This answer refers to GPT, GPT_Pro better to solve the problem
This problem may be caused by your incorrectly configured CUDA. When calling the GPU, ValueError may occur. CUDA is a parallel computing platform developed by NVIDIA that speeds up the training process for deep learning tasks. If you want to use the GPU to speed up your YOLOv3 network, you need to configure your CUDA environment properly first.
Before setting up a CUDA environment, you need to check if your computer has a CUDA-enabled graphics card, if not, then you won't be able to use the GPU properly to speed up the YOLOv3 training process.
Before you start configuring CUDA, download the CUDA installation package from NVIDIA and install and run CUDA. After the installation is complete, open Device Manager in Windows to check whether the installation is successful. If NVIDIA CUDA is displayed, CUDA is successfully installed.
Next, there are two ways to configure the CUDA running environment: one is to use Windows command line tools; The second is to use the Visual Studio IDE(integrated development environment).
To use the command line tool, open the Windows command line tool and enter the nvcc -V command to test whether CUDA runs properly. If normal version information is displayed, CUDA is properly installed and ready for use. After that, you can enter the corresponding command to call the GPU.
If you are using the Visual Studio IDE to configure the CUDA runtime environment, you can create a C++/CLI DLL project by opening the Visual Studio IDE and selecting "C++/CLI Dynamic Library(DLL)" in the new project. The next step is to configure the CUDA runtime environment in your new project. First, CUDA-related library files should be added to the project; Then you want to modify the C/C++ TAB in the configuration properties "Attach Include Directory", "Attach Library Directory", "Enable GPU Code generation", "GPU Code generation - Immediate number of GPU examples", "GPU Code generation - maximum number of GPU waves", "GPU Code generation - maximum number of GPU waves"(SM 3.0), "GPU Code Generation - Maximum Number of GPU Waves(SM 3.5)", "GPU Code generation - Maximum Number of GPU Waves(SM 5.0)", "GPU Code generation -GPU storage bit Width(bytes)", "GPU Code generation -GPU Core Performance time allocation density(%)", etc. Finally, you can write the yolov3 code into a new C++/CLI DLL project and save it.
After the above steps are completed, you can use the GPU to speed up the yolov3 training process.
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