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2024年07月22日

神经风格迁移简介及实现（风格迁移神经网络）

介绍

神经网络在过去几年中已被广泛应用于图像识别。最酷的应用之一是Leon A. Gatys最初提出的神经风格迁移算法。该算法使用预训练的模型和简单的优化过程，将两个独立图像的风格和内容组合成一副图像。

直觉

任何图像都可以被认为有两个组成部分：

  • 内容：图像中的对象及其空间排列
  • 风格：纹理，视觉图案和配色方案

让我们将我们想要复制的内容的图像定义为I_CONTENT，并将我们想要复制的风格图像定义为I_STYLE。然后，目标是生成一个内容为I_CONTENT且风格为I_STYLE的图像（I_TARGET）。

为了实现这一点，首先对一个最初填充随机像素的图像进行迭代修改，直到实现所需的内容和风格。

它是如何工作的？

我们可以看到，对最初有噪声的图像进行迭代修改以匹配所选图像的内容和风格。

但是，我们如何衡量两个图像之间的内容相似性或风格相似度呢？这就是预训练卷积神经网络(CNN)发挥作用的地方。

C???，S???和T???是层l的第i个特征映射的第j个激活，内容、风格和目标图像通过预训练网络传递。H，W和C指的是层l的高度，宽度和通道数。

内容损失

在用于对象检测的巨大图像数据集上训练的卷积神经网络(CNN)的高层学习，显式地识别和学习图像中对象(内容)表示，而不是学习精确的像素值。

因此，在较高层生成类似feature map激活的图像将具有类似的内容。形式上，我们可以将层l测得的内容损失定义为:

这基本上是当通过卷积神经网络(CNN)时图像X和图像Y的各个激活之间的归一化平方误差。

风格损失

风格被测量为特定层处的不同feature map激活之间的相关性。这在Gram矩阵(G)中正式表示为:：

Gram Matrix：当图像X通过时，捕获神经网络的第1层的不同feature map之间的相关性

G???捕获层l处第i个和第j个之间的feature map相关性，因此，第l层测得的风格损失定义为:

请注意，可以在网络的不同层测量内容损失和风格损失，以形成最终的损失函数。

c?和s?是分别在第l层计算的内容和风格损失的权重。α和β分别是给予总体内容和风格损失的权重。

在最初的实现中，VGG-19被用作预训练的CNN，并且已经使用了内容和风格层的不同组合。

Python实现

步骤1：我们需要预训练的VGG19模型权重来计算每次迭代时的损失。我们将构建一个字典，将层名称映射到包含每层VGG19图层权重的张量。

import tensorflow as tf
from tensorflow import keras
import numpy as np
import os, shutil
import random
import matplotlib.pyplot as plt
import string
import cv2
from google.colab.patches import cv2_imshow
import scipy

model.layers包含每层的权重张量。

对于每个卷积层i，model.layers [i] .weights [0]有filter权重，model.layers [i] .weights [1]有偏差权重。

我们将提取这些值并将它们添加到字典weights_dict中。

def getModelWeightsAsDict():
 keras.backend.clear_session()
 tf.reset_default_graph()
 model = keras.applications.VGG19(input_shape=(IMAGE_SIZE,IMAGE_SIZE,3),include_top=False,weights='imagenet')
 
 with keras.backend.get_session() as sess:
 weights_dict = {}
 for i in range(len(model.layers)):
 weights_dict['layer_' + str(i)] = []
 for j in range(len(model.layers[i].weights)):
 weights_dict['layer_' + str(i)].append(sess.run(model.layers[i].weights[j]))
 tf.reset_default_graph()
 return weights_dict

第2步：我们将构建一个复制VGG19网络连接和权重的Tensorflow图。但是，我们将把输入张量设置为tf.Variable()，并使用步骤1中存储的权重定义图。这是因为我们不会训练网络的权值，我们只会修改输入张量来最小化定义的损失。

