onnx model shows slower inference when setting cuda as backend

asked 2020-10-21 10:26:37 -0600

xz1990 gravatar image

I have a net loaded from onnx:

net = cv2.dnn.readNetFromONNX(xxx.onnx)

when i directly do net.forward(), the inference time is around 0.2s

If I set cuda as backend after loading model:

net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA) net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

The inference time will be around 0.4s

(But this backend setting is working for YOLOv3 readNetFromDarknet, I can see an inference time reduce after setting cuda as backend)

Is it because the model is not supporting cuda backend?

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the model is not supporting cuda backend?

it's for sure not about onnx, probably more like: some layers in your network have better or worse cuda support than others (or none at all)

yolo3 is a well tested network, while we don't know anything about yours.

berak gravatar imageberak ( 2020-10-21 10:46:24 -0600 )edit