13.12. Neural Style Transfer¶
If you use social sharing apps or happen to be an amateur photographer, you are familiar with filters. Filters can alter the color styles of photos to make the background sharper or people’s faces whiter. However, a filter generally can only change one aspect of a photo. To create the ideal photo, you often need to try many different filter combinations. This process is as complex as tuning the hyperparameters of a model.
In this section, we will discuss how we can use convolution neural networks (CNNs) to automatically apply the style of one image to another image, an operation known as style transfer [Gatys et al., 2016]. Here, we need two input images, one content image and one style image. We use a neural network to alter the content image so that its style mirrors that of the style image. In Fig. 13.12.1, the content image is a landscape photo the author took in Mount Rainier National Part near Seattle. The style image is an oil painting of oak trees in autumn. The output composite image retains the overall shapes of the objects in the content image, but applies the oil painting brushwork of the style image and makes the overall color more vivid.
Fig. 13.12.1 Content and style input images and composite image produced by style transfer.¶
13.12.1. Technique¶
The CNN-based style transfer model is shown in Fig. 13.12.2. First, we initialize the composite image. For example, we can initialize it as the content image. This composite image is the only variable that needs to be updated in the style transfer process, i.e., the model parameter to be updated in style transfer. Then, we select a pre-trained CNN to extract image features. These model parameters do not need to be updated during training. The deep CNN uses multiple neural layers that successively extract image features. We can select the output of certain layers to use as content features or style features. If we use the structure in Fig. 13.12.2, the pre-trained neural network contains three convolutional layers. The second layer outputs the image content features, while the outputs of the first and third layers are used as style features. Next, we use forward propagation (in the direction of the solid lines) to compute the style transfer loss function and backward propagation (in the direction of the dotted lines) to update the model parameter, constantly updating the composite image. The loss functions used in style transfer generally have three parts: 1. Content loss is used to make the composite image approximate the content image as regards content features. 2. Style loss is used to make the composite image approximate the style image in terms of style features. 3. Total variation loss helps reduce the noise in the composite image. Finally, after we finish training the model, we output the style transfer model parameters to obtain the final composite image.
Fig. 13.12.2 CNN-based style transfer process. Solid lines show the direction of forward propagation and dotted lines show backward propagation.¶
Next, we will perform an experiment to help us better understand the technical details of style transfer.
13.12.2. Reading the Content and Style Images¶
First, we read the content and style images. By printing out the image coordinate axes, we can see that they have different dimensions.
%matplotlib inline
from d2l import mxnet as d2l
from mxnet import autograd, gluon, image, init, np, npx
from mxnet.gluon import nn
npx.set_np()
d2l.set_figsize()
content_img = image.imread('../img/rainier.jpg')
d2l.plt.imshow(content_img.asnumpy());
style_img = image.imread('../img/autumn-oak.jpg')
d2l.plt.imshow(style_img.asnumpy());
%matplotlib inline
from d2l import torch as d2l
import torch
import torchvision
from torch import nn
d2l.set_figsize()
content_img = d2l.Image.open('../img/rainier.jpg')
d2l.plt.imshow(content_img);
style_img = d2l.Image.open('../img/autumn-oak.jpg')
d2l.plt.imshow(style_img);
13.12.3. Preprocessing and Postprocessing¶
Below, we define the functions for image preprocessing and
postprocessing. The preprocess function normalizes each of the three
RGB channels of the input images and transforms the results to a format
that can be input to the CNN. The postprocess function restores the
pixel values in the output image to their original values before
normalization. Because the image printing function requires that each
pixel has a floating point value from 0 to 1, we use the clip
function to replace values smaller than 0 or greater than 1 with 0 or 1,
respectively.
