FastDeploy部署参考

一、FastDeploy的gitee地址
https://gitee.com/leiqing1/FastDeploy/blob/release/0.3.0/docs/cn/faq/use_sdk_on_windows.md#21-%E4%B8%8B%E8%BD%BD%E9%A2%84%E7%BC%96%E8%AF%91%E5%BA%93%E6%88%96%E8%80%85%E4%BB%8E%E6%BA%90%E7%A0%81%E7%BC%96%E8%AF%91%E6%9C%80%E6%96%B0%E7%9A%84sdk

https://gitee.com/leiqing1/FastDeploy/blob/release/0.3.0/docs/cn/build_and_install/download_prebuilt_libraries.md

二、某知乎大神使用FastDeploy Windows c++ 预编译库进行部署的流程
https://zhuanlan.zhihu.com/p/598740190

三、FastDeploy的github地址
https://github.com/PaddlePaddle/FastDeploy
https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_windows_build.md#VisualStudio2019

重要：参考以上链接完全可以自己实现部署，以下内容是为了方便个人理解。

FastDeploy使用Cmake进行部署的程序

project(infer_ppyoloe_demo C CXX)
cmake_minimum_required(VERSION 3.12)

# Only support "Release" mode now  
set(CMAKE_BUILD_TYPE "Release")

# Set FastDeploy install dir
set(FASTDEPLOY_INSTALL_DIR "D:/qiuyanjun/fastdeploy-win-x64-gpu-0.2.1"
    CACHE PATH "Path to downloaded or built fastdeploy sdk.")

# Set CUDA_DIRECTORY (CUDA 11.x) for GPU SDK
set(CUDA_DIRECTORY "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.7"
    CACHE PATH "Path to installed CUDA Toolkit.")

include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)

include_directories(${FASTDEPLOY_INCS})

add_executable(infer_ppyoloe_demo ${PROJECT_SOURCE_DIR}/infer_ppyoloe.cpp)
target_link_libraries(infer_ppyoloe_demo ${FASTDEPLOY_LIBS})  

# Optional: install all DLLs to binary dir.
install_fastdeploy_libraries(${CMAKE_CURRENT_BINARY_DIR}/Release)

需要编译使用的 infer_ppyoloe.cpp

#include "fastdeploy/vision.h"

#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif

void CpuInfer(const std::string& model_dir, const std::string& image_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
  auto params_file = model_dir + sep + "model.pdiparams";
  auto config_file = model_dir + sep + "infer_cfg.yml";
  auto option = fastdeploy::RuntimeOption();
  option.UseCpu();
  auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
                                                      config_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }

  auto im = cv::imread(image_file);

  fastdeploy::vision::DetectionResult res;
  if (!model.Predict(im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }

  std::cout << res.Str() << std::endl;
  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
  cv::imwrite("vis_result.jpg", vis_im);
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}

void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
  auto params_file = model_dir + sep + "model.pdiparams";
  auto config_file = model_dir + sep + "infer_cfg.yml";
  auto option = fastdeploy::RuntimeOption();
  option.UseKunlunXin();
  auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
                                                      config_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }

  auto im = cv::imread(image_file);

  fastdeploy::vision::DetectionResult res;
  if (!model.Predict(im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }

  std::cout << res.Str() << std::endl;
  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
  cv::imwrite("vis_result.jpg", vis_im);
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}

void GpuInfer(const std::string& model_dir, const std::string& image_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
  auto params_file = model_dir + sep + "model.pdiparams";
  auto config_file = model_dir + sep + "infer_cfg.yml";

  auto option = fastdeploy::RuntimeOption();
  option.UseGpu();
  auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
                                                      config_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }

  auto im = cv::imread(image_file);

  fastdeploy::vision::DetectionResult res;
  if (!model.Predict(im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }

  std::cout << res.Str() << std::endl;
  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
  cv::imwrite("vis_result.jpg", vis_im);
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}

void TrtInfer(const std::string& model_dir, const std::string& image_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
  auto params_file = model_dir + sep + "model.pdiparams";
  auto config_file = model_dir + sep + "infer_cfg.yml";

  auto option = fastdeploy::RuntimeOption();
  option.UseGpu();
  option.UseTrtBackend();
  auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
                                                      config_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }

  auto im = cv::imread(image_file);

  fastdeploy::vision::DetectionResult res;
  if (!model.Predict(im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }

  std::cout << res.Str() << std::endl;
  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
  cv::imwrite("vis_result.jpg", vis_im);
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}

int main(int argc, char* argv[]) {
  if (argc < 4) {
    std::cout
        << "Usage: infer_demo path/to/model_dir path/to/image run_option, "
           "e.g ./infer_model ./ppyoloe_model_dir ./test.jpeg 0"
        << std::endl;
    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with kunlunxin."
              << std::endl;
    return -1;
  }

  if (std::atoi(argv[3]) == 0) {
    CpuInfer(argv[1], argv[2]);
  } else if (std::atoi(argv[3]) == 1) {
    GpuInfer(argv[1], argv[2]);
  } else if (std::atoi(argv[3]) == 2) {
    TrtInfer(argv[1], argv[2]);
  } else if (std::atoi(argv[3]) == 3) {
    KunlunXinInfer(argv[1], argv[2]);
  }
  return 0;
}

编译之后需要运行时，需要的操作：

cd Release
infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 0  # CPU
infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 1  # GPU
infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 2  # GPU + TensorRT