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整理代码结构

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整理代码结构

Co-Authored-By: Chris Kong <609027949@qq.com>
This commit is contained in:
ChrisKong
2019-12-28 17:48:50 +08:00
parent d7a51fe42e
commit 729eecac2e
8 changed files with 301 additions and 404 deletions
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437
src/facenet.cpp
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@@ -4,102 +4,6 @@
#include "facenet.h"
facenet::facenet() {
}
facenet::~facenet() {
}
void facenet::printData(pBox *in) {
for (long i = 0; i < in->height * in->width * in->channel; ++i) {
// if (in->pdata[i] != 0)
printf("%f\n", in->pdata[i]);
}
cout << "printData" << endl;
}
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;
for (int i = 0; i < 5; ++i) {
// model_128/block35_1_list.txt
string filepath = "../model_" + to_string(Num) + "/block35_" + to_string((i + 1)) + "_list.txt";
Inception_resnet_A(input, output, filepath, 0.17);
input = output;
output = new pBox;
}
cout << "Inception_resnet_A Finally" << endl;
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
string filepath = "../model_" + to_string(Num) + "/block17_" + to_string((j + 1)) + "_list.txt";
Inception_resnet_B(input, output, filepath, 0.1);
input = output;
output = new pBox;
}
cout << "Inception_resnet_B Finally" << endl;
Reduction_B(input, output);
cout << "Reduciotn_B Finally" << endl;
input = output;
// freepBox(output);
output = new pBox;
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;
Inception_resnet_C_None(input, output, "../model_" + to_string(Num) + "/Block8_list.txt");
cout << "Inception_resnet_C_None Finally" << endl;
input = output;
// freepBox(output);
output = new pBox;
AveragePooling(input, output);
cout << "AveragePooling Finally" << endl;
input = output;
// freepBox(output);
output = new pBox;
Flatten(input, output);
cout << "Flatten Finally" << endl;
input = output;
output = new pBox;
fully_connect(input, output, "../model_" + to_string(Num) + "/Bottleneck_list.txt");
cout << "Fully_Connect Finally" << endl;
mydataFmt sq = 0, sum = 0;
for (int i = 0; i < Num; ++i) {
sq = pow(output->pdata[i], 2);
sum += sq;
}
mydataFmt divisor = 0;
if (sum < 1e-10) {
divisor = sqrt(1e-10);
} else {
divisor = sqrt(sum);
}
for (int j = 0; j < Num; ++j) {
// o[j] = output->pdata[j] / divisor;
o.push_back(output->pdata[j] / divisor);
}
// memcpy(o, output->pdata, Num * sizeof(mydataFmt));
freepBox(output);
}
void facenet::Stem(Mat &image, pBox *output) {
pBox *rgb = new pBox;
@@ -142,18 +46,18 @@ void facenet::Stem(Mat &image, pBox *output) {
struct BN *conv6_mean = new BN;
struct BN *conv6_beta = new BN;
long conv1 = initConvAndFc(conv1_wb, 32, 3, 3, 2, 0);
initBN(conv1_var, conv1_mean, conv1_beta, 32);
long conv2 = initConvAndFc(conv2_wb, 32, 32, 3, 1, 0);
initBN(conv2_var, conv2_mean, conv2_beta, 32);
long conv3 = initConvAndFc(conv3_wb, 64, 32, 3, 1, 1);
initBN(conv3_var, conv3_mean, conv3_beta, 64);
long conv4 = initConvAndFc(conv4_wb, 80, 64, 1, 1, 0);
initBN(conv4_var, conv4_mean, conv4_beta, 80);
long conv5 = initConvAndFc(conv5_wb, 192, 80, 3, 1, 0);
initBN(conv5_var, conv5_mean, conv5_beta, 192);
long conv6 = initConvAndFc(conv6_wb, 256, 192, 3, 2, 0);
initBN(conv6_var, conv6_mean, conv6_beta, 256);
long conv1 = ConvAndFcInit(conv1_wb, 32, 3, 3, 2, 0);
BatchNormInit(conv1_var, conv1_mean, conv1_beta, 32);
long conv2 = ConvAndFcInit(conv2_wb, 32, 32, 3, 1, 0);
BatchNormInit(conv2_var, conv2_mean, conv2_beta, 32);
long conv3 = ConvAndFcInit(conv3_wb, 64, 32, 3, 1, 1);
BatchNormInit(conv3_var, conv3_mean, conv3_beta, 64);
long conv4 = ConvAndFcInit(conv4_wb, 80, 64, 1, 1, 0);
BatchNormInit(conv4_var, conv4_mean, conv4_beta, 80);
long conv5 = ConvAndFcInit(conv5_wb, 192, 80, 3, 1, 0);
BatchNormInit(conv5_var, conv5_mean, conv5_beta, 192);
long conv6 = ConvAndFcInit(conv6_wb, 256, 192, 3, 2, 0);
BatchNormInit(conv6_var, conv6_mean, conv6_beta, 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};
@@ -301,24 +205,24 @@ void facenet::Inception_resnet_A(pBox *input, pBox *output, string filepath, flo
struct BN *conv6_beta = new BN;
long conv1 = initConvAndFc(conv1_wb, 32, 256, 1, 1, 0);
initBN(conv1_var, conv1_mean, conv1_beta, 32);
long conv1 = ConvAndFcInit(conv1_wb, 32, 256, 1, 1, 0);
BatchNormInit(conv1_var, conv1_mean, conv1_beta, 32);
long conv2 = initConvAndFc(conv2_wb, 32, 256, 1, 1, 0);
initBN(conv2_var, conv2_mean, conv2_beta, 32);
long conv3 = initConvAndFc(conv3_wb, 32, 32, 3, 1, 1);
initBN(conv3_var, conv3_mean, conv3_beta, 32);
long conv2 = ConvAndFcInit(conv2_wb, 32, 256, 1, 1, 0);
BatchNormInit(conv2_var, conv2_mean, conv2_beta, 32);
