cmake cannot find -lopencv_dep_cudart

本文介绍如何使用CMake配置CUDA静态运行时为关闭状态,并设置OpenCV库的路径及版本,以便于进行计算机视觉与GPU加速的相关开发。
部署运行你感兴趣的模型镜像

加入:

set(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
include_directories("/usr/local/cuda-8.0/include")



##### CMake entry point


cmake_minimum_required (VERSION 3.0)
project (mld_crf)


##### make release version

#set(CMAKE_BUILD_TYPE Release)
set(CMAKE_BUILD_TYPE debug)
set(CUDA_USE_STATIC_CUDA_RUNTIME OFF)



##### external library setting
find_package(OpenCV REQUIRED)
# OpenCV
#set( OPENCV_VER       320 )
#set( OPENCV_VER       2413 )
#set( OPENCV_PATH "C:/programming/lib/opencv-master/build-test/install")
set( OPENCV_INC_DIR "/usr/local/include" )
set( OPENCV_LIB_DIR "/usr/local/lib" )
#set( OPENCV_LIB   
#  optimized opencv_world.so      debug opencv_worldd
#  optimized opencv_xfeatures2d.so   debug opencv_xfeatures2dd
#)


########################## NO CHANGES BEYOND THIS POINT ##########################


##### include & link
 
# main project
include_directories(
  ${OPENCV_INC_DIR}
)
include_directories("/usr/local/cuda-8.0/include")

link_directories(
  ${OPENCV_LIB_DIR}
)

##### project

FILE(GLOB MLD_CRF_SRC_FILES "src/*.cpp" "src/*.c" "src/*.h")



##### build demo program

add_executable(mld_crf
  ${MLD_CRF_SRC_FILES}
)
target_link_libraries(mld_crf
   ${OpenCV_LIBS}
)
#target_link_libraries(mld_crf
#  ${OPENCV_LIB}
#)

