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Thank you for contribution!
Comments about the algorithm are valid only for the case of continuous matrices, but you assume it in a few places, so we could benefit from optimization.
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I had a few ideas for speeding this up further, if you don't mind. The code should behave exactly the same. I ran tests, but might've missed something. Please take a look.
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Fixes #22528
Merge with: opencv/opencv_extra#1009
Checklist:
ONNX operator doc: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Scatter
Benchmark
Benchmark was done on M1 Macbook Air (16G mem). Time is in milliseconds.
*: Shapes of input, indices, updates are all [8, 256, 128, 100].
*: Shapes of input, indices, updates are [8, 256, 128, 100], [8, 256, 128, 100, 4], [8, 256, 128, 100] respectively.
Potential issue
ScatterND should support duplicate indices if reduction is not none. Also ScatterND permits negative indices. See here for more details.
Notice: Current impl does not check for duplicate indices if reduction is none.
Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
Patch to opencv_extra has the same branch name.