long conv3 = ConvAndFcInit(conv3_wb, 32, 32, 3, 1, 1);
BatchNormInit(conv3_var, conv3_mean, conv3_beta, 32);
long conv4 = initConvAndFc(conv4_wb, 32, 256, 1, 1, 0);
initBN(conv4_var, conv4_mean, conv4_beta, 32);
long conv5 = initConvAndFc(conv5_wb, 32, 32, 3, 1, 1);
initBN(conv5_var, conv5_mean, conv5_beta, 32);
long conv6 = initConvAndFc(conv6_wb, 32, 32, 3, 1, 1);
initBN(conv6_var, conv6_mean, conv6_beta, 32);
long conv4 = ConvAndFcInit(conv4_wb, 32, 256, 1, 1, 0);
BatchNormInit(conv4_var, conv4_mean, conv4_beta, 32);
long conv5 = ConvAndFcInit(conv5_wb, 32, 32, 3, 1, 1);
BatchNormInit(conv5_var, conv5_mean, conv5_beta, 32);
long conv6 = ConvAndFcInit(conv6_wb, 32, 32, 3, 1, 1);
BatchNormInit(conv6_var, conv6_mean, conv6_beta, 32);
long conv7 = initConvAndFc(conv7_wb, 256, 96, 1, 1, 0);
long conv7 = ConvAndFcInit(conv7_wb, 256, 96, 1, 1, 0);
long conv8 = initConvAndFc(conv8_wb, 256, 0, 0, 0, 0);
long conv8 = ConvAndFcInit(conv8_wb, 256, 0, 0, 0, 0);
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};
@@ -450,15 +354,15 @@ void facenet::Reduction_A(pBox *input, pBox *output) {
struct BN *conv4_beta = new BN;
long conv1 = initConvAndFc(conv1_wb, 384, 256, 3, 2, 0);
initBN(conv1_var, conv1_mean, conv1_beta, 384);
long conv1 = ConvAndFcInit(conv1_wb, 384, 256, 3, 2, 0);
BatchNormInit(conv1_var, conv1_mean, conv1_beta, 384);
long conv2 = initConvAndFc(conv2_wb, 192, 256, 1, 1, 0);
initBN(conv2_var, conv2_mean, conv2_beta, 192);
long conv3 = initConvAndFc(conv3_wb, 192, 192, 3, 1, 0);
initBN(conv3_var, conv3_mean, conv3_beta, 192);
long conv4 = initConvAndFc(conv4_wb, 256, 192, 3, 2, 0);
initBN(conv4_var, conv4_mean, conv4_beta, 256);
long conv2 = ConvAndFcInit(conv2_wb, 192, 256, 1, 1, 0);
BatchNormInit(conv2_var, conv2_mean, conv2_beta, 192);
long conv3 = ConvAndFcInit(conv3_wb, 192, 192, 3, 1, 0);
BatchNormInit(conv3_var, conv3_mean, conv3_beta, 192);
long conv4 = ConvAndFcInit(conv4_wb, 256, 192, 3, 2, 0);
BatchNormInit(conv4_var, conv4_mean, conv4_beta, 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, \
@@ -554,19 +458,19 @@ void facenet::Inception_resnet_B(pBox *input, pBox *output, string filepath, flo
struct BN *conv4_beta = new BN;
long conv1 = initConvAndFc(conv1_wb, 128, 896, 1, 1, 0);
initBN(conv1_var, conv1_mean, conv1_beta, 128);
long conv1 = ConvAndFcInit(conv1_wb, 128, 896, 1, 1, 0);
BatchNormInit(conv1_var, conv1_mean, conv1_beta, 128);
long conv2 = initConvAndFc(conv2_wb, 128, 896, 1, 1, 0);
initBN(conv2_var, conv2_mean, conv2_beta, 128);
long conv3 = initConvAndFc(conv3_wb, 128, 128, 0, 1, -1, 7, 1, 3, 0);//[1,7]
initBN(conv3_var, conv3_mean, conv3_beta, 128);
long conv4 = initConvAndFc(conv4_wb, 128, 128, 0, 1, -1, 1, 7, 0, 3);//[7,1]
initBN(conv4_var, conv4_mean, conv4_beta, 128);
long conv2 = ConvAndFcInit(conv2_wb, 128, 896, 1, 1, 0);
BatchNormInit(conv2_var, conv2_mean, conv2_beta, 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);
long conv4 = ConvAndFcInit(conv4_wb, 128, 128, 0, 1, -1, 1, 7, 0, 3);//[7,1]
BatchNormInit(conv4_var, conv4_mean, conv4_beta, 128);
long conv5 = initConvAndFc(conv5_wb, 896, 256, 1, 1, 0);
long conv5 = ConvAndFcInit(conv5_wb, 896, 256, 1, 1, 0);
long conv6 = initConvAndFc(conv6_wb, 896, 0, 0, 0, 0);
long conv6 = ConvAndFcInit(conv6_wb, 896, 0, 0, 0, 0);
long dataNumber[20] = {conv1, 128, 128, 128, conv2, 128, 128, 128, conv3, 128, 128, 128, conv4, 128, 128, 128,
conv5, 896, conv6, 0};
@@ -688,22 +592,22 @@ void facenet::Reduction_B(pBox *input, pBox *output) {
struct BN *conv7_beta = new BN;
long conv1 = initConvAndFc(conv1_wb, 256, 896, 1, 1, 0);
initBN(conv1_var, conv1_mean, conv1_beta, 256);
long conv2 = initConvAndFc(conv2_wb, 384, 256, 3, 2, 0);
initBN(conv2_var, conv2_mean, conv2_beta, 384);
long conv1 = ConvAndFcInit(conv1_wb, 256, 896, 1, 1, 0);
BatchNormInit(conv1_var, conv1_mean, conv1_beta, 256);
long conv2 = ConvAndFcInit(conv2_wb, 384, 256, 3, 2, 0);
BatchNormInit(conv2_var, conv2_mean, conv2_beta, 384);
long conv3 = initConvAndFc(conv3_wb, 256, 896, 1, 1, 0);
initBN(conv3_var, conv3_mean, conv3_beta, 256);
long conv4 = initConvAndFc(conv4_wb, 256, 256, 3, 2, 0);
initBN(conv4_var, conv4_mean, conv4_beta, 256);
long conv3 = ConvAndFcInit(conv3_wb, 256, 896, 1, 1, 0);
BatchNormInit(conv3_var, conv3_mean, conv3_beta, 256);
long conv4 = ConvAndFcInit(conv4_wb, 256, 256, 3, 2, 0);
BatchNormInit(conv4_var, conv4_mean, conv4_beta, 256);
long conv5 = initConvAndFc(conv5_wb, 256, 896, 1, 1, 0);
initBN(conv5_var, conv5_mean, conv5_beta, 256);
long conv6 = initConvAndFc(conv6_wb, 256, 256, 3, 1, 1);
initBN(conv6_var, conv6_mean, conv6_beta, 256);
long conv7 = initConvAndFc(conv7_wb, 256, 256, 3, 2, 0);
