转换模型

ncnn(https://github.com/Tencent/ncnn)编译好后，tools内有一个onnx2ncnn的工具，直接使用此工具即可将onnx模型转换为ncnn模型，命令如下：

  1. cd /home/darrenzhang/ncnn/build/tools/onnx
  3. ./onnx2ncnn  resnet18-v2-7.onnx resnet18.param resnet18.bin

sendpix1.jpg
生成resnet18的param文件和bin文件，其中，param文件保存了模型结构，bin文件保存了模型参数。

C++ 调用

测试ncnn模型的前向推理结果的正确性，导入ncnn的库和头文件后，调用代码如下：

CMakeLists.txt文件如下

  1. cmake_minimum_required(VERSION 3.17)
  2. project(ncnn_res_cls)
  4. set( OpenCV_DIR /home/darrenzhang/opencv/usr/local/lib/cmake/opencv4)
  5. find_package(OpenCV REQUIRED)
  6. FIND_PACKAGE( OpenMP REQUIRED)
  8. if(OPENMP_FOUND)
  9.     message("OPENMP FOUND")
  10.     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
  11.     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
  12.     set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
  13. endif()
  15. set(CMAKE_CXX_STANDARD 11)
  17. include_directories(/home/darrenzhang/ncnn/build/install/include/ncnn)
  18. link_directories(/home/darrenzhang/ncnn/build/install/lib)
  20. add_executable(ncnn_res_cls main.cpp)
  21. target_link_libraries(ncnn_res_cls ${OpenCV_LIBS}
  22.         /home/darrenzhang/ncnn/build/install/lib/libncnn.a)

main.cpp代码如下

  1. //
  2. // Created by darrenzhang on 2021/3/3.
  3. //
  5. #include <opencv2/opencv.hpp>
  6. #include <fstream>
  8. #include "platform.h"
  9. #include "net.h"
  11. using namespace std;
  12. using namespace cv;
  14. vector<string> split(const string& str, const string& pattern) {
  15.     vector<string> ret;
  16.     if (pattern.empty()) return ret;
  17.     size_t start = 0, index = str.find_first_of(pattern, 0);
  18.     while (index != str.npos) {
  19.         if (start != index)
  20.             ret.push_back(str.substr(start, index - start));
  21.         start = index + 1;
  22.         index = str.find_first_of(pattern, start);
  23.     }
  24.     if (!str.substr(start).empty())
  25.         ret.push_back(str.substr(start));
  26.     return ret;
  27. }
  29. void get_label(vector<string> &labels, const char* file_path) {
  30.     FILE *fp = fopen(file_path, "r");
  31.     ifstream infile;
  32.     string temp;
  33.     const char*d = ":";
  34.     infile.open(file_path);
  35.     while(getline(infile, temp)) {
  36.         vector<string> line = split(temp, ":");
  37.         labels.push_back((line[1]));
  38.     }
  39. }
  41. static int print_topk(const std::vector<float>& cls_scores, int topk,vector<string> labels)
  42. {
  43.     // partial sort topk with index
  44.     int size = cls_scores.size();
  45.     std::vector< std::pair<float, int> > vec;
  46.     vec.resize(size);
  47.     for (int i=0; i<size; i++)
  48.     {
  49.         vec[i] = std::make_pair(cls_scores[i], i);
  50.     }
  52.     std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
  53.                       std::greater< std::pair<float, int> >());
  55.     // print topk and score
  56.     for (int i=0; i<topk; i++)
  57.     {
  58.         float score = vec[i].first;
  59.         int index = vec[i].second;
  60.         string label = labels[index];
  61.         fprintf(stderr, "%d = %f  %s \n", index, score ,label.c_str());
  63.     }
  65.     return 0;
  66. }
  69. int main() {
  70.     vector<string> labels;
  71.     get_label(labels, "/home/darrenzhang/CLionProjects/ncnn_res_cls/data/label.txt");
  73.     cv::Mat img = cv::imread("/home/darrenzhang/CLionProjects/ncnn_res_cls/data/test.jpg");
  75.     int img_w = img.cols;
  76.     int img_h = img.rows;
  78.     ncnn::Mat input_img = ncnn::Mat::from_pixels_resize(
  79.             img.data, ncnn::Mat::PIXEL_BGR, img.cols, img.rows, 224, 224
  80.     );
  82.     const float mean_vals[3] = {104.0f,117.0f,123.0f};
  83.     const float norm_vals[3] = {0.007843f, 0.007843f, 0.007843f};
  85.     input_img.substract_mean_normalize(mean_vals, norm_vals);  // normalize for input img
  87.     ncnn::Net resnet18;
  88.     resnet18.load_param("/home/darrenzhang/CLionProjects/ncnn_res_cls/model/resnet.param");
  89.     resnet18.load_model("/home/darrenzhang/CLionProjects/ncnn_res_cls/model/resnet.bin");
  91.     ncnn::Extractor ex = resnet18.create_extractor();  // 定义解析器，解析特征平面
  92.     ex.set_num_threads(4);
  93.     ex.input("data", input_img);
  95.     ncnn::Mat out;  // define the ncnn`s output
  96.     ex.extract("resnetv22_dense0_fwd", out);  // extract the last layer`s output of the model
  98.     vector<float> output;
  99.     output.resize(out.w);
  101.     for (int i = 0; i < out.w; i ++)
  102.         output[i] = out[i];
  104.     print_topk(output, 2, labels);
  105. }

报错如下：
sendpix2.jpg

locate  libopencv_imgproc.so.4.1

image.png
进入到/etc/ld.so.conf.d

cd etc/ld.so.conf.d
sudo vi OpenCV.conf

/home/darrenzhang/opencv/usr/local/lib/

sudo ldconfig -v

报错内容2：
内存溢出，使用断点调试发现如下错误，模型加载问题

fopen resnet.param failed
fopen resnet.bin failed
find_blob_index_by_name data failed
find_blob_index_by_name prob failed

image.png
在debug或者运行可执行文件的时候，可执行文件在cmake-build-debug文件夹中，所以模型路径和测试文件路径要根据可执行文件的路径来设置。