rgb_mean = np.array([0.485, 0.456, 0.406])
rgb_std = np.array([0.229, 0.224, 0.225])
def preprocess(img, image_shape):
img = image.imresize(img, *image_shape)
img = (img.astype('float32') / 255 - rgb_mean) / rgb_std
return np.expand_dims(img.transpose(2, 0, 1), axis=0)
def postprocess(img):
img = img[0].as_in_ctx(rgb_std.ctx)
return (img.transpose(1, 2, 0) * rgb_std + rgb_mean).clip(0, 1)
rgb_mean = torch.tensor([0.485, 0.456, 0.406])
rgb_std = torch.tensor([0.229, 0.224, 0.225])
def preprocess(img, image_shape):
transforms = torchvision.transforms.Compose([
torchvision.transforms.Resize(image_shape),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=rgb_mean, std=rgb_std)])
return transforms(img).unsqueeze(0)
def postprocess(img):
img = img[0].to(rgb_std.device)
img = torch.clamp(img.permute(1, 2, 0) * rgb_std + rgb_mean, 0, 1)
return torchvision.transforms.ToPILImage()(img.permute(2, 0, 1))
13.12.4. Extracting Features¶
We use the VGG-19 model pre-trained on the ImageNet dataset to extract image features[1].
pretrained_net = gluon.model_zoo.vision.vgg19(pretrained=True)
pretrained_net = torchvision.models.vgg19(pretrained=True)
To extract image content and style features, we can select the outputs
of certain layers in the VGG network. In general, the closer an output
is to the input layer, the easier it is to extract image detail
information. The farther away an output is, the easier it is to extract
global information. To prevent the composite image from retaining too
many details from the content image, we select a VGG network layer near
the output layer to output the image content features. This layer is
called the content layer. We also select the outputs of different layers
from the VGG network for matching local and global styles. These are
called the style layers. As we mentioned in Section 7.2, VGG
networks have five convolutional blocks. In this experiment, we select
the last convolutional layer of the fourth convolutional block as the
content layer and the first layer of each block as style layers. We can
obtain the indexes for these layers by printing the pretrained_net
instance.
style_layers, content_layers = [0, 5, 10, 19, 28], [25]
style_layers, content_layers = [0, 5, 10, 19, 28], [25]
During feature extraction, we only need to use all the VGG layers from
the input layer to the content or style layer nearest the output layer.
Below, we build a new network, net, which only retains the layers in
the VGG network we need to use. We then use net to extract features.
net = nn.Sequential()
for i in range(max(content_layers + style_layers) + 1):
net.add(pretrained_net.features[i])
net = nn.Sequential(*[pretrained_net.features[i] for i in
range(max(content_layers + style_layers) + 1)])
Given input X, if we simply call the forward computation net(X),
we can only obtain the output of the last layer. Because we also need
the outputs of the intermediate layers, we need to perform
layer-by-layer computation and retain the content and style layer
outputs.
def extract_features(X, content_layers, style_layers):
contents = []
styles = []
for i in range(len(net)):
X = net[i](X)
if i in style_layers:
styles.append(X)
if i in content_layers:
contents.append(X)
return contents, styles
def extract_features(X, content_layers, style_layers):
contents = []
styles = []
for i in range(len(net)):
X = net[i](X)
if i in style_layers:
styles.append(X)
if i in content_layers:
contents.append(X)
return contents, styles
Next, we define two functions: The get_contents function obtains the
content features extracted from the content image, while the
get_styles function obtains the style features extracted from the
style image. Because we do not need to change the parameters of the
pre-trained VGG model during training, we can extract the content
features from the content image and style features from the style image
before the start of training. As the composite image is the model
parameter that must be updated during style transfer, we can only call
the extract_features function during training to extract the content
and style features of the composite image.
def get_contents(image_shape, device):
content_X = preprocess(content_img, image_shape).copyto(device)
contents_Y, _ = extract_features(content_X, content_layers, style_layers)
return content_X, contents_Y
def get_styles(image_shape, device):
style_X = preprocess(style_img, image_shape).copyto(device)
_, styles_Y = extract_features(style_X, content_layers, style_layers)
return style_X, styles_Y
def get_contents(image_shape, device):
content_X = preprocess(content_img, image_shape).to(device)
contents_Y, _ = extract_features(content_X, content_layers, style_layers)
return content_X, contents_Y
def get_styles(image_shape, device):
style_X = preprocess(style_img, image_shape).to(device)
_, styles_Y = extract_features(style_X, content_layers, style_layers)
return style_X, styles_Y
13.12.5. Defining the Loss Function¶
Next, we will look at the loss function used for style transfer. The loss function includes the content loss, style loss, and total variation loss.
