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zhiqwang avatar zhiqwang commented on May 22, 2024

Hi @CPFelix ,

As far as I can recall, torchscript does not provide an interface to modify the names of the input and output layers. But I think that their names are fixed once you export the torchscript model. And you can check out the name of it via https://netron.app/.

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CPFelix avatar CPFelix commented on May 22, 2024

@zhiqwang can I modify the name when make model?The layer name seems to be name auto.

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zhiqwang avatar zhiqwang commented on May 22, 2024

Seems a bit tricky, and need to check the documentation for exporting torchscript more carefully.

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zhiqwang avatar zhiqwang commented on May 22, 2024

Besides, it doesn't care about the names of the input and output layers if you are using libtorch. (I did't have much experience on Triton)

https://github.com/zhiqwang/yolov5-rt-stack/blob/76096edb32b71981552cc68e61b1e0026d2c74ff/deployment/libtorch/main.cpp#L197

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CPFelix avatar CPFelix commented on May 22, 2024

For Triton,model input and output name must be named like "name__index", otherwise will come error:
"
UNAVAILABLE: Internal: input 'x' does not follow naming convention i.e. name__index.
"
Thank you for your earnest reply.

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CPFelix avatar CPFelix commented on May 22, 2024

@zhiqwang And I find that onnx model is slow than .pt. For my test, yolov5s.pt gets 76.18fps, while yolov5s.onnx gets 24.78fps.
Is it normal?

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zhiqwang avatar zhiqwang commented on May 22, 2024

For my test, yolov5s.pt gets 76.18fps, while yolov5s.onnx gets 24.78fps. Is it normal?

I think this is possible. Judging the speed of model inference requires more information, such as batch_size, whether the currently used device is specifically optimized and so on.

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CPFelix avatar CPFelix commented on May 22, 2024

I use batchsieze=1 and the same GPU to test the two model.

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