使用 TensorFlow 进行神经风格迁移

简介

神经风格迁移（Neural Style Transfer）是一种利用深度学习技术将一幅图像的“风格”应用在另一幅图像上的方法。深度学习模型首先学习如何表示一幅图像的内容和风格，然后将这两部分内容组合在一起，生成一幅新的图像。

TensorFlow 是一种流行的深度学习框架，可以用来实现神经风格迁移。

准备

在使用 TensorFlow 进行神经风格迁移之前，需要安装 TensorFlow 并下载一幅“内容图像”和一幅“风格图像”。

可以使用以下代码片段安装 TensorFlow：

!pip install tensorflow

可以使用以下代码片段下载一幅“内容图像”和一幅“风格图像”：

!wget https://github.com/tensorflow/models/raw/master/research/slim/nets/mobilenet_v1/examples/flower_photos/daisy/5547758_eea9edfd54_n.jpg -O content.jpg
!wget https://github.com/tensorflow/models/raw/master/research/slim/nets/mobilenet_v1/examples/flower_photos/sunflowers/6953297_8576bf4ea3.jpg -O style.jpg

实现

可以使用以下代码片段实现神经风格迁移：

import tensorflow as tf
import numpy as np
from PIL import Image

# 定义常量
CONTENT_IMAGE_PATH = 'content.jpg'
STYLE_IMAGE_PATH = 'style.jpg'
IMAGE_SIZE = 400
NUM_ITERATIONS = 200
CONTENT_WEIGHT = 1
STYLE_WEIGHT = 1e4
CONTENT_LAYER = 'conv4_2'
STYLE_LAYERS = ['conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1']

# 加载图像
def load_image(path):
    image = Image.open(path)
    image = np.array(image)
    image = image.astype(np.float32)
    image = np.expand_dims(image, axis=0)
    image /= 255.0
    image = tf.constant(image)
    return image

# 保存图像
def save_image(image, path):
    image = image.numpy()
    image *= 255.0
    image = np.squeeze(image, axis=0)
    image = image.astype(np.uint8)
    image = Image.fromarray(image)
    image.save(path)

# 加载模型
def load_model():
    model = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
    model.trainable = False
    outputs = [model.get_layer(name).output for name in STYLE_LAYERS + [CONTENT_LAYER]]
    model = tf.keras.models.Model(inputs=model.input, outputs=outputs)
    return model

# 计算内容损失
def compute_content_loss(content_features, combination_features):
    content_loss = tf.reduce_mean(tf.square(combination_features[CONTENT_LAYER] - content_features))
    return CONTENT_WEIGHT * content_loss

# 计算风格损失
def compute_style_loss(style_features, combination_features):
    style_loss = 0
    for style_layer in STYLE_LAYERS:
        style_loss += tf.reduce_mean(tf.square(combination_features[style_layer] - style_features[style_layer]))
    return STYLE_WEIGHT * style_loss

# 计算总损失
def compute_loss(model, combination_image, content_image, style_image):
    content_layer_size = tf.size(model(content_image)[CONTENT_LAYER])
    combination_layer_size = tf.size(model(combination_image)[CONTENT_LAYER])
    total_loss = 0
    content_features = model(content_image)[CONTENT_LAYER]
    combination_features = model(combination_image)[CONTENT_LAYER]
    total_loss += compute_content_loss(content_features, combination_features)
    style_features = {layer:model(style_image)[layer] for layer in STYLE_LAYERS}
    total_loss += compute_style_loss(style_features, combination_features) / len(STYLE_LAYERS)
    return total_loss

# 计算梯度
@tf.function()
def compute_gradient(model, combination_image, content_image, style_image):
    with tf.GradientTape() as tape:
        loss = compute_loss(model, combination_image, content_image, style_image)
    gradient = tape.gradient(loss, combination_image)
    return gradient, loss

# 迭代优化
def optimize(model, content_image, style_image):
    combination_image = tf.Variable(tf.random.uniform(shape=[1, IMAGE_SIZE, IMAGE_SIZE, 3]))
    optimizer = tf.optimizers.Adam(learning_rate=5, beta_1=0.99, epsilon=1e-1)
    for i in range(NUM_ITERATIONS):
        gradient, loss = compute_gradient(model, combination_image, content_image, style_image)
        optimizer.apply_gradients([(gradient, combination_image)])
        combination_image.assign(tf.clip_by_value(combination_image, 0, 1))
        if i % 10 == 0:
            print("Iteration %d: loss=%.2f" % (i, loss))
    return combination_image

# 主函数
def main():
    content_image = load_image(CONTENT_IMAGE_PATH)
    style_image = load_image(STYLE_IMAGE_PATH)
    model = load_model()
    combination_image = optimize(model, content_image, style_image)
    save_image(combination_image, 'output.jpg')

if __name__ == '__main__':
    main()

结果

运行上述代码之后，会生成一幅迁移后的图像，并在控制台上输出优化过程中的损失值。

可以使用以下代码片段查看生成的图像：

from IPython.display import Image
Image(filename='output.jpg')

总结

使用 TensorFlow 进行神经风格迁移需要进行以下步骤：

1. 安装 TensorFlow 并下载一幅“内容图像”和一幅“风格图像”
2. 加载图像并定义常量
3. 加载模型
4. 计算总损失并计算梯度
5. 迭代优化
6. 保存图像

最终可以生成一幅迁移后的图像，示例代码中生成的图像如下所示：