initBN(conv7_var, conv7_mean, conv7_beta, 256);
long conv5 = ConvAndFcInit(conv5_wb, 256, 896, 1, 1, 0);
BatchNormInit(conv5_var, conv5_mean, conv5_beta, 256);
long conv6 = ConvAndFcInit(conv6_wb, 256, 256, 3, 1, 1);
BatchNormInit(conv6_var, conv6_mean, conv6_beta, 256);
long conv7 = ConvAndFcInit(conv7_wb, 256, 256, 3, 2, 0);
BatchNormInit(conv7_var, conv7_mean, conv7_beta, 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};
@@ -839,18 +743,18 @@ void facenet::Inception_resnet_C(pBox *input, pBox *output, string filepath, flo
struct BN *conv4_beta = new BN;
long conv1 = initConvAndFc(conv1_wb, 192, 1792, 1, 1, 0);
initBN(conv1_var, conv1_mean, conv1_beta, 192);
long conv2 = initConvAndFc(conv2_wb, 192, 1792, 1, 1, 0);
initBN(conv2_var, conv2_mean, conv2_beta, 192);
long conv3 = initConvAndFc(conv3_wb, 192, 192, 0, 1, -1, 3, 1, 1, 0);
initBN(conv3_var, conv3_mean, conv3_beta, 192);
long conv4 = initConvAndFc(conv4_wb, 192, 192, 0, 1, -1, 1, 3, 0, 1);
initBN(conv4_var, conv4_mean, conv4_beta, 192);
long conv1 = ConvAndFcInit(conv1_wb, 192, 1792, 1, 1, 0);
BatchNormInit(conv1_var, conv1_mean, conv1_beta, 192);
long conv2 = ConvAndFcInit(conv2_wb, 192, 1792, 1, 1, 0);
BatchNormInit(conv2_var, conv2_mean, conv2_beta, 192);
long conv3 = ConvAndFcInit(conv3_wb, 192, 192, 0, 1, -1, 3, 1, 1, 0);
BatchNormInit(conv3_var, conv3_mean, conv3_beta, 192);
long conv4 = ConvAndFcInit(conv4_wb, 192, 192, 0, 1, -1, 1, 3, 0, 1);
BatchNormInit(conv4_var, conv4_mean, conv4_beta, 192);
long conv5 = initConvAndFc(conv5_wb, 1792, 384, 1, 1, 0);
long conv5 = ConvAndFcInit(conv5_wb, 1792, 384, 1, 1, 0);
long conv6 = initConvAndFc(conv6_wb, 1792, 0, 0, 0, 0);
long conv6 = ConvAndFcInit(conv6_wb, 1792, 0, 0, 0, 0);
long dataNumber[20] = {conv1, 192, 192, 192, conv2, 192, 192, 192, conv3, 192, 192, 192, conv4, 192, 192, 192,
conv5, 1792, conv6, 0};
@@ -959,15 +863,15 @@ void facenet::Inception_resnet_C_None(pBox *input, pBox *output, string filepath
struct BN *conv4_mean = new BN;
struct BN *conv4_beta = new BN;
long conv1 = initConvAndFc(conv1_wb, 192, 1792, 1, 1, 0);
initBN(conv1_var, conv1_mean, conv1_beta, 192);
long conv2 = initConvAndFc(conv2_wb, 192, 1792, 1, 1, 0);
initBN(conv2_var, conv2_mean, conv2_beta, 192);
long conv3 = initConvAndFc(conv3_wb, 192, 192, 0, 1, -1, 3, 1, 1, 0);
initBN(conv3_var, conv3_mean, conv3_beta, 192);
long conv4 = initConvAndFc(conv4_wb, 192, 192, 0, 1, -1, 1, 3, 0, 1);
initBN(conv4_var, conv4_mean, conv4_beta, 192);
long conv5 = initConvAndFc(conv5_wb, 1792, 384, 1, 1, 0);
long conv1 = ConvAndFcInit(conv1_wb, 192, 1792, 1, 1, 0);
BatchNormInit(conv1_var, conv1_mean, conv1_beta, 192);
long conv2 = ConvAndFcInit(conv2_wb, 192, 1792, 1, 1, 0);
BatchNormInit(conv2_var, conv2_mean, conv2_beta, 192);
long conv3 = ConvAndFcInit(conv3_wb, 192, 192, 0, 1, -1, 3, 1, 1, 0);
BatchNormInit(conv3_var, conv3_mean, conv3_beta, 192);
long conv4 = ConvAndFcInit(conv4_wb, 192, 192, 0, 1, -1, 1, 3, 0, 1);
BatchNormInit(conv4_var, conv4_mean, conv4_beta, 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};
@@ -1067,8 +971,8 @@ void facenet::fully_connect(pBox *input, pBox *output, string filepath) {
struct BN *conv1_var = new BN;
struct BN *conv1_mean = new BN;
struct BN *conv1_beta = new BN;
long conv1 = initConvAndFc(conv1_wb, Num, 1792, input->height, 1, 0);
initBN(conv1_var, conv1_mean, conv1_beta, Num);
long conv1 = ConvAndFcInit(conv1_wb, Num, 1792, input->height, 1, 0);
BatchNormInit(conv1_var, conv1_mean, conv1_beta, Num);
long dataNumber[4] = {conv1, Num, Num, Num};
// cout << to_string(sum) << endl;
@@ -1083,117 +987,104 @@ void facenet::fully_connect(pBox *input, pBox *output, string filepath) {
fullconnect(conv1_wb, input, output);
BatchNorm(output, conv1_var, conv1_mean, conv1_beta);
// relu(output, conv1_wb->pbias, prelu_gmma1->pdata);
freeWeight(conv1_wb);
freeBN(conv1_var);
freeBN(conv1_mean);
freeBN(conv1_beta);
}
void facenet::conv_mergeInit(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
output->channel = 0;
output->height = c1->height;
output->width = c1->width;
if (c1 != 0) {
output->channel = c1->channel;
if (c2 != 0) {
output->channel += c2->channel;
if (c3 != 0) {
output->channel += c3->channel;
if (c4 != 0) {
output->channel += c4->channel;
}
}
}
} else { cout << "conv_mergeInit" << endl; }
output->pdata = (mydataFmt *) malloc(output->width * output->height * output->channel * sizeof(mydataFmt));
if (output->pdata == NULL)cout << "the conv_mergeInit is failed!!" << endl;
memset(output->pdata, 0, output->width * output->height * output->channel * sizeof(mydataFmt));
facenet::facenet() {
}
