I would like to run dnn in opencv3.3 parallel to increase the speed of object recognition: this is my code

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/utils/trace.hpp>
using namespace cv;
using namespace cv::dnn;

#include <fstream>
#include <iostream>
#include <cstdlib>
using namespace std;

// Parallel Programming

#include "tbb/parallel_for.h"
#include "tbb/blocked_range.h"
using namespace tbb;
String modelTxt = "caffenet_deploy_2.prototxt";
String modelBin = "caffe_model_2_iter_15000.caffemodel";

static void getMaxClass( Mat &probBlob, int *classId, double *classProb)
{

    Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
    Point classNumber;

    minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
    *classId = classNumber.x;
}



class Parallel_process : public cv::ParallelLoopBody
{

private:

    std::vector<cv::dnn::experimental_dnn_v1::Net> net ;
    int numberofClass;
    double probabilityOfClass;
    std::string PictureName;
    int n;
    vector<int> classId_array_parallel;
    vector<double> classProb_array_parallel;
    vector<cv::String> fnClass;
    Mat prob;

public:

    Parallel_process(vector<cv::String>& fn)
        : fnClass(fn){}
    void operator()(const cv::Range& range) const
    {
        net = dnn::readNetFromCaffe(modelTxt, modelBin);
        for(int y = range.start(); y < range.end(); y++)
        {
            net = dnn::readNetFromCaffe(modelTxt, modelBin);
            cv::Mat inputIm = cv::imread(fnClass[y]);
            Mat inputBlob = blobFromImage(inputIm, 1.0f, Size(227, 227),
                                          Scalar(104, 117, 123),false);

            net.setInput(inputBlob, "data");
            prob = net.forward("prob");
            getMaxClass(prob, &numberofClass, &probabilityOfClass);//find the best class
            std::cout << "Best class: #" << numberofClass << " '" << ",  image = " << fnClass[y] <<std::endl;
            std::cout << "Probability: " << probabilityOfClass * 100 << "%" << std::endl;
            //String label = String(classNames[classId_array[k]]);
            //std::cout << "Best class: #" << classId_array_parallel[y] << " '" << ",  image = " << fnClass[y] <<std::endl;
            //std::cout << "Probability: " << classProb_array_parallel[y] * 100 << "%" << std::endl;

        }
    }
};


int main(int argc, char **argv)
{

    CV_TRACE_FUNCTION();

    String path("Images/*.png"); 
    vector<cv::String> fn;
    vector<cv::Mat> data;
    cv::glob(path,fn,true);

    int classId;
    double classProb;
    vector<int> classId_array;
    vector<double> classProb_array;

    Mat prob;
    cv::TickMeter t;
    int numberImage = fn.size();

    /// Parallel loop

    parallel_for_(blocked_range(0,numberImage), Parallel_process(fn));


    return 0;
}

$ g++ -o test_1 google_parallel.cpp `pkg-config opencv --cflags --libs`
google_parallel.cpp: In member function ‘void Parallel_process::operator()(const tbb::blocked_range<int>&) const’:
google_parallel.cpp:107:17: error: no match for ‘operator=’ (operand types are ‘const std::vector<cv::dnn::experimental_dnn_v1::Net>’ and ‘cv::dnn::experimental_dnn_v1::Net’)
             net = dnn::readNetFromCaffe(modelTxt, modelBin);
                 ^
In file included from /usr/include/c++/5/vector:69:0,
                 from /usr/local/include/opencv2/dnn/dnn.hpp:45,
                 from /usr/local/include/opencv2/dnn.hpp:62,
                 from google_parallel.cpp:42:
/usr/include/c++/5/bits/vector.tcc:167:5: note: candidate: std::vector<_Tp, _Alloc>& std::vector<_Tp, _Alloc>::operator=(const std::vector<_Tp, _Alloc>&) [with _Tp = cv::dnn::experimental_dnn_v1::Net; _Alloc = std::allocator<cv::dnn::experimental_dnn_v1::Net>]
     vector<_Tp, _Alloc>::
     ^
/usr/include/c++/5/bits/vector.tcc:167:5: note:   no known conversion for argument 1 from ‘cv::dnn::experimental_dnn_v1::Net’ to ‘const std::vector<cv::dnn::experimental_dnn_v1::Net>&’
google_parallel.cpp:112:17: error: ‘const class std::vector<cv::dnn::experimental_dnn_v1::Net>’ has no member named ‘setInput’
             net.setInput(inputBlob, "data");
                 ^
google_parallel.cpp:113:24: error: ‘const class std::vector<cv::dnn::experimental_dnn_v1::Net>’ has no member named ‘forward’
             prob = net.forward("prob");
                        ^
google_parallel.cpp:114:25: error: binding ‘const cv::Mat’ to reference of type ‘cv::Mat&’ discards qualifiers
             getMaxClass(prob, &numberofClass, &probabilityOfClass);//find the best class
                         ^
google_parallel.cpp:63:13: note:   initializing argument 1 of ‘void getMaxClass(cv::Mat&, int*, double*)’
 static void getMaxClass( Mat &probBlob, int *classId, double *classProb)
             ^
google_parallel.cpp: In function ‘int main(int, char**)’:
google_parallel.cpp:195:5: error: ‘start’ was not declared in this scope
     start = (double)getTickCount();
     ^
google_parallel.cpp:196:32: error: missing template arguments before ‘(’ token
     parallel_for_(blocked_range(0,numberImage), Parallel_process(fn));
                                ^
google_parallel.cpp:196:68: error: invalid cast to abstract class type ‘Parallel_process’
     parallel_for_(blocked_range(0,numberImage), Parallel_process(fn));
                                                                    ^
google_parallel.cpp:75:7: note:   because the following virtual functions are pure within ‘Parallel_process’:
 class Parallel_process : public cv::ParallelLoopBody
       ^
In file included from /usr/local/include/opencv2/core.hpp:3224:0,
                 from /usr/local/include/opencv2/dnn/dnn.hpp:46,
                 from /usr/local/include/opencv2/dnn.hpp:62,
                 from google_parallel.cpp:42:
/usr/local/include/opencv2/core/utility.hpp:495:18: note:   virtual void cv::ParallelLoopBody::operator()(const cv::Range&) const
     virtual void operator() (const Range& range) const = 0;
                  ^