mobileFacenet-ncnn 为什么mobilefacenet提取的任意图片的特征向量都是一样的，下面是我的主程序

#include <stdio.h>
#include
#include
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp> #include //using namespace std;
using namespace cv;
#include "net.h"

static int detect_squeezenet(const cv::Mat& bgr, std::vector& cls_scores)
{
ncnn::Net squeezenet;
squeezenet.load_param("/home1/syy/DownLoad/ncnn-mtcnn/ncnn-master/mobilefacenet/mobilefacenet.param");
squeezenet.load_model("/home1/syy/DownLoad/ncnn-mtcnn/ncnn-master/mobilefacenet/mobilefacenet.bin");

ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, 112, 112);  

const float mean_vals[3] = {127.5, 127.5, 127.5};
const float norm_vals[3] = {0.0078125, 0.0078125, 0.0078125};
in.substract_mean_normalize(mean_vals, norm_vals);  

ncnn::Extractor ex = squeezenet.create_extractor();  
ex.set_light_mode(true);  

ex.input("data", in);  

ncnn::Mat out;  
ex.extract("fc1", out);  

cls_scores.resize(out.c);  
for (int j=0; j<out.c; j++)  
{  
    const float* prob = out.data + out.cstep * j;  
    cls_scores[j] = prob[0]; 
    std::cout<< prob[0]<<std::endl;
}  

return 0;

}
double calculSimilar(std::vector& v1, std::vector& v2) { assert(v1.size() == v2.size()); double ret = 0.0, mod1 = 0.0, mod2 = 0.0; for (std::vector::size_type i = 0; i != v1.size(); ++i) { ret += v1[i] * v2[i]; mod1 += v1[i] * v1[i]; mod2 += v2[i] * v2[i]; } return (ret / sqrt(mod1) / sqrt(mod2) + 1) / 2.0; //std::cout<<ret<<" "<<mod1<<" "<<mod2<<std::endl; //return ret / sqrt(mod1) / sqrt(mod2); }
int main(int argc, char** argv)
{
cv::Mat img1 = cv::imread("/home1/syy/DownLoad/ncnn-mtcnn/ncnn-master/mobilefacenet/1.jpg", CV_LOAD_IMAGE_COLOR);

cv::Mat img2 = cv::imread("/home1/syy/DownLoad/ncnn-mtcnn/ncnn-master/mobilefacenet/2.jpg", CV_LOAD_IMAGE_COLOR);
std::vector<float> feature1;
std::vector<float> feature2;
    detect_squeezenet(img1, feature1) ;
    std::cout<<"$$$$$$$$$$$$$$$$$$$$$"<<std::endl;
    detect_squeezenet(img1, feature2) ;
    double similar = calculSimilar(feature1, feature2);
   // printf(similar);
    std::cout << "similarity is : " << similar <<std::endl;

    return 0;

}