timm_efficientdet: benchmark coverage for custom devices #2374
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Pls hold until the custom devices support for https://github.com/rwightman/efficientdet-pytorch is merged. |
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The PR for third-party dependency rwightman/efficientdet-pytorch#299 is merged. @EikanWang pls help review again and I will mark this PR as "ready for review" if no change. |
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Works for Roadmap #1293 to increase benchmark coverage.
This model implementation is hard-code with CUDA due to the 3rd-party repo dependency which makes that running on the custom devices except for CUDA(e.g. XPU) will raise the runtime error.
In this PR, we accept the device arg as a parameter within the training and inference processes, which will cover the model initializing and data transposition for these custom devices.