# 图像风格迁移

图像的风格迁移是卷积神经网络有趣的应用之一。那什么是风格迁移呢？下图第一列左边的图为相机拍摄的一张普通图片，右边的图为梵高的名画星空。那如何让左边的普通图片拥有星空的风格呢。神经网络的风格迁移就可以帮助你生成第二列的这样的图片。

<div align=center>
 <img src="images/markdown/img1.png" width = "600" height = "300"  />
</br>
 <img src="images/markdown/img2.png" width = "300" height = "300"  divalign=center />
<div align=left>


## 基本原理
风格迁移的目标就是使得生成图片的内容与内容图片（content image）尽可能相似。由于在计算机中，我们用一个一个像素点表示图片，所以两个图片的相似程度我们可以用每个像素点的欧式距离来表示。而两个图片的风格相似度，我们采用两个图片在卷积神经网络中相同的一层特征图的gram矩阵的欧式距离来表示。对于一个特征图gram矩阵的计算如下所示：

```python
# tensor shape is [1, c, h, w]
_, c, h, w = tensor.shape
tensor = paddle.reshape(tensor, [c, h * w])
# gram matrix with shape: [c, c]
gram_matrix = paddle.matmul(tensor, paddle.transpose(tensor, [1, 0]))
```

最终风格迁移的问题转化为优化上述的两个欧式距离的问题。这里要注意的是，我们使用一个在imagenet上预训练好的模型vgg16，并且固定参数，优化器只更新输入的生成图像的值。

## 具体实现
接下来，使用代码一步一步来实现上述图片的风格迁移

```python
# 导入所需的模块
%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

import paddle

from paddle.vision.models import vgg16
from paddle.vision.transforms import transforms
from paddle import fluid

import cv2
import copy

# 图像预处理函数，和tensor恢复到自然图像的函数
from .style_transfer import load_image, image_restore
```


```python
# 启动动态图模式
paddle.disable_static()
```

```python
# 内容图像，用于风格迁移
content_path = './images/chicago_cropped.jpg'
# 风格图像
style_path = './images/Starry-Night-by-Vincent-Van-Gogh-painting.jpg'
```


```python
# 可视化两个图像
content = load_image(content_path)
style = load_image(style_path, shape=tuple(content.shape[-2:]))

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 10))
ax1.imshow(image_restore(content))
ax2.imshow(image_restore(style))
```


![png](images/markdown/output_10_1.png)



```python
# 定义风格迁移模型，使用在imagenet上预训练好的vgg16作为基础模型
class StyleTransferModel(paddle.nn.Layer):
    def __init__(self):
        super(StyleTransferModel, self).__init__()
        # pretrained设置为true，会自动下载imagenet上的预训练权重并加载
        vgg = vgg16(pretrained=True)
        self.base_model = vgg.features

        for p in self.base_model.parameters():
            p.stop_gradient = True
        self.layers = {
            '0': 'conv1_1',
            '5': 'conv2_1',
            '10': 'conv3_1',
            '17': 'conv4_1',
            '19': 'conv4_2',  ## content representation
            '24': 'conv5_1'
        }

    def forward(self, image):
        outputs = []
        for name, layer in self.base_model.named_sublayers():
            image = layer(image)
            if name in self.layers:
                outputs.append(image)
        return outputs
```


```python
# 定义风格迁移个损失函数
class StyleTransferLoss(paddle.nn.Layer):
    def __init__(self,
                 content_loss_weight=1,
                 style_loss_weight=1e5,
                 style_weights=[1.0, 0.8, 0.5, 0.3, 0.1]):
        super(StyleTransferLoss, self).__init__()
        self.content_loss_weight = content_loss_weight
        self.style_loss_weight = style_loss_weight
        self.style_weights = style_weights

    def forward(self, *features):
        outputs = features[:6]
        labels = features[6:]
        content_features = labels[-1]
        style_features = labels[:-1]

        # 计算图像内容相似度的loss
        content_loss = paddle.mean((outputs[-2] - content_features)**2)

        # 计算风格相似度的loss
        style_loss = 0
        style_grams = [self.gram_matrix(feat) for feat in style_features]
        style_weights = self.style_weights
        for i, weight in enumerate(style_weights):
            target_gram = self.gram_matrix(outputs[i])
            layer_loss = weight * paddle.mean((target_gram - style_grams[
                i])**2)
            b, d, h, w = outputs[i].shape
            style_loss += layer_loss / (d * h * w)

        total_loss = self.content_loss_weight * content_loss + self.style_loss_weight * style_loss
        return total_loss

    def gram_matrix(self, A):
        if len(A.shape) == 4:
            _, c, h, w = A.shape
            A = paddle.reshape(A, (c, h * w))
        GA = paddle.matmul(A, paddle.transpose(A, [1, 0]))

        return GA
```


```python
# 创建模型
net = StyleTransferModel()
model = paddle.Model(net)
```


```python
# 创建损失函数
style_loss = StyleTransferLoss()
```


```python
# 使用内容图像初始化要生成的图像
target = net.create_parameter(shape=content.shape)
target.set_value(content.numpy())
```


```python
# 创建优化器
optimizer = fluid.optimizer.Adam(parameter_list=[target], learning_rate=0.001)
```


```python
# 初始化高级api
model.prepare(optimizer, style_loss)
```


```python
# 使用内容图像和风格图像获取内容特征和风格特征
content_fetures = model.test_batch(content)
style_features = model.test_batch(style)
```


```python
# 将两个特征组合，作为损失函数的label传给模型
feats = style_features + [content_fetures[-2]]
```


```python
# 训练5000个step，每500个step画一下生成的图像查看效果
steps = 5000
for i in range(steps):
    outs = model.train_batch(target, feats)

    if i % 500 == 0:
        print('iters:', i, 'loss:', outs[0])
        plt.imshow(image_restore(target))
        plt.show()
```

    iters: 0 loss: [8.829961e+09]



![png](images/markdown/output_20_1.png)


    iters: 500 loss: [3.728548e+08]



![png](images/markdown/output_20_3.png)


    iters: 1000 loss: [1.6327214e+08]



![png](images/markdown/output_20_5.png)


    iters: 1500 loss: [1.0806553e+08]



![png](images/markdown/output_20_7.png)


    iters: 2000 loss: [81069480.]



![png](images/markdown/output_20_9.png)


    iters: 2500 loss: [64284104.]



![png](images/markdown/output_20_11.png)


    iters: 3000 loss: [52580884.]



![png](images/markdown/output_20_13.png)


    iters: 3500 loss: [43825304.]



![png](images/markdown/output_20_15.png)


    iters: 4000 loss: [37048400.]



![png](images/markdown/output_20_17.png)


    iters: 4500 loss: [31719670.]



![png](images/markdown/output_20_19.png)



```python
# 风格迁移后的图像
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 10))
ax1.imshow(image_restore(content))
ax2.imshow(image_restore(target))
ax3.imshow(image_restore(style))
```




![png](images/markdown/output_21_1.png)



## 总结
上述可运行的代码都在[style-transfer.ipynb](./style-transfer.ipynb)中， 同时我们提供了[style-transfer.py](./style-transfer.py)脚本，可以直接执行如下命令，实现图片的风格迁移：

```shell
python -u style-transfer.py --content-image /path/to/your-content-image --style-image /path/to/your-style-image --save-dir /path/to/your-output-dir
```

风格迁移生成的图像保存在```--save-dir```中。

## 参考

[A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576)