使用预训练模型VGG19进行神经风格迁移实战

• 一笔一划，一个世界

前言

• 本次搭建的卷积神经网络使用了VGG19预训练模型搭建神经风格迁移模型，所以使用CPU整个训练过程也很快。
• 环境和库：windows10+pycharm+tensorflow+keras+python+CPU
• 使用tensorflow_hub可以快速体验神经风格迁移，不过需要VPN

import tensorflow_hub as hub

# 如果搭了VPN还是出现超时的问题，可以直接点开该url，
# 然后直接download压缩文件，
# 然后解压后把解压后的文件夹路径替换该url即可
hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')
stylized_image = hub_module(tf.constant(content_image), tf.constant(style_image))[0]
# 自定义函数tensor_to_image，实现见下文
tensor_to_image(stylized_image)

正文

库、包、模板

import numpy as np
import pandas as pd

import tensorflow as tf
import keras.preprocessing.image as process_im
from keras.applications import vgg19
from keras.models import Model
from tensorflow.python.keras import models
from tensorflow.python.keras import losses
from tensorflow.python.keras import layers
from tensorflow.python.keras import backend as k

from PIL import Image		# Python Image Library
import matplotlib.pyplot as plt	# 可视化库
import functools			# 用以为可调用对象（callable objects）定义高阶函数或操作
import IPython.display		# python的交互式shell

---

图像预处理

• PIL中的image对象调用resize方法，此处将图像进行缩放为某高宽的高质量图像
• img_to_array转换前后类型都是一样的，唯一区别是转换前元素类型是整型，转换后元素类型是浮点型(和keras等机器学习框架相适应的图像类型。Keras introduced a special function called img_to_array which accepts an input image and then orders the channels correctly based on the image_data_format setting)
• expand_dims在数组中增加一个维度，reshape能够达到相同的效果
• 作用：加载图像；将图像等比例缩放，最长边为512；并返回图像对应三维数组
• 源码：

# 加载图像；将图像进行缩放，最长边为512；并将图像转换为array输出
def load_img(img_path):
    """

    :param img_path: 
    :return: 
    """
    max_dim = 512
    img = tf.io.read_file(img_path)  # coded
    img = tf.image.decode_image(img, channels=3)  # 0-255
    img = tf.image.convert_image_dtype(img, tf.float32)  # 0-1

    shape = tf.cast(tf.shape(img)[:-1], tf.float32)
    long_dim = max(shape)
    scale = max_dim / long_dim

    new_shape = tf.cast(shape * scale, tf.int32)

    img = tf.image.resize(img, new_shape)
    img = img[tf.newaxis, :]        # 升维，便于卷积神经网络处理
    return img

---

构建神经风格迁移模型

VGG19预训练模型

• 函数原型如下，返回一个Kera Model对象

keras.applications.vgg19.VGG19(include_top=True,
							   weights='imagenet', 	
							   input_tensor=None,	
							   input_shape=None, 
							   pooling=None,
							   classes=1000)		

• 有点儿不理解include_top的用法，文档中说：是否包括顶层的全连接层，但是还是有点儿不清楚，所以直接调用model.summary()来一探究竟，结果是：是否删去所有的全连接层
• 还有对于pooling的用法，实验说明：表示在include_top=False时，最后一层是否要加上一层什么样的池化层。注意：最后一块的最后一层本来就有一层最大池化层
• 此处我们使用VGG19预训练模型对于VGG19的需求如下：不需要最后的全连接层（include_top=False）、需要预训练权重（weights=‘imagenet’）、其他为默认值。

基于VGG19自定义模型

• 整个模型的输入即为VGG19的输入，而输出则需要根据神经风格迁移网络的代价函数来定，我们需要提取内容图像中的内容、风格图像中的风格(blog)，由此可知：内容代价函数我们需要提取靠近中间的某层的激活值输出，风格代价函数我们需要提取每一层的激活值输出。
• 值得一提的是：我们自定义好的模型之后，一些完全无关的层会被自动剔除，可用model.summary()验证
• 对于VGG19来说，模型结构如下图所示：
  
• 需要提取的层的激活值如下所示

content_layers = ['block5_conv2']
style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1',
                'block4_conv1',
                'block5_conv1']

