Inference speed is weird.
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- C++
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Description
I test inference in GeForce 3090 and Jeston TX2.
The env of 3090 is
TensorRT Version: 8.4.1.5
NVIDIA GPU: GeForce 3090
CUDA Version: 11.4
CUDNN Version: 8.4.0
Operating System: ubuntu 18.04
The env of TX2 is
TensorRT Version: 7.1.3-1
CUDA Version: 10.2
In 3090, I use darknet/yolov3 to inference and the inference time is 0.031s. And I also use yolov3_onnx of Tensorrt samples to inference, but the inference speed, 1.983s, is slower. By the way, the inference time is calculated as follows.
At the same time，I run the sample, yolov3_onnx of Tensorrt samples in TX2, the inference time is 0.334s. I am very confused, can u help me?
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Research direction
Start with the TensorRT yolov3_onnx sample and compare its inference timing method, shown in the attached image, with darknet/yolov3. Reproduce the reported timings on the GeForce 3090 and Jetson TX2 using the listed environments, then document the cause of the discrepancy and establish a comparable measurement.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100