Fast-BEV How to test speed on my own device?

Hi, thanks for the great work ! I want to test speed on my own RTX3090 with fp32. Which file do I need to run and where do I modify the config? We want to cite your method and make a fair comparison.

Mar 05 '23 11:03 exiawsh

We would appreciate it if you could provide the speed on RTX3090 (fp32 pytorch version).

Mar 05 '23 11:03 exiawsh

I have tested the speed of your resnet-34 model with a single frame on RTX3090. I only got 20 FPS, is that normal?

Mar 05 '23 13:03 exiawsh

I have tested the speed of your resnet-34 model with a single frame on RTX3090. I only got 20 FPS, is that normal?

I have tested their M0 model (Res18), only 3.3FPS （for the temporal version with 4 frame).

Mar 11 '23 16:03 JudasDie

I have tested the speed of your resnet-34 model with a single frame on RTX3090. I only got 20 FPS, is that normal?

I have tested their M0 model (Res18), only 3.3FPS （for the temporal version with 4 frame).

Thank you for the result. I set the number of frame to 1, And get the above results. It seems that the pytorch version of the code is not efficient. The speed reported in the paper is mainly due to onnx/tensorrt…

Mar 11 '23 16:03 exiawsh

I have tested the speed of your resnet-34 model with a single frame on RTX3090. I only got 20 FPS, is that normal?

I have tested their M0 model (Res18), only 3.3FPS （for the temporal version with 4 frame).

Thank you for the result. I set the number of frame to 1, And get the above results. It seems that the pytorch version of the code is not efficient. The speed reported in the paper is mainly due to onnx/tensorrt…

Have you already transferred their model to TRT?

Mar 11 '23 16:03 JudasDie

I have tested the speed of your resnet-34 model with a single frame on RTX3090. I only got 20 FPS, is that normal?

I have tested their M0 model (Res18), only 3.3FPS （for the temporal version with 4 frame).

Thank you for the result. I set the number of frame to 1, And get the above results. It seems that the pytorch version of the code is not efficient. The speed reported in the paper is mainly due to onnx/tensorrt…

Have you already transferred their model Have not yet, I don't have this plan now.

Mar 12 '23 00:03 exiawsh