fuse constant padding into conv kernels #7515
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csullivan
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Feb 24, 2021
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Thanks @mbrookhart!
Support for other operators that handle would be a nice follow on, but this handles the majority of problematic cases of padding happening explicitly from ONNX's use of auto_pad. On deeplabv3-mobilenetv2 this reduces the number of explicit nn.pad calls from 54 -> 11. Those remaining are part of a space to depth transformation.
csullivan
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Feb 26, 2021
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Note that given the layout inference of the implementation, users should consider running SimplifyExpr prior to layout transformations via ConvertLayout or otherwise. Else it may be the case that a |
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masahi
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Mar 2, 2021
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Thanks @mbrookhart @csullivan |
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* fuse constant padding into conv kernels * change the kernel to support other layouts * add channel-last test * add a comment about bailing early
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May 11, 2021
* fuse constant padding into conv kernels * change the kernel to support other layouts * add channel-last test * add a comment about bailing early
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cc @masahi @csullivan
Currently supports conv1d/2d/3d. I can probably do the same thing for conv transpose and pooling, but this is getting a little long, what do you guys think?