void facenet::conv_merge(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
// cout << "output->channel:" << output->channel << endl;
if (c1 != 0) {
long count1 = c1->height * c1->width * c1->channel;
//output->pdata = c1->pdata;
for (long i = 0; i < count1; i++) {
output->pdata[i] = c1->pdata[i];
}
if (c2 != 0) {
long count2 = c2->height * c2->width * c2->channel;
for (long i = 0; i < count2; i++) {
output->pdata[count1 + i] = c2->pdata[i];
}
if (c3 != 0) {
long count3 = c3->height * c3->width * c3->channel;
for (long i = 0; i < count3; i++) {
output->pdata[count1 + count2 + i] = c3->pdata[i];
}
if (c4 != 0) {
long count4 = c4->height * c4->width * c4->channel;
for (long i = 0; i < count4; i++) {
output->pdata[count1 + count2 + count3 + i] = c4->pdata[i];
}
}
}
}
} else { cout << "conv_mergeInit" << endl; }
// cout << "output->pdata:" << *(output->pdata) << endl;
facenet::~facenet() {
}
void facenet::mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale) {
outpBox->channel = temppbox->channel;
outpBox->width = temppbox->width;
outpBox->height = temppbox->height;
outpBox->pdata = (mydataFmt *) malloc(outpBox->width * outpBox->height * outpBox->channel * sizeof(mydataFmt));
if (outpBox->pdata == NULL)cout << "the mulandaddInit is failed!!" << endl;
memset(outpBox->pdata, 0, outpBox->width * outpBox->height * outpBox->channel * sizeof(mydataFmt));
}
void facenet::mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale) {
mydataFmt *ip = inpbox->pdata;
mydataFmt *tp = temppbox->pdata;
mydataFmt *op = outpBox->pdata;
long dis = inpbox->width * inpbox->height * inpbox->channel;
for (long i = 0; i < dis; i++) {
op[i] = ip[i] + tp[i] * scale;
void facenet::printData(pBox *in) {
for (long i = 0; i < in->height * in->width * in->channel; ++i) {
// if (in->pdata[i] != 0)
printf("%f\n", in->pdata[i]);
}
cout << "printData" << endl;
}
void facenet::prewhiten(Mat &image) {
double mean, stddev, sqr, stddev_adj;
int size;
Mat temp_m, temp_sd;
meanStdDev(image, temp_m, temp_sd);
mean = temp_m.at<double>(0, 0);
stddev = temp_sd.at<double>(0, 0);
size = image.cols * image.rows * image.channels();
sqr = sqrt(double(size));
if (stddev > 1.0 / sqr) {
stddev_adj = stddev;
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;
for (int i = 0; i < 5; ++i) {
// model_128/block35_1_list.txt
string filepath = "../model_" + to_string(Num) + "/block35_" + to_string((i + 1)) + "_list.txt";
Inception_resnet_A(input, output, filepath, 0.17);
input = output;
output = new pBox;
}
cout << "Inception_resnet_A Finally" << endl;
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
string filepath = "../model_" + to_string(Num) + "/block17_" + to_string((j + 1)) + "_list.txt";
Inception_resnet_B(input, output, filepath, 0.1);
input = output;
output = new pBox;
}
cout << "Inception_resnet_B Finally" << endl;
Reduction_B(input, output);
cout << "Reduciotn_B Finally" << endl;
input = output;
// freepBox(output);
output = new pBox;
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;
Inception_resnet_C_None(input, output, "../model_" + to_string(Num) + "/Block8_list.txt");
cout << "Inception_resnet_C_None Finally" << endl;
input = output;
// freepBox(output);
output = new pBox;
AveragePooling(input, output);
cout << "AveragePooling Finally" << endl;
input = output;
// output = new pBox;
// Flatten(input, output);
// cout << "Flatten Finally" << endl;
// input = output;
output = new pBox;
fully_connect(input, output, "../model_" + to_string(Num) + "/Bottleneck_list.txt");
cout << "Fully_Connect Finally" << endl;
mydataFmt sq = 0, sum = 0;
for (int i = 0; i < Num; ++i) {
sq = pow(output->pdata[i], 2);
sum += sq;
}
mydataFmt divisor = 0;
if (sum < 1e-10) {
divisor = sqrt(1e-10);
} else {
stddev_adj = 1.0 / sqr;
divisor = sqrt(sum);
}
Mat temp_image(image.rows, image.cols, CV_64F);
for (int i = 0; i < image.rows; i++) {
for (int j = 0; j < image.cols; j++) {
image.at<uchar>(i, j);
temp_image.at<Vec3b>(i, j)[0] = (image.at<Vec3b>(i, j)[0] - mean) / stddev_adj;
temp_image.at<Vec3b>(i, j)[0] = (image.at<Vec3b>(i, j)[0] - mean) / stddev_adj;
temp_image.at<Vec3b>(i, j)[0] = (image.at<Vec3b>(i, j)[0] - mean) / stddev_adj;
cout << 1 << endl;
}
for (int j = 0; j < Num; ++j) {
// o[j] = output->pdata[j] / divisor;
o.push_back(output->pdata[j] / divisor);
}
// double max, min;
// minMaxLoc(temp_image, &min, &max);
// for (int i = 0; i < image.rows; i++) {
// for (int j = 0; j < image.cols; j++) {
// double pixelVal = temp_image.at<double>(i, j);
// image.at<uchar>(i, j) = temp_image.at<double>(i, j);
// }
// }
// imshow("New Image", image);
// waitKey(0);
}
// memcpy(o, output->pdata, Num * sizeof(mydataFmt));
freepBox(output);
}