13.12.5.1. Content Loss¶
Similar to the loss function used in linear regression, content loss
uses a square error function to measure the difference in content
features between the composite image and content image. The two inputs
of the square error function are both content layer outputs obtained
from the extract_features function.
def content_loss(Y_hat, Y):
return np.square(Y_hat - Y).mean()
def content_loss(Y_hat, Y):
# we 'detach' the target content from the tree used
# to dynamically compute the gradient: this is a stated value,
# not a variable. Otherwise the loss will throw an error.
return torch.square(Y_hat - Y.detach()).mean()
13.12.5.2. Style Loss¶
Style loss, similar to content loss, uses a square error function to
measure the difference in style between the composite image and style
image. To express the styles output by the style layers, we first use
the extract_features function to compute the style layer output.
Assuming that the output has 1 example, \(c\) channels, and a height
and width of \(h\) and \(w\), we can transform the output into
the matrix \(\mathbf{X}\), which has \(c\) rows and
\(h \cdot w\) columns. You can think of matrix \(\mathbf{X}\) as
the combination of the \(c\) vectors
\(\mathbf{x}_1, \ldots, \mathbf{x}_c\), which have a length of
\(hw\). Here, the vector \(\mathbf{x}_i\) represents the style
feature of channel \(i\). In the Gram matrix of these vectors
\(\mathbf{X}\mathbf{X}^\top \in \mathbb{R}^{c \times c}\), element
\(x_{ij}\) in row \(i\) column \(j\) is the inner product of
vectors \(\mathbf{x}_i\) and \(\mathbf{x}_j\). It represents the
correlation of the style features of channels \(i\) and \(j\).
We use this type of Gram matrix to represent the style output by the
style layers. You must note that, when the \(h \cdot w\) value is
large, this often leads to large values in the Gram matrix. In addition,
the height and width of the Gram matrix are both the number of channels
\(c\). To ensure that the style loss is not affected by the size of
these values, we define the gram function below to divide the Gram
matrix by the number of its elements, i.e., \(c \cdot h \cdot w\).
def gram(X):
num_channels, n = X.shape[1], X.size // X.shape[1]
X = X.reshape((num_channels, n))
return np.dot(X, X.T) / (num_channels * n)
def gram(X):
num_channels, n = X.shape[1], X.numel() // X.shape[1]
X = X.reshape((num_channels, n))
return torch.matmul(X, X.T) / (num_channels * n)
Naturally, the two Gram matrix inputs of the square error function for
style loss are taken from the composite image and style image style
layer outputs. Here, we assume that the Gram matrix of the style image,
gram_Y, has been computed in advance.
def style_loss(Y_hat, gram_Y):
return np.square(gram(Y_hat) - gram_Y).mean()
def style_loss(Y_hat, gram_Y):
return torch.square(gram(Y_hat) - gram_Y.detach()).mean()
13.12.5.3. Total Variance Loss¶
Sometimes, the composite images we learn have a lot of high-frequency noise, particularly bright or dark pixels. One common noise reduction method is total variation denoising. We assume that \(x_{i, j}\) represents the pixel value at the coordinate \((i, j)\), so the total variance loss is:
We try to make the values of neighboring pixels as similar as possible.
def tv_loss(Y_hat):
return 0.5 * (np.abs(Y_hat[:, :, 1:, :] - Y_hat[:, :, :-1, :]).mean() +
np.abs(Y_hat[:, :, :, 1:] - Y_hat[:, :, :, :-1]).mean())
def tv_loss(Y_hat):
return 0.5 * (torch.abs(Y_hat[:, :, 1:, :] - Y_hat[:, :, :-1, :]).mean() +
torch.abs(Y_hat[:, :, :, 1:] - Y_hat[:, :, :, :-1]).mean())
13.12.5.4. Loss Function¶
The loss function for style transfer is the weighted sum of the content loss, style loss, and total variance loss. By adjusting these weight hyperparameters, we can balance the retained content, transferred style, and noise reduction in the composite image according to their relative importance.
