From 528cc0d7886580f3f29e2d01786f0c63e19c8f00 Mon Sep 17 00:00:00 2001
From: ChrisKong <609027949@qq.com>
Date: Fri, 3 Jan 2020 17:39:55 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=B3=A8=E9=87=8A?=
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添加注释

Co-Authored-By: Chris Kong <609027949@qq.com>
---
 src/facenet.cpp |  83 ++++++++++++++++++++---
 src/network.cpp | 171 +++++++++++++++++++++++++++++++++++++++++++++++-
 src/network.h   |   2 +-
 src/pikaqiu.cpp |   2 +-
 4 files changed, 243 insertions(+), 15 deletions(-)

diff --git a/src/facenet.cpp b/src/facenet.cpp
index 4aee9c4..04c2e82 100644
--- a/src/facenet.cpp
+++ b/src/facenet.cpp
@@ -4,7 +4,11 @@
 
 #include "facenet.h"
 
-
+/**
+ * stem网络
+ * @param image 输入图片
+ * @param output 输出featuremap 指针形式
+ */
 void facenet::Stem(Mat &image, pBox *output) {
     pBox *rgb = new pBox;
     pBox *conv1_out = new pBox;
@@ -161,6 +165,13 @@ void facenet::Stem(Mat &image, pBox *output) {
     freeBN(conv6_beta);
 }
 
+/**
+ * Inception_resnet_A网络
+ * @param input 输入featuremap
+ * @param output 输出featuremap
+ * @param filepath 模型文件路径
+ * @param scale 比例系数
+ */
 void facenet::Inception_resnet_A(pBox *input, pBox *output, string filepath, float scale) {
     pBox *conv1_out = new pBox;
     pBox *conv2_out = new pBox;
@@ -280,7 +291,7 @@ void facenet::Inception_resnet_A(pBox *input, pBox *output, string filepath, flo
     convolution(conv7_wb, conv7_out, conv8_out);
     addbias(conv8_out, conv7_wb->pbias);
 
-    mulandaddInit(input, conv8_out, output, scale);
+    mulandaddInit(input, conv8_out, output);
     mulandadd(input, conv8_out, output, scale);
     relu(output, conv8_wb->pbias);
 
@@ -327,6 +338,11 @@ void facenet::Inception_resnet_A(pBox *input, pBox *output, string filepath, flo
     freeBN(conv6_beta);
 }
 
+/**
+ * Reduction_A
+ * @param input 输入featuremap
+ * @param output 输出featuremap
+ */
 void facenet::Reduction_A(pBox *input, pBox *output) {
     pBox *conv1_out = new pBox;
     pBox *conv2_out = new pBox;
@@ -429,6 +445,13 @@ void facenet::Reduction_A(pBox *input, pBox *output) {
     freeBN(conv4_beta);
 }
 
+/**
+ * Inception_resnet_B网络
+ * @param input 输入featuremap
+ * @param output 输出featuremap
+ * @param filepath 模型文件路径
+ * @param scale 比例系数
+ */
 void facenet::Inception_resnet_B(pBox *input, pBox *output, string filepath, float scale) {
     pBox *conv1_out = new pBox;
     pBox *conv2_out = new pBox;
@@ -518,7 +541,7 @@ void facenet::Inception_resnet_B(pBox *input, pBox *output, string filepath, flo
     convolution(conv5_wb, conv5_out, conv6_out);
     addbias(conv6_out, conv5_wb->pbias);
 
-    mulandaddInit(input, conv6_out, output, scale);
+    mulandaddInit(input, conv6_out, output);
     mulandadd(input, conv6_out, output, scale);
     relu(output, conv6_wb->pbias);
 
@@ -550,6 +573,11 @@ void facenet::Inception_resnet_B(pBox *input, pBox *output, string filepath, flo
     freeBN(conv4_beta);
 }
 
