caffe：使用训练好的模型进行训练（使用mnist模型）

在使用模型的时候，不能直接调用该模型，还需要改写之前的训练网络，最后将更改之后的网络(deploy)和模型结合在一起使用

1.把数据层（Data Layer）和连接数据层的Layers去掉(即top:data的层)，图左彩色部分删除

2. 去掉输出层和连接输出层的Layers（即bottom:label）图右彩色部分删除

   

3. 重新建立输入，将第一步删除的部分粘贴下面的代码

input: "data"
input_shape {
  dim: 1 # batchsize,每次forward的时候输入的图片个数
  dim: 3 # number of colour channels - rgb.如果是1，测试灰度值
  dim: 28 # width
  dim: 28 # height
}

4.重新建立输出，将第二部删除的部分粘贴下面的代码
layer {
  name: "prob"
  type: "Softmax"
  bottom: "ip2"
  top: "prob"
}
* 修改后的mnist的deploy文件可以参考caffe/example/mnist/lenet_train.prototxt

修改后的deploy的文件如下：

 

name: "LeNet"
input: "data"
input_shape {
  dim: 1 # batchsize
  dim: 1 # number of colour channels - rgb
  dim: 28 # width
  dim: 28 # height
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool2"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "ip2"
  top: "prob"
}

 

 

 

 

 

 

建立一个test_mnist.cpp文件输入以下代码

 

 

#include "opencv2/dnn.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"

using namespace cv;
using namespace cv::dnn;

#include 
#include 
#include 
using namespace std;

/* Find best class for the blob (i. e. class with maximal probability) */ 
void getMaxClass(dnn::Blob &probBlob, int *classId, double *classProb)
{
    Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix
    Point classNumber;
    minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
    *classId = classNumber.x;
}


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

    String modelTxt = "mnist_deploy.prototxt";
    String modelBin = "lenet_iter_10000.caffemodel";
    String imageFile = (argc > 1) ? argv[1] : "5.jpg";

    //! [Create the importer of Caffe model] 导入一个caffe模型接口 
    Ptr importer; 
    importer = dnn::createCaffeImporter(modelTxt, modelBin);
  
    if (!importer){
        std::cerr << "Can't load network by using the following files: " << std::endl;
        std::cerr << "prototxt:   " << modelTxt << std::endl;
        std::cerr << "caffemodel: " << modelBin << std::endl;
        exit(-1);
    }

    //! [Initialize network] 通过接口创建和初始化网络
    Net net;
    importer->populateNet(net);  
    importer.release();

    //! [Prepare blob] 读取一张图片并转换到blob数据存储
    Mat img = imread(imageFile,0); //[] "0" for 1 channel, Mnist accepts 1 channel
    if (img.empty())
    {
        std::cerr << "Can't read image from the file: " << imageFile << std::endl;
        exit(-1);
    }
    resize(img, img, Size(28, 28));                   //[]Mnist accepts only 28x28 RGB-images

    dnn::Blob inputBlob = cv::dnn::Blob(img);   //Convert Mat to dnn::Blob batch of images

    //! [Set input blob] 将blob输入到网络
    net.setBlob(".data", inputBlob);        //set the network input

    //! [Make forward pass] 进行前向传播
    net.forward();                          //compute output

    //! [Gather output] 获取概率值
    dnn::Blob prob = net.getBlob("prob");   //[] gather output of "prob" layer
    int classId;
    double classProb;
    getMaxClass(prob, &classId, &classProb);//find the best class

    //! [Print results] 输出结果
    std::cout << "Best class: #" << classId << "'" << std::endl;
    std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
    
    return 0;
}

 

 

 

 

 

4.使用修改后的mnist的deploy文件，输入一张图片，输出分类结果。将mnist训练好的模型放在一个文件夹下，该文件夹下有以下几个文件

 

执行

g++-o test_mnist test_mnist.cpp -lopencv_dnn -lopencv_highgui-lopencv_imgcodecs -lopencv_imgproc -lstdc++ -lopencv_core

出现了如下错误：

输入以下命令：

sudo apt-get autoremove libopencv-dev，选择y。再次执行g++-o test_mnist test_mnist.cpp -lopencv_dnn -lopencv_highgui-lopencv_imgcodecs -lopencv_imgproc -lstdc++ -lopencv_core。会在文件中生成test_mnist文件。

然后执行：./test_mnist

出现结果：