像以前一样，我们将对应于每一层的张量存储在字典中。

def get_nst_model(weights_dict):
 layers = {}
 image_shape = (IMAGE_SIZE,IMAGE_SIZE,3)
 layers['input'] = tf.Variable(initial_value=tf.initializers.random_normal().__call__((1,)+image_shape),expected_shape=(1,)+image_shape,
 name='nst_output',dtype=tf.float32)
 layers['conv1_1'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['input'],weights_dict['layer_1'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_1'][1]))
 layers['conv1_2'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv1_1'],weights_dict['layer_2'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_2'][1]))
 layers['pool1'] = tf.nn.avg_pool(layers['conv1_2'],ksize=(1,2,2,1),strides=(1,2,2,1),padding='VALID')
 layers['conv2_1'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['pool1'],weights_dict['layer_4'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_4'][1]))
 layers['conv2_2'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv2_1'],weights_dict['layer_5'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_5'][1]))
 layers['pool2'] = tf.nn.avg_pool(layers['conv2_2'],ksize=(1,2,2,1),strides=(1,2,2,1),padding='VALID')
 layers['conv3_1'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['pool2'],weights_dict['layer_7'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_7'][1]))
 layers['conv3_2'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv3_1'],weights_dict['layer_8'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_8'][1]))
 layers['conv3_3'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv3_2'],weights_dict['layer_9'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_9'][1]))
 layers['conv3_4'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv3_3'],weights_dict['layer_10'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_10'][1]))
 layers['pool3'] = tf.nn.avg_pool(layers['conv3_4'],ksize=(1,2,2,1),strides=(1,2,2,1),padding='VALID')
 layers['conv4_1'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['pool3'],weights_dict['layer_12'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_12'][1]))
 layers['conv4_2'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv4_1'],weights_dict['layer_13'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_13'][1]))
 layers['conv4_3'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv4_2'],weights_dict['layer_14'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_14'][1]))
 layers['conv4_4'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv4_3'],weights_dict['layer_15'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_15'][1]))
 layers['pool4'] = tf.nn.avg_pool(layers['conv4_4'],ksize=(1,2,2,1),strides=(1,2,2,1),padding='VALID')
 layers['conv5_1'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['pool4'],weights_dict['layer_17'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_17'][1]))
 layers['conv5_2'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv5_1'],weights_dict['layer_18'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_18'][1]))
 layers['conv5_3'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv5_2'],weights_dict['layer_19'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_19'][1]))
 layers['conv5_4'] = tf.nn.relu(tf.nn.bias_add(tf.nn.convolution(layers['conv5_3'],weights_dict['layer_20'][0],padding='SAME',strides=(1,1)),
 weights_dict['layer_20'][1]))
 layers['pool5'] = tf.nn.avg_pool(layers['conv5_4'],ksize=(1,2,2,1),strides=(1,2,2,1),padding='VALID')
 
 return layers

第3步：让我们加载I_CONTENT和I_STYLE图像。我们将通过减去像素方式来预处理这些。

此外，我们定义save_image（）函数，它接受模型输出图像，添加MEANS，将值剪辑到范围[0,255]并保存图像。

MEANS = np.array([123.68, 116.779, 103.939]).reshape((1,1,1,3)) 
def load_image(filename):
 
 image = cv2.imread(filename)
 image = cv2.resize(image,(IMAGE_SIZE,IMAGE_SIZE))
 cv2_imshow(image)
 image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 image = image.reshape((1,)+image.shape)
 
 image = image - MEANS
 return image
def save_image(image,filename):
 image = image + MEANS
 image = np.clip(image[0], 0, 255).astype('uint8')
 scipy.misc.imsave(filename, image)
 return image

第4步：让我们定义层（和相应的权重）来计算内容和风格损失。

CONTENT_LAYERS = [
 ('conv4_2', 1.0),
]
STYLE_LAYERS = [
 ('conv1_1', 0.2),
 ('conv2_1', 0.2),
 ('conv3_1', 0.2),
 ('conv4_1', 0.2),
 ('conv5_1', 0.2)
]

让我们定义损失项和优化器以最小化损失：

def getContentLoss(layers,content_target,content_layer_name): 
 m,H,W,C = content_target.shape
 return tf.reduce_sum(tf.pow(content_target-layers[content_layer_name],2))/(4.0*C*H*W)
# Calculate tensor for content loss
J_content = 0.0
with tf.Session() as sess:
 for content_layer_name, weight in CONTENT_LAYERS:
 content_layer = layers[content_layer_name]
 content_target = sess.run(content_layer,feed_dict={layers['input']:content_image})
 J_content = J_content + weight * getContentLoss(layers,content_target, content_layer_name)
print('Content loss defined...\n')
 
 
def getStyleLoss(layers,style_target,style_layer_name):
 m,H,W,C = style_target.shape
 style_target = tf.transpose(tf.reshape(style_target,[H*W,C]))
 gram_target = tf.matmul(style_target,tf.transpose(style_target)) 
 style_tensor = tf.transpose(tf.reshape(layers[style_layer_name],[H*W,C]))
 gram_tensor = tf.matmul(style_tensor,tf.transpose(style_tensor)) 
 return tf.reduce_sum(tf.pow(gram_tensor-gram_target,2))/(4.0*C*C*H*H*W*W)
 
# Calculate tensor for style loss
J_style = 0.0
with tf.Session() as sess:
 for style_layer_name, weight in STYLE_LAYERS:
 style_layer = layers[style_layer_name]
 style_target = sess.run(style_layer,feed_dict={layers['input']:style_image})
 J_style = J_style + weight * getStyleLoss(layers,style_target,style_layer_name)
print('Style loss defined...\n')
 
 
def getTotalLoss(J_content,J_style,alpha,beta):
 return J_content*alpha + J_style*beta
# Calculate tensor for total loss
J_total = getTotalLoss(J_content,J_style,alpha=alpha,beta=beta)
# Define loss optimizer
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(J_total)

第5步：我们需要做的就是用白噪声图像初始化layer['input'] 并运行优化器。

每100次迭代，我们将使用可定义的路径前缀保存图像。这是在前面定义的save_image（）函数中完成的。

我已将所有这些包装在函数run_style_transfer（）中，您可以将文件路径传递给内容和风格图像，内容和风格权重（α和β），学习率和epochs数。

def run_style_transfer(content_image_filename,
 style_image_filename,
 initialImage=None,
 alpha=10,beta=1e-1,
 epochs=1200,
 learning_rate=5.0,
 prefix=None):
 
 if prefix is None:
 print('Output file prefix not defined...')
 return
 
 content_image = load_image(content_image_filename)
 style_image = load_image(style_image_filename)
 weights_dict = getModelWeightsAsDict()
 tf.reset_default_graph()
 layers = get_nst_model(weights_dict)
 print('Model graph generated...\n')
 
 # Calculate tensor for content loss
 J_content = 0.0
 with tf.Session() as sess:
 for content_layer_name, weight in CONTENT_LAYERS:
 content_layer = layers[content_layer_name]
 content_target = sess.run(content_layer,feed_dict={layers['input']:content_image})
 J_content = J_content + weight * getContentLoss(layers,content_target, content_layer_name)
 print('Content loss defined...\n')
 
 
 # Calculate tensor for style loss
 J_style = 0.0
 with tf.Session() as sess:
 for style_layer_name, weight in STYLE_LAYERS:
 style_layer = layers[style_layer_name]
 style_target = sess.run(style_layer,feed_dict={layers['input']:style_image})
 J_style = J_style + weight * getStyleLoss(layers,style_target,style_layer_name)
 print('Style loss defined...\n')
 
 J_total = getTotalLoss(J_content,J_style,alpha=alpha,beta=beta)
 optimizer = tf.train.AdamOptimizer(learning_rate).minimize(J_total)
 print('Losses defined...\n')
 
 print('Trainable variables:\n',tf.trainable_variables())
 
 with tf.Session() as sess:
 
 # Initialize image
 sess.run(tf.global_variables_initializer())
 if initialImage is not None:
 sess.run(layers['input'].assign(initialImage))
 else:
 sess.run(layers['input'].assign(generate_noise_image()))
 
 for epoch in range(epochs):
 epoch_loss, epoch_content_loss, epoch_style_loss, _ = sess.run([J_total,J_content,J_style,optimizer])
 if (epoch+1) % 100 == 0:
 generated_image = sess.run(layers['input'])
 generated_image = save_image(generated_image,prefix + '_' + str(epoch+1) + '.jpg')
 print('Loss after epoch %d: \nT: %f, \nC: %f, \nS: %f'%(epoch,epoch_loss,epoch_content_loss,epoch_style_loss))
 generated_image = cv2.cvtColor(generated_image, cv2.COLOR_RGB2BGR)
 cv2_imshow(generated_image)

设置GPU

# CHECK AND SETUP GPU
device_name = tf.test.gpu_device_name()
print('Found GPU at: {}'.format(device_name))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True

调用的Python示例代码

# Example Usage
IMAGE_SIZE = 900
content_image_filename = './knight.jpg'
style_image_filename = './/patterned_leaves.jpg'
with tf.device('/gpu:0'):
 run_style_transfer(content_image_filename=content_image_filename,
 style_image_filename=style_image_filename,
 epochs=3000,
 alpha=18,
 beta=1e-1,
 learning_rate=10.0,
 prefix='knight-patterned_leaves')

将α设定在[10,20]范围内，β为~1e-1，学习率为~5.0。能够在大约1000次迭代中产生良好的结果。

标签：tf.pow 

作者:sishi88 , 分类:技术教程 , 浏览:20 , 评论:0

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