dnn module and cuda [closed]

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in short -- no.

Well i assumed so after reading the code and seeing a cv::gpu::GpuMat. But i wanted to clarify - maybe i was overlooking something. Well thx for the clarification.

Ok so in general if i want to evaluate a dnn model and that should be fast - i would used them directly instead of going through open cv (but that worked nice for me).

I will make some further measurement (with and without cuda - with and without open cv dnn), i hope i can stick to open cv.

where's the GpuMat ?

Ok i reread my sentence - what i meant was "After reading the code" - and looking at the cuda example code for open cv ( cv::gpu::GpuMat). It really wrote this wrong.

So cuda support is not "transparent" - u need specialized code to gain from it. My current understanding.

ah, ok. there is indeed cuda support for many opencv algorithms, just not for the dnn. (and it's not planned to add it)

if you have a halfway decent GPU, you might want to try the opencl dnn backend.

So cuda support is not "transparent" - u need specialized code to gain from it

exactly.

Wait, wait a second berak. I can set a backend for dnn.

 net.setPreferableBackend()

You are saying opencl will use my gpu(wikipedia says it could do that )? I want to use my gpu - thats what i am trying to do(nice rhyme). Wikipedia suggest open cli provides you the level of transparency i was complaining about missing on cuda.

It has not to be cuda if something else works. I will give it a try and measure the results. I have a google cloud account on a strong machine, hope it produces reasonable results - wish me luck :-)

Thank you very much again - i hope i can stick to open cv for fast dnn evaluation, its nice and its working. I really prefer it to all the solutions i tried out yet(darknet(yolo), tensorflow(ssd)).

yea, nice rhyme ;)

and apologies, it's "target", not "backend", see e.g. here

Well ok - you are right.For the people reading this - the method for activating cli for dnn is like berak metioned

net.setPreferableTarget()

more precise

net.setPreferableTarget(1)