更改模型参数结构

更改模型参数结构

.gitignore

@@ -35,4 +35,5 @@
 .idea/
 cmake-build-debug/
 cmake-build-release/
-model_128/
+model_128/
+model_512/

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;
@@ -47,27 +51,35 @@ void facenet::Stem(Mat &image, pBox *output) {
     struct BN *conv6_beta = new BN;
 
     long conv1 = ConvAndFcInit(conv1_wb, 32, 3, 3, 2, 0);
-    BatchNormInit(conv1_var, conv1_mean, conv1_beta, 32);
+    BatchNormInit(conv1_beta, conv1_mean, conv1_var, 32);
     long conv2 = ConvAndFcInit(conv2_wb, 32, 32, 3, 1, 0);
-    BatchNormInit(conv2_var, conv2_mean, conv2_beta, 32);
+    BatchNormInit(conv2_beta, conv2_mean, conv2_var, 32);
     long conv3 = ConvAndFcInit(conv3_wb, 64, 32, 3, 1, 1);
-    BatchNormInit(conv3_var, conv3_mean, conv3_beta, 64);
+    BatchNormInit(conv3_beta, conv3_mean, conv3_var, 64);
     long conv4 = ConvAndFcInit(conv4_wb, 80, 64, 1, 1, 0);
-    BatchNormInit(conv4_var, conv4_mean, conv4_beta, 80);
+    BatchNormInit(conv4_beta, conv4_mean, conv4_var, 80);
     long conv5 = ConvAndFcInit(conv5_wb, 192, 80, 3, 1, 0);
-    BatchNormInit(conv5_var, conv5_mean, conv5_beta, 192);
+    BatchNormInit(conv5_beta, conv5_mean, conv5_var, 192);
     long conv6 = ConvAndFcInit(conv6_wb, 256, 192, 3, 2, 0);
-    BatchNormInit(conv6_var, conv6_mean, conv6_beta, 256);
+    BatchNormInit(conv6_beta, conv6_mean, conv6_var, 256);
 
     long dataNumber[24] = {conv1, 32, 32, 32, conv2, 32, 32, 32, conv3, 64, 64, 64, conv4, 80, 80, 80, conv5, 192, 192,
                            192, conv6, 256, 256, 256};
 
-    mydataFmt *pointTeam[24] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
+//    mydataFmt *pointTeam[24] = {
-                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
+//                            conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
-                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
-                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
-                            conv5_wb->pdata, conv5_var->pdata, conv5_mean->pdata, conv5_beta->pdata, \
+//                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
-                            conv6_wb->pdata, conv6_var->pdata, conv6_mean->pdata, conv6_beta->pdata};
+//                            conv5_wb->pdata, conv5_var->pdata, conv5_mean->pdata, conv5_beta->pdata, \
+//                            conv6_wb->pdata, conv6_var->pdata, conv6_mean->pdata, conv6_beta->pdata};
+    mydataFmt *pointTeam[24] = {
+                            conv1_wb->pdata, conv1_beta->pdata, conv1_mean->pdata, conv1_var->pdata, \
+                            conv2_wb->pdata, conv2_beta->pdata, conv2_mean->pdata, conv2_var->pdata, \
+                            conv3_wb->pdata, conv3_beta->pdata, conv3_mean->pdata, conv3_var->pdata, \
+                            conv4_wb->pdata, conv4_beta->pdata, conv4_mean->pdata, conv4_var->pdata, \
+                            conv5_wb->pdata, conv5_beta->pdata, conv5_mean->pdata, conv5_var->pdata, \
+                            conv6_wb->pdata, conv6_beta->pdata, conv6_mean->pdata, conv6_var->pdata};
     string filename = "../model_" + to_string(Num) + "/stem_list.txt";
     readData(filename, dataNumber, pointTeam, 24);
 
@@ -78,21 +90,19 @@ void facenet::Stem(Mat &image, pBox *output) {
     convolutionInit(conv1_wb, rgb, conv1_out);
     //conv1 149 x 149 x 32
     convolution(conv1_wb, rgb, conv1_out);
-//    printData(conv1_out);
+    BatchNorm(conv1_out, conv1_beta, conv1_mean, conv1_var);
-    BatchNorm(conv1_out, conv1_var, conv1_mean, conv1_beta);
-//    printData(conv1_out);
     relu(conv1_out, conv1_wb->pbias);
 
     convolutionInit(conv2_wb, conv1_out, conv2_out);
     //conv2 147 x 147 x 32
     convolution(conv2_wb, conv1_out, conv2_out);
-    BatchNorm(conv2_out, conv2_var, conv2_mean, conv2_beta);
+    BatchNorm(conv2_out, conv2_beta, conv2_mean, conv2_var);
     relu(conv2_out, conv2_wb->pbias);
 
     convolutionInit(conv3_wb, conv2_out, conv3_out);
     //conv3 147 x 147 x 64
     convolution(conv3_wb, conv2_out, conv3_out);
-    BatchNorm(conv3_out, conv3_var, conv3_mean, conv3_beta);
+    BatchNorm(conv3_out, conv3_beta, conv3_mean, conv3_var);
     relu(conv3_out, conv3_wb->pbias);
 
     maxPoolingInit(conv3_out, pooling1_out, 3, 2);
@@ -102,20 +112,23 @@ void facenet::Stem(Mat &image, pBox *output) {
     convolutionInit(conv4_wb, pooling1_out, conv4_out);
     //conv4 73 x 73 x 80
     convolution(conv4_wb, pooling1_out, conv4_out);
-    BatchNorm(conv4_out, conv4_var, conv4_mean, conv4_beta);
+    BatchNorm(conv4_out, conv4_beta, conv4_mean, conv4_var);
+//    BatchNorm(conv4_out, conv4_var, conv4_mean, conv4_beta);
     relu(conv4_out, conv4_wb->pbias);
 
     convolutionInit(conv5_wb, conv4_out, conv5_out);
     //conv5 71 x 71 x 192
     convolution(conv5_wb, conv4_out, conv5_out);
-    BatchNorm(conv5_out, conv5_var, conv5_mean, conv5_beta);
+    BatchNorm(conv5_out, conv5_beta, conv5_mean, conv5_var);
+//    BatchNorm(conv5_out, conv5_var, conv5_mean, conv5_beta);
     relu(conv5_out, conv5_wb->pbias);
 
