paddle预测库

git config --global http.postBuffer 1048576000
git clone --recursive https://github.com/PaddlePaddle/Paddle.git

修改CMakeLists.txt

mkdir build
cd build

cmake  -DWITH_CONTRIB=OFF -DWITH_MKL=OFF -DWITH_MKLDNN=OFF  -DWITH_TESTING=OFF -DCMAKE_BUILD_TYPE=Release -DWITH_INFERENCE_API_TEST=OFF -DON_INFER=ON -DWITH_PYTHON=ON -DWITH_GPU=ON -DWITH_AVX=OFF -DCUDA_ARCH_NAME=Auto ..

安装依赖

apt-get install -y patchelf

1.Fast

1.1)编译GPU版本的

第三个库的配置（可选，如果没有定义以下选项，预构建的第三个库将在构建 FastDeploy 时自动下载）。

编译时Linux需要

• gcc/g++ >= 5.4 (8.2 is recommended)
• cmake >= 3.18.0
• cuda >= 11.2
• cudnn >= 8.2

建议手动安装OpenCV库，并定义-DOPENCV_DIRECTORY设置OpenCV库的路径（如果没有定义标志，构建FastDeploy时会自动下载一个预建的OpenCV库，但预建的OpenCV不支持读取视频文件或其他功能，例如imshow）

 apt-get install libopencv-dev

git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy
mkdir buildgpu && cd buildgpu
cmake .. -DENABLE_ORT_BACKEND=ON \
         -DENABLE_PADDLE_BACKEND=ON \
         -DENABLE_OPENVINO_BACKEND=OFF \
         -DENABLE_TRT_BACKEND=ON \
         -DWITH_GPU=ON \
         -DTRT_DIRECTORY=/TensorRT-8.4.1.5 \
         -DCUDA_DIRECTORY=/usr/local/cuda \
         -DCMAKE_INSTALL_PREFIX=${PWD}/compiled_fastdeploy_sdk \
         -DENABLE_VISION=ON \
         -DOPENCV_DIRECTORY=/usr/lib/x86_64-linux-gnu/cmake/opencv4
         

make -j12
make install

DTRT_DIRECTORY的目录在于找到下面的Include和lib目录，我的tensorrt最初是安装的deb包，

系统中头文件在/usr/include/x86_64-linux-gnu，依赖库在/usr/lib/x86_64-linux-gnu

注意：

但在CMakeLists.txt中,需要用到tensorrt的include和lib

 include_directories(${TRT_DIRECTORY}/include)
  find_library(TRT_INFER_LIB nvinfer ${TRT_DIRECTORY}/lib)
  find_library(TRT_ONNX_LIB nvonnxparser ${TRT_DIRECTORY}/lib)
  find_library(TRT_PLUGIN_LIB nvinfer_plugin ${TRT_DIRECTORY}/lib)
  list(APPEND DEPEND_LIBS ${TRT_INFER_LIB} ${TRT_ONNX_LIB} ${TRT_PLUGIN_LIB})

可以在https://developer.nvidia.com/nvidia-tensorrt-8x-download下载tensorrt的tar包

否则，会报如下错误

编译报如下错误

 此处我下载了tensorrt的tar包

测试demo

创建fastdeploy_test,在该目录下创建3rdparty,将/baidu_paddle/FastDeploy/buildgpu/compiled_fastdeploy_sdk/include和/baidu_paddle/FastDeploy/buildgpu/compiled_fastdeploy_sdk/lib拷贝到3rdparty目录下

注意：不知道为啥直接拷贝/baidu_paddle/FastDeploy/buildgpu/compiled_fastdeploy_sdk/lib下的so无法找到某些backend,使用

cp -r /baidu_paddle/FastDeploy/buildgpu/libfastdeploy.so* ./

/baidu_paddle/FastDeploy/buildgpu

cp -r /usr/include/opencv4/opencv2/  /baidu_paddle/fastdeploy_test/3rdparty/include

1)获取模型和测试图像

wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
tar xvf ppyoloe_crn_l_300e_coco.tgz

2)创建infer_demo.cc,内容如下

#include "fastdeploy/vision.h"
int main() {
  std::string model_file = "/baidu_paddle/fastdeplot_test/ppyoloe_crn_l_300e_coco/model.pdmodel";
  std::string params_file = "/baidu_paddle/fastdeplot_test/ppyoloe_crn_l_300e_coco/model.pdiparams";
  std::string infer_cfg_file = "/baidu_paddle/fastdeplot_test/ppyoloe_crn_l_300e_coco/infer_cfg.yml";
  auto option = fastdeploy::RuntimeOption();
  option.UseGpu();
  auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
                                                      infer_cfg_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return 0;
  }

  cv::Mat im = cv::imread("../000000014439.jpg");

  fastdeploy::vision::DetectionResult res;
  if (!model.Predict(im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return 0;
  }

  std::cout << res.Str() << std::endl;
  cv::Mat 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;
  return 0;
}
r

option.UseCpu()或option.UseGpu()此处似乎没有影响

3)编写CMakeLists.txt内容如下

cmake_minimum_required(VERSION 3.20)
SET(CMAKE_CXX_STANDARD 17)

project(test)
set(INCLUDE_PATH ${CMAKE_SOURCE_DIR}/3rdparty/include)
set(LIB_PATH ${CMAKE_SOURCE_DIR}/3rdparty/lib)

include_directories(${INCLUDE_PATH})
link_directories(${LIB_PATH})

add_executable(
  infer_demo
  infer_demo.cc
)
target_link_libraries(
  infer_demo
  fastdeploy
  opencv_calib3d opencv_core opencv_dnn opencv_features2d opencv_flann opencv_highgui opencv_imgcodecs opencv_imgproc opencv_ml opencv_objdetect opencv_photo opencv_stitching opencv_video opencv_videoio
)