content_weight, style_weight, tv_weight = 1, 1e3, 10
def compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram):
# Calculate the content, style, and total variance losses respectively
contents_l = [content_loss(Y_hat, Y) * content_weight for Y_hat, Y in zip(
contents_Y_hat, contents_Y)]
styles_l = [style_loss(Y_hat, Y) * style_weight for Y_hat, Y in zip(
styles_Y_hat, styles_Y_gram)]
tv_l = tv_loss(X) * tv_weight
# Add up all the losses
l = sum(styles_l + contents_l + [tv_l])
return contents_l, styles_l, tv_l, l
content_weight, style_weight, tv_weight = 1, 1e3, 10
def compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram):
# Calculate the content, style, and total variance losses respectively
contents_l = [content_loss(Y_hat, Y) * content_weight for Y_hat, Y in zip(
contents_Y_hat, contents_Y)]
styles_l = [style_loss(Y_hat, Y) * style_weight for Y_hat, Y in zip(
styles_Y_hat, styles_Y_gram)]
tv_l = tv_loss(X) * tv_weight
# Add up all the losses
l = sum(styles_l + contents_l + [tv_l])
return contents_l, styles_l, tv_l, l
13.12.6. Creating and Initializing the Composite Image¶
In style transfer, the composite image is the only variable that needs
to be updated. Therefore, we can define a simple model,
GeneratedImage, and treat the composite image as a model parameter.
In the model, forward computation only returns the model parameter.
class GeneratedImage(nn.Block):
def __init__(self, img_shape, **kwargs):
super(GeneratedImage, self).__init__(**kwargs)
self.weight = self.params.get('weight', shape=img_shape)
def forward(self):
return self.weight.data()
class GeneratedImage(nn.Module):
def __init__(self, img_shape, **kwargs):
super(GeneratedImage, self).__init__(**kwargs)
self.weight = nn.Parameter(torch.rand(*img_shape))
def forward(self):
return self.weight
Next, we define the get_inits function. This function creates a
composite image model instance and initializes it to the image X.
The Gram matrix for the various style layers of the style image,
styles_Y_gram, is computed prior to training.
def get_inits(X, device, lr, styles_Y):
gen_img = GeneratedImage(X.shape)
gen_img.initialize(init.Constant(X), ctx=device, force_reinit=True)
trainer = gluon.Trainer(gen_img.collect_params(), 'adam',
{'learning_rate': lr})
styles_Y_gram = [gram(Y) for Y in styles_Y]
return gen_img(), styles_Y_gram, trainer
def get_inits(X, device, lr, styles_Y):
gen_img = GeneratedImage(X.shape).to(device)
gen_img.weight.data.copy_(X.data)
trainer = torch.optim.Adam(gen_img.parameters(), lr=lr)
styles_Y_gram = [gram(Y) for Y in styles_Y]
return gen_img(), styles_Y_gram, trainer
13.12.7. Training¶
During model training, we constantly extract the content and style
features of the composite image and calculate the loss function. Recall
our discussion of how synchronization functions force the front end to
wait for computation results in Section 12.2. Because we only
call the asnumpy synchronization function every 10 epochs, the
process may occupy a great deal of memory. Therefore, we call the
waitall synchronization function during every epoch.