23
src/facenet.h
View File

@@ -16,8 +16,6 @@ public:
void run(Mat &image, vector<mydataFmt> &o, int count = 1);
void prewhiten(Mat &image);
private:
void Stem(Mat &image, pBox *output);
@@ -37,30 +35,9 @@ private:
void fully_connect(pBox *input, pBox *output, string filepath = "");
void conv_merge(pBox *output, pBox *c1 = 0, pBox *c2 = 0, pBox *c3 = 0, pBox *c4 = 0);
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 mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale = 1);
void Flatten(pBox *input, pBox *output);
void printData(pBox *output);
// Mat reImage;
// float nms_threshold[3];
// vector<float> scales_;
// vector<struct Bbox> firstBbox_;
// vector<struct orderScore> firstOrderScore_;
// vector<struct Bbox> secondBbox_;
// vector<struct orderScore> secondBboxScore_;
// vector<struct Bbox> thirdBbox_;
// vector<struct orderScore> thirdBboxScore_;
};
#endif //MAIN_FACENET_H

62
src/mtcnn.cpp
View File

@@ -23,14 +23,14 @@ Pnet::Pnet() {
this->conv4c1_wb = new Weight;
this->conv4c2_wb = new Weight;
// w sc lc ks s p
long conv1 = initConvAndFc(this->conv1_wb, 10, 3, 3, 1, 0);
initpRelu(this->prelu_gmma1, 10);
long conv2 = initConvAndFc(this->conv2_wb, 16, 10, 3, 1, 0);
initpRelu(this->prelu_gmma2, 16);
long conv3 = initConvAndFc(this->conv3_wb, 32, 16, 3, 1, 0);
initpRelu(this->prelu_gmma3, 32);
long conv4c1 = initConvAndFc(this->conv4c1_wb, 2, 32, 1, 1, 0);
long conv4c2 = initConvAndFc(this->conv4c2_wb, 4, 32, 1, 1, 0);
long conv1 = ConvAndFcInit(this->conv1_wb, 10, 3, 3, 1, 0);
pReluInit(this->prelu_gmma1, 10);
long conv2 = ConvAndFcInit(this->conv2_wb, 16, 10, 3, 1, 0);
pReluInit(this->prelu_gmma2, 16);
long conv3 = ConvAndFcInit(this->conv3_wb, 32, 16, 3, 1, 0);
pReluInit(this->prelu_gmma3, 32);
long conv4c1 = ConvAndFcInit(this->conv4c1_wb, 2, 32, 1, 1, 0);
long conv4c2 = ConvAndFcInit(this->conv4c2_wb, 4, 32, 1, 1, 0);
long dataNumber[13] = {conv1, 10, 10, conv2, 16, 16, conv3, 32, 32, conv4c1, 2, conv4c2, 4};
mydataFmt *pointTeam[13] = {this->conv1_wb->pdata, this->conv1_wb->pbias, this->prelu_gmma1->pdata, \
this->conv2_wb->pdata, this->conv2_wb->pbias, this->prelu_gmma2->pdata, \
@@ -171,16 +171,16 @@ Rnet::Rnet() {
this->score_wb = new Weight;
this->location_wb = new Weight;
// // w sc lc ks s p
long conv1 = initConvAndFc(this->conv1_wb, 28, 3, 3, 1, 0);
initpRelu(this->prelu_gmma1, 28);
long conv2 = initConvAndFc(this->conv2_wb, 48, 28, 3, 1, 0);
initpRelu(this->prelu_gmma2, 48);
long conv3 = initConvAndFc(this->conv3_wb, 64, 48, 2, 1, 0);
initpRelu(this->prelu_gmma3, 64);
long fc4 = initConvAndFc(this->fc4_wb, 128, 576, 1, 1, 0);
initpRelu(this->prelu_gmma4, 128);
long score = initConvAndFc(this->score_wb, 2, 128, 1, 1, 0);
long location = initConvAndFc(this->location_wb, 4, 128, 1, 1, 0);
long conv1 = ConvAndFcInit(this->conv1_wb, 28, 3, 3, 1, 0);
pReluInit(this->prelu_gmma1, 28);
long conv2 = ConvAndFcInit(this->conv2_wb, 48, 28, 3, 1, 0);
pReluInit(this->prelu_gmma2, 48);
long conv3 = ConvAndFcInit(this->conv3_wb, 64, 48, 2, 1, 0);
pReluInit(this->prelu_gmma3, 64);
long fc4 = ConvAndFcInit(this->fc4_wb, 128, 576, 1, 1, 0);
pReluInit(this->prelu_gmma4, 128);
long score = ConvAndFcInit(this->score_wb, 2, 128, 1, 1, 0);
long location = ConvAndFcInit(this->location_wb, 4, 128, 1, 1, 0);
long dataNumber[16] = {conv1, 28, 28, conv2, 48, 48, conv3, 64, 64, fc4, 128, 128, score, 2, location, 4};
mydataFmt *pointTeam[16] = {this->conv1_wb->pdata, this->conv1_wb->pbias, this->prelu_gmma1->pdata, \
this->conv2_wb->pdata, this->conv2_wb->pbias, this->prelu_gmma2->pdata, \
@@ -303,19 +303,19 @@ Onet::Onet() {
this->keyPoint_wb = new Weight;
// // w sc lc ks s p
long conv1 = initConvAndFc(this->conv1_wb, 32, 3, 3, 1, 0);
initpRelu(this->prelu_gmma1, 32);
long conv2 = initConvAndFc(this->conv2_wb, 64, 32, 3, 1, 0);
initpRelu(this->prelu_gmma2, 64);
long conv3 = initConvAndFc(this->conv3_wb, 64, 64, 3, 1, 0);
initpRelu(this->prelu_gmma3, 64);
long conv4 = initConvAndFc(this->conv4_wb, 128, 64, 2, 1, 0);
initpRelu(this->prelu_gmma4, 128);
long fc5 = initConvAndFc(this->fc5_wb, 256, 1152, 1, 1, 0);
initpRelu(this->prelu_gmma5, 256);
long score = initConvAndFc(this->score_wb, 2, 256, 1, 1, 0);
long location = initConvAndFc(this->location_wb, 4, 256, 1, 1, 0);
long keyPoint = initConvAndFc(this->keyPoint_wb, 10, 256, 1, 1, 0);
long conv1 = ConvAndFcInit(this->conv1_wb, 32, 3, 3, 1, 0);
pReluInit(this->prelu_gmma1, 32);
long conv2 = ConvAndFcInit(this->conv2_wb, 64, 32, 3, 1, 0);
pReluInit(this->prelu_gmma2, 64);
long conv3 = ConvAndFcInit(this->conv3_wb, 64, 64, 3, 1, 0);
pReluInit(this->prelu_gmma3, 64);
long conv4 = ConvAndFcInit(this->conv4_wb, 128, 64, 2, 1, 0);
pReluInit(this->prelu_gmma4, 128);
long fc5 = ConvAndFcInit(this->fc5_wb, 256, 1152, 1, 1, 0);
pReluInit(this->prelu_gmma5, 256);
long score = ConvAndFcInit(this->score_wb, 2, 256, 1, 1, 0);
long location = ConvAndFcInit(this->location_wb, 4, 256, 1, 1, 0);
long keyPoint = ConvAndFcInit(this->keyPoint_wb, 10, 256, 1, 1, 0);
long dataNumber[21] = {conv1, 32, 32, conv2, 64, 64, conv3, 64, 64, conv4, 128, 128, fc5, 256, 256, score, 2,
location, 4, keyPoint, 10};
mydataFmt *pointTeam[21] = {this->conv1_wb->pdata, this->conv1_wb->pbias, this->prelu_gmma1->pdata, \

