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duanzhiihao / RAPiD

RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images (CVPR 2020 Workshops)
http://vip.bu.edu/rapid/
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Export model to tensorrt #26

Open MHerique opened 3 years ago

MHerique commented 3 years ago

Hi!

I would like to know if you have already tried to convert RAPID pytorch model into tensorrt model with success?

I'm trying to do it but it's look a bite difficult. I pass through onnx model but passing from onnx to tensorrt is a bite difficult.

thanks

duanzhiihao commented 3 years ago

Hello, thank you for your interest.

Unfortunately, we did not try it, and we are not planning to do it. We are moving on to a new project and probably will not develop RAPiD for exporting to ONNX or TensorRT. Sorry for the inconvenience!

That said, if there are more people expecting this feature, I will take some time to learn and implement the TensorRT export.

stevebottos commented 3 years ago

I posted an issue earlier and came across this one. I plan to extend the model to allow ONNX and TensorRT support but I am waiting on a non-prohibitive license to be put into the repo as I'm hesitant to make any modifications otherwise.

duanzhiihao commented 3 years ago

Hi, I add a script that converts our model to ONNX. You can choose the pre-trained weights and input shape upon your preference. https://github.com/duanzhiihao/RAPiD/blob/master/export_onnx.py

However, I didn't check if the ONNX model output is correct comparing with the original Pytorch model.

I also managed to convert the ONNX model to TENSORRT without errors by following the TENSORRT docs. That is, simply trtexec --onnx=rapid.onnx --saveEngine=rapid.trt --explicitBatch after we have the ONNX model, which I named rapid.onnx here.

Let me know if there are any problems.

abhigoku10 commented 3 years ago

@duanzhiihao thanks for sharing the file but when i am inference onnx model i am not getting any outputs at all i have shared the image below any thoughts on these ONNX w

duanzhiihao commented 3 years ago

It may be a bug in the ONNX version since I didn't test the ONNX model.

It seems that the predictions' scale is different from the image as the predictions concentrate at the top-left corner. Did you resize the image after input it into the model?

Also, it would be helpful if you provide the code that you produce this image.

deepsrm commented 1 year ago

Regarding the bug in the ONNX model inference, I am getting the same results as [abhigoku10]. Is there any upgrade or clues on how to resolve this issue?

I've just used the same input, without resizing at all, maybe is something related to rapid_export.py module?

Thanks