def train(X, contents_Y, styles_Y, device, lr, num_epochs, lr_decay_epoch):
X, styles_Y_gram, trainer = get_inits(X, device, lr, styles_Y)
animator = d2l.Animator(xlabel='epoch', ylabel='loss',
xlim=[10, num_epochs],
legend=['content', 'style', 'TV'],
ncols=2, figsize=(7, 2.5))
for epoch in range(num_epochs):
with autograd.record():
contents_Y_hat, styles_Y_hat = extract_features(
X, content_layers, style_layers)
contents_l, styles_l, tv_l, l = compute_loss(
X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram)
l.backward()
trainer.step(1)
npx.waitall()
if (epoch + 1) % lr_decay_epoch == 0:
trainer.set_learning_rate(trainer.learning_rate * 0.1)
if (epoch + 1) % 10 == 0:
animator.axes[1].imshow(postprocess(X).asnumpy())
animator.add(epoch + 1, [float(sum(contents_l)),
float(sum(styles_l)), float(tv_l)])
return X
def train(X, contents_Y, styles_Y, device, lr, num_epochs, lr_decay_epoch):
X, styles_Y_gram, trainer = get_inits(X, device, lr, styles_Y)
scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_decay_epoch)
animator = d2l.Animator(xlabel='epoch', ylabel='loss',
xlim=[10, num_epochs],
legend=['content', 'style', 'TV'],
ncols=2, figsize=(7, 2.5))
for epoch in range(num_epochs):
trainer.zero_grad()
contents_Y_hat, styles_Y_hat = extract_features(
X, content_layers, style_layers)
contents_l, styles_l, tv_l, l = compute_loss(
X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram)
l.backward()
trainer.step()
scheduler.step()
if (epoch + 1) % 10 == 0:
animator.axes[1].imshow(postprocess(X))
animator.add(epoch + 1, [float(sum(contents_l)),
float(sum(styles_l)), float(tv_l)])
return X
Next, we start to train the model. First, we set the height and width of the content and style images to 150 by 225 pixels. We use the content image to initialize the composite image.
device, image_shape = d2l.try_gpu(), (225, 150)
net.collect_params().reset_ctx(device)
content_X, contents_Y = get_contents(image_shape, device)
_, styles_Y = get_styles(image_shape, device)
output = train(content_X, contents_Y, styles_Y, device, 0.01, 500, 200)
device, image_shape = d2l.try_gpu(), (150, 225) # PIL Image (h, w)
net = net.to(device)
content_X, contents_Y = get_contents(image_shape, device)
_, styles_Y = get_styles(image_shape, device)
output = train(content_X, contents_Y, styles_Y, device, 0.01, 500, 200)
As you can see, the composite image retains the scenery and objects of the content image, while introducing the color of the style image. Because the image is relatively small, the details are a bit fuzzy.
To obtain a clearer composite image, we train the model using a larger image size: \(900 \times 600\). We increase the height and width of the image used before by a factor of four and initialize a larger composite image.
image_shape = (900, 600)
_, content_Y = get_contents(image_shape, device)
_, style_Y = get_styles(image_shape, device)
X = preprocess(postprocess(output) * 255, image_shape)
output = train(X, content_Y, style_Y, device, 0.01, 300, 100)
d2l.plt.imsave('../img/neural-style.jpg', postprocess(output).asnumpy())
image_shape = (600, 900) # PIL Image (h, w)
_, content_Y = get_contents(image_shape, device)
_, style_Y = get_styles(image_shape, device)
X = preprocess(postprocess(output), image_shape).to(device)
output = train(X, content_Y, style_Y, device, 0.01, 300, 100)
d2l.plt.imsave('../img/neural-style.jpg', postprocess(output))
As you can see, each epoch takes more time due to the larger image size. As shown in Fig. 13.12.3, the composite image produced retains more detail due to its larger size. The composite image not only has large blocks of color like the style image, but these blocks even have the subtle texture of brush strokes.
Fig. 13.12.3 \(900 \times 600\) composite image.¶
13.12.8. Summary¶
The loss functions used in style transfer generally have three parts: 1. Content loss is used to make the composite image approximate the content image as regards content features. 2. Style loss is used to make the composite image approximate the style image in terms of style features. 3. Total variation loss helps reduce the noise in the composite image.
We can use a pre-trained CNN to extract image features and minimize the loss function to continuously update the composite image.
We use a Gram matrix to represent the style output by the style layers.
13.12.9. Exercises¶
How does the output change when you select different content and style layers?
Adjust the weight hyperparameters in the loss function. Does the output retain more content or have less noise?
Use different content and style images. Can you create more interesting composite images?
Can we apply style transfer for text? Hint: you may refer to the survey paper by Hu et al. [Hu et al., 2020].