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nihui
80 lines
1.9 KiB
Markdown
80 lines
1.9 KiB
Markdown
### broadcasting rule
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ncnn BinaryOp accepts blobs with different shape
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C = BinaryOp(A, B)
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shape notation convention is [w], [w,h], [w,h,c], [w,h,d,c]
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* binaryop with scalar and scalar-like
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|A|B|C|
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|---|---|---|
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|[2]|scalar / [1]|[2]|
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|[2,3]|scalar / [1] / [1,1]|[2,3]|
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|[2,3,4]|scalar / [1] / [1,1] / [1,1,1]|[2,3,4]|
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|[2,3,4,5]|scalar / [1] / [1,1] / [1,1,1] / [1,1,1,1]|[2,3,4,5]|
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* no broadcast
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|A|B|C|
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|---|---|---|
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|[2]|[2]|[2]|
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|[2,3]|[2,3]|[2,3]|
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|[2,3,4]|[2,3,4]|[2,3,4]|
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|[2,3,4,5]|[2,3,4,5]|[2,3,4,5]|
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* explicit broadcast B
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|A|B|C|
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|---|---|---|
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|[2,3]|[1,3]|[2,3]|
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|[2,3]|[2,1]|[2,3]|
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|[2,3,4]|[1,3,4]|[2,3,4]|
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|[2,3,4]|[2,1,4]|[2,3,4]|
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|[2,3,4]|[2,3,1]|[2,3,4]|
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|[2,3,4]|[1,1,4]|[2,3,4]|
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|[2,3,4]|[1,3,1]|[2,3,4]|
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|[2,3,4]|[2,1,1]|[2,3,4]|
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|[2,3,4,5]|[1,3,4,5]|[2,3,4,5]|
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|[2,3,4,5]|[2,1,4,5]|[2,3,4,5]|
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|[2,3,4,5]|[2,3,1,5]|[2,3,4,5]|
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|[2,3,4,5]|[2,3,4,1]|[2,3,4,5]|
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|[2,3,4,5]|[1,1,4,5]|[2,3,4,5]|
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|[2,3,4,5]|[1,3,1,5]|[2,3,4,5]|
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|[2,3,4,5]|[1,3,4,1]|[2,3,4,5]|
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|[2,3,4,5]|[2,1,1,5]|[2,3,4,5]|
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|[2,3,4,5]|[2,1,4,1]|[2,3,4,5]|
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|[2,3,4,5]|[2,3,1,1]|[2,3,4,5]|
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|[2,3,4,5]|[1,1,1,5]|[2,3,4,5]|
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|[2,3,4,5]|[1,1,4,1]|[2,3,4,5]|
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|[2,3,4,5]|[1,3,1,1]|[2,3,4,5]|
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|[2,3,4,5]|[2,1,1,1]|[2,3,4,5]|
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* implicit broadcast B for inner axis
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It broadcasts in the opposite direction of the numpy's implicit broadcasting behavior.
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pnnx will insert reshape operator at the appropriate position to convert it to explicit broadcast automatically.
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|A|B|C|
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|---|---|---|
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|[2,3]|[3]|[2,3]|
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|[2,3,4]|[4]|[2,3,4]|
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|[2,3,4]|[3,4]|[2,3,4]|
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|[2,3,4,5]|[5]|[2,3,4,5]|
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|[2,3,4,5]|[4,5]|[2,3,4,5]|
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|[2,3,4,5]|[3,4,5]|[2,3,4,5]|
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* implicit broadcast B with 1 dimension rank for outer axis
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This exists only for compatibility.
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When the size is the same, eg. [2,2] and [2], broadcast B for inner axis will be prioritized.
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|A|B|C|
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|---|---|---|
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|[2,3]|[2]|[2,3]|
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|[2,3,4]|[2]|[2,3,4]|
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|[2,3,4,5]|[2]|[2,3,4,5]|
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