ChrisKong/MTCNN-FaceNet-light
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

更改模型参数结构

Browse Source 
更改模型参数结构
This commit is contained in:
ChrisKong
2020-01-07 11:35:09 +08:00
parent 461147900d
commit 0b5a672af4
4 changed files with 187 additions and 122 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.gitignore vendored
View File

@@ -17,3 +17,5 @@ emb_img/*.jpg
kkk.jpg
result0.jpg
result1.jpg
model_128_old/

285
src/facenet.cpp
View File

@@ -51,27 +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, \
conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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_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_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
// conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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_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);
@@ -82,21 +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_var, conv1_mean, conv1_beta);
// printData(conv1_out);
BatchNorm(conv1_out, conv1_beta, conv1_mean, conv1_var);
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);
@@ -106,20 +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;
// }
@@ -217,19 +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);
@@ -238,12 +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, \
conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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_var->pdata, conv5_mean->pdata, conv5_beta->pdata, \
conv6_wb->pdata, conv6_var->pdata, conv6_mean->pdata, conv6_beta->pdata, \
// mydataFmt *pointTeam[28] = {
// conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
// conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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_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};
@@ -252,34 +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);
@@ -371,20 +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, \
conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
// conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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);
@@ -395,25 +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
@@ -482,14 +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);
@@ -498,10 +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, \
conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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[20] = {
// conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
// conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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};
@@ -512,24 +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);
@@ -621,32 +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, \
conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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_var->pdata, conv5_mean->pdata, conv5_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_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
// conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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_var->pdata, conv5_mean->pdata, conv5_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);
@@ -658,43 +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);
@@ -779,13 +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);
@@ -795,10 +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, \
conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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[20] = {
// conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
// conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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};
@@ -809,25 +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);
@@ -906,23 +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, \
conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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[18] = {
// conv1_wb->pdata, conv1_var->pdata, conv1_mean->pdata, conv1_beta->pdata, \
// conv2_wb->pdata, conv2_var->pdata, conv2_mean->pdata, conv2_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";
@@ -932,25 +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);
@@ -1026,15 +1086,16 @@ void facenet::Flatten(pBox *input, pBox *output) {
//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);
@@ -1043,7 +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);

17
src/network.cpp
View File

@@ -869,14 +869,15 @@ 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 beta beta
* @param mean 平均值
* @param var 方差
* @param width 参数个数
*/
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) {
var->width = width;
var->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
if (var->pdata == NULL)cout << "prelu apply for memory failed!!!!";
@@ -896,11 +897,11 @@ void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width)
/**
* BN实现
* @param pbox　输入feature map
* @param var　方差
* @param mean　平均值
* @param beta　beta
* @param beta beta
* @param mean 平均值
* @param var 方差
*/
void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *beta) {
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;

5
src/network.h
View File

@@ -72,7 +72,8 @@ 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