c++编写神经网络（二）MTCNN的程序主程序

痛定思痛。 2022-03-29 08:57 282阅读 0赞

**背景：**我们得出了MTCNN的内存调用，内存调用是开辟了一系列的存储空间。

**目的：**固定相应的存储的大小，编写主程序。

**目录**

一、MTCNN内存占用表

1.1 权重尺寸

6.2 feature大小

二、程序思路

--------------------

# 一、MTCNN内存占用表 #

## 1.1 权重尺寸 ##

<table> 
 <tbody> 
  <tr> 
   <td> <p>Pnet</p> <p>weight</p> </td> 
   <td>layer name</td> 
   <td>weight data byte</td> 
   <td>weight bias byte</td> 
   <td>total byte</td> 
  </tr> 
  <tr> 
   <td>conv1</td> 
   <td>1080</td> 
   <td>40</td> 
   <td>1120</td> 
  </tr> 
  <tr> 
   <td>pool1_conv1</td> 
   <td>5760</td> 
   <td>64</td> 
   <td>5824</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td>18432</td> 
   <td>128</td> 
   <td>18560</td> 
  </tr> 
  <tr> 
   <td>conv3</td> 
   <td>36864</td> 
   <td>128</td> 
   <td>36992</td> 
  </tr> 
  <tr> 
   <td>conv4c1</td> 
   <td>256</td> 
   <td>8</td> 
   <td>264</td> 
  </tr> 
  <tr> 
   <td>conv4c2</td> 
   <td>512</td> 
   <td>16</td> 
   <td>528</td> 
  </tr> 
 </tbody> 
</table>

<table> 
 <tbody> 
  <tr> 
   <td> <p>Rnet</p> <p>weight</p> </td> 
   <td>layer name</td> 
   <td>weight data byte</td> 
   <td>weight bias byte</td> 
   <td>total byte</td> 
  </tr> 
  <tr> 
   <td>conv1</td> 
   <td>3024</td> 
   <td>112</td> 
   <td>3136</td> 
  </tr> 
  <tr> 
   <td>pool_conv1</td> 
   <td>28224</td> 
   <td>112</td> 
   <td>28336</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td>48384</td> 
   <td>192</td> 
   <td>48576</td> 
  </tr> 
  <tr> 
   <td>pool2_conv3</td> 
   <td>82944</td> 
   <td>192</td> 
   <td>83136</td> 
  </tr> 
  <tr> 
   <td>conv3</td> 
   <td>110592</td> 
   <td>256</td> 
   <td>110848</td> 
  </tr> 
  <tr> 
   <td>fc4</td> 
   <td>294912</td> 
   <td>512</td> 
   <td>295424</td> 
  </tr> 
  <tr> 
   <td>score</td> 
   <td>1024</td> 
   <td>8</td> 
   <td>1032</td> 
  </tr> 
  <tr> 
   <td>location</td> 
   <td>2048</td> 
   <td>16</td> 
   <td>2064</td> 
  </tr> 
 </tbody> 
</table>

<table> 
 <tbody> 
  <tr> 
   <td> <p>Onet</p> <p>weight</p> </td> 
   <td>layer name</td> 
   <td>weight data byte</td> 
   <td>weight bias byte</td> 
   <td>total byte</td> 
  </tr> 
  <tr> 
   <td>conv1</td> 
   <td>3456</td> 
   <td>128</td> 
   <td>3584</td> 
  </tr> 
  <tr> 
   <td>conv2</td> 
   <td>36864</td> 
   <td>128</td> 
   <td>36992</td> 
  </tr> 
  <tr> 
   <td>conv3</td> 
   <td>73728</td> 
   <td>256</td> 
   <td>73984</td> 
  </tr> 
  <tr> 
   <td>conv4_</td> 
   <td>147456</td> 
   <td>256</td> 
   <td>147712</td> 
  </tr> 
  <tr> 
   <td>conv5_</td> 
   <td>294912</td> 
   <td>512</td> 
   <td>295424</td> 
  </tr> 
  <tr> 
   <td>conv6_</td> 
   <td>589824</td> 
   <td>512</td> 
   <td>590336</td> 
  </tr> 
  <tr> 
   <td>fc5</td> 
   <td>1179648</td> 
   <td>1024</td> 
   <td>1180672</td> 
  </tr> 
  <tr> 
   <td>score</td> 
   <td>2048</td> 
   <td>8</td> 
   <td>2056</td> 
  </tr> 
  <tr> 
   <td>location</td> 
   <td>4096</td> 
   <td>16</td> 
   <td>4112</td> 
  </tr> 
 </tbody> 
</table>

