tensorflow(八)tensorflow加载VGG19模型数据并可视化每一层的输出

时间:2022-01-26 00:03:31

一、简介

VGG网络在2014年的 ILSVRC localization and classification 两个问题上分别取得了第一名和第二名。VGG网络非常深,通常有16-19层,如果自己训练网络模型的话很浪费时间和计算资源。因此这里采用一种方法获取VGG19模型的模型数据,从而能够更快速的应用到自己的任务中来,

本文在加载模型数据的同时,还可视化图片在网络传播过程中,每一层的输出特征图。让我们能够更直接的观察网络传播的状况。

运行环境为spyder,Python3.5,tensorflow1.2.1
模型名称为: imagenet-vgg-verydeep-19.mat 大家可以在网上下载。

二、VGG19模型结构

模型的每一层结构如下图所示:
tensorflow(八)tensorflow加载VGG19模型数据并可视化每一层的输出

三、代码

    #加载VGG19模型并可视化一张图片前向传播的过程中每一层的输出
    #引入包
    import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt
    import scipy.io
    import scipy.misc
    #定义一些函数
    #卷积
    def _conv_layer(input, weights, bias):
           conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),
                   padding='SAME')
           return tf.nn.bias_add(conv, bias)
    #池化
    def _pool_layer(input):
           return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
                   padding='SAME')
    #减像素均值操作
    def preprocess(image, mean_pixel):
           return image - mean_pixel
    #加像素均值操作
    def unprocess(image, mean_pixel):
           return image + mean_pixel
    #读
    def imread(path):
           return scipy.misc.imread(path).astype(np.float)
    #保存
    def imsave(path, img):
           img = np.clip(img, 0, 255).astype(np.uint8)
           scipy.misc.imsave(path, img)
    print ("Functions for VGG ready")
    #定义VGG的网络结构,用来存储网络的权重和偏置参数
    def net(data_path, input_image):
            #拿到每一层对应的参数
            layers = (
                'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
                'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
                'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
                'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
                'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
                'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
                'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
                'relu5_3', 'conv5_4', 'relu5_4'
            )
            data = scipy.io.loadmat(data_path)
            #原网络在训练的过程中,对每张图片三通道都执行了减均值的操作,这里也要减去均值
            mean = data['normalization'][0][0][0]
            mean_pixel = np.mean(mean, axis=(0, 1))
            #print(mean_pixel)
            #取到权重参数W和b,这里运气好的话,可以查到VGG模型中每层的参数含义,查不到的
            #话可以打印出weights,然后打印每一层的shape,推出其中每一层代表的含义
            weights = data['layers'][0]
            #print(weights)
            net = {}
            current = input_image
            #取到w和b
            for i, name in enumerate(layers):
                #:4的含义是只看每一层的前三个字母,从而进行判断
                kind = name[:4]
                if kind == 'conv':
                    kernels, bias = weights[i][0][0][0][0]
                    # matconvnet: weights are [width, height, in_channels, out_channels]\n",
                    # tensorflow: weights are [height, width, in_channels, out_channels]\n",
                    #这里width和height是颠倒的,所以要做一次转置运算
                    kernels = np.transpose(kernels, (1, 0, 2, 3))
                    #将bias转换为一个维度
                    bias = bias.reshape(-1)
                    current = _conv_layer(current, kernels, bias)
                elif kind == 'relu':
                    current = tf.nn.relu(current)
                elif kind == 'pool':
                    current = _pool_layer(current)
                net[name] = current
            assert len(net) == len(layers)
            return net, mean_pixel, layers
    print ("Network for VGG ready")
    #cwd = os.getcwd()
    #这里用的是绝对路径
    VGG_PATH = "F:/mnist/imagenet-vgg-verydeep-19.mat"
    #需要可视化的图片路径,这里是一只小猫
    IMG_PATH = "D:/VS2015Program/cat.jpg"
    input_image = imread(IMG_PATH)
    #获取图像shape
    shape = (1,input_image.shape[0],input_image.shape[1],input_image.shape[2]) 
    #开始会话
    with tf.Session() as sess:
            image = tf.placeholder('float', shape=shape)
            #调用net函数
            nets, mean_pixel, all_layers = net(VGG_PATH, image)
            #减均值操作(由于VGG网络图片传入前都做了减均值操作,所以这里也用相同的预处理
            input_image_pre = np.array([preprocess(input_image, mean_pixel)])
            layers = all_layers # For all layers \n",
            # layers = ('relu2_1', 'relu3_1', 'relu4_1')\n",
            for i, layer in enumerate(layers):
                print ("[%d/%d] %s" % (i+1, len(layers), layer))
                features = nets[layer].eval(feed_dict={image: input_image_pre})
                print (" Type of 'features' is ", type(features))
                print (" Shape of 'features' is %s" % (features.shape,))
                # Plot response \n",
                #画出每一层
                if 1:
                    plt.figure(i+1, figsize=(10, 5))
                    plt.matshow(features[0, :, :, 0], cmap=plt.cm.gray, fignum=i+1)
                    plt.title("" + layer)
                    plt.colorbar()
                    plt.show()

四、程序运行结果

1、print(weights)的结果:
tensorflow(八)tensorflow加载VGG19模型数据并可视化每一层的输出

2、程序运行最终结果:
tensorflow(八)tensorflow加载VGG19模型数据并可视化每一层的输出
中间层数太多,这里就不展示了。程序最后两层的可视化结果:
tensorflow(八)tensorflow加载VGG19模型数据并可视化每一层的输出