133
src/network.cpp
View File

@@ -50,7 +50,7 @@ void image2Matrix(const Mat &image, const struct pBox *pbox, int num) {
mydataFmt mymean, mystddev;
// prewhiten
if (num != 0) {
meanAndDev(image, mymean, mystddev);
MeanAndDev(image, mymean, mystddev);
cout << mymean << "----" << mystddev << endl;
size = image.cols * image.rows * image.channels();
sqr = sqrt(double(size));
@@ -78,14 +78,11 @@ void image2Matrix(const Mat &image, const struct pBox *pbox, int num) {
}
}
void meanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q) {
void MeanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q) {
mydataFmt meansum = 0, stdsum = 0;
for (int rowI = 0; rowI < image.rows; rowI++) {
for (int colK = 0; colK < image.cols; colK++) {
meansum += image.at<Vec3b>(rowI, colK)[0] + image.at<Vec3b>(rowI, colK)[1] + image.at<Vec3b>(rowI, colK)[2];
// cout << int(image.at<Vec3b>(rowI, colK)[0]) << endl;
// cout << int(image.at<Vec3b>(rowI, colK)[1]) << endl;
// cout << int(image.at<Vec3b>(rowI, colK)[2]) << endl;
}
}
p = meansum / (image.cols * image.rows * image.channels());
@@ -172,12 +169,6 @@ void convolutionInit(const Weight *weight, pBox *pbox, pBox *outpBox) {
}
void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox) {
// if (weight->pad != 0) {
// pBox *padpbox = new pBox;
// featurePadInit(outpBox, padpbox, weight->pad, weight->padw, weight->padh);
// featurePad(outpBox, padpbox, weight->pad, weight->padw, weight->padh);
// *outpBox = *padpbox;
// }
int ckh, ckw, ckd, stride, cknum, ckpad, imginputh, imginputw, imginputd, Nh, Nw;
mydataFmt *ck, *imginput;
// float *output = outpBox->pdata;
@@ -214,15 +205,6 @@ void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox) {
+ (k * stride + i1)
+ m * imginputh * imginputw]
* ck[i * ckh * ckw * ckd + m * ckh * ckw + n * ckw + i1];
// cout << "(" << imginput[(j * stride + n) * imginputw
// + (k * stride + i1)
// + m * imginputh * imginputw] << ")x("
// << ck[i * ckh * ckw * ckd + m * ckh * ckw + n * ckw + i1] << ")="
// << imginput[(j * stride + n) * imginputw
// + (k * stride + i1)
// + m * imginputh * imginputw]
// * ck[i * ckh * ckw * ckd + m * ckh * ckw + n * ckw + i1] << endl;
// cout << temp << endl;
}
}
}
@@ -231,7 +213,6 @@ void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox) {
}
}
}
// cout << "output->pdata:" << (outpBox->pdata[10]) << endl;
}
void maxPoolingInit(const pBox *pbox, pBox *Matrix, int kernelSize, int stride, int flag) {
@@ -340,6 +321,13 @@ void avePooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride) {
}
}
/**
* 激活函数 有系数
* @param pbox
* @param pbias
* @param prelu_gmma
*/
void prelu(struct pBox *pbox, mydataFmt *pbias, mydataFmt *prelu_gmma) {
if (pbox->pdata == NULL) {
cout << "the pRelu feature is NULL!!" << endl;
@@ -365,6 +353,11 @@ void prelu(struct pBox *pbox, mydataFmt *pbias, mydataFmt *prelu_gmma) {
}
}
/**
* 激活函数 没有系数
* @param pbox
* @param pbias
*/
void relu(struct pBox *pbox, mydataFmt *pbias) {
if (pbox->pdata == NULL) {
cout << "the Relu feature is NULL!!" << endl;
@@ -411,24 +404,17 @@ void fullconnect(const Weight *weight, const pBox *pbox, pBox *outpBox) {
// row no trans A's row A'col
//cblas_sgemv(CblasRowMajor, CblasNoTrans, weight->selfChannel, weight->lastChannel, 1, weight->pdata, weight->lastChannel, pbox->pdata, 1, 0, outpBox->pdata, 1);
vectorXmatrix(pbox->pdata, weight->pdata,
pbox->width * pbox->height * pbox->channel,
weight->lastChannel, weight->selfChannel,
outpBox->pdata);
}
void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int size, int v_w, int v_h, mydataFmt *p) {
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;
for (int j = 0; j < v_w; j++) {
p[i] += matrix[j] * v[i * v_w + j];
// cout << p[i] << endl;
}
// cout << p[i] << endl;
// p[i] = -0.0735729;
// cout << "...." << endl;
// break;
}
// cout << "...." << endl;
}
void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length) {
@@ -481,19 +467,15 @@ void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length