## 6.2 feature大小 ##

<table> 
 <tbody> 
  <tr> 
   <td> <p>Rnet</p> <p>feature</p> </td> 
   <td>feature name</td> 
   <td>bytesize</td> 
  </tr> 
  <tr> 
   <td>rgb</td> 
   <td>6912</td> 
  </tr> 
  <tr> 
   <td>rgb_pad</td> 
   <td>8112</td> 
  </tr> 
  <tr> 
   <td>conv1_out</td> 
   <td>64512</td> 
  </tr> 
  <tr> 
   <td>conv1_out_pad</td> 
   <td>70000</td> 
  </tr> 
  <tr> 
   <td>pool_conv1_out</td> 
   <td>16128</td> 
  </tr> 
  <tr> 
   <td>pool_conv1_out_pad</td> 
   <td>21952</td> 
  </tr> 
  <tr> 
   <td>conv2_out</td> 
   <td>27648</td> 
  </tr> 
  <tr> 
   <td>conv2_out_pad</td> 
   <td>32448</td> 
  </tr> 
  <tr> 
   <td>pool2_conv3_out</td> 
   <td>6912</td> 
  </tr> 
  <tr> 
   <td>pool2_conv3_out_pad</td> 
   <td>9408</td> 
  </tr> 
  <tr> 
   <td>conv3_out</td> 
   <td>2304</td> 
  </tr> 
  <tr> 
   <td>fc4_out</td> 
   <td>512</td> 
  </tr> 
  <tr> 
   <td>score_</td> 
   <td>8</td> 
  </tr> 
  <tr> 
   <td>location_</td> 
   <td>16</td> 
  </tr> 
 </tbody> 
</table>

<table> 
 <tbody> 
  <tr> 
   <td> <p>Onet</p> <p>feature</p> </td> 
   <td>feature name</td> 
   <td>bytesize</td> 
  </tr> 
  <tr> 
   <td>rgb</td> 
   <td>27648</td> 
  </tr> 
  <tr> 
   <td>rgb_pad</td> 
   <td>30000</td> 
  </tr> 
  <tr> 
   <td>conv1_out</td> 
   <td>294912</td> 
  </tr> 
  <tr> 
   <td>conv1_out_pad</td> 
   <td>307328</td> 
  </tr> 
  <tr> 
   <td>conv2_out</td> 
   <td>73728</td> 
  </tr> 
  <tr> 
   <td>conv2_out_pad</td> 
   <td>86528</td> 
  </tr> 
  <tr> 
   <td>conv3_out</td> 
   <td>147456</td> 
  </tr> 
  <tr> 
   <td>conv3_out_pad</td> 
   <td>160000</td> 
  </tr> 
  <tr> 
   <td>conv4_out</td> 
   <td>36864</td> 
  </tr> 
  <tr> 
   <td>conv4_out_pad</td> 
   <td>43264</td> 
  </tr> 
  <tr> 
   <td>conv5_out</td> 
   <td>18432</td> 
  </tr> 
  <tr> 
   <td>conv5_out_pad</td> 
   <td>25088</td> 
  </tr> 
  <tr> 
   <td>conv6_out</td> 
   <td>4608</td> 
  </tr> 
  <tr> 
   <td>fc5_out</td> 
   <td>1024</td> 
  </tr> 
  <tr> 
   <td>score_</td> 
   <td>8</td> 
  </tr> 
  <tr> 
   <td>location_</td> 
   <td>16</td> 
  </tr> 
 </tbody> 
</table>

