MTCNN（十）输出python端权重到c端

布满荆棘的人生 2022-04-11 03:10 255阅读 0赞

**背景：**python端运用tensorFlow训练权重，很快速。c代码端为了获得更好的运行性能，只保留网络前馈运算的部分。

**目的：**将python端通过tensorFlow训练的权重输出出来，c端读取。

**前期的工作：**

MTCNN（三）基于python代码的网络结构更改 [https://blog.csdn.net/weixin\_36474809/article/details/82856171][https_blog.csdn.net_weixin_36474809_article_details_82856171]

MTCNN（六）c代码网络结构的更改 [https://blog.csdn.net/weixin\_36474809/article/details/83056795][https_blog.csdn.net_weixin_36474809_article_details_83056795]

**目录**

一、中间调试程序

1.1 c代码端输出中间结果

1.2 python端输出中间结果

二、原结构与原结构

2.1  读入tensorFlow模型与参数

2.2 创建相应文档

2.3 写入

Pconv1\_w卷积核

Pconv1\_b偏移

PPReLU1斜率

Pconv41\_w全连接层

三、新结构

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

权重输出程序的实现：庄铭泳 [https://me.csdn.net/qqqzmy][https_me.csdn.net_qqqzmy]

# 一、中间调试程序 #

初期较多bug，无法很快实现权重转移。需要来回实验，将tensorFlow与c端代码的中间结果进行对比。所以需要输出TF与c端代码的中间结果，以便进行调试。

## 1.1 c代码端输出中间结果 ##

MTCNN原程序自带输出中间结果的pBoxshow函数。

void pBoxShow(const struct pBox *pbox){
    	if (pbox->pdata == NULL){
    		cout << "pbox is NULL, please check it !" << endl;
    		return;
    	}
    	cout << "the data is :" << endl;
    	mydataFmt *p = pbox->pdata;
    	//pbox->channel
    	for (int channel = 0; channel < pbox->channel; channel++){
    		cout << "the " << channel <<"th channel data is :"<< endl;
    		//pbox->height
    		for (int i = 0; i < pbox->height; i++){
    			for (int k = 0; k < pbox->width; k++){
    				cout << *p++ << " ";
    			}
    			cout << endl;
    		}
    	}
    	p = NULL;
    }

在c程序之中输出中间结果的值。

feature2Matrix(this->maxPooling1, this->maxPooling_matrix, this->conv2_wb);
    convolution(this->conv2_wb, this->maxPooling1, this->conv2, this->maxPooling_matrix);
    prelu(this->conv2, this->conv2_wb->pbias, this->prelu_gmma2->pdata);
    
    printf("\n########################conv2 ##########################\n");    
    pBoxShow(this->conv2);

## 1.2 python端输出中间结果 ##

#PNet debug
       if(1==1):
           scale = scales[12]
           
           hs = int(np.ceil(h * scale))
           ws = int(np.ceil(w * scale))
           print("scale", scale, "hs", hs, "ws", ws)
           im_data = imresample(imgDebug, (hs, ws))
    #        im_data = (im_data - 127.5) * (1. / 128.0)
           print("im_data.shape", im_data.shape)
           
           if(im_data.shape == (17, 21, 3)):
               print("orignal img ###########\n")
               for c in range(3):
                   print("channel:",c)
                   tempImg = []
                   for y in range(17):
                       for x in range(21):
                           tempImg.append(im_data[y,x,c])
                       print(tempImg)
                       tempImg = []
                   print("\n")
               print("\n\n\n")

# 二、原结构与原结构 #

MTCNN初始的程序为人脸识别程序，原结构与原结构需要验证对齐上。

## 2.1  读入tensorFlow模型与参数 ##

import tensorflow as tf
    #import cv2
    #import numpy as np
    import os
    import struct
    from src.mtcnn import PNet, RNet, ONet
    from tools import get_model_filenames
    os.environ["CUDA_VISIBLE_DEVICES"] = "3"
    
    model_dir = "/1t_second/myzhuang2/MTCNN/wangbm_tf/save_model/sepatate/"
    file_paths = get_model_filenames(model_dir)
    
    with tf.device('/cpu'):
        with tf.Graph().as_default():
            config = tf.ConfigProto(allow_soft_placement=True)
            with tf.Session(config=config) as sess:
                if len(file_paths) == 3:
                    image_pnet = tf.placeholder(
                        tf.float32, [None, None, None, 3])
                    pnet = PNet({'data': image_pnet}, mode='test')
                    out_tensor_pnet = pnet.get_all_output()
    
                    image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
                    rnet = RNet({'data': image_rnet}, mode='test')
                    out_tensor_rnet = rnet.get_all_output()
    
