RoIHeads: remove low-scoring boxes statically#4
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sprt merged 1 commit intojysohn23:tpu_compatfrom Dec 11, 2019
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One of the postprocessing step of RoIHeads is to filter low-scoring (in
terms of confidence level) boxes from the output. This commit makes that
operation static for TPUs.
For now, it's needed to make it static manually as the experimental
support for dynamic nonzero/masked_select would raise the following
issue in this case:
RuntimeError: Internal: From /job:tpu_worker/replica:0/task:0:
RET_CHECK failure (learning/brain/google/xla/tpu_execute.cc:1066) size >= 0
[[{{node XRTExecute}}]]
jysohn23
approved these changes
Dec 11, 2019
|
@sprt Could you elaborate on how this makes it static? what does making static "manually" mean? |
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@taylanbil I mean "manually" in the sense that I'm not relying on the experimental support for dynamic shapes with nonzero/masked_select (because it seems buggy atm, see exception), and rather making the shapes |
taylanbil
approved these changes
Dec 11, 2019
|
ah I see. Thanks. |
sprt
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that referenced
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Dec 14, 2019
pytorch#1019 introduced a change that removes empty boxes in postprocessing on the basis that, previously, empty boxes would actually reach this postprocessing step, and that the model could therefore output empty boxes (even though NMS would've most likely filtered them out). It's essentially a safety check that'd be seldom needed. However, that filtering causes dynamicity on the TPU (because the number of empty boxes, if any, would be unknown). And in any case, we recently introduced a change in PR #4 that purposefully pads the boxes tensor with empty boxes, to avoid dynamicity. Therefore there's no point trying to remove boxes that we use as padding, we'll just filter those out of the output on the CPU.
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One of the postprocessing step of RoIHeads is to filter low-scoring (in terms of confidence level) boxes from the output. This commit makes that operation static for TPUs.
For now, it's needed to make it static manually as the experimental support for dynamic nonzero/masked_select would raise the following issue in this case: