VGG19模型训练+读取

Bertha 。 2022-01-10 05:41 609阅读 0赞

*  VGG-19模型简单介绍
 *  VGG-19模型文件介绍
    
     *  分析模型文件
        
         *  mean值查看
         *  Weight和Bias查看
         *  读取代码
     *  读取模型
 *  训练代码
 *  参考资料

>  *  VGG-19的介绍和训练这里不做说明,网上资源很多,而且相对比较简单.
>  *  本博文主要介绍VGG-19模型调用官方已经训练好的模型,进行测试使用.

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

# VGG-19模型简单介绍 #

![VGG模型使用][VGG]

![VGG结构模型][VGG 1]

![VGG具体参数模型][VGG 2]

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

# VGG-19模型文件介绍 #

> **这里是重难点,VGG-19模型存储的方式有点复杂**
> 
>  *  可以通过作者文档说明去查看
>  *  可以通过在线调试查看结构,对比模型得出结论

1.  [imagenet-vgg-verydeep-19.mat文件下载地址][imagenet-vgg-verydeep-19.mat]
2.  [ImageNet 1000种分类以及排列文件下载地址][ImageNet 1000]

## 分析模型文件 ##

首先我们通过scipy模块(当然你可以用其它方式入opencv / sklearn等)读取`scipy.io.loadmat()`文件

data = scipy.io.loadmat(data_path)

接下来使用Pycharm在线查看`data`数据结构:

*  总共有很多参数,我们只关心我们需要关注的,W和B在哪里就行了
 *  注意这里还有一个mean(平均值),因为VGG使用了图像预处理方式是 input - mean,当然这种处理方式在现在看来不怎么好,但是现在我们用人家的模型,需要遵照人家的意思.
 *  这里主要关注"layers"和"normalization"

![data内存一][data]

### mean值查看 ###

首先查看normalization的mean值:

*  注意我们使用的是RGB输入,且图像大小为224\*224,也就是说图像是\[1 \* 224 \* 224 \*3\]格式
 *  那么我们的mean肯定就是\[X,Y,Z\]的格式,因为是RGB的平均值

这里使用笨方法,直接带入:

`data["normalization"][0][0][0][0][0]`

**先看大概看一下数据结构,然后一个一个测试,因为在线调试,这样很快,比你精确的去找要快很多.**

![1033571-20180612210605108-1637367328.png][]

### Weight和Bias查看 ###

![layer(一)][layer]

**这里和mean查看方法不同,W和B得自习查看数据结构,因为太复杂了**

*  从下面的图看到存储的43个参数
 *  注意里面的`Relu`是没有数据的,因为`Relu`就是一个函数
 *  注意`Pool`的参数是固定的,因为大小为:\[1,2,2,1\],步长\[1,2,2,1\],这里可以自己写,也可以读取参数
 *  `Weight` `Bias`是存放在`Relu` `Pool` 中间的,而且两个值存在一起的.
 *  后期代码需要进一步处理,后面会说到.
 *  再不明白的,自己画个VGG-19网络就知道了.

![layer(二)][layer 1]

### 读取代码 ###

mean = data["normalization"][0][0][0][0][0]

data['layers'][0][i][0][0][0][0])

## 读取模型 ##

> **这里默认大家已经会CNN的基本操作了,代码不做详细说明**

import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt
    import os
    import scipy.io
    import scipy.misc
    from imagenet_classes import class_names
    
    def _conv_layer(input,weight,bias):
        conv = tf.nn.conv2d(input,weight,strides=[1,1,1,1],padding="SAME")
        return tf.nn.bias_add(conv,bias)
    def _pool_layer(input):
        return tf.nn.max_pool(input,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")
    def preprocess(image,mean_pixel):
        '''简单预处理,全部图片减去平均值'''
        return image-mean_pixel
    def unprocess(image,mean_pixel):
        return image+mean_pixel
    def imread(path):
        return scipy.misc.imread(path)
    def imsave(image,path):
        img = np.clip(image,0,255).astype(np.int8)
        scipy.misc.imsave(path,image)
    def net(data_path,input_image,sess=None):
        """
        读取VGG模型参数,搭建VGG网络
        :param data_path: VGG模型文件位置
        :param input_image: 输入测试图像
        :return:
        """
        layers = (
            'conv1_1', 'conv1_2', 'pool1',
            'conv2_1', 'conv2_2', 'pool2',
            'conv3_1', 'conv3_2', 'conv3_3','conv3_4', 'pool3',
            'conv4_1', 'conv4_2', 'conv4_3','conv4_4', 'pool4',
            'conv5_1', 'conv5_2', 'conv5_3','conv5_4', 'pool5',
              'fc1'  ,   'fc2'  ,   'fc3'  ,
            'softmax'
        )
        data = scipy.io.loadmat(data_path)
        mean = data["normalization"][0][0][0][0][0]
        input_image = np.array([preprocess(input_image, mean)]).astype(np.float32)#去除平均值
        net = {}
        current = input_image
        net["src_image"] = tf.constant(current)  # 存储数据
        count = 0 #计数存储
        for i in range(43):
            if str(data['layers'][0][i][0][0][0][0])[:4] == ("relu"):
                continue
            if str(data['layers'][0][i][0][0][0][0])[:4] == ("pool"):
                current = _pool_layer(current)
            elif str(data['layers'][0][i][0][0][0][0]) == ("softmax"):
                current = tf.nn.softmax(current)
            elif i == (37):
                shape = int(np.prod(current.get_shape()[1:]))
                current = tf.reshape(current, [-1, shape])
                kernels, bias = data['layers'][0][i][0][0][0][0]
                kernels = np.reshape(kernels,[-1,4096])
                bias = bias.reshape(-1)
                current = tf.nn.relu(tf.add(tf.matmul(current,kernels),bias))
            elif i == (39):
                kernels, bias = data['layers'][0][i][0][0][0][0]
                kernels = np.reshape(kernels,[4096,4096])
                bias = bias.reshape(-1)
                current = tf.nn.relu(tf.add(tf.matmul(current,kernels),bias))
            elif i == 41:
                kernels, bias = data['layers'][0][i][0][0][0][0]
                kernels = np.reshape(kernels, [4096, 1000])
                bias = bias.reshape(-1)
                current = tf.add(tf.matmul(current, kernels), bias)
            else:
                kernels,bias = data['layers'][0][i][0][0][0][0]
                #注意VGG存储方式为[,]
                #kernels = np.transpose(kernels,[1,0,2,3])
                bias = bias.reshape(-1)#降低维度
                current = tf.nn.relu(_conv_layer(current,kernels,bias))
            net[layers[count]] = current #存储数据
            count += 1
        return net, mean
    
    if __name__ == '__main__':
        VGG_PATH = os.getcwd()+"/imagenet-vgg-verydeep-19.mat"
    
        input_image = scipy.misc.imread("234.jpeg")
        input_image = scipy.misc.imresize(input_image,[224,224,3])
    
        shape = (1, input_image.shape[0], input_image.shape[1], input_image.shape[2])
        #image = tf.placeholder('float', shape=shape)
    
        with tf.Session() as sess:
            nets, mean_pixel, = net(VGG_PATH, input_image, sess=sess)
            #print(sess.run(nets,feed_dict={image:input_image}))
            nets = sess.run(nets)
            '''
            for key, values in nets.items():
                if len(values.shape)<4:
                    continue
                plt.figure(key)
                plt.matshow(values[0, :, :, 0],)
                plt.title(key)
                plt.colorbar()
                plt.show()
            '''
            #打印概率最大的三个数据
            net_sort    = list(reversed(np.argsort(nets["softmax"]).reshape(-1).tolist()))
            net_softmax = nets["softmax"].reshape(-1).tolist()
            for i in range(3):
                print(class_names[net_sort[i]],": ",net_softmax[net_sort[i]])

输入图片:

![测试图片][1033571-20180612212241408-1190806303.jpg]

结果显示:

![结果图][1033571-20180612212123893-1926137751.png]

百度验证:

![验证图][1033571-20180612212424630-281199402.png]

完整模型下载地址:

*  **网盘老是被封,请留言邮箱发送**

# 训练代码 #

以下是训练的代码:

> 这部分随便看看就好,比较简单~~主要看思想和细节!

########################################################################################
    # Davi Frossard, 2016                                                                  #
    # VGG16 implementation in TensorFlow                                                   #
    # Details:                                                                             #
    # http://www.cs.toronto.edu/~frossard/post/vgg16/                                      #
    #                                                                                      #
    # Model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md     #
    # Weights from Caffe converted using https://github.com/ethereon/caffe-tensorflow      #
    ########################################################################################
    
    import tensorflow as tf
    import numpy as np
    from scipy.misc import imread, imresize
    from imagenet_classes import class_names
    
    
    class vgg16:
        def __init__(self, imgs, weights=None, sess=None):
            self.imgs = imgs
            self.convlayers()
            self.fc_layers()
            self.probs = tf.nn.softmax(self.fc3l)
            if weights is not None and sess is not None:
                self.load_weights(weights, sess)
    
    
        def convlayers(self):
            self.parameters = []
    
            # zero-mean input
            with tf.name_scope('preprocess') as scope:
                mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
                images = self.imgs-mean
    
            # conv1_1
            with tf.name_scope('conv1_1') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv1_1 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # conv1_2
            with tf.name_scope('conv1_2') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv1_2 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # pool1
            self.pool1 = tf.nn.max_pool(self.conv1_2,
                                   ksize=[1, 2, 2, 1],
                                   strides=[1, 2, 2, 1],
                                   padding='SAME',
                                   name='pool1')
    
            # conv2_1
            with tf.name_scope('conv2_1') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv2_1 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # conv2_2
            with tf.name_scope('conv2_2') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv2_2 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # pool2
            self.pool2 = tf.nn.max_pool(self.conv2_2,
                                   ksize=[1, 2, 2, 1],
                                   strides=[1, 2, 2, 1],
                                   padding='SAME',
                                   name='pool2')
    
            # conv3_1
            with tf.name_scope('conv3_1') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv3_1 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # conv3_2
            with tf.name_scope('conv3_2') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv3_2 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # conv3_3
            with tf.name_scope('conv3_3') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv3_3 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # pool3
            self.pool3 = tf.nn.max_pool(self.conv3_3,
                                   ksize=[1, 2, 2, 1],
                                   strides=[1, 2, 2, 1],
                                   padding='SAME',
                                   name='pool3')
    
            # conv4_1
            with tf.name_scope('conv4_1') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.pool3, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv4_1 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # conv4_2
            with tf.name_scope('conv4_2') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv4_2 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # conv4_3
            with tf.name_scope('conv4_3') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv4_3 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # pool4
            self.pool4 = tf.nn.max_pool(self.conv4_3,
                                   ksize=[1, 2, 2, 1],
                                   strides=[1, 2, 2, 1],
                                   padding='SAME',
                                   name='pool4')
    
            # conv5_1
            with tf.name_scope('conv5_1') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv5_1 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # conv5_2
            with tf.name_scope('conv5_2') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.conv5_1, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv5_2 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # conv5_3
            with tf.name_scope('conv5_3') as scope:
                kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
                                                         stddev=1e-1), name='weights')
                conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
                                     trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                self.conv5_3 = tf.nn.relu(out, name=scope)
                self.parameters += [kernel, biases]
    
            # pool5
            self.pool5 = tf.nn.max_pool(self.conv5_3,
                                   ksize=[1, 2, 2, 1],
                                   strides=[1, 2, 2, 1],
                                   padding='SAME',
                                   name='pool4')
    
        def fc_layers(self):
            # fc1
            with tf.name_scope('fc1') as scope:
                shape = int(np.prod(self.pool5.get_shape()[1:]))
                fc1w = tf.Variable(tf.truncated_normal([shape, 4096],
                                                             dtype=tf.float32,
                                                             stddev=1e-1), name='weights')
                fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
                                     trainable=True, name='biases')
                pool5_flat = tf.reshape(self.pool5, [-1, shape])
                fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)
                self.fc1 = tf.nn.relu(fc1l)
                self.parameters += [fc1w, fc1b]
    
            # fc2
            with tf.name_scope('fc2') as scope:
                fc2w = tf.Variable(tf.truncated_normal([4096, 4096],
                                                             dtype=tf.float32,
                                                             stddev=1e-1), name='weights')
                fc2b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
                                     trainable=True, name='biases')
                fc2l = tf.nn.bias_add(tf.matmul(self.fc1, fc2w), fc2b)
                self.fc2 = tf.nn.relu(fc2l)
                self.parameters += [fc2w, fc2b]
    
            # fc3
            with tf.name_scope('fc3') as scope:
                fc3w = tf.Variable(tf.truncated_normal([4096, 1000],
                                                             dtype=tf.float32,
                                                             stddev=1e-1), name='weights')
                fc3b = tf.Variable(tf.constant(1.0, shape=[1000], dtype=tf.float32),
                                     trainable=True, name='biases')
                self.fc3l = tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b)
                self.parameters += [fc3w, fc3b]
    
        def load_weights(self, weight_file, sess):
            weights = np.load(weight_file)
            keys = sorted(weights.keys())
            for i, k in enumerate(keys):
                print i, k, np.shape(weights[k])
                sess.run(self.parameters[i].assign(weights[k]))
    
    if __name__ == '__main__':
        sess = tf.Session()
        imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
        vgg = vgg16(imgs, 'vgg16_weights.npz', sess)
    
        img1 = imread('laska.png', mode='RGB')
        img1 = imresize(img1, (224, 224))
    
        prob = sess.run(vgg.probs, feed_dict={vgg.imgs: [img1]})[0]
        preds = (np.argsort(prob)[::-1])[0:5]
        for p in preds:
            print class_names[p], prob[p]

# 参考资料 #

*  [https://www.cs.toronto.edu/~frossard/post/vgg16/][https_www.cs.toronto.edu_frossard_post_vgg16]
 *  [http://x-algo.cn/index.php/2017/01/08/1471/][http_x-algo.cn_index.php_2017_01_08_1471]
 *  [https://blog.csdn.net/wyl1987527/article/details/68168115][https_blog.csdn.net_wyl1987527_article_details_68168115]

转载于:https://www.cnblogs.com/wjy-lulu/p/9175529.html

[VGG]: /images/20220109/023e38a2b7d24ca5997a1c1c3ba500a0.png
[VGG 1]: /images/20220109/63273dd48fa34ef6ad3000baf688c7f0.png
[VGG 2]: /images/20220109/8ed9ed45790d4d07a82da3d85697dd84.png
[imagenet-vgg-verydeep-19.mat]: http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat
[ImageNet 1000]: https://download.csdn.net/download/u011046017/10474965
[data]: /images/20220109/bfc3420636bb4a888d5f5cb432f86f3f.png
[1033571-20180612210605108-1637367328.png]: /images/20220109/a84fbf1a163d4787ae98482b6788effd.png
[layer]: /images/20220109/0ce201e277fe4342b14e63a04a877fb1.png
[layer 1]: /images/20220109/4a72e12f5a1b46e1aa875b15bbb60d84.png
[1033571-20180612212241408-1190806303.jpg]: /images/20220109/03c1230beb864b89a6fcf2cf3c414a18.png
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[https_www.cs.toronto.edu_frossard_post_vgg16]: https://www.cs.toronto.edu/~frossard/post/vgg16/
[http_x-algo.cn_index.php_2017_01_08_1471]: http://x-algo.cn/index.php/2017/01/08/1471/
[https_blog.csdn.net_wyl1987527_article_details_68168115]: https://blog.csdn.net/wyl1987527/article/details/68168115