+/**
+ * Reduction_B
+ * @param input 输入featuremap
+ * @param output 输出featuremap
+ */
 void facenet::Reduction_B(pBox *input, pBox *output) {
     pBox *conv1_out = new pBox;
     pBox *conv2_out = new pBox;
@@ -714,6 +742,13 @@ void facenet::Reduction_B(pBox *input, pBox *output) {
     freeBN(conv7_beta);
 }
 
+/**
+ * Inception_resnet_C网络
+ * @param input 输入featuremap
+ * @param output 输出featuremap
+ * @param filepath 模型文件路径
+ * @param scale 比例系数
+ */
 void facenet::Inception_resnet_C(pBox *input, pBox *output, string filepath, float scale) {
     pBox *conv1_out = new pBox;
     pBox *conv2_out = new pBox;
@@ -804,7 +839,7 @@ void facenet::Inception_resnet_C(pBox *input, pBox *output, string filepath, flo
     convolution(conv5_wb, conv5_out, conv6_out);
     addbias(conv6_out, conv5_wb->pbias);
 
-    mulandaddInit(input, conv6_out, output, scale);
+    mulandaddInit(input, conv6_out, output);
     mulandadd(input, conv6_out, output, scale);
     relu(output, conv6_wb->pbias);
 
@@ -836,6 +871,13 @@ void facenet::Inception_resnet_C(pBox *input, pBox *output, string filepath, flo
     freeBN(conv4_beta);
 }
 
+/**
+ * Inception_resnet_C网络 最后无激活函数
+ * @param input 输入featuremap
+ * @param output 输出featuremap
+ * @param filepath 模型文件路径
+ * @param scale 比例系数
+ */
 void facenet::Inception_resnet_C_None(pBox *input, pBox *output, string filepath) {
     pBox *conv1_out = new pBox;
     pBox *conv2_out = new pBox;
@@ -920,7 +962,7 @@ void facenet::Inception_resnet_C_None(pBox *input, pBox *output, string filepath
     convolution(conv5_wb, conv5_out, conv6_out);
     addbias(conv6_out, conv5_wb->pbias);
 
-    mulandaddInit(input, conv6_out, output, 1);
+    mulandaddInit(input, conv6_out, output);
     mulandadd(input, conv6_out, output);
 
     freepBox(conv1_out);
@@ -950,12 +992,22 @@ void facenet::Inception_resnet_C_None(pBox *input, pBox *output, string filepath
     freeBN(conv4_beta);
 }
 
+/**
+ * 平均池化
+ * @param input 输入featuremap
+ * @param output 输出featuremap
+ */
 void facenet::AveragePooling(pBox *input, pBox *output) {
 //    cout << "size:" << input->height << endl;
     avePoolingInit(input, output, input->height, 2);
     avePooling(input, output, input->height, 2);
 }
 
+/**
+ * flatten 多维转换到一维
+ * @param input
+ * @param output
+ */
 void facenet::Flatten(pBox *input, pBox *output) {
     output->width = input->channel;
     output->height = 1;
@@ -965,6 +1017,12 @@ void facenet::Flatten(pBox *input, pBox *output) {
     memcpy(output->pdata, input->pdata, output->channel * output->width * output->height * sizeof(mydataFmt));
 }
 
+/**
+ * 全连接网络
+ * @param input 输入featuremap
+ * @param output 输出featuremap
+ * @param filepath 网络模型参数文件路径
+ */
 //参数还未设置
 void facenet::fully_connect(pBox *input, pBox *output, string filepath) {
     struct Weight *conv1_wb = new Weight;
@@ -1009,13 +1067,17 @@ void facenet::printData(pBox *in) {
     cout << "printData" << endl;
 }
 