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PyTorch 2.5

PyTorch 2.5

PyTorch
Cuda

PyTorch 是一个开源的 Python 机器学习库,基于 Torch 库,底层由 C++ 实现,应用于人工智能领域,如计算机视觉和自然语言处理

PowerShell 7 环境已加载 (版本: 7.5.2) PowerShell 7 环境已加载 (版本: 7.5.2) PS C:\Users\Administrator\Desktop> # 进入构建目录 PS C:\Users\Administrator\Desktop> Set-Location E:\PyTorch_Build\pytorch\build PS E:\PyTorch_Build\pytorch\build> PS E:\PyTorch_Build\pytorch\build> # 查找缺失的torch_python.lib PS E:\PyTorch_Build\pytorch\build> $libPath = Get-ChildItem -Path . -Recurse -Filter "torch_python.lib" | Select-Object -First 1 PS E:\PyTorch_Build\pytorch\build> if (-not $libPath) { >> Write-Host "未找到torch_python.lib,重新编译..." >> cmake --build . --target torch_python >> $libPath = Get-ChildItem -Path . -Recurse -Filter "torch_python.lib" >> } PS E:\PyTorch_Build\pytorch\build> PS E:\PyTorch_Build\pytorch\build> # 复制到Python包目录 PS E:\PyTorch_Build\pytorch\build> $torchDir = python -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())" PS E:\PyTorch_Build\pytorch\build> Copy-Item -Path $libPath.FullName -Destination "$torchDir\torch" -Force PS E:\PyTorch_Build\pytorch\build> # 获取正确的CUDA运行时库 PS E:\PyTorch_Build\pytorch\build> $cudaDir = "E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin" PS E:\PyTorch_Build\pytorch\build> $cudart = Get-ChildItem -Path $cudaDir -Filter "cudart64_*.dll" | >> Where-Object { $_.Name -match "cudart64_\d+" } | >> Select-Object -First 1 PS E:\PyTorch_Build\pytorch\build> PS E:\PyTorch_Build\pytorch\build> # 复制到torch包目录 PS E:\PyTorch_Build\pytorch\build> Copy-Item -Path $cudart.FullName -Destination "$torchDir\torch\lib" -Force PropertyNotFoundException: The property 'FullName' cannot be found on this object. Verify that the property exists. PS E:\PyTorch_Build\pytorch\build> PS E:\PyTorch_Build\pytorch\build> # 添加环境变量 PS E:\PyTorch_Build\pytorch\build> $env:PATH = "$cudaDir;$env:PATH" PS E:\PyTorch_Build\pytorch\build> # 创建纯净虚拟环境 PS E:\PyTorch_Build\pytorch\build> python -m venv .venv PS E:\PyTorch_Build\pytorch\build> .\.venv\Scripts\activate (.venv) PS E:\PyTorch_Build\pytorch\build> (.venv) PS E:\PyTorch_Build\pytorch\build> # 安装基础依赖 (.venv) PS E:\PyTorch_Build\pytorch\build> pip install numpy pyyaml cmake ninja Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting numpy Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a3/dd/4b822569d6b96c39d1215dbae0582fd99954dcbcf0c1a13c61783feaca3f/numpy-2.2.6-cp310-cp310-win_amd64.whl (12.9 MB) Collecting pyyaml Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b5/84/0fa4b06f6d6c958d207620fc60005e241ecedceee58931bb20138e1e5776/PyYAML-6.0.2-cp310-cp310-win_amd64.whl (161 kB) Collecting cmake Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7c/d0/73cae88d8c25973f2465d5a4457264f95617c16ad321824ed4c243734511/cmake-4.1.0-py3-none-win_amd64.whl (37.6 MB) Collecting ninja Using cached https://pypi.tuna.tsinghua.edu.cn/packages/29/45/c0adfbfb0b5895aa18cec400c535b4f7ff3e52536e0403602fc1a23f7de9/ninja-1.13.0-py3-none-win_amd64.whl (309 kB) Installing collected packages: pyyaml, numpy, ninja, cmake Successfully installed cmake-4.1.0 ninja-1.13.0 numpy-2.2.6 pyyaml-6.0.2 [notice] A new release of pip available: 22.3.1 -> 25.2 [notice] To update, run: python.exe -m pip install --upgrade pip (.venv) PS E:\PyTorch_Build\pytorch\build> (.venv) PS E:\PyTorch_Build\pytorch\build> # 设置PYTHONPATH强制使用本地编译版本 (.venv) PS E:\PyTorch_Build\pytorch\build> $env:PYTHONPATH = "E:\PyTorch_Build\pytorch;$env:PYTHONPATH" (.venv) PS E:\PyTorch_Build\pytorch\build> (.venv) PS E:\PyTorch_Build\pytorch\build> # 创建纯净虚拟环境 (.venv) PS E:\PyTorch_Build\pytorch\build> python -m venv .venv Error: [Errno 13] Permission denied: 'E:\\PyTorch_Build\\pytorch\\build\\.venv\\Scripts\\python.exe' (.venv) PS E:\PyTorch_Build\pytorch\build> .\.venv\Scripts\activate (.venv) PS E:\PyTorch_Build\pytorch\build> (.venv) PS E:\PyTorch_Build\pytorch\build> # 安装基础依赖 (.venv) PS E:\PyTorch_Build\pytorch\build> pip install numpy pyyaml cmake ninja Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: numpy in e:\pytorch_build\pytorch\build\.venv\lib\site-packages (2.2.6) Requirement already satisfied: pyyaml in e:\pytorch_build\pytorch\build\.venv\lib\site-packages (6.0.2) Requirement already satisfied: cmake in e:\pytorch_build\pytorch\build\.venv\lib\site-packages (4.1.0) Requirement already satisfied: ninja in e:\pytorch_build\pytorch\build\.venv\lib\site-packages (1.13.0) [notice] A new release of pip available: 22.3.1 -> 25.2 [notice] To update, run: python.exe -m pip install --upgrade pip (.venv) PS E:\PyTorch_Build\pytorch\build> (.venv) PS E:\PyTorch_Build\pytorch\build> # 设置PYTHONPATH强制使用本地编译版本 (.venv) PS E:\PyTorch_Build\pytorch\build> $env:PYTHONPATH = "E:\PyTorch_Build\pytorch;$env:PYTHONPATH" (.venv) PS E:\PyTorch_Build\pytorch\build> # save as verify_torch.ps1 (.venv) PS E:\PyTorch_Build\pytorch\build> $env:PATH = "E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin;$env:PATH" (.venv) PS E:\PyTorch_Build\pytorch\build> $env:PYTHONPATH = "E:\PyTorch_Build\pytorch" (.venv) PS E:\PyTorch_Build\pytorch\build> (.venv) PS E:\PyTorch_Build\pytorch\build> python -c @" >> import os >> import sys >> print(f'Python路径: {sys.executable}') >> print(f'PYTHONPATH: {os.getenv("PYTHONPATH")}') >> >> try: >> import torch >> print(f'PyTorch版本: {torch.__version__}') >> print(f'编译路径: {os.path.dirname(torch.__file__)}') >> print(f'CUDA可用: {torch.cuda.is_available()}') >> >> if torch.cuda.is_available(): >> print(f'GPU设备: {torch.cuda.get_device_name(0)}') >> # 简单计算验证 >> a = torch.ones(1000, 1000).cuda() >> b = torch.ones(1000, 1000).cuda() >> c = a @ b >> print(f'计算验证通过: 总和={c.sum().item()}') >> >> except ImportError as e: >> print(f'导入错误: {e}') >> # 诊断缺失DLL >> if hasattr(e, 'name'): >> print(f'缺失模块: {e.name}') >> "@ Python路径: E:\PyTorch_Build\pytorch\build\.venv\Scripts\python.exe PYTHONPATH: E:\PyTorch_Build\pytorch 导入错误: No module named 'typing_extensions' 缺失模块: typing_extensions (.venv) PS E:\PyTorch_Build\pytorch\build>
最新发布
09-02
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