}
// w sc lc ks s p kw kh
long initConvAndFc(struct Weight *weight, int schannel, int lchannel, int kersize,
long ConvAndFcInit(struct Weight *weight, int schannel, int lchannel, int kersize,
int stride, int pad, int w, int h, int padw, int padh) {
weight->selfChannel = schannel;
weight->lastChannel = lchannel;
weight->kernelSize = kersize;
// if (kersize == 0) {
weight->h = h;
weight->w = w;
// }
// if (pad == -1) {
weight->padh = padh;
weight->padw = padw;
// }
weight->stride = stride;
weight->pad = pad;
weight->pbias = (mydataFmt *) malloc(schannel * sizeof(mydataFmt));
@@ -511,7 +493,7 @@ long initConvAndFc(struct Weight *weight, int schannel, int lchannel, int kersiz
return byteLenght;
}
void initpRelu(struct pRelu *prelu, int width) {
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!!!!";
@@ -652,7 +634,76 @@ void refineAndSquareBbox(vector<struct Bbox> &vecBbox, const int &height, const
}
}
void initBN(struct BN *var, struct BN *mean, struct BN *beta, int width) {
void conv_mergeInit(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
output->channel = 0;
output->height = c1->height;
output->width = c1->width;
if (c1 != 0) {
output->channel = c1->channel;
if (c2 != 0) {
output->channel += c2->channel;
if (c3 != 0) {
output->channel += c3->channel;
if (c4 != 0) {
output->channel += c4->channel;
}
}
}
}
output->pdata = (mydataFmt *) malloc(output->width * output->height * output->channel * sizeof(mydataFmt));
if (output->pdata == NULL)cout << "the conv_mergeInit is failed!!" << endl;
memset(output->pdata, 0, output->width * output->height * output->channel * sizeof(mydataFmt));
}
void conv_merge(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
// cout << "output->channel:" << output->channel << endl;
if (c1 != 0) {
long count1 = c1->height * c1->width * c1->channel;
//output->pdata = c1->pdata;
for (long i = 0; i < count1; i++) {
output->pdata[i] = c1->pdata[i];
}
if (c2 != 0) {
long count2 = c2->height * c2->width * c2->channel;
for (long i = 0; i < count2; i++) {
output->pdata[count1 + i] = c2->pdata[i];
}
if (c3 != 0) {
long count3 = c3->height * c3->width * c3->channel;
for (long i = 0; i < count3; i++) {
output->pdata[count1 + count2 + i] = c3->pdata[i];
}
if (c4 != 0) {
long count4 = c4->height * c4->width * c4->channel;
for (long i = 0; i < count4; i++) {
output->pdata[count1 + count2 + count3 + i] = c4->pdata[i];
}
}
}
}
} else { cout << "conv_mergeInit" << endl; }
}
void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale) {
outpBox->channel = temppbox->channel;
outpBox->width = temppbox->width;
outpBox->height = temppbox->height;
outpBox->pdata = (mydataFmt *) malloc(outpBox->width * outpBox->height * outpBox->channel * sizeof(mydataFmt));
if (outpBox->pdata == NULL)cout << "the mulandaddInit is failed!!" << endl;
memset(outpBox->pdata, 0, outpBox->width * outpBox->height * outpBox->channel * sizeof(mydataFmt));
}
void mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale) {
mydataFmt *ip = inpbox->pdata;
mydataFmt *tp = temppbox->pdata;
mydataFmt *op = outpBox->pdata;
long dis = inpbox->width * inpbox->height * inpbox->channel;
for (long i = 0; i < dis; i++) {
op[i] = ip[i] + tp[i] * scale;
}
}
void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width) {
var->width = width;
var->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
if (var->pdata == NULL)cout << "prelu apply for memory failed!!!!";
@@ -689,18 +740,8 @@ void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *be
for (int channel = 0; channel < pbox->channel; channel++) {
temp = gamma / sqrt(((vp[channel]) + epsilon));
for (int col = 0; col < dis; col++) {
// *pp = *pp + *vp;
// cout << ((*pp) / (sqrt(*vp + bias))) << endl;
// cout << ((*pp) * (*mp) / (sqrt(*vp + bias))) << endl;
// if (*pp == 0) {
// cout << *vp << "===" << *mp << "===" << *bp << endl;
// }
*pp = temp * (*pp) + ((bp[channel]) - temp * (mp[channel]));
// cout << *pp << endl;
pp++;
}
// vp++;
// mp++;
// bp++;
}
}