+/**
+ * facenet网络运行入口
+ * @param image
+ * @param o
+ * @param count
+ */
 void facenet::run(Mat &image, vector<mydataFmt> &o, int count) {
     cout << "=====This is No." + to_string(count) + " Picture=====" << endl;
     pBox *output = new pBox;
     pBox *input;
     Stem(image, output);
-//    printData(output);
-//    return;
     cout << "Stem Finally" << endl;
     input = output;
     output = new pBox;
@@ -1030,7 +1092,6 @@ void facenet::run(Mat &image, vector<mydataFmt> &o, int count) {
     Reduction_A(input, output);
     cout << "Reduction_A Finally" << endl;
     input = output;
-//    freepBox(output);
     output = new pBox;
     for (int j = 0; j < 10; ++j) {
 //        model_128/block17_1_list.txt
@@ -1048,10 +1109,8 @@ void facenet::run(Mat &image, vector<mydataFmt> &o, int count) {
     for (int k = 0; k < 5; ++k) {
 //        model_128/block8_1_list.txt
         string filepath = "../model_" + to_string(Num) + "/block8_" + to_string((k + 1)) + "_list.txt";
-//        cout << filepath << endl;
         Inception_resnet_C(input, output, filepath, 0.2);
         input = output;
-//        freepBox(output);
         output = new pBox;
     }
     cout << "Inception_resnet_C Finally" << endl;
@@ -1070,6 +1129,10 @@ void facenet::run(Mat &image, vector<mydataFmt> &o, int count) {
     output = new pBox;
     fully_connect(input, output, "../model_" + to_string(Num) + "/Bottleneck_list.txt");
     cout << "Fully_Connect Finally" << endl;
+
+    /**
+     * L2归一化
+     */
     mydataFmt sq = 0, sum = 0;
     for (int i = 0; i < Num; ++i) {
         sq = pow(output->pdata[i], 2);
diff --git a/src/network.cpp b/src/network.cpp
index b21e1d6..df25610 100644
--- a/src/network.cpp
+++ b/src/network.cpp
@@ -1,5 +1,10 @@
 #include "network.h"
 
+/**
+ * 卷积以后偏移
+ * @param pbox　feature map
+ * @param pbias　偏移量
+ */
 void addbias(struct pBox *pbox, mydataFmt *pbias) {
     if (pbox->pdata == NULL) {
         cout << "Relu feature is NULL!!" << endl;
@@ -22,6 +27,11 @@ void addbias(struct pBox *pbox, mydataFmt *pbias) {
     }
 }
 
+/**
+ * mat图片转成pbox结构体初始化
+ * @param image　mat格式的图片
+ * @param pbox　结构体pbox
+ */
 void image2MatrixInit(Mat &image, struct pBox *pbox) {
     if ((image.data == NULL) || (image.type() != CV_8UC3)) {
         cout << "image's type is wrong!!Please set CV_8UC3" << endl;
@@ -36,6 +46,12 @@ void image2MatrixInit(Mat &image, struct pBox *pbox) {
     memset(pbox->pdata, 0, pbox->channel * pbox->height * pbox->width * sizeof(mydataFmt));
 }
 
+/**
+ * mat图片转成pbox结构体
+ * @param image　mat格式的图片
+ * @param pbox　结构体pbox
+ * @param num　选择mtcnn还是facenet 0-mtcnn  非0-facenet  缺省为０
+ */
 void image2Matrix(const Mat &image, const struct pBox *pbox, int num) {
     if ((image.data == NULL) || (image.type() != CV_8UC3)) {
         cout << "image's type is wrong!!Please set CV_8UC3" << endl;
@@ -78,6 +94,12 @@ void image2Matrix(const Mat &image, const struct pBox *pbox, int num) {
     }
 }
 
+/**
+ * 求图片像素的平均值和标准差
+ * @param image 图片
+ * @param p 平均值
+ * @param q 标准差
+ */
 void MeanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q) {
     mydataFmt meansum = 0, stdsum = 0;
     for (int rowI = 0; rowI < image.rows; rowI++) {
@@ -96,6 +118,14 @@ void MeanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q) {
     q = sqrt(stdsum / (image.cols * image.rows * image.channels()));
 }
 
