Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
Inference time is more with fp16 compared to fp32
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Description
YOLOX-s model, when evaluated using the below command line (without fuse enabled) took an average inference time of 22.712ms, with RTX2080Ti
python -m yolox.tools.eval -n yolox-s -c yolox_s.pth -b 1 -d 1 --conf 0.001 --fp16
The same YOLOX-s model, without fp16 using the below command line, took only 15.309ms with RTX2080Ti.
python -m yolox.tools.eval -n yolox-s -c yolox_s.pth -b 1 -d 1 --conf 0.001
With fp16, it should have taken lesser time. Instead it took more time. What could be the reason for this?
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Research direction
Start with the python -m yolox.tools.eval entry point and reproduce both YOLOX-s commands on an RTX2080Ti, with and without --fp16. Compare the timing path and surrounding evaluation configuration; done means identifying and documenting the cause of the slower fp16 result, or confirming the behavior with evidence.
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Assessment
- Tech stack
- python, pytorch
- 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