SLOW Semantic Segmentation on NVIDIA DRIVE Open Script
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Paolo Rosettani
2022 年 9 月 27 日
コメント済み: Hariprasad Ravishankar
2022 年 10 月 3 日
Hello,
I'm using a NVIDIA Jetson AGX Xavier
I'm trying the Semantic Segmentation on NVIDIA DRIVE Open Script: https://it.mathworks.com/help/supportpkg/nvidia/ug/semantic-segmentation-on-nvidia-drive.html
I've only chaned the
opencv_link_flags = '`pkg-config --cflags --libs opencv`';
to
opencv_link_flags = '`pkg-config --cflags --libs opencv4`';
because without it it doesn't compile.
There is another problem: Why the FPS are so slow? Mine goes at 0.39 FPS (as shown in the screenshot below)
I've checked the screenshot in the example and it goes at 8.73 FPS.

Thank you for your help.
Paolo R
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採用された回答
Hariprasad Ravishankar
2022 年 9 月 30 日
Hi Paolo,
Can you try setting the deep learning target library to TensorRT?
cfg = coder.gpuConfig('exe');
cfg.DeepLearningConfig = coder.DeepLearningConfig(TargetLibrary = 'tensorrt');
Hari
1 件のコメント
Hariprasad Ravishankar
2022 年 10 月 3 日
In addition to this, you can also try the following to get a little bit more peformance.
1.TensorRT FP16 mode. Note that FP16 computation can result in lower accuracy from baseline FP32 computation.
cfg = coder.gpuConfig('exe');
dlcfg = coder.DeepLearningConfig(TargetLibrary = 'tensorrt');
dlcfg.DataType = 'FP16';
cfg.DeepLearningConfig = dlcfg;
2.You can use the nvpmodel tool to change the clock mode
For example:
sudo nvpmodel -m 0
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