【标题 C++环境下tensorrt加速推理部署测试】

标题 C++环境下tensorrt加速推理部署测试

延续上一篇，基于yolov5利用C++进行检测分类测试。

C++环境下tensorrt加速推理部署测试

1.文件下载

2.部署实施

1.生成yolov5s.wts文件

1）将下载后的Dirent里面dirent.h复制到tensorrtx/yolov5的文件夹中

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2）将tensorrtx/yolov5文件夹中的gen_wts.py文件复制到下载好的yolov5源码中，运行得到wts文件

源码工程目录下直接cmd 运行命令python gen_wts.py -w yolov5s.pt -o yolov5s.wts

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2.生成yolov5.exe文件

1.CMakeList.txt修改

主要修改后面带#部分的对应路径，与自己工程实际路径相对应

/
cmake_minimum_required(VERSION 3.2)
if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
  set(CMAKE_CUDA_ARCHITECTURES 50)###########
endif()

set(CudaToolkitDir "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1")###########

project(yolov5)
set(OpenCV_DIR "D:\\AppMy\\opencv\\opencv\\build")  #1
set(TRT_DIR "D:\\Myproject\\TensorRT-8.2.5.1.Windows10.x86_64.cuda-11.4.cudnn8.2\\TensorRT-8.2.5.1")  #2
set(OpenCV_INCLUDE_DIRS "D:\\AppMy\\opencv\\opencv\\build\\include")   #3
set(OpenCV_LIBS "D:\\AppMy\\opencv\\opencv\\build\\x64\\vc15\\lib\\opencv_world340.lib")   #4

add_definitions(-std=c++11)
add_definitions(-DAPI_EXPORTS)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)

set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads)

# setup CUDA
find_package(CUDA REQUIRED)
message(STATUS "    libraries: ${CUDA_LIBRARIES}")
message(STATUS "    include path: ${CUDA_INCLUDE_DIRS}")

include_directories(${CUDA_INCLUDE_DIRS})

####
enable_language(CUDA)  # add this line, then no need to setup cuda path in vs
####
include_directories(${PROJECT_SOURCE_DIR}/include)
include_directories(${TRT_DIR}\\include)
include_directories(D:\\yanyi\\project_process\\Completed_projects\\yolo-deploy\\tensorrtx-master\\yolov5)  # 5


##### find package(opencv)
include_directories(${OpenCV_INCLUDE_DIRS})
include_directories(${OpenCV_INCLUDE_DIRS}\\opencv2)  #6


# -D_MWAITXINTRIN_H_INCLUDED for solving error: identifier "__builtin_ia32_mwaitx" is undefined
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -D_MWAITXINTRIN_H_INCLUDED")

# setup opencv
find_package(OpenCV QUIET
    NO_MODULE
    NO_DEFAULT_PATH
    NO_CMAKE_PATH
    NO_CMAKE_ENVIRONMENT_PATH
    NO_SYSTEM_ENVIRONMENT_PATH
    NO_CMAKE_PACKAGE_REGISTRY
    NO_CMAKE_BUILDS_PATH
    NO_CMAKE_SYSTEM_PATH
    NO_CMAKE_SYSTEM_PACKAGE_REGISTRY
)

message(STATUS "OpenCV library status:")
message(STATUS "    version: ${OpenCV_VERSION}")
message(STATUS "    libraries: ${OpenCV_LIBS}")
message(STATUS "    include path: ${OpenCV_INCLUDE_DIRS}")

include_directories(${OpenCV_INCLUDE_DIRS})
link_directories(${TRT_DIR}\\lib)           #7
link_directories(${OpenCV_DIR}\\x64\\vc15\\lib)                         #8

#add_executable(yolov5 ${PROJECT_SOURCE_DIR}/calibrator.cpp ${PROJECT_SOURCE_DIR}/yolov5.cpp ${PROJECT_SOURCE_DIR}/yololayer.cu ${PROJECT_SOURCE_DIR}/yololayer.h ${PROJECT_SOURCE_DIR}/preprocess.cu ${PROJECT_SOURCE_DIR}/preprocess.h)
add_executable(yolov5 ${PROJECT_SOURCE_DIR}/yolov5.cpp ${PROJECT_SOURCE_DIR}/common.hpp ${PROJECT_SOURCE_DIR}/yololayer.cu ${PROJECT_SOURCE_DIR}/yololayer.h ${PROJECT_SOURCE_DIR}/preprocess.cu ${PROJECT_SOURCE_DIR}/preprocess.h)   #17
target_link_libraries(yolov5 "nvinfer" "nvinfer_plugin")   #5
target_link_libraries(yolov5 ${OpenCV_LIBS})          #6
target_link_libraries(yolov5 ${CUDA_LIBRARIES})   #7
target_link_libraries(yolov5 Threads::Threads)       #8

/

2.利用CMake软件进行文件配置

然后打开CMake软件，输入tensorrtx下的yolov5文件路径和build（文件夹名称自定义）路径，会自动创建文件夹，勾选以下选项
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点击configure后，弹出如下对话框，选择对应vs版本和系统版本

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点击finish后出现configure done

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依次点击generate open project
注意：若此处报错，检查cmakelist文件中的路径设置

选择Realse和x64，点击生成解决方案
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编译成功后会显示成功，只要没有失败即可，在yolov5-build-Realse文件中生成exe文件

3.生成yolov5s.engine文件

将上述生成的wts文件复制到yolov5-Build-Release文件下，cmd执行：
yolov5.exe -s yolov5s.wts yolov5s.engine s
等待几分钟后，会在当前目录生成一个yolov5s.engine文件
此处注意yolov5源码中的类别数量和训练模型中的类别数量一致，否则报错
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