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python-3.xopencvgpucvlib

opencv doesn't use all GPU memory

I'm trying to use the cvlib package which use yolov3 model to recognize objects on images on windows 10. Let's take an easy example:

import cvlib as cv
import time
from cvlib.object_detection import draw_bbox


inittimer=time.time()
bbox, label, conf = cv.detect_common_objects(img,confidence=0.5,model='yolov3-worker',enable_gpu=True)
print('The process tooks %.3f s'%(time.time()-inittimer)

output_image = draw_bbox(img, bbox, label, conf)

The results give ~60ms.

cvlib use opencv to compute this cnn part.

If now I try to see how much GPU tensorflow used, using subprocess, It tooks only 824MiB.

while the program runs, if I start nvidia-smi it gives me this result:

enter image description here

As u can see there is much more memory available here. My question is simple.. why Cvlib (and so tensorflow) doesn't use all of it to improve the time's detection?

EDIT:

As far as I understand, cvlib use tensorflow but it also use opencv detector. I installed opencv using cmake and Cuda 10.2 I don't understand why but in the nvidia-smi it's written CUDA Version : 11.0 which is not. Maybe that's the part of the problem?

Solution

  • I got it! The problem come from the fact that I created a new Net object for each itteration.

    Here is the related issue on github where you can follow it: https://github.com/opencv/opencv/issues/16348

    With a custom function, it now works at ~60 fps. Be aware that cvlib is, maybe, not done for real time computation.