//scales 0.400000  h:116  w:142
    //scales 0.340000  h:98  w:120
    //scales 0.289000  h:84  w:102
    //scales 0.245650  h:72  w:88
    //scales 0.208803  h:62  w:74
    //scales 0.177482  h:52  w:64
    //scales 0.150860  h:44  w:54
    //scales 0.128231  h:38  w:46
    //scales 0.108996  h:32  w:40
    //scales 0.092647  h:28  w:34
    //scales 0.078750  h:24  w:28
    //scales 0.066937  h:20  w:24
    //scales 0.056897  h:18  w:22
    //scales 0.048362  h:14  w:18
    Create Pnet,Read Pnet.bin ,12 pointers
    ------Start find Face function
    scales 0.400000  h:116  w:142
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 197664 ----------------
    --------convolution buffer byte size 638400 -------
    --------Feature pad buffer byte size 648600 -------
    --------convolution buffer byte size 255360 -------
    --------convolution buffer byte size 478720 -------
    --------convolution buffer byte size 447744 -------
    --------convolution buffer byte size 27984 -------
    --------convolution buffer byte size 55968 -------
    scales 0.340000  h:98  w:120
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 141120 ----------------
    --------convolution buffer byte size 453120 -------
    --------Feature pad buffer byte size 461720 -------
    --------convolution buffer byte size 181248 -------
    --------convolution buffer byte size 335616 -------
    --------convolution buffer byte size 309760 -------
    --------convolution buffer byte size 19360 -------
    --------convolution buffer byte size 38720 -------
    scales 0.289000  h:84  w:102
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 102816 ----------------
    --------convolution buffer byte size 328000 -------
    --------Feature pad buffer byte size 335320 -------
    --------convolution buffer byte size 131200 -------
    --------convolution buffer byte size 239616 -------
    --------convolution buffer byte size 217856 -------
    --------convolution buffer byte size 13616 -------
    --------convolution buffer byte size 27232 -------
    scales 0.245650  h:72  w:88
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 76032 ----------------
    --------convolution buffer byte size 240800 -------
    --------Feature pad buffer byte size 247080 -------
    --------convolution buffer byte size 96320 -------
    --------convolution buffer byte size 173184 -------
    --------convolution buffer byte size 154752 -------
    --------convolution buffer byte size 9672 -------
    --------convolution buffer byte size 19344 -------
    scales 0.208803  h:62  w:74
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 55056 ----------------
    --------convolution buffer byte size 172800 -------
    --------Feature pad buffer byte size 178120 -------
    --------convolution buffer byte size 69120 -------
    --------convolution buffer byte size 121856 -------
    --------convolution buffer byte size 106496 -------
    --------convolution buffer byte size 6656 -------
    --------convolution buffer byte size 13312 -------
    scales 0.177482  h:52  w:64
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 39936 ----------------
    --------convolution buffer byte size 124000 -------
    --------Feature pad buffer byte size 128520 -------
    --------convolution buffer byte size 49600 -------
    --------convolution buffer byte size 85376 -------
    --------convolution buffer byte size 72576 -------
    --------convolution buffer byte size 4536 -------
    --------convolution buffer byte size 9072 -------
    scales 0.150860  h:44  w:54
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 28512 ----------------
    --------convolution buffer byte size 87360 -------
    --------Feature pad buffer byte size 91160 -------
    --------convolution buffer byte size 34944 -------
    --------convolution buffer byte size 58368 -------
    --------convolution buffer byte size 47872 -------
    --------convolution buffer byte size 2992 -------
    --------convolution buffer byte size 5984 -------
    scales 0.128231  h:38  w:46
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 20976 ----------------
    --------convolution buffer byte size 63360 -------
    --------Feature pad buffer byte size 66600 -------
    --------convolution buffer byte size 25344 -------
    --------convolution buffer byte size 40960 -------
    --------convolution buffer byte size 32256 -------
    --------convolution buffer byte size 2016 -------
    --------convolution buffer byte size 4032 -------
    scales 0.108996  h:32  w:40
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 15360 ----------------
    --------convolution buffer byte size 45600 -------
    --------Feature pad buffer byte size 48360 -------
    --------convolution buffer byte size 18240 -------
    --------convolution buffer byte size 28288 -------
    --------convolution buffer byte size 21120 -------
    --------convolution buffer byte size 1320 -------
    --------convolution buffer byte size 2640 -------
    scales 0.092647  h:28  w:34
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 11424 ----------------
    --------convolution buffer byte size 33280 -------
    --------Feature pad buffer byte size 35640 -------
    --------convolution buffer byte size 13312 -------
    --------convolution buffer byte size 19712 -------
    --------convolution buffer byte size 13824 -------
    --------convolution buffer byte size 864 -------
    --------convolution buffer byte size 1728 -------
    scales 0.078750  h:24  w:28
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 8064 ----------------
    --------convolution buffer byte size 22880 -------
    --------Feature pad buffer byte size 24840 -------
    --------convolution buffer byte size 9152 -------
    --------convolution buffer byte size 12672 -------
    --------convolution buffer byte size 8064 -------
    --------convolution buffer byte size 504 -------
    --------convolution buffer byte size 1008 -------
    scales 0.066937  h:20  w:24
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 5760 ----------------
    --------convolution buffer byte size 15840 -------
    --------Feature pad buffer byte size 17480 -------
    --------convolution buffer byte size 6336 -------
    --------convolution buffer byte size 8064 -------
    --------convolution buffer byte size 4480 -------
    --------convolution buffer byte size 280 -------
    --------convolution buffer byte size 560 -------
    scales 0.056897  h:18  w:22
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 4752 ----------------
    --------convolution buffer byte size 12800 -------
    --------Feature pad buffer byte size 14280 -------
    --------convolution buffer byte size 5120 -------
    --------convolution buffer byte size 6144 -------
    --------convolution buffer byte size 3072 -------
    --------convolution buffer byte size 192 -------
    --------convolution buffer byte size 384 -------
    scales 0.048362  h:14  w:18
    Start run Pnet
    Pnet feature buffer init
    ----------image to matrix buffer byte size 3024 ----------------
    --------convolution buffer byte size 7680 -------
    --------Feature pad buffer byte size 8840 -------
    --------convolution buffer byte size 3072 -------
    --------convolution buffer byte size 3072 -------
    --------convolution buffer byte size 1024 -------
    --------convolution buffer byte size 64 -------
    --------convolution buffer byte size 128 -------

# 二、程序思路 #

*  开辟网络，存入权重，Pnet，Rnet，Onet
 *  开辟Rnet，Onet，Pnet的存储空间，其中Pnet的feaute以最大的实现
 *  读入图片，存入feature
 *  运行程序，直接根据指针来运行