• 在搞明白以上内容之后，我们可以调用models.Model(input=, output=)构建我们需要的神经风格迁移模型了。

content_layers = ['block5_conv2']
style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1',
                'block4_conv1',
                'block5_conv1']
# get the neural transfer model
def get_model(content_layers_names, style_layers_names):
    """
    :param content_layers_names: content layers names list
    :param style_layers_names: style layers name list
    :return: neural transfer model
    """
    vgg = tf.keras.applications.vgg19.VGG19(include_top=False)  # 删掉最后一层；默认加载ImageNet上的预训练权重
    vgg.trainable = False  # 参数不可训练

    # get the content layer and style layer
    content_output = [vgg.get_layer(name=layer).output for layer in content_layers_names]
    style_output = [vgg.get_layer(name=layer).output for layer in style_layers_names]
    model_output = style_output + content_output  # list combine

    # get the neural transfer model
    neural_transfer_model = models.Model([vgg.input], model_output)
    return neural_transfer_model

代价函数

内容代价函数

• 内容直接定义为内容层的激活值，所以内容代价函数定义为激活值的均方差误差MSE

# get content loss
def content_loss_layer(output_layer, target_layer):
    """
    :param output_layer:
    :param target_layer:
    :return: some layer content loss
    """
    # MSE, all elements
    content_loss = tf.reduce_mean(tf.square(output_layer - target_layer))
    return content_loss

风格代价函数

• 风格定义为不同通道之间激活值的相关系数，每一层有一个风格矩阵（即为Gram矩阵），风格代价函数定义类似于风格矩阵的均方误差

# get the gram matrix for style cost
def gram_matrix(layer):
    """
    :param layer: some layer of model
    :return: Gram matrix
    """
    channels = int(layer.shape[-1])		# 通道数为layer最后一层的维度
    vector = tf.reshape(layer, [-1, channels])  # reshape to n*channel
    n = tf.shape(vector)[0]			# n=nw*nh

    # vector.T*vector
    gram = tf.matmul(vector, vector, transpose_a=True)
    return gram / tf.cast(n, tf.float32)


# get some layer style loss
def style_loss_layer(output_layer, target_layer):
    """
    :param output_layer:
    :param target_layer:
    :return:
    """
    # get the gram matrix of the layer
    gram_output = gram_matrix(output_layer)
    gram_target = gram_matrix(target_layer)

    # use the gram to compute the loss of some layer
    # /(nw*nh*nc)
    style_loss = tf.reduce_mean(tf.square(gram_target - gram_output))
    return style_loss

总代价函数

• 总代价函数由内容代价函数和风格代价函数通过加权和得来
• 为什么在get_features方法中截取content_feature&style_feature的时候用的layer[0]，要解决这个问题必须要理解每一层的激活值到底是什么样的结构！
• 同样的问题：在neural_transfer_cost方法中对输出图像的输出激活值只去第零维的值
• 源码：

# get the style layer output and content layer output
def get_features(neural_transfer_model, content_image, style_image):
    """

    :param neural_transfer_model:
    :param content_image:
    :param style_image:
    :return: 
    """
	content_image = content_image*255.
	style_image = style_image*255.
	
    # preprocess the content image and style image
    content_image = tf.keras.applications.vgg19.preprocess_input(content_image)
    style_image = tf.keras.applications.vgg19.preprocess_input(style_image)

    # input the preprocessed content image and style image
    # get the output of the neural transfer model
    content_output = neural_transfer_model(content_image)
    style_output = neural_transfer_model(style_image)

    # extract the content layer output and style layer output
    content_feature = [layer for layer in content_output[number_style:]]
    style_feature = [layer for layer in style_output[:number_style]]

    return content_feature, style_feature


# get the cost of neural transfer model
def neural_transfer_cost(neural_transfer_model, loss_weights, output, target_style_features, target_content_features):
    """

    :param neural_transfer_model:
    :param loss_weights:
    :param output:
    :param target_style_features:
    :param target_content_features:
    :return: model cost
    """
    # initial var
    style_weight, content_weight = loss_weights
    content_loss = 0
    style_loss = 0
    
    # get output image features
    # 0-1 --> 0.-255.
    output = output*255.
   	output = tf.keras.applications.vgg19.preprocess_input(output)
    output_features = neural_transfer_model(output)
    output_style_features = output_features[:number_style]
    output_content_features = output_features[number_style:]

    # get the style loss
    for i, j in zip(target_style_features, output_style_features):
        style_loss += style_loss_layer(i, j)
    style_loss *= 1. / number_style