4)编译运行

mkdir build && cd build
cmake .. && make
./infer_demo

1.2）编译CPU版本

mkdir buildcpu && cd buildcpu
cmake .. -DENABLE_ORT_BACKEND=ON \
         -DENABLE_PADDLE_BACKEND=ON \
         -DENABLE_OPENVINO_BACKEND=ON \
         -DCMAKE_INSTALL_PREFIX=${PWD}/compiled_fastdeploy_sdk \
         -DENABLE_VISION=ON \
         -DOPENCV_DIRECTORY=/usr/lib/x86_64-linux-gnu/cmake/opencv4 

make -j12
make install

-DWITH_TESTING=ON目的在于开启单元测试

在make -j12时遇到了如下错误

解决方法如下：

##检查python
root@hpcinf02:/baidu_paddle/FastDeploy# type python python2 python3
bash: type: python: not found
bash: type: python2: not found
python3 is hashed (/usr/bin/python3)

发现存在python3,要做的就是将python链接到python3


在.bashrc文件中创建一个永久别名
alias python='python3'
这样，运行Python命令，系统运行python3
多数情况下都起作用，除非某些程序期望运行/usr/bin/python.现在，你可以在 ​​/usr/bin/python​​ 和 ​​/usr/bin/python3​​ 之间建立符号链接，但对于 Ubuntu 用户来说，存在一个更简单的选择。

对于 Ubuntu 20.04 和更高版本，如果你安装了 ​​python-is-python3​​ 软件包，你有一个软件包可以自动完成所有链接创建。这也是原始错误信息所提示的。
apt-get install python-is-python3

然后重新编译，成功后头文件和lib文件在/baidu_paddle/FastDeploy/buildcpu/compiled_fastdeploy_sdk目录下

添加环境变量

export LD_LIBRARY_PATH=/baidu_paddle/FastDeploy/buildcpu/compiled_fastdeploy_sdk/third_libs/install/paddle2onnx/lib:/baidu_paddle/FastDeploy/buildcpu/compiled_fastdeploy_sdk/third_libs/install/opencv/lib:/baidu_paddle/FastDeploy/buildcpu/compiled_fastdeploy_sdk/third_libs/install/onnxruntime/lib:/baidu_paddle/FastDeploy/buildcpu:${LD_LIBRARY_PATH}

测试demo

创建fastdeploy_test,在该目录下创建3rdparty,将/baidu_paddle/FastDeploy/buildcpu/compiled_fastdeploy_sdk/include和/baidu_paddle/FastDeploy/buildcpu/compiled_fastdeploy_sdk/lib拷贝到3rdparty目录下

注意：不知道为啥直接拷贝/baidu_paddle/FastDeploy/buildcpu/compiled_fastdeploy_sdk/lib下的so无法找到某些backend,使用

cp -r /baidu_paddle/FastDeploy/buildcpu/libfastdeploy.so* ./

/baidu_paddle/FastDeploy/buildcpu

cp -r /usr/include/opencv4/opencv2/  /baidu_paddle/fastdeploy_test/3rdparty/include

1)获取模型和测试图像

wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
tar xvf ppyoloe_crn_l_300e_coco.tgz

2)创建infer_demo.cc,内容如下

#include "fastdeploy/vision.h"
int main() {
  std::string model_file = "/baidu_paddle/fastdeplot_test/ppyoloe_crn_l_300e_coco/model.pdmodel";
  std::string params_file = "/baidu_paddle/fastdeplot_test/ppyoloe_crn_l_300e_coco/model.pdiparams";
  std::string infer_cfg_file = "/baidu_paddle/fastdeplot_test/ppyoloe_crn_l_300e_coco/infer_cfg.yml";
  auto option = fastdeploy::RuntimeOption();
  option.UseCpu();
  auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
                                                      infer_cfg_file, option);
  if (!model.Initialized()) {
    std::cerr << "Failed to initialize." << std::endl;
    return 0;
  }

  cv::Mat im = cv::imread("../000000014439.jpg");

  fastdeploy::vision::DetectionResult res;
  if (!model.Predict(im, &res)) {
    std::cerr << "Failed to predict." << std::endl;
    return 0;
  }

  std::cout << res.Str() << std::endl;
  cv::Mat 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;
  return 0;
}
r

option.UseCpu()或option.UseGpu()此处似乎没有影响

3)编写CMakeLists.txt内容如下

cmake_minimum_required(VERSION 3.20)
SET(CMAKE_CXX_STANDARD 17)

project(test)
set(INCLUDE_PATH ${CMAKE_SOURCE_DIR}/3rdparty/include)
set(LIB_PATH ${CMAKE_SOURCE_DIR}/3rdparty/lib)

include_directories(${INCLUDE_PATH})
link_directories(${LIB_PATH})

add_executable(
  infer_demo
  infer_demo.cc
)
target_link_libraries(
  infer_demo
  fastdeploy
  opencv_calib3d opencv_core opencv_dnn opencv_features2d opencv_flann opencv_highgui opencv_imgcodecs opencv_imgproc opencv_ml opencv_objdetect opencv_photo opencv_stitching opencv_video opencv_videoio
)

4)编译运行

mkdir build && cd build
cmake .. && make
./infer_demo