 
     convolutionInit(conv6_wb, conv5_out, output);
     //conv6 35 x 35 x 256
     convolution(conv6_wb, conv5_out, output);
-    BatchNorm(output, conv6_var, conv6_mean, conv6_beta);
+    BatchNorm(output, conv6_beta, conv6_mean, conv6_var);
+//    BatchNorm(output, conv6_var, conv6_mean, conv6_beta);
     relu(output, conv6_wb->pbias);
 //        firstFlag = false;
 //    }
@@ -161,6 +174,13 @@ 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;
@@ -206,19 +226,19 @@ void facenet::Inception_resnet_A(pBox *input, pBox *output, string filepath, flo
 
 
     long conv1 = ConvAndFcInit(conv1_wb, 32, 256, 1, 1, 0);
-    BatchNormInit(conv1_var, conv1_mean, conv1_beta, 32);
+    BatchNormInit(conv1_beta, conv1_mean, conv1_var, 32);
 
     long conv2 = ConvAndFcInit(conv2_wb, 32, 256, 1, 1, 0);
-    BatchNormInit(conv2_var, conv2_mean, conv2_beta, 32);
+    BatchNormInit(conv2_beta, conv2_mean, conv2_var, 32);
     long conv3 = ConvAndFcInit(conv3_wb, 32, 32, 3, 1, 1);
-    BatchNormInit(conv3_var, conv3_mean, conv3_beta, 32);
+    BatchNormInit(conv3_beta, conv3_mean, conv3_var, 32);
 
     long conv4 = ConvAndFcInit(conv4_wb, 32, 256, 1, 1, 0);
-    BatchNormInit(conv4_var, conv4_mean, conv4_beta, 32);
+    BatchNormInit(conv4_beta, conv4_mean, conv4_var, 32);
     long conv5 = ConvAndFcInit(conv5_wb, 32, 32, 3, 1, 1);
-    BatchNormInit(conv5_var, conv5_mean, conv5_beta, 32);
+    BatchNormInit(conv5_beta, conv5_mean, conv5_var, 32);
     long conv6 = ConvAndFcInit(conv6_wb, 32, 32, 3, 1, 1);
-    BatchNormInit(conv6_var, conv6_mean, conv6_beta, 32);
+    BatchNormInit(conv6_beta, conv6_mean, conv6_var, 32);
 
     long conv7 = ConvAndFcInit(conv7_wb, 256, 96, 1, 1, 0);
 
@@ -227,12 +247,22 @@ void facenet::Inception_resnet_A(pBox *input, pBox *output, string filepath, flo
     long dataNumber[28] = {conv1, 32, 32, 32, conv2, 32, 32, 32, conv3, 32, 32, 32, conv4, 32, 32, 32,
                            conv5, 32, 32, 32, conv6, 32, 32, 32, conv7, 256, conv8, 0};
 
-    mydataFmt *pointTeam[28] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
+//    mydataFmt *pointTeam[28] = {
-                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
+//                            conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
-                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
-                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
-                            conv5_wb->pdata, conv5_var->pdata, conv5_mean->pdata, conv5_beta->pdata, \
+//                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
-                            conv6_wb->pdata, conv6_var->pdata, conv6_mean->pdata, conv6_beta->pdata, \
+//                            conv5_wb->pdata, conv5_var->pdata, conv5_mean->pdata, conv5_beta->pdata, \
+//                            conv6_wb->pdata, conv6_var->pdata, conv6_mean->pdata, conv6_beta->pdata, \
+//                            conv7_wb->pdata, conv7_wb->pbias, \
+//                            conv8_wb->pdata, conv8_wb->pbias};
+    mydataFmt *pointTeam[28] = {
+                            conv1_wb->pdata, conv1_beta->pdata, conv1_mean->pdata, conv1_var->pdata, \
+                            conv2_wb->pdata, conv2_beta->pdata, conv2_mean->pdata, conv2_var->pdata, \
+                            conv3_wb->pdata, conv3_beta->pdata, conv3_mean->pdata, conv3_var->pdata, \
+                            conv4_wb->pdata, conv4_beta->pdata, conv4_mean->pdata, conv4_var->pdata, \
+                            conv5_wb->pdata, conv5_beta->pdata, conv5_mean->pdata, conv5_var->pdata, \
+                            conv6_wb->pdata, conv6_beta->pdata, conv6_mean->pdata, conv6_var->pdata, \
                             conv7_wb->pdata, conv7_wb->pbias, \
                             conv8_wb->pdata, conv8_wb->pbias};
 
@@ -241,34 +271,34 @@ void facenet::Inception_resnet_A(pBox *input, pBox *output, string filepath, flo
     convolutionInit(conv1_wb, input, conv1_out);
     //conv1 35 x 35 x 32
     convolution(conv1_wb, input, conv1_out);
-    BatchNorm(conv1_out, conv1_var, conv1_mean, conv1_beta);
+    BatchNorm(conv1_out, conv1_beta, conv1_mean, conv1_var);
     relu(conv1_out, conv1_wb->pbias);
 
     convolutionInit(conv2_wb, input, conv2_out);
     //conv2 35 x 35 x 32
     convolution(conv2_wb, input, conv2_out);
-    BatchNorm(conv2_out, conv2_var, conv2_mean, conv2_beta);
+    BatchNorm(conv2_out, conv2_beta, conv2_mean, conv2_var);
     relu(conv2_out, conv2_wb->pbias);
     convolutionInit(conv3_wb, conv2_out, conv3_out);
     //conv3 35 x 35 x 32
     convolution(conv3_wb, conv2_out, conv3_out);
-    BatchNorm(conv3_out, conv3_var, conv3_mean, conv3_beta);
+    BatchNorm(conv3_out, conv3_beta, conv3_mean, conv3_var);
     relu(conv3_out, conv3_wb->pbias);
 
     convolutionInit(conv4_wb, input, conv4_out);
     //conv4 35 x 35 x 32
     convolution(conv4_wb, input, conv4_out);
-    BatchNorm(conv4_out, conv4_var, conv4_mean, conv4_beta);
+    BatchNorm(conv4_out, conv4_beta, conv4_mean, conv4_var);
     relu(conv4_out, conv4_wb->pbias);
     convolutionInit(conv5_wb, conv4_out, conv5_out);
     //conv5 35 x 35 x 32
     convolution(conv5_wb, conv4_out, conv5_out);
-    BatchNorm(conv5_out, conv5_var, conv5_mean, conv5_beta);
+    BatchNorm(conv5_out, conv5_beta, conv5_mean, conv5_var);
     relu(conv5_out, conv5_wb->pbias);
     convolutionInit(conv6_wb, conv5_out, conv6_out);
     //conv6 35 x 35 x 32
     convolution(conv6_wb, conv5_out, conv6_out);
-    BatchNorm(conv6_out, conv6_var, conv6_mean, conv6_beta);
+    BatchNorm(conv6_out, conv6_beta, conv6_mean, conv6_var);
     relu(conv6_out, conv6_wb->pbias);
 