+/**
+ * 卷积补偿初始化
+ * @param pbox　输入feature map
+ * @param outpBox　输出feature map
+ * @param pad　补偿 正方形算子(-1为不规则补偿，0为不需要补偿)
+ * @param padw 补偿　不规则算子的宽度
+ * @param padh　补偿　不规则算子的高度
+ */
 void featurePadInit(const pBox *pbox, pBox *outpBox, const int pad, const int padw, const int padh) {
     if (pad < -1) {
         cout << "the data needn't to pad,please check you network!" << endl;
@@ -115,6 +145,14 @@ void featurePadInit(const pBox *pbox, pBox *outpBox, const int pad, const int pa
     memset(outpBox->pdata, 0, outpBox->channel * outpBox->height * RowByteNum);
 }
 
+/**
+ * 卷积补偿
+ * @param pbox　输入feature map
+ * @param outpBox 输出feature map
+ * @param pad　补偿 正方形算子(-1为不规则补偿，0为不需要补偿)
+ * @param padw 补偿　不规则算子的宽度
+ * @param padh　补偿　不规则算子的高度
+ */
 void featurePad(const pBox *pbox, pBox *outpBox, const int pad, const int padw, const int padh) {
     mydataFmt *p = outpBox->pdata;
     mydataFmt *pIn = pbox->pdata;
@@ -143,6 +181,12 @@ void featurePad(const pBox *pbox, pBox *outpBox, const int pad, const int padw,
     }
 }
 
+/**
+ * 卷积初始化
+ * @param weight 卷积权重
+ * @param pbox　输入feature map
+ * @param outpBox 输出feature map
+ */
 void convolutionInit(const Weight *weight, pBox *pbox, pBox *outpBox) {
     outpBox->channel = weight->selfChannel;
 //    ((imginputh - ckh + 2 * ckpad) / stride) + 1;
@@ -168,6 +212,12 @@ void convolutionInit(const Weight *weight, pBox *pbox, pBox *outpBox) {
     }
 }
 
+/**
+ * 卷积
+ * @param weight 卷积权重
+ * @param pbox　输入feature map
+ * @param outpBox 输出feature map
+ */
 void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox) {
     int ckh, ckw, ckd, stride, cknum, ckpad, imginputh, imginputw, imginputd, Nh, Nw;
     mydataFmt *ck, *imginput;
@@ -215,6 +265,14 @@ void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox) {
     }
 }
 
+/**
+ * 最大值池化初始化
+ * @param pbox 输入feature map
+ * @param Matrix 输出feature map
+ * @param kernelSize 池化算子大小
+ * @param stride　步长
+ * @param flag　标志位
+ */
 void maxPoolingInit(const pBox *pbox, pBox *Matrix, int kernelSize, int stride, int flag) {
     if (flag == 1) {
         Matrix->width = floor((float) (pbox->width - kernelSize) / stride + 1);
@@ -229,6 +287,13 @@ void maxPoolingInit(const pBox *pbox, pBox *Matrix, int kernelSize, int stride,
     memset(Matrix->pdata, 0, Matrix->channel * Matrix->width * Matrix->height * sizeof(mydataFmt));
 }
 
+/**
+ * 最大值池化
+ * @param pbox 输入feature map
+ * @param Matrix 输出feature map
+ * @param kernelSize 池化算子大小
+ * @param stride　步长
+ */
 void maxPooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride) {
     if (pbox->pdata == NULL) {
         cout << "the feature2Matrix pbox is NULL!!" << endl;
@@ -281,6 +346,13 @@ void maxPooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride) {
     }
 }
 
