485 Introduced a bug in rank filtering kernels that was not caught by our current test cases (so this bug is only present in the 23.02 release) This MR fixes the bug and adds test coverage for this case. Authors: - Gregory Lee (https://github.com/grlee77) - https://github.com/jakirkham Approvers: - https://github.com/jakirkham URL: #521
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This is a follow-up to #485 allowing the overhead of creating a weights array of uniform values to be omitted when possible for all binary morphology operators. This gives a large performance benefit for small footprints (and small images) with the benefit becoming minimal for large footprint size. As currently implemented, a shape tuple can be passed to `structure` to indicate the equivalent of an array of ones of that shape. This is a deviation from SciPy's API which does not currently allow this. I am not positive if this is the best API choice or if it might be better to instead add a `size` keyword-only argument like the grayscale morphology functions currently have. The footprint generation methods `square`, `rectangle`, `cube` etc. use this new tuple format when possible to reduce overhead. I will post benchmarks below. Authors: - Gregory Lee (https://github.com/grlee77) Approvers: - Gigon Bae (https://github.com/gigony) URL: #517
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In many cases such as rank filtering, if all elements of the kernel array are equal to one, then there is no reason to pay the overhead of allocating the footprint or checking its values during the filtering operation.
This MR is a small update to vendored CuPy ndimage code to avoid overhead of footprint creation and use when possible.
Will update with benchmark results of acceleration vs. the kernel size. It is approximately a 2x improvement for small kernels (e.g. 3x3, 5x5, 3x3x3) approaching no acceleration for larger kernel sizes.