     conv_mergeInit(conv7_out, conv1_out, conv3_out, conv6_out);
@@ -280,7 +310,7 @@ 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 +357,11 @@ 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;
@@ -355,20 +390,26 @@ void facenet::Reduction_A(pBox *input, pBox *output) {
 
 
     long conv1 = ConvAndFcInit(conv1_wb, 384, 256, 3, 2, 0);
-    BatchNormInit(conv1_var, conv1_mean, conv1_beta, 384);
+    BatchNormInit(conv1_beta, conv1_mean, conv1_var, 384);
 
     long conv2 = ConvAndFcInit(conv2_wb, 192, 256, 1, 1, 0);
-    BatchNormInit(conv2_var, conv2_mean, conv2_beta, 192);
+    BatchNormInit(conv2_beta, conv2_mean, conv2_var, 192);
     long conv3 = ConvAndFcInit(conv3_wb, 192, 192, 3, 1, 0);
-    BatchNormInit(conv3_var, conv3_mean, conv3_beta, 192);
+    BatchNormInit(conv3_beta, conv3_mean, conv3_var, 192);
     long conv4 = ConvAndFcInit(conv4_wb, 256, 192, 3, 2, 0);
-    BatchNormInit(conv4_var, conv4_mean, conv4_beta, 256);
+    BatchNormInit(conv4_beta, conv4_mean, conv4_var, 256);
     long dataNumber[16] = {conv1, 384, 384, 384, conv2, 192, 192, 192, conv3, 192, 192, 192, conv4, 256, 256, 256};
 
-    mydataFmt *pointTeam[16] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
+//    mydataFmt *pointTeam[16] = {
-                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
+//                            conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
-                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
-                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata};
+//                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata};
+    mydataFmt *pointTeam[16] = {
+                            conv1_wb->pdata, conv1_beta->pdata, conv1_mean->pdata, conv1_var->pdata, \
+                            conv2_wb->pdata, conv2_beta->pdata, conv2_mean->pdata, conv2_var->pdata, \
+                            conv3_wb->pdata, conv3_beta->pdata, conv3_mean->pdata, conv3_var->pdata, \
+                            conv4_wb->pdata, conv4_beta->pdata, conv4_mean->pdata, conv4_var->pdata};
     string filename = "../model_" + to_string(Num) + "/Mixed_6a_list.txt";
     readData(filename, dataNumber, pointTeam, 16);
 
@@ -379,25 +420,25 @@ void facenet::Reduction_A(pBox *input, pBox *output) {
     convolutionInit(conv1_wb, input, conv1_out);
     //conv1 17 x 17 x 384
     convolution(conv1_wb, input, conv1_out);
-    BatchNorm(conv1_out, conv1_var, conv1_mean, conv1_beta);
+    BatchNorm(conv1_out, conv1_beta, conv1_mean, conv1_var);
     relu(conv1_out, conv1_wb->pbias);
 
     convolutionInit(conv2_wb, input, conv2_out);
     //conv2 35 x 35 x 192
     convolution(conv2_wb, input, conv2_out);
-    BatchNorm(conv2_out, conv2_var, conv2_mean, conv2_beta);
+    BatchNorm(conv2_out, conv2_beta, conv2_mean, conv2_var);
     relu(conv2_out, conv2_wb->pbias);
 
     convolutionInit(conv3_wb, conv2_out, conv3_out);
     //conv3 35 x 35 x 192
     convolution(conv3_wb, conv2_out, conv3_out);
-    BatchNorm(conv3_out, conv3_var, conv3_mean, conv3_beta);
+    BatchNorm(conv3_out, conv3_beta, conv3_mean, conv3_var);
     relu(conv3_out, conv3_wb->pbias);
 
     convolutionInit(conv4_wb, conv3_out, conv4_out);
     //conv4 17 x 17 x 256
     convolution(conv4_wb, conv3_out, conv4_out);
-    BatchNorm(conv4_out, conv4_var, conv4_mean, conv4_beta);
+    BatchNorm(conv4_out, conv4_beta, conv4_mean, conv4_var);
     relu(conv4_out, conv4_wb->pbias);
     conv_mergeInit(output, pooling1_out, conv1_out, conv4_out);
     //17×17×896
@@ -429,6 +470,13 @@ 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;
@@ -459,14 +507,14 @@ void facenet::Inception_resnet_B(pBox *input, pBox *output, string filepath, flo
 
 
     long conv1 = ConvAndFcInit(conv1_wb, 128, 896, 1, 1, 0);
-    BatchNormInit(conv1_var, conv1_mean, conv1_beta, 128);
+    BatchNormInit(conv1_beta, conv1_mean, conv1_var, 128);
 
     long conv2 = ConvAndFcInit(conv2_wb, 128, 896, 1, 1, 0);
-    BatchNormInit(conv2_var, conv2_mean, conv2_beta, 128);
+    BatchNormInit(conv2_beta, conv2_mean, conv2_var, 128);
     long conv3 = ConvAndFcInit(conv3_wb, 128, 128, 0, 1, -1, 7, 1, 3, 0);//[1,7]
-    BatchNormInit(conv3_var, conv3_mean, conv3_beta, 128);
+    BatchNormInit(conv3_beta, conv3_mean, conv3_var, 128);
     long conv4 = ConvAndFcInit(conv4_wb, 128, 128, 0, 1, -1, 1, 7, 0, 3);//[7,1]
-    BatchNormInit(conv4_var, conv4_mean, conv4_beta, 128);
+    BatchNormInit(conv4_beta, conv4_mean, conv4_var, 128);
 
     long conv5 = ConvAndFcInit(conv5_wb, 896, 256, 1, 1, 0);
 
@@ -475,10 +523,18 @@ void facenet::Inception_resnet_B(pBox *input, pBox *output, string filepath, flo
     long dataNumber[20] = {conv1, 128, 128, 128, conv2, 128, 128, 128, conv3, 128, 128, 128, conv4, 128, 128, 128,
                            conv5, 896, conv6, 0};
 
-    mydataFmt *pointTeam[20] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
+//    mydataFmt *pointTeam[20] = {
-                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
+//                            conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
-                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
-                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv5_wb->pdata, conv5_wb->pbias, \
+//                            conv6_wb->pdata, conv6_wb->pbias};
+    mydataFmt *pointTeam[20] = {
+                            conv1_wb->pdata, conv1_beta->pdata, conv1_mean->pdata, conv1_var->pdata, \
+                            conv2_wb->pdata, conv2_beta->pdata, conv2_mean->pdata, conv2_var->pdata, \
+                            conv3_wb->pdata, conv3_beta->pdata, conv3_mean->pdata, conv3_var->pdata, \
+                            conv4_wb->pdata, conv4_beta->pdata, conv4_mean->pdata, conv4_var->pdata, \
                             conv5_wb->pdata, conv5_wb->pbias, \
                             conv6_wb->pdata, conv6_wb->pbias};
 
@@ -489,24 +545,24 @@ void facenet::Inception_resnet_B(pBox *input, pBox *output, string filepath, flo
     convolutionInit(conv1_wb, input, conv1_out);
     //conv1 17*17*128
     convolution(conv1_wb, input, conv1_out);
-    BatchNorm(conv1_out, conv1_var, conv1_mean, conv1_beta);
+    BatchNorm(conv1_out, conv1_beta, conv1_mean, conv1_var);
     relu(conv1_out, conv1_wb->pbias);
 
     convolutionInit(conv2_wb, input, conv2_out);
     //conv2 17*17*128
     convolution(conv2_wb, input, conv2_out);
-    BatchNorm(conv2_out, conv2_var, conv2_mean, conv2_beta);
+    BatchNorm(conv2_out, conv2_beta, conv2_mean, conv2_var);
     relu(conv2_out, conv2_wb->pbias);
 
     convolutionInit(conv3_wb, conv2_out, conv3_out);
     //conv3 17*17*128
     convolution(conv3_wb, conv2_out, conv3_out);
-    BatchNorm(conv3_out, conv3_var, conv3_mean, conv3_beta);
+    BatchNorm(conv3_out, conv3_beta, conv3_mean, conv3_var);
     relu(conv3_out, conv3_wb->pbias);
     convolutionInit(conv4_wb, conv3_out, conv4_out);
     //conv4 17*17*128
     convolution(conv4_wb, conv3_out, conv4_out);
-    BatchNorm(conv4_out, conv4_var, conv4_mean, conv4_beta);
+    BatchNorm(conv4_out, conv4_beta, conv4_mean, conv4_var);
     relu(conv4_out, conv4_wb->pbias);
 
     conv_mergeInit(conv5_out, conv1_out, conv4_out);
@@ -518,7 +574,7 @@ 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 +606,11 @@ 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;
@@ -593,32 +654,41 @@ void facenet::Reduction_B(pBox *input, pBox *output) {
 
 
     long conv1 = ConvAndFcInit(conv1_wb, 256, 896, 1, 1, 0);
-    BatchNormInit(conv1_var, conv1_mean, conv1_beta, 256);
+    BatchNormInit(conv1_beta, conv1_mean, conv1_var, 256);
     long conv2 = ConvAndFcInit(conv2_wb, 384, 256, 3, 2, 0);
-    BatchNormInit(conv2_var, conv2_mean, conv2_beta, 384);
+    BatchNormInit(conv2_beta, conv2_mean, conv2_var, 384);
 
     long conv3 = ConvAndFcInit(conv3_wb, 256, 896, 1, 1, 0);
-    BatchNormInit(conv3_var, conv3_mean, conv3_beta, 256);
+    BatchNormInit(conv3_beta, conv3_mean, conv3_var, 256);
     long conv4 = ConvAndFcInit(conv4_wb, 256, 256, 3, 2, 0);
-    BatchNormInit(conv4_var, conv4_mean, conv4_beta, 256);
+    BatchNormInit(conv4_beta, conv4_mean, conv4_var, 256);
 
     long conv5 = ConvAndFcInit(conv5_wb, 256, 896, 1, 1, 0);
-    BatchNormInit(conv5_var, conv5_mean, conv5_beta, 256);
+    BatchNormInit(conv5_beta, conv5_mean, conv5_var, 256);
     long conv6 = ConvAndFcInit(conv6_wb, 256, 256, 3, 1, 1);
-    BatchNormInit(conv6_var, conv6_mean, conv6_beta, 256);
+    BatchNormInit(conv6_beta, conv6_mean, conv6_var, 256);
     long conv7 = ConvAndFcInit(conv7_wb, 256, 256, 3, 2, 0);
-    BatchNormInit(conv7_var, conv7_mean, conv7_beta, 256);
+    BatchNormInit(conv7_beta, conv7_mean, conv7_var, 256);
 
     long dataNumber[28] = {conv1, 256, 256, 256, conv2, 384, 384, 384, conv3, 256, 256, 256, conv4, 256, 256, 256,
                            conv5, 256, 256, 256, conv6, 256, 256, 256, conv7, 256, 256, 256};
 
-    mydataFmt *pointTeam[28] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
+//    mydataFmt *pointTeam[28] = {
-                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
+//                            conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
-                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
-                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
-                            conv5_wb->pdata, conv5_var->pdata, conv5_mean->pdata, conv5_beta->pdata, \
+//                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
-                            conv6_wb->pdata, conv6_var->pdata, conv6_mean->pdata, conv6_beta->pdata, \
+//                            conv5_wb->pdata, conv5_var->pdata, conv5_mean->pdata, conv5_beta->pdata, \
-                            conv7_wb->pdata, conv7_var->pdata, conv7_mean->pdata, conv7_beta->pdata};
+//                            conv6_wb->pdata, conv6_var->pdata, conv6_mean->pdata, conv6_beta->pdata, \
+//                            conv7_wb->pdata, conv7_var->pdata, conv7_mean->pdata, conv7_beta->pdata};
+    mydataFmt *pointTeam[28] = {
+                            conv1_wb->pdata, conv1_beta->pdata, conv1_mean->pdata, conv1_var->pdata, \
+                            conv2_wb->pdata, conv2_beta->pdata, conv2_mean->pdata, conv2_var->pdata, \
+                            conv3_wb->pdata, conv3_beta->pdata, conv3_mean->pdata, conv3_var->pdata, \
+                            conv4_wb->pdata, conv4_beta->pdata, conv4_mean->pdata, conv4_var->pdata, \
+                            conv5_wb->pdata, conv5_beta->pdata, conv5_mean->pdata, conv5_var->pdata, \
+                            conv6_wb->pdata, conv6_beta->pdata, conv6_mean->pdata, conv6_var->pdata, \
+                            conv7_wb->pdata, conv7_beta->pdata, conv7_mean->pdata, conv7_var->pdata};
     string filename = "../model_" + to_string(Num) + "/Mixed_7a_list.txt";
     readData(filename, dataNumber, pointTeam, 28);
 
@@ -630,43 +700,46 @@ void facenet::Reduction_B(pBox *input, pBox *output) {
     convolutionInit(conv1_wb, input, conv1_out);
     //conv1 17 x 17 x 256
     convolution(conv1_wb, input, conv1_out);
-    BatchNorm(conv1_out, conv1_var, conv1_mean, conv1_beta);
+    BatchNorm(conv1_out, conv1_beta, conv1_mean, conv1_var);
+//    BatchNorm(conv1_out, conv1_var, conv1_mean, conv1_beta);
     relu(conv1_out, conv1_wb->pbias);
 
     convolutionInit(conv2_wb, conv1_out, conv2_out);
     //conv2 8 x 8 x 384
     convolution(conv2_wb, conv1_out, conv2_out);
-    BatchNorm(conv2_out, conv2_var, conv2_mean, conv2_beta);
+    BatchNorm(conv2_out, conv2_beta, conv2_mean, conv2_var);
+//    BatchNorm(conv2_out, conv2_var, conv2_mean, conv2_beta);
     relu(conv2_out, conv2_wb->pbias);
 
     convolutionInit(conv3_wb, input, conv3_out);
     //conv3 17 x 17 x 256
     convolution(conv3_wb, input, conv3_out);
-    BatchNorm(conv3_out, conv3_var, conv3_mean, conv3_beta);
+    BatchNorm(conv3_out, conv3_beta, conv3_mean, conv3_var);
+//    BatchNorm(conv3_out, conv3_var, conv3_mean, conv3_beta);
     relu(conv3_out, conv3_wb->pbias);
 
     convolutionInit(conv4_wb, conv3_out, conv4_out);
     //conv4 8 x 8 x 256
     convolution(conv4_wb, conv3_out, conv4_out);
-    BatchNorm(conv4_out, conv4_var, conv4_mean, conv4_beta);
+    BatchNorm(conv4_out, conv4_beta, conv4_mean, conv4_var);
     relu(conv4_out, conv4_wb->pbias);
 
     convolutionInit(conv5_wb, input, conv5_out);
     //conv5 17 x 17 x 256
     convolution(conv5_wb, input, conv5_out);
-    BatchNorm(conv5_out, conv5_var, conv5_mean, conv5_beta);
+    BatchNorm(conv5_out, conv5_beta, conv5_mean, conv5_var);
     relu(conv5_out, conv5_wb->pbias);
 
     convolutionInit(conv6_wb, conv5_out, conv6_out);
     //conv6 17 x 17 x 256
     convolution(conv6_wb, conv5_out, conv6_out);
-    BatchNorm(conv6_out, conv6_var, conv6_mean, conv6_beta);
+    BatchNorm(conv6_out, conv6_beta, conv6_mean, conv6_var);
     relu(conv6_out, conv6_wb->pbias);
 
     convolutionInit(conv7_wb, conv6_out, conv7_out);
     //conv6 8 x 8 x 256
     convolution(conv7_wb, conv6_out, conv7_out);
-    BatchNorm(conv7_out, conv7_var, conv7_mean, conv7_beta);
+    BatchNorm(conv7_out, conv7_beta, conv7_mean, conv7_var);
     relu(conv7_out, conv7_wb->pbias);
 
     conv_mergeInit(output, conv2_out, conv4_out, conv7_out, pooling1_out);
@@ -714,6 +787,13 @@ 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;
@@ -744,13 +824,13 @@ void facenet::Inception_resnet_C(pBox *input, pBox *output, string filepath, flo
 
 
     long conv1 = ConvAndFcInit(conv1_wb, 192, 1792, 1, 1, 0);
-    BatchNormInit(conv1_var, conv1_mean, conv1_beta, 192);
+    BatchNormInit(conv1_beta, conv1_mean, conv1_var, 192);
     long conv2 = ConvAndFcInit(conv2_wb, 192, 1792, 1, 1, 0);
-    BatchNormInit(conv2_var, conv2_mean, conv2_beta, 192);
+    BatchNormInit(conv2_beta, conv2_mean, conv2_var, 192);
     long conv3 = ConvAndFcInit(conv3_wb, 192, 192, 0, 1, -1, 3, 1, 1, 0);
-    BatchNormInit(conv3_var, conv3_mean, conv3_beta, 192);
+    BatchNormInit(conv3_beta, conv3_mean, conv3_var, 192);
     long conv4 = ConvAndFcInit(conv4_wb, 192, 192, 0, 1, -1, 1, 3, 0, 1);
-    BatchNormInit(conv4_var, conv4_mean, conv4_beta, 192);
+    BatchNormInit(conv4_beta, conv4_mean, conv4_var, 192);
 
     long conv5 = ConvAndFcInit(conv5_wb, 1792, 384, 1, 1, 0);
 
@@ -760,10 +840,18 @@ void facenet::Inception_resnet_C(pBox *input, pBox *output, string filepath, flo
                            conv5, 1792, conv6, 0};
 
 
-    mydataFmt *pointTeam[20] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
+//    mydataFmt *pointTeam[20] = {
-                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
+//                            conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
-                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
-                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv5_wb->pdata, conv5_wb->pbias, \
+//                            conv6_wb->pdata, conv6_wb->pbias};
+    mydataFmt *pointTeam[20] = {
+                            conv1_wb->pdata, conv1_beta->pdata, conv1_mean->pdata, conv1_var->pdata, \
+                            conv2_wb->pdata, conv2_beta->pdata, conv2_mean->pdata, conv2_var->pdata, \
+                            conv3_wb->pdata, conv3_beta->pdata, conv3_mean->pdata, conv3_var->pdata, \
+                            conv4_wb->pdata, conv4_beta->pdata, conv4_mean->pdata, conv4_var->pdata, \
                             conv5_wb->pdata, conv5_wb->pbias, \
                             conv6_wb->pdata, conv6_wb->pbias};
 
@@ -774,25 +862,25 @@ void facenet::Inception_resnet_C(pBox *input, pBox *output, string filepath, flo
     convolutionInit(conv1_wb, input, conv1_out);
     //conv1 8 x 8 x 192
     convolution(conv1_wb, input, conv1_out);
-    BatchNorm(conv1_out, conv1_var, conv1_mean, conv1_beta);
+    BatchNorm(conv1_out, conv1_beta, conv1_mean, conv1_var);
     relu(conv1_out, conv1_wb->pbias);
 
     convolutionInit(conv2_wb, input, conv2_out);
     //conv2 8 x 8 x 192
     convolution(conv2_wb, input, conv2_out);
-    BatchNorm(conv2_out, conv2_var, conv2_mean, conv2_beta);
+    BatchNorm(conv2_out, conv2_beta, conv2_mean, conv2_var);
     relu(conv2_out, conv2_wb->pbias);
 
     convolutionInit(conv3_wb, conv2_out, conv3_out);
     //conv3 8 x 8 x 192
     convolution(conv3_wb, conv2_out, conv3_out);
-    BatchNorm(conv3_out, conv3_var, conv3_mean, conv3_beta);
+    BatchNorm(conv3_out, conv3_beta, conv3_mean, conv3_var);
     relu(conv3_out, conv3_wb->pbias);
 
     convolutionInit(conv4_wb, conv3_out, conv4_out);
     //conv4 8 x 8 x 192
     convolution(conv4_wb, conv3_out, conv4_out);
-    BatchNorm(conv4_out, conv4_var, conv4_mean, conv4_beta);
+    BatchNorm(conv4_out, conv4_beta, conv4_mean, conv4_var);
     relu(conv4_out, conv4_wb->pbias);
 
     conv_mergeInit(conv5_out, conv1_out, conv4_out);
@@ -804,7 +892,7 @@ 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 +924,13 @@ 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;
@@ -864,23 +959,30 @@ void facenet::Inception_resnet_C_None(pBox *input, pBox *output, string filepath
     struct BN *conv4_beta = new BN;
 
     long conv1 = ConvAndFcInit(conv1_wb, 192, 1792, 1, 1, 0);
-    BatchNormInit(conv1_var, conv1_mean, conv1_beta, 192);
+    BatchNormInit(conv1_beta, conv1_mean, conv1_var, 192);
     long conv2 = ConvAndFcInit(conv2_wb, 192, 1792, 1, 1, 0);
-    BatchNormInit(conv2_var, conv2_mean, conv2_beta, 192);
+    BatchNormInit(conv2_beta, conv2_mean, conv2_var, 192);
     long conv3 = ConvAndFcInit(conv3_wb, 192, 192, 0, 1, -1, 3, 1, 1, 0);
-    BatchNormInit(conv3_var, conv3_mean, conv3_beta, 192);
+    BatchNormInit(conv3_beta, conv3_mean, conv3_var, 192);
     long conv4 = ConvAndFcInit(conv4_wb, 192, 192, 0, 1, -1, 1, 3, 0, 1);
-    BatchNormInit(conv4_var, conv4_mean, conv4_beta, 192);
+    BatchNormInit(conv4_beta, conv4_mean, conv4_var, 192);
     long conv5 = ConvAndFcInit(conv5_wb, 1792, 384, 1, 1, 0);
 
     long dataNumber[18] = {conv1, 192, 192, 192, conv2, 192, 192, 192, conv3, 192, 192, 192, conv4, 192, 192, 192,
                            conv5, 1792};
 
 
-    mydataFmt *pointTeam[18] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
+//    mydataFmt *pointTeam[18] = {
-                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
+//                            conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
-                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_beta->pdata, \
-                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv3_wb->pdata, conv3_var->pdata, conv3_mean->pdata, conv3_beta->pdata, \
+//                            conv4_wb->pdata, conv4_var->pdata, conv4_mean->pdata, conv4_beta->pdata, \
+//                            conv5_wb->pdata, conv5_wb->pbias};
+    mydataFmt *pointTeam[18] = {
+                            conv1_wb->pdata, conv1_beta->pdata, conv1_mean->pdata, conv1_var->pdata, \
+                            conv2_wb->pdata, conv2_beta->pdata, conv2_mean->pdata, conv2_var->pdata, \
+                            conv3_wb->pdata, conv3_beta->pdata, conv3_mean->pdata, conv3_var->pdata, \
+                            conv4_wb->pdata, conv4_beta->pdata, conv4_mean->pdata, conv4_var->pdata, \
                             conv5_wb->pdata, conv5_wb->pbias};
 
 //    string filename = "../model_128/Repeat_2_list.txt";
@@ -890,25 +992,25 @@ void facenet::Inception_resnet_C_None(pBox *input, pBox *output, string filepath
     convolutionInit(conv1_wb, input, conv1_out);
     //conv1 8 x 8 x 192
     convolution(conv1_wb, input, conv1_out);
-    BatchNorm(conv1_out, conv1_var, conv1_mean, conv1_beta);
+    BatchNorm(conv1_out, conv1_beta, conv1_mean, conv1_var);
     relu(conv1_out, conv1_wb->pbias);
 
     convolutionInit(conv2_wb, input, conv2_out);
     //conv2 8 x 8 x 192
     convolution(conv2_wb, input, conv2_out);
-    BatchNorm(conv2_out, conv2_var, conv2_mean, conv2_beta);
+    BatchNorm(conv2_out, conv2_beta, conv2_mean, conv2_var);
     relu(conv2_out, conv2_wb->pbias);
 
     convolutionInit(conv3_wb, conv2_out, conv3_out);
     //conv3 8 x 8 x 192
     convolution(conv3_wb, conv2_out, conv3_out);
-    BatchNorm(conv3_out, conv3_var, conv3_mean, conv3_beta);
+    BatchNorm(conv3_out, conv3_beta, conv3_mean, conv3_var);
     relu(conv3_out, conv3_wb->pbias);
 
     convolutionInit(conv4_wb, conv3_out, conv4_out);
     //conv4 8 x 8 x 192
     convolution(conv4_wb, conv3_out, conv4_out);
-    BatchNorm(conv4_out, conv4_var, conv4_mean, conv4_beta);
+    BatchNorm(conv4_out, conv4_beta, conv4_mean, conv4_var);
     relu(conv4_out, conv4_wb->pbias);
 
     conv_mergeInit(conv5_out, conv1_out, conv4_out);
@@ -920,7 +1022,7 @@ 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 +1052,22 @@ 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;
@@ -965,18 +1077,25 @@ 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;
-    struct BN *conv1_var = new BN;
-    struct BN *conv1_mean = new BN;
     struct BN *conv1_beta = new BN;
+    struct BN *conv1_mean = new BN;
+    struct BN *conv1_var = new BN;
     long conv1 = ConvAndFcInit(conv1_wb, Num, 1792, input->height, 1, 0);
-    BatchNormInit(conv1_var, conv1_mean, conv1_beta, Num);
+    BatchNormInit(conv1_beta, conv1_mean, conv1_var, Num);
     long dataNumber[4] = {conv1, Num, Num, Num};
 
 //    cout << to_string(sum) << endl;
-    mydataFmt *pointTeam[4] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata};
+//    mydataFmt *pointTeam[4] = {conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata};
+    mydataFmt *pointTeam[4] = {conv1_wb->pdata, conv1_beta->pdata, conv1_mean->pdata, conv1_var->pdata};
 //    string filename = "../model_128/Bottleneck_list.txt";
 //    int length = sizeof(dataNumber) / sizeof(*dataNumber);
     readData(filepath, dataNumber, pointTeam, 4);
@@ -985,7 +1104,7 @@ void facenet::fully_connect(pBox *input, pBox *output, string filepath) {
 
     //conv1 8 x 8 x 192
     fullconnect(conv1_wb, input, output);
-    BatchNorm(output, conv1_var, conv1_mean, conv1_beta);
+    BatchNorm(output, conv1_beta, conv1_mean, conv1_var);
 
     freeWeight(conv1_wb);
     freeBN(conv1_var);
@@ -1009,13 +1128,17 @@ 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 +1153,6 @@ 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 +1170,8 @@ 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 +1190,10 @@ 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);

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;
@@ -321,10 +400,53 @@ void avePooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride) {
     }
 }
 
+/**
+ * 激活函数 有系数 初始化
+ * @param prelu　激活函数权重
+ * @param width　长度
+ */
+void pReluInit(struct pRelu *prelu, int width) {
+    prelu->width = width;
+    prelu->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
+    if (prelu->pdata == NULL)cout << "prelu apply for memory failed!!!!";
+    memset(prelu->pdata, 0, width * sizeof(mydataFmt));
+}
+
+/**
+ * 激活函数 有系数
+ * @param pbox　输入feature
+ * @param pbias 偏移
+ * @param prelu_gmma　激活函数权重
+ */
+void prelu(struct pBox *pbox, mydataFmt *pbias, mydataFmt *prelu_gmma) {
+    if (pbox->pdata == NULL) {
+        cout << "the  pRelu feature is NULL!!" << endl;
+        return;
+    }
+    if (pbias == NULL) {
+        cout << "the  pRelu bias is NULL!!" << endl;
+        return;
+    }
+    mydataFmt *op = pbox->pdata;
+    mydataFmt *pb = pbias;
+    mydataFmt *pg = prelu_gmma;
+
+    long dis = pbox->width * pbox->height;
+    for (int channel = 0; channel < pbox->channel; channel++) {
+        for (int col = 0; col < dis; col++) {
+            *op = *op + *pb;
+            *op = (*op > 0) ? (*op) : ((*op) * (*pg));
+            op++;
+        }
+        pb++;
+        pg++;
+    }
+}
+
 /**
  * 激活函数 没有系数
- * @param pbox
+ * @param pbox　输入feature
- * @param pbias
+ * @param pbias 偏移
  */
 void relu(struct pBox *pbox, mydataFmt *pbias) {
     if (pbox->pdata == NULL) {
@@ -349,6 +471,11 @@ 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 +485,12 @@ 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 +509,14 @@ 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 +526,13 @@ 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 +582,20 @@ 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 +623,159 @@ long ConvAndFcInit(struct Weight *weight, int schannel, int lchannel, int kersiz
     return byteLenght;
 }
 
+/**
+ * softmax
+ * @param pbox  feature map
+ */
+void softmax(const struct pBox *pbox) {
+    if (pbox->pdata == NULL) {
+        cout << "the softmax's pdata is NULL , Please check !" << endl;
+        return;
+    }
+    mydataFmt *p2D = pbox->pdata;
+    mydataFmt *p3D = NULL;
+    long mapSize = pbox->width * pbox->height;
+    mydataFmt eleSum = 0;
+    for (int row = 0; row < pbox->height; row++) {
+        for (int col = 0; col < pbox->width; col++) {
+            eleSum = 0;
+            for (int channel = 0; channel < pbox->channel; channel++) {
+                p3D = p2D + channel * mapSize;
+                *p3D = exp(*p3D);
+                eleSum += *p3D;
+            }
+            for (int channel = 0; channel < pbox->channel; channel++) {
+                p3D = p2D + channel * mapSize;
+                *p3D = (*p3D) / eleSum;
+            }
+            p2D++;
+        }
+    }
+}
+
+bool cmpScore(struct orderScore lsh, struct orderScore rsh) {
+    if (lsh.score < rsh.score)
+        return true;
+    else
+        return false;
+}
+
+/**
+ * 非极大值抑制
+ * @param boundingBox_
+ * @param bboxScore_
+ * @param overlap_threshold
+ * @param modelname
+ */
+void nms(vector<struct Bbox> &boundingBox_, vector<struct orderScore> &bboxScore_, const mydataFmt overlap_threshold,
+         string modelname) {
+    if (boundingBox_.empty()) {
+        return;
+    }
+    std::vector<int> heros;
+    //sort the score
+    sort(bboxScore_.begin(), bboxScore_.end(), cmpScore);
+
+    int order = 0;
+    float IOU = 0;
+    float maxX = 0;
+    float maxY = 0;
+    float minX = 0;
+    float minY = 0;
+    while (bboxScore_.size() > 0) {
+        order = bboxScore_.back().oriOrder;
+        bboxScore_.pop_back();
+        if (order < 0)continue;
+        heros.push_back(order);
+        boundingBox_.at(order).exist = false;//delete it
+
+        for (int num = 0; num < boundingBox_.size(); num++) {
+            if (boundingBox_.at(num).exist) {
+                //the iou
+                maxX = (boundingBox_.at(num).x1 > boundingBox_.at(order).x1) ? boundingBox_.at(num).x1
+                                                                             : boundingBox_.at(order).x1;
+                maxY = (boundingBox_.at(num).y1 > boundingBox_.at(order).y1) ? boundingBox_.at(num).y1
+                                                                             : boundingBox_.at(order).y1;
+                minX = (boundingBox_.at(num).x2 < boundingBox_.at(order).x2) ? boundingBox_.at(num).x2
+                                                                             : boundingBox_.at(order).x2;
+                minY = (boundingBox_.at(num).y2 < boundingBox_.at(order).y2) ? boundingBox_.at(num).y2
+                                                                             : boundingBox_.at(order).y2;
+                //maxX1 and maxY1 reuse
+                maxX = ((minX - maxX + 1) > 0) ? (minX - maxX + 1) : 0;
+                maxY = ((minY - maxY + 1) > 0) ? (minY - maxY + 1) : 0;
+                //IOU reuse for the area of two bbox
+                IOU = maxX * maxY;
+                if (!modelname.compare("Union"))
+                    IOU = IOU / (boundingBox_.at(num).area + boundingBox_.at(order).area - IOU);
+                else if (!modelname.compare("Min")) {
+                    IOU = IOU /
+                          ((boundingBox_.at(num).area < boundingBox_.at(order).area) ? boundingBox_.at(num).area
+                                                                                     : boundingBox_.at(
+                                          order).area);
+                }
+                if (IOU > overlap_threshold) {
+                    boundingBox_.at(num).exist = false;
+                    for (vector<orderScore>::iterator it = bboxScore_.begin(); it != bboxScore_.end(); it++) {
+                        if ((*it).oriOrder == num) {
+                            (*it).oriOrder = -1;
+                            break;
+                        }
+                    }
+                }
+            }
+        }
+    }
+    for (int i = 0; i < heros.size(); i++)
+        boundingBox_.at(heros.at(i)).exist = true;
+}
+
+void refineAndSquareBbox(vector<struct Bbox> &vecBbox, const int &height, const int &width) {
+    if (vecBbox.empty()) {
+        cout << "Bbox is empty!!" << endl;
+        return;
+    }
+    float bbw = 0, bbh = 0, maxSide = 0;
+    float h = 0, w = 0;
+    float x1 = 0, y1 = 0, x2 = 0, y2 = 0;
+    for (vector<struct Bbox>::iterator it = vecBbox.begin(); it != vecBbox.end(); it++) {
+        if ((*it).exist) {
+            bbh = (*it).x2 - (*it).x1 + 1;
+            bbw = (*it).y2 - (*it).y1 + 1;
+            x1 = (*it).x1 + (*it).regreCoord[1] * bbh;
+            y1 = (*it).y1 + (*it).regreCoord[0] * bbw;
+            x2 = (*it).x2 + (*it).regreCoord[3] * bbh;
+            y2 = (*it).y2 + (*it).regreCoord[2] * bbw;
+
+            h = x2 - x1 + 1;
+            w = y2 - y1 + 1;
+
+            maxSide = (h > w) ? h : w;
+            x1 = x1 + h * 0.5 - maxSide * 0.5;
+            y1 = y1 + w * 0.5 - maxSide * 0.5;
+            (*it).x2 = round(x1 + maxSide - 1);
+            (*it).y2 = round(y1 + maxSide - 1);
+            (*it).x1 = round(x1);
+            (*it).y1 = round(y1);
+
+            //boundary check
+            if ((*it).x1 < 0)(*it).x1 = 0;
+            if ((*it).y1 < 0)(*it).y1 = 0;
+            if ((*it).x2 > height)(*it).x2 = height - 1;
+            if ((*it).y2 > width)(*it).y2 = width - 1;
+
+            it->area = (it->x2 - it->x1) * (it->y2 - it->y1);
+        }
+    }
+}
+
+/**
+ * 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 +797,14 @@ 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 +834,16 @@ 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 +852,13 @@ 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;
@@ -530,7 +869,15 @@ void mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float sc
     }
 }
 
-void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width) {
+
+/**
+ * BN初始化
+ * @param beta beta
+ * @param mean 平均值
+ * @param var 方差
+ * @param width 参数个数
+ */
+void BatchNormInit(struct BN *beta, struct BN *mean, struct BN *var, int width) {
     var->width = width;
     var->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
     if (var->pdata == NULL)cout << "prelu apply for memory failed!!!!";
@@ -547,7 +894,14 @@ void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width)
     memset(beta->pdata, 0, width * sizeof(mydataFmt));
 }
 
-void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *beta) {
+/**
+ * BN实现
+ * @param pbox　输入feature map
+ * @param beta beta
+ * @param mean 平均值
+ * @param var 方差
+ */
+void BatchNorm(struct pBox *pbox, struct BN *beta, struct BN *mean, struct BN *var) {
     if (pbox->pdata == NULL) {
         cout << "Relu feature is NULL!!" << endl;
         return;

src/network.h

@@ -24,6 +24,8 @@ void avePooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride);
 
 void featurePad(const pBox *pbox, pBox *outpBox, const int pad, const int padw = 0, const int padh = 0);
 
+void prelu(struct pBox *pbox, mydataFmt *pbias, mydataFmt *prelu_gmma);
+
 void relu(struct pBox *pbox, mydataFmt *pbias);
 
 void fullconnect(const Weight *weight, const pBox *pbox, pBox *outpBox);
@@ -33,6 +35,10 @@ void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length
 long ConvAndFcInit(struct Weight *weight, int schannel, int lchannel, int kersize, int stride, int pad,
                    int w = 0, int h = 0, int padw = 0, int padh = 0);
 
+void pReluInit(struct pRelu *prelu, int width);
+
+void softmax(const struct pBox *pbox);
+
 void image2MatrixInit(Mat &image, struct pBox *pbox);
 
 void featurePadInit(const pBox *pbox, pBox *outpBox, const int pad, const int padw = 0, const int padh = 0);
@@ -45,6 +51,13 @@ void convolutionInit(const Weight *weight, pBox *pbox, pBox *outpBox);
 
 void fullconnectInit(const Weight *weight, pBox *outpBox);
 
+bool cmpScore(struct orderScore lsh, struct orderScore rsh);
+
+void nms(vector<struct Bbox> &boundingBox_, vector<struct orderScore> &bboxScore_, const mydataFmt overlap_threshold,
+         string modelname = "Union");
+
+void refineAndSquareBbox(vector<struct Bbox> &vecBbox, const int &height, const int &width);
+
 void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int v_w, int v_h, mydataFmt *p);
 
 void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox);
@@ -55,11 +68,12 @@ 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);
 
-void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width);
+void BatchNormInit(struct BN *beta, struct BN *mean, struct BN *var, int width);
+
+void BatchNorm(struct pBox *pbox, struct BN *beta, struct BN *mean, struct BN *var);
 
-void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *beta);
 #endif