setUpNet DNN module was not built with CUDA backend; switching to CPU

C++
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1

I am working with Object Detection ( training with YOLOv3) on Jetson Orin with OpenCV
My Objective: get the CUDA working for the object detection.

**OpenCV = 4.5.4**
**Operating System / Platform => NVIDIA JETSON Orin (Tegra)**
**Compiler => Visual Studio 2019*
**CUDNN 8.6 and CUDA 11.4.**

I have configured the opencv with cmake-gui, enabling,

WITH_CUDNN=ON
OPENCV_DNN_CUDA=ON
ENABLE_FAST_MATH=1
CUDA_FAST_MATH=1
CUDA_ARCH_BIN=8.7
WITH_CUBLAS=1
WITH_CUDA=ON
BUILD_OPENCV_DNN=ON
BUILD_OPENCV_WORLD=ON
OPENCV_EXTRA_MODULES=/path/opencv_contrib/modules
PYTHON3_EXECUTABLES=/path/
PYTHON3_INCLUDE_DIR=/path/
PYTHON3_LIB=/path/
PYTHON3_PATH=/path/
PYTHON3_NUMPY=/path/
CUDNN_VERSION=8.6.0

i also linked ln -s /usr/local/lib/python3/dist-packages/cv2/python-3.8/cv2.cpython-38-aarch64-linux-gnu.so cv2.so

The opencv test : import cv2 also works.

cv2.version
4.5.4

And I use this segment in my code.

cout << “Using GPU device” << endl;
net.setPreferableBackend(DNN_BACKEND_CUDA);
net.setPreferableTarget(DNN_TARGET_CUDA);

And i use, my own tested,
.cfg,
.weights
data.names
test.png (these work perfectly in around 72 milliseconds from the direct command line)

When run, i do get the image output with the objects detected, But the problem is
: it switches to CPU and compiles giving the result in 400 milliseconds.

The issue is displayed as below:

[ WARN:0] global /home/ubuntu/build_opencv/opencv/modules/dnn/src/dnn.cpp (1447) setUpNet DNN module was not built with CUDA backend; switching to CPU

But, the dnn.cpp is in the path: tk_ws/opencv-4.5.4/modules/dnn/src/dnn.cpp
My directory structure is ( i cloned the opencv into my tk_ws workspace)

tk_ws/opencv-4.5.4/build
tk_ws/opencv_contrib-4.5.4

while as per the instructions on GSoC 2019 | OpenCV | Adding a CUDA backend to the DNN module · GitHub

I can see the opencv_test_dnn in green.

I could not figure out why I am repeatedly failing. with the backend switching automatically to CPU instead of CUDA every time

I would be immensely grateful if you could help me, please.

Warm Regards,
Karishma

2

When you configure with CMake does it pick up cuDNN? i.e. Do you see something similar to the below?

--   NVIDIA CUDA:                   YES (ver 11.7, CUFFT CUBLAS NVCUVID NVCUVENC FAST_MATH)
--     NVIDIA GPU arch:             86
--     NVIDIA PTX archs:
--
--   cuDNN:                         YES (ver 8.4.1)
3

Yes I do. it configures to my CUDNN - 8.6.0 version, every time.
2022-11-17 07_49_10-Window