[OpenCL] Fix a bug when running an external model with the image-loader transposing the input to NCHW #3951
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…er transposing the input to NCHW
SameerAsal
approved these changes
Dec 30, 2019
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Would it be useful to propagate the layout from the loader into Glow placeholder? We can track that in an issue for now? |
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The ideal solution would have the loader adding a transpose node to the graph instead of the way |
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…osing the input to NCHW (pytorch#3951) Summary: See the in-source comment for workaround information, but: We have a model we load from the outside with no-way of knowing the constant/placeholder input layout, the default assumption for 4-D tensors (images) is NHWC format which is the canonical Glow format, PNG files are in NHWC format. Our image loader, when using the `image-layout` flag, transposes the image outside the Glow graph, since there's no easy way to propagate that information, weaken the OpenCL verifier, not the canonical verifier: for placeholders and constants, assume that the loader knows what it is doing and they are in the right format. Fixes pytorch#3815 Pull Request resolved: pytorch#3951 Test Plan: `ninja test` Differential Revision: D19252774 Pulled By: shajrawi fbshipit-source-id: f850c504245ee947794446b144b00df635a68497
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Summary:
See the in-source comment for workaround information, but: We have a model we load from the outside with no-way of knowing the constant/placeholder input layout, the default assumption for 4-D tensors (images) is NHWC format which is the canonical Glow format, PNG files are in NHWC format.
Our image loader, when using the
image-layoutflag, transposes the image outside the Glow graph, since there's no easy way to propagate that information, weaken the OpenCL verifier, not the canonical verifier: for placeholders and constants, assume that the loader knows what it is doing and they are in the right format.Fixes #3815
Test Plan:
ninja test