                    image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
                    onet = ONet({'data': image_onet}, mode='test')
                    out_tensor_onet = onet.get_all_output()
    
                    saver_pnet = tf.train.Saver(
                                    [v for v in tf.global_variables()
                                     if v.name[0:5] == "pnet/"])
                    saver_rnet = tf.train.Saver(
                                    [v for v in tf.global_variables()
                                     if v.name[0:5] == "rnet/"])
                    saver_onet = tf.train.Saver(
                                    [v for v in tf.global_variables()
                                     if v.name[0:5] == "onet/"])
    
                    saver_pnet.restore(sess, file_paths[0])
    
                    def pnet_fun(img): return sess.run(
                        out_tensor_pnet, feed_dict={image_pnet: img})
    
                    saver_rnet.restore(sess, file_paths[1])
    
                    def rnet_fun(img): return sess.run(
                        out_tensor_rnet, feed_dict={image_rnet: img})
    
                    saver_onet.restore(sess, file_paths[2])
    
                    def onet_fun(img): return sess.run(
                        out_tensor_onet, feed_dict={image_onet: img})

## 2.2 创建相应文档 ##

##PNET Params Write
               variable_names = [v.name for v in tf.global_variables()]
               pnet_var = []
               for var in variable_names:
                   if 'pnet' in var:
                       pnet_var.append(var)
                       print(var)
               PnetOutFile = "Pnet.bin"
               PnetBinFile = open(PnetOutFile,'wb')
               print("tensorflow model load seccess")
    
               writeNum = 0

## 2.3 写入 ##

### Pconv1\_w卷积核 ###

Conv层输出顺序：先确定输出核（num\_out\_kerns），再确定输入核（num\_in\_map），再先列后行输出。相应顺序需要保持一致。此顺序为c代码端的顺序。**注意相应TensorFlow的权重为Pconv1\_w\[ ky, kx ,num\_in\_map, num\_out\_map \]**

可见TensorFlow之中权重的排列为\[列数，行数，输入通道，输出通道\]

#Pconv1_w
               Pconv1_w = sess.run(pnet_var[0])
               ky, kx, num_in_map, num_out_kerns = Pconv1_w.shape
               print(0,Pconv1_w.shape)
               for j in range(num_out_kerns):
                   for i in range(num_in_map):
                       for y in range(ky):
                           for x in range(kx):
                               PnetBinFile.write(struct.pack('f', Pconv1_w[y,x,i,j]))
                               writeNum += 1

### Pconv1\_b偏移 ###

Conv层偏置、PReLU层的输出顺序TF和C代码中一致，按照顺序输出即可.

#Pconv1_b
               Pconv1_b = sess.run(pnet_var[1])
               print(1,Pconv1_b.shape)
               for item in range(0,len(Pconv1_b[:])):
                   PnetBinFile.write(struct.pack('f',Pconv1_b[item]))
                   writeNum += 1

### PPReLU1斜率 ###

#PPReLU1
               PPReLU1 = sess.run(pnet_var[2])
               print(2,PPReLU1.shape)
               for item in range(0,len(PPReLU1[:])):
                   PnetBinFile.write(struct.pack('f',PPReLU1[item]))
                   writeNum += 1

### Pconv41\_w全连接层 ###

全连接层输出按照先输出通道（num\_out\_kerns）再输入通道(num\_in\_map)。这是c代码之中的权重排列顺序。

TensorFlow之中的权重排列顺序为：\[宽，高，输入通道，输出通道\]

#Pconv41_b
               Pconv41_b = sess.run(pnet_var[10])
               print(10,Pconv41_b.shape)
               for item in range(0,len(Pconv41_b[:])):
                   PnetBinFile.write(struct.pack('f',Pconv41_b[item]))
                   writeNum += 1                
    #Pconv42_w
               Pconv42_w = sess.run(pnet_var[11])
               print(11,Pconv42_w.shape)
               ky, kx, num_in_map, num_out_kerns = Pconv42_w.shape
               for j in range(num_out_kerns):
                   for i in range(num_in_map):
                       for y in range(ky):
                           for x in range(kx):                    
                               PnetBinFile.write(struct.pack('f', Pconv42_w[y,x,i,j]))
                               writeNum += 1

# 三、新结构 #

人头数据集上python代码训练，并且测试成功，c端直接运用python端的网络结构来进行更改。

基本的层操作都一样，只要与同上的顺序输出即可

[https_blog.csdn.net_weixin_36474809_article_details_82856171]: https://blog.csdn.net/weixin_36474809/article/details/82856171
[https_blog.csdn.net_weixin_36474809_article_details_83056795]: https://blog.csdn.net/weixin_36474809/article/details/83056795
[https_me.csdn.net_qqqzmy]: https://me.csdn.net/qqqzmy