    # get the content loss
    for i, j in zip(target_content_features, output_content_features):
        content_loss += content_loss_layer(i, j)
    content_loss *= 1. / number_content

    # get the total cost of neural transfer model
    total_cost = content_weight * content_loss + style_weight * style_loss

    return total_cost

---

训练模型

• 使用参数字典作为方法参数
• with tf.GradientTape() as tape:在context manager中监视计算过程，随后可以调用tape来计算偏导
• tf.clip_by_value将tensor的值限制在一定范围内
• var.numpy()是什么意思？

def run_style_transfer(content_image, style_image, epochs=500, content_weight=1e3, style_weight=1e-2):
    """

    :param content_image:
    :param style_image:
    :param epochs:
    :param content_weight:
    :param style_weight:
    :return:
    """
    # get the neural style model
    neural_transfer_model = get_model(content_layers_names=content_layers, style_layers_names=style_layers)

    # get the content layer and style layer features of content image and style image
    target_content_feature, target_style_feature = get_features(neural_transfer_model, content_image, style_image)

    # output image
    output = tf.keras.applications.vgg19.preprocess_input(content_image)
    output = tf.Variable(output, dtype=tf.float32)

    # loss weights
    loss_weights = (style_weight, content_weight)

    dictionary = {
     'neural_transfer_model': neural_transfer_model,
                  'loss_weights': loss_weights,
                  'output': output,
                  'target_style_features': target_style_feature,
                  'target_content_features': target_content_feature}

    # get the optimizer
    optimizer = tf.keras.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)

    start_time = time.time()
    start_50_epochs = time.time()

    for i in range(epochs):
        with tf.GradientTape() as tape:
            model_cost = neural_transfer_cost(**dictionary)
        grad = tape.gradient(model_cost, output)
        optimizer.apply_gradients([(grad, output)])
        clipped = tf.clip_by_value(output, 0, 1)
        output.assign(clipped)

        # for visualization
        if i % 50 == 0:
            end_50_epochs = time.time()
            print('Epoch:{}, duration:{}s'.format(i, end_50_epochs-start_50_epochs))
            print('Total loss: {:.4e}, '.format(model_cost))
            print('----------------------------------------------')
            start_50_epochs = time.time()

            plt.imshow(tensor_to_image(output))     # 0-1, (1 512 512 3) tf.Variable float32
            plt.show()

    IPython.display.clear_output(wait=True)

    end_time = time.time()
    print(str(end_time - start_time) + 's')
    return output

---

结果展示

• 第一次用的广州塔的图片和梵高的星空，然后效果不是特别好，可能在参数方面需要调整。图我就不放了，效果不太行，有点丑。
• 然后我就换成了小黄狗，左图迭代10次，右图迭代大概100次，可以看到效果是不错的。

• 源码：

# 显示图像和图像标题
def show_im(img, title=None):
    """
    :param img:
    :param title:
    """
    if len(img.shape) > 3:
        image = tf.squeeze(img, axis=0)
    plt.imshow(image)
    if title:
        plt.title(title)
    plt.show()


# get and show content image and style image 
content_path = './input/to.jpg'
style_path = './input/vagao.jpg'
content_image = load_img(content_path)
style_image = load_img(style_path)  # （1 512 512 3）, ndarry, float32, 0-1
show_im(content_image)
show_im(style_image)

# content layers and style layers
content_layers = ['block5_conv2']
style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1',
                'block4_conv1',
                'block5_conv1']
number_content = len(content_layers)
number_style = len(style_layers)

# run the style transfer
image_neural_transfer = run_style_transfer(content_image, style_image, epochs=1000)

# show the result
image_neural_transfer = tensor_to_image(image_neural_transfer)
plt.imshow(image_neural_transfer)
plt.show()

总结

• 主要练习两个内容：如何使用预训练模型、如何搭建神经风格迁移模型
• 在实现过程中遇到了不少麻烦，一直感觉：输出的图像有问题，但是应该不是模型的问题，而是数据类型转换的时候搞错了@.@
• 第二天更新：输出图像怪怪的（指广州塔）。现在解决了一个问题：弄清楚了图像输入的格式、图像显示的格式。vgg19.preprocess_input()的输入要求0.-255.，输出层的输出0.-1.，计算代价函数的输入0.-1.，所以要特别注意

源码

• 想了想还是不直接贴全部源码了

# 无

参考

1. kaggle
2. VGGpaper
3. csdn
4. tensorflow
5. img_to_array csdn