20
src/network.h
View File

@@ -11,7 +11,6 @@
#include <string>
#include <math.h>
#include "pBox.h"
//#include <cblas.h>
using namespace cv;
@@ -33,10 +32,10 @@ void fullconnect(const Weight *weight, const pBox *pbox, pBox *outpBox);
void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length = 0);
long initConvAndFc(struct Weight *weight, int schannel, int lchannel, int kersize, int stride, int pad,
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 initpRelu(struct pRelu *prelu, int width);
void pReluInit(struct pRelu *prelu, int width);
void softmax(const struct pBox *pbox);
@@ -59,14 +58,21 @@ void nms(vector<struct Bbox> &boundingBox_, vector<struct orderScore> &bboxScore
void refineAndSquareBbox(vector<struct Bbox> &vecBbox, const int &height, const int &width);
void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int size, int v_w, int v_h, mydataFmt *p);
void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int v_w, int v_h, mydataFmt *p);
void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox);
void meanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q);
void MeanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q);
void initBN(struct BN *var, struct BN *mean, struct BN *beta, int width);
void conv_merge(pBox *output, pBox *c1 = 0, pBox *c2 = 0, pBox *c3 = 0, pBox *c4 = 0);
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 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 BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *beta);
#endif

2
src/pBox.cpp
View File

@@ -30,4 +30,4 @@ void freeBN(struct BN *bn) {
free(bn->pdata);
bn->pdata = NULL;
delete bn;
}
}

26
src/pBox.h
View File

@@ -6,12 +6,15 @@
#include <opencv2/core/cvstd.hpp>
#include <vector>
/**
* 声明结构体
*/
using namespace std;
//#define mydataFmt double
#define Num 128
typedef float mydataFmt;
struct pBox : public cv::String {
mydataFmt *pdata;
int width;
@@ -19,7 +22,6 @@ struct pBox : public cv::String {
int channel;
};
struct pRelu {
mydataFmt *pdata;
int width;
@@ -30,7 +32,6 @@ struct BN {
int width;
};
struct Weight {
mydataFmt *pdata;
mydataFmt *pbias;
@@ -45,25 +46,6 @@ struct Weight {
int padh;
};
class pBox1 {
public:
vector<vector<vector<mydataFmt>>> pdata;
};
class pRelu1 {
public:
vector<mydataFmt> pdata;
};
class Weight1 {
public:
vector<vector<vector<vector<mydataFmt>>>> pdata;
vector<mydataFmt> pbias;
int stride;
int padw;
int padh;
};
struct Bbox {
float score;
int x1;

2
src/pikaqiu.cpp
View File

@@ -183,7 +183,7 @@ void run() {
}
void test() {
Mat image0 = imread("../kkk.jpg");
Mat image0 = imread("../hejiong1.jpeg");
Mat image1 = imread("../hejiong0.jpeg");
clock_t start;