+/**
+ * 平均值池化初始化
+ * @param pbox 输入feature map
+ * @param Matrix 输出feature map
+ * @param kernelSize 池化算子大小
+ * @param stride　步长
+ */
 void avePoolingInit(const pBox *pbox, pBox *Matrix, int kernelSize, int stride) {
     Matrix->width = ceil((float) (pbox->width - kernelSize) / stride + 1);
     Matrix->height = ceil((float) (pbox->height - kernelSize) / stride + 1);
@@ -290,6 +362,13 @@ void avePoolingInit(const pBox *pbox, pBox *Matrix, int kernelSize, int stride)
     memset(Matrix->pdata, 0, Matrix->channel * Matrix->width * Matrix->height * sizeof(mydataFmt));
 }
 
+/**
+ * 平均值池化
+ * @param pbox 输入feature map
+ * @param Matrix 输出feature map
+ * @param kernelSize 池化算子大小
+ * @param stride　步长
+ */
 void avePooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride) {
     if (pbox->pdata == NULL) {
         cout << "the feature2Matrix pbox is NULL!!" << endl;
@@ -323,8 +402,8 @@ void avePooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride) {
 
 /**
  * 激活函数 没有系数
- * @param pbox
- * @param pbias
+ * @param pbox　输入feature
+ * @param pbias 偏移
  */
 void relu(struct pBox *pbox, mydataFmt *pbias) {
     if (pbox->pdata == NULL) {
@@ -349,6 +428,11 @@ void relu(struct pBox *pbox, mydataFmt *pbias) {
     }
 }
 
+/**
+ * 全连接初始化
+ * @param weight　权重参数
+ * @param outpBox　输出feature map
+ */
 void fullconnectInit(const Weight *weight, pBox *outpBox) {
     outpBox->channel = weight->selfChannel;
     outpBox->width = 1;
@@ -358,6 +442,12 @@ void fullconnectInit(const Weight *weight, pBox *outpBox) {
     memset(outpBox->pdata, 0, weight->selfChannel * sizeof(mydataFmt));
 }
 
+/**
+ * 全连接
+ * @param weight　权重参数
+ * @param pbox 　输入feature map
+ * @param outpBox　输出feature map
+ */
 void fullconnect(const Weight *weight, const pBox *pbox, pBox *outpBox) {
     if (pbox->pdata == NULL) {
         cout << "the fc feature is NULL!!" << endl;
@@ -376,6 +466,14 @@ void fullconnect(const Weight *weight, const pBox *pbox, pBox *outpBox) {
                   outpBox->pdata);
 }
 
+/**
+ * 一维数组与二位矩阵相乘
+ * @param matrix　输入feature map
+ * @param v 权重
+ * @param v_w　权重矩阵的宽度
+ * @param v_h　权重矩阵的高度
+ * @param p　输出feature map
+ */
 void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int v_w, int v_h, mydataFmt *p) {
     for (int i = 0; i < v_h; i++) {
         p[i] = 0;
@@ -385,6 +483,13 @@ void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int v_w, int v_h, mydataFmt
     }
 }
 
+/**
+ * 读取模型文件
+ * @param filename　文件路径
+ * @param dataNumber　参数个数数组
+ * @param pTeam　变量数组
+ * @param length
+ */
 void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length) {
     ifstream in(filename.data());
     string line;
@@ -434,6 +539,20 @@ void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length
     }
 }
 
+/**
+ * 卷积和全连接初始化
+ * @param weight　权重
+ * @param schannel　卷积核个数
+ * @param lchannel　上一层feature map个数
+ * @param kersize　卷积核大小
+ * @param stride　卷积步长
+ * @param pad　卷积是否补偿
+ * @param w　卷积核宽度
+ * @param h　卷积核高度
+ * @param padw　补偿宽度
+ * @param padh　补偿高度
+ * @return　参数长度
+ */
 //									w			  sc			 lc			ks				s		p    kw       kh
 long ConvAndFcInit(struct Weight *weight, int schannel, int lchannel, int kersize,
                    int stride, int pad, int w, int h, int padw, int padh) {
@@ -461,6 +580,14 @@ long ConvAndFcInit(struct Weight *weight, int schannel, int lchannel, int kersiz
     return byteLenght;
 }
 
