caffe2 first sight

#include <boost/lexical_cast.hpp>// Caffe2#include <google/protobuf/text_format.h>#include "caffe2/core/context.h"#include "caffe2/core/operator.h"#include "caffe2/core/predictor.h"#include "caffe2/core/tensor.h"#include "caffe2/utils/math.h"#include <caffe2/core/context.h>#include <caffe2/core/context_gpu.h>#include <caffe2/core/workspace.h>#include <gtest/gtest.h>// OpenCV#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <stdio.h>#include <stdlib.h>#include <time.h>void setgpu(caffe2::NetDef& net, int id){    net.mutable_device_option()->set_device_type(1);    net.mutable_device_option()->set_cuda_gpu_id(id);    for(int i = 0; i < net.op_size(); ++i){        net.mutable_op(i)->mutable_device_option()->set_device_type(1);        net.mutable_op(i)->mutable_device_option()->set_cuda_gpu_id(0);    }}int main(int argc, char* argv[]){    std::cout <<"1" <<std::endl;    //typedef caffe2::TensorCPU tensorType;    typedef caffe2::TensorCUDA TensorType;    // std::string image_filename = us::any_cast<std::string>(parsedArgs["image"]);    // cv::Mat img = cv::imread(image_filename, CV_LOAD_IMAGE_COLOR);    // cv::Mat img = cv::Mat(64,64,CV_32FC3,128);    // cv::namedWindow( "Display window", cv::WINDOW_AUTOSIZE );// Create a window for display.    // cv::imshow( "Display w30indow", img2 ); // Show our image inside it.    // cv::waitKey(0); // Wait for a keystroke in the window    caffe2::NetDef init_net, predict_net;    CAFFE_ENFORCE(ReadProtoFromFile("/home/amax/Yang/caffe2/models/init_net.pb", &init_net));    setgpu(init_net, 1);    std::cout <<"2" <<std::endl;    CAFFE_ENFORCE(ReadProtoFromFile("/home/amax/Yang/caffe2/models/predict_net.pb", &predict_net));    setgpu(predict_net, 1);    std::cout <<"3" <<std::endl;    std::vector<TensorType*> outputVec;    int w,h,c; w=224; h=224; c=3;    TensorType input(std::vector<int>({1, c, h, w}));    //// TRYING TO ACCESS TENSOR DATA (RESULTS IN ERROR FOR caffe2::TensorCUDA)    // caffe2::CPUContext cpu_context;    // caffe2::TensorCPU cpu_input(input, &cpu_context);    // float* input_data = cpu_input.template mutable_data<float>();    float* input_data = input.mutable_data<float>();    // create input image (simply a square of size 10x10 in a 64x64 image)    // test image for segmentation    /*int counter = 0;    for (int x = 0; x < h; x++)        for (int y = 0; y < w; y++)        {            for (int j = 0; j < c; j++)            {                input_data[counter] = 0;                if (x>20 && x<30 && y>20 && y<30)                    input_data[counter] = 1;                counter++;        }    }*/    std::cout <<"4" <<std::endl;    // show input image    cv::Mat img = cv::Mat(224,224,CV_32FC3);    cv::namedWindow( "Display window", cv::WINDOW_AUTOSIZE );// Create a window for display.    cv::imshow( "Display window", img ); // Show our image inside it.    cv::waitKey(0); // Wait for a keystroke in the window    // run network    clock_t t1,t2;    std::vector<TensorType> inputVec{input};    caffe2::Workspace ws;    t1=clock();    ws.RunNetOnce(init_net);    t2=clock();    std::cout <<(double)(t2-t1) / CLOCKS_PER_SEC <<std::endl;    ws.CreateNet(predict_net, true);    std::cout << inputVec.size() << std::endl;    for (auto i = 0; i < inputVec.size(); ++i)    {        auto* blob = ws.GetBlob(predict_net.external_input(i));        TensorType* tensor = blob->template GetMutable<TensorType>();        tensor->ResizeLike(inputVec[i]);        tensor->ShareData(inputVec[i]);    }    std::cout <<"5" <<std::endl;    t1=clock();    CAFFE_ENFORCE(ws.RunNet(predict_net.name()));    t2=clock();    std::cout <<(double)(t2-t1) / CLOCKS_PER_SEC <<std::endl;    t1=clock();    for(int i = 0; i < 1000; ++i)        ws.RunNet(predict_net.name());    t2=clock();    std::cout <<(double)(t2-t1) / CLOCKS_PER_SEC/1000 <<std::endl;    t1=clock();    ws.RunNet(predict_net.name());    t2=clock();    std::cout <<(double)(t2-t1) / CLOCKS_PER_SEC <<std::endl;    std::cout <<"6" <<std::endl;    return 1;}