yolov5 c++ tensorrt推理

代码：https://github.com/shouxieai/tensorRT_Pro
运行环境：nvidia nx

cd tensorRT_Pro-main/
sudo make yolo -j8
sudo vim Makefile

报错protobuf，opencv，cudnn等未正确配置。
解决：
1.安装protobuf-3.11.4

sudo unzip protobuf-cpp-3.11.4.zip 
cd protobuf-3.11.4/
sudo ./autogen.sh 
sudo ./configure 
sudo make -j6
sudo make install
sudo ldconfig
sudo protoc –h

2.修改CMakeLists.txt

cmake_minimum_required(VERSION 2.6)
project(pro)

option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/workspace)
set(HAS_PYTHON OFF)

# 如果要支持python则设置python路径
set(PythonRoot "/data/datav/newbb/lean/anaconda3/envs/torch1.8")
set(PythonName "python3.9")

# 如果你是不同显卡，请设置为显卡对应的号码参考这里：https://developer.nvidia.com/zh-cn/cuda-gpus#compute
set(CUDA_GEN_CODE "-gencode=arch=compute_72,code=sm_72")

# 如果你的opencv找不到，可以自己指定目录
set(OpenCV_DIR   "/usr/include/opencv4/")

set(CUDA_TOOLKIT_ROOT_DIR     "/usr/local/cuda-10.2")
set(CUDNN_DIR    "/usr/include/aarch64-linux-gnu")
set(TENSORRT_DIR "/data/sxai/lean/TensorRT-8.0.1.6-cuda10.2-cudnn8.2")

# set(CUDA_TOOLKIT_ROOT_DIR     "/data/sxai/lean/cuda-10.2")
# set(CUDNN_DIR    "/data/sxai/lean/cudnn7.6.5.32-cuda10.2")
# set(TENSORRT_DIR "/data/sxai/lean/TensorRT-7.0.0.11")

# set(CUDA_TOOLKIT_ROOT_DIR  "/data/sxai/lean/cuda-11.1")
# set(CUDNN_DIR    "/data/sxai/lean/cudnn8.2.2.26")
# set(TENSORRT_DIR "/data/sxai/lean/TensorRT-7.2.1.6")

# 因为protobuf，需要用特定版本，所以这里指定路径
set(PROTOBUF_DIR "/data/protobuf-3.11.4")


find_package(CUDA REQUIRED)
find_package(OpenCV)

include_directories(
    ${PROJECT_SOURCE_DIR}/src
    ${PROJECT_SOURCE_DIR}/src/application
    ${PROJECT_SOURCE_DIR}/src/tensorRT
    ${PROJECT_SOURCE_DIR}/src/tensorRT/common
    ${OpenCV_INCLUDE_DIRS}
    ${CUDA_TOOLKIT_ROOT_DIR}/include
    ${PROTOBUF_DIR}/src
    #${TENSORRT_DIR}/include
    /usr/include/aarch64-linux-gnu
    ${CUDNN_DIR}/include
)

# 切记，protobuf的lib目录一定要比tensorRT目录前面，因为tensorRTlib下带有protobuf的so文件
# 这可能带来错误
link_directories(
    #${PROTOBUF_DIR}/lib
    /usr/local/lib
    #${TENSORRT_DIR}/lib
    /usr/include/aarch64-linux-gnu
    ${CUDA_TOOLKIT_ROOT_DIR}/lib64
    ${CUDNN_DIR}/lib
)

if("${HAS_PYTHON}" STREQUAL "ON")
    message("Usage Python ${PythonRoot}")
    include_directories(${PythonRoot}/include/${PythonName})
    link_directories(${PythonRoot}/lib)
    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DHAS_PYTHON")
endif()

set(CMAKE_CXX_FLAGS  "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -O0 -Wfatal-errors -pthread -w -g")
set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -std=c++11 -O0 -Xcompiler -fPIC -g -w ${CUDA_GEN_CODE}")
file(GLOB_RECURSE cpp_srcs ${PROJECT_SOURCE_DIR}/src/*.cpp)
file(GLOB_RECURSE cuda_srcs ${PROJECT_SOURCE_DIR}/src/*.cu)
cuda_add_library(plugin_list SHARED ${cuda_srcs})
target_link_libraries(plugin_list nvinfer nvinfer_plugin)
target_link_libraries(plugin_list cuda cublas cudart cudnn)
target_link_libraries(plugin_list protobuf pthread)
target_link_libraries(plugin_list ${OpenCV_LIBS})

add_executable(pro ${cpp_srcs})

# 如果提示插件找不到，请使用dlopen(xxx.so, NOW)的方式手动加载可以解决插件找不到问题
target_link_libraries(pro nvinfer nvinfer_plugin)
target_link_libraries(pro cuda cublas cudart cudnn)
target_link_libraries(pro protobuf pthread plugin_list)
target_link_libraries(pro ${OpenCV_LIBS})

if("${HAS_PYTHON}" STREQUAL "ON")
    set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/python/trtpy)
    add_library(trtpyc SHARED ${cpp_srcs})
    target_link_libraries(trtpyc nvinfer nvinfer_plugin)
    target_link_libraries(trtpyc cuda cublas cudart cudnn)
    target_link_libraries(trtpyc protobuf pthread plugin_list)
    target_link_libraries(trtpyc ${OpenCV_LIBS})
    target_link_libraries(trtpyc "${PythonName}")
    target_link_libraries(pro "${PythonName}")
endif()

add_custom_target(
    yolo
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro yolo
)

add_custom_target(
    yolo_fast
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro yolo_fast
)

add_custom_target(
    centernet
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro centernet
)

add_custom_target(
    alphapose 
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro alphapose
)

add_custom_target(
    retinaface
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro retinaface
)

add_custom_target(
    dbface
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro dbface
)


add_custom_target(
    bert 
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro bert
)

add_custom_target(
    fall
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro fall_recognize
)

add_custom_target(
    scrfd
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro scrfd
)

add_custom_target(
    lesson
    DEPENDS pro
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/workspace
    COMMAND ./pro lesson
)

add_custom_target(
    pyscrfd
    DEPENDS trtpyc
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python
    COMMAND python test_scrfd.py
)

add_custom_target(
    pyinstall
    DEPENDS trtpyc
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python
    COMMAND python setup.py install
)

add_custom_target(
    pytorch
    DEPENDS trtpyc
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python
    COMMAND python test_torch.py
)

add_custom_target(
    pyyolov5
    DEPENDS trtpyc
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python
    COMMAND python test_yolov5.py
)

add_custom_target(
    pycenternet
    DEPENDS trtpyc
    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python
    COMMAND python test_centernet.py
)

sudo vim CMakeLists.txt 
sudo cmake .
sudo make yolo -j6
workspace/
./pro yolo

yolov5 c++ tensorrt推理

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