+/**
+ * 残差融合初始化
+ * @param output 输出feature map
+ * @param c1 输入feature map
+ * @param c2 输入feature map
+ * @param c3 输入feature map
+ * @param c4 输入feature map
+ */
 void conv_mergeInit(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
     output->channel = 0;
     output->height = c1->height;
@@ -482,6 +609,14 @@ void conv_mergeInit(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
     memset(output->pdata, 0, output->width * output->height * output->channel * sizeof(mydataFmt));
 }
 
+/**
+ * 残差网络融合
+ * @param output 输出feature map
+ * @param c1 输入feature map
+ * @param c2 输入feature map
+ * @param c3 输入feature map
+ * @param c4 输入feature map
+ */
 void conv_merge(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
 //    cout << "output->channel:" << output->channel << endl;
     if (c1 != 0) {
@@ -511,7 +646,16 @@ void conv_merge(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
     } else { cout << "conv_mergeInit" << endl; }
 }
 
-void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale) {
+/**
+ * 残差网络做多次按比例相加初始化
+ * @param inpbox 输入feature map
+ * @param temppbox 输入feature map
+ * @param outpBox 输出feature map
+ */
+void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox) {
+    if (!((inpbox->width == temppbox->width) && (inpbox->height == temppbox->height) &&
+          (inpbox->channel == temppbox->channel)))
+        cout << "the mulandaddInit is failed!!" << endl;
     outpBox->channel = temppbox->channel;
     outpBox->width = temppbox->width;
     outpBox->height = temppbox->height;
@@ -520,6 +664,13 @@ void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, floa
     memset(outpBox->pdata, 0, outpBox->width * outpBox->height * outpBox->channel * sizeof(mydataFmt));
 }
 
+/**
+ * 残差网络做多次按比例相加
+ * @param inpbox 输入feature map
+ * @param temppbox 输入feature map
+ * @param outpBox 输出feature map
+ * @param scale 比例系数
+ */
 void mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale) {
     mydataFmt *ip = inpbox->pdata;
     mydataFmt *tp = temppbox->pdata;
@@ -530,6 +681,13 @@ void mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float sc
     }
 }
 
+/**
+ * BN初始化
+ * @param var　方差
+ * @param mean　平均值
+ * @param beta　beta
+ * @param width 参数个数
+ */
 void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width) {
     var->width = width;
     var->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
@@ -547,6 +705,13 @@ void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width)
     memset(beta->pdata, 0, width * sizeof(mydataFmt));
 }
 
+/**
+ * BN实现
+ * @param pbox　输入feature map
+ * @param var　方差
+ * @param mean　平均值
+ * @param beta　beta
+ */
 void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *beta) {
     if (pbox->pdata == NULL) {
         cout << "Relu feature is NULL!!" << endl;
diff --git a/src/network.h b/src/network.h
index d7432f7..fcab202 100644
--- a/src/network.h
+++ b/src/network.h
@@ -55,7 +55,7 @@ void conv_merge(pBox *output, pBox *c1 = 0, pBox *c2 = 0, pBox *c3 = 0, pBox *c4
 
 void conv_mergeInit(pBox *output, pBox *c1 = 0, pBox *c2 = 0, pBox *c3 = 0, pBox *c4 = 0);
 
-void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale);
+void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox);
 
 void mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale = 1);
 
diff --git a/src/pikaqiu.cpp b/src/pikaqiu.cpp
index fe90636..59349fc 100644
--- a/src/pikaqiu.cpp
+++ b/src/pikaqiu.cpp
@@ -10,7 +10,7 @@ int main() {
     facenet ggg;
     vector<mydataFmt> o;
     ggg.run(Image, o, 0);
-//        imshow("result", Image);
+    imshow("result", Image);
     imwrite("../result.jpg", Image);
 
     for (int i = 0; i < Num; ++i) {