Artistic image generation using Neural Style Transfer

In this post, we will learn how to use deep learning to generate images (base image) in the style of another image (style image). This is known as neural style transfer! This is a technique outlined in Leon A. Gatys’ paper, A Neural Algorithm of Artistic Style which is a great read, and you should definitely check it out.

So, neural style transfer is an optimization technique which takes two images, a content image, a style reference image (such as an artwork by a famous painter), and the input image you want to style — and blend them together such that the input image is transformed to look like the content image, but “painted” in the style of the style image.

The principle of neural style transfer is to define two distance functions, one that describes how different the content of two images are , Lcontent, and one that describes the difference between two images in terms of their style, Lstyle.

Given these two images, a desired style image, and the input image (initialized with the content image), we try to transform the input image to minimize the content distance with the content image and its style distance with the style image. In summary, we’ll take the base input image, a content image that we want to match, and the style image that we want to match. We’ll transform the base input image by minimizing the content and style distances (losses) with backpropagation, creating an image that matches the content of the content image and the style of the style image.

We will follow the general steps to perform style transfer:

1. Visualize data
2. Basic Preprocessing/preparing our data
3. Set up loss functions
4. Create model
5. Optimize for loss function

Visualize data

def load_img(path_to_img):
    max_dim = 512
    img = Image.open(path_to_img)
    long = max(img.size)
    scale = max_dim/long
    img = img.resize((round(img.size[0]*scale), round(img.size[1]*scale)), Image.ANTIALIAS)

    img = kp_image.img_to_array(img)

    # We need to broadcast the image array such that it has a batch dimension 
    img = np.expand_dims(img, axis=0)
    return img

def imshow(img, title=None):
    # Remove the batch dimension
    out = np.squeeze(img, axis=0)
    # Normalize for display 
    out = out.astype('uint8')
    plt.imshow(out)
    if title is not None:
        plt.title(title)
    plt.imshow(out)

plt.figure(figsize=(10,10))

content = load_img(content_path).astype('uint8')
style = load_img(style_path).astype('uint8')

plt.subplot(1, 2, 1)
imshow(content, 'Content Image')

plt.subplot(1, 2, 2)
imshow(style, 'Style Image')
plt.show()

These are input content and style images. We hope to “create” an image with the content of our content image, but with the style of the style image.

Prepare the data

Let’s create methods that will allow us to load and preprocess our images easily. We perform the same preprocessing process as are expected according to the VGG training process. VGG networks are trained on image with each channel normalized by mean = [103.939, 116.779, 123.68]and with channels BGR.

def load_and_process_img(path_to_img):
    img = load_img(path_to_img)
    img = tf.keras.applications.vgg19.preprocess_input(img)
    return img

In order to view the outputs of our optimization, we are required to perform the inverse preprocessing step. Furthermore, since our optimized image may take its values anywhere between -infinity and infinity and we must clip to maintain our values from within the 0-255 range.

def deprocess_img(processed_img):
    x = processed_img.copy()
    if len(x.shape) == 4:
        x = np.squeeze(x, 0)
    assert len(x.shape) == 3, ("Input to deprocess image must be an image of "
                             "dimension [1, height, width, channel] or [height, width, channel]")
    if len(x.shape) != 3:
        raise ValueError("Invalid input to deprocessing image")

    # perform the inverse of the preprocessiing step
    x[:, :, 0] += 103.939
    x[:, :, 1] += 116.779
    x[:, :, 2] += 123.68
    x = x[:, :, ::-1]

    x = np.clip(x, 0, 255).astype('uint8')
    return x

Define content and style representations

In order to get both the content and style representations of our image, we will look at some intermediate layers within our model. As we go deeper into the model, these intermediate layers represent higher and higher order features. In this case, we are using the network architecture VGG19, a pretrained image classification network. These intermediate layers are necessary to define the representation of content and style from our images. For an input image, we will try to match the corresponding style and content target representations at these intermediate layers.

# Content layer where will pull our feature maps
content_layers = ['block5_conv2'] 

# Style layer we are interested in
style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1', 
                'block4_conv1', 
                'block5_conv1'
               ]

num_content_layers = len(content_layers)
num_style_layers = len(style_layers)

Build the Model

In this case, we load VGG19, and feed in our input tensor to the model. This will allow us to extract the feature maps (and subsequently the content and style representations) of the content, style, and generated images.

We use VGG19, as suggested in the paper. In addition, since VGG19 is a relatively simple model (compared with ResNet, Inception, etc) the feature maps actually work better for style transfer.

In order to access the intermediate layers corresponding to our style and content feature maps, we get the corresponding outputs and using the Keras Functional API, we define our model with the desired output activations.

With the Functional API defining a model simply involves defining the input and output:

model = Model(inputs, outputs)

def get_model():
    """ Creates our model with access to intermediate layers. 

    This function will load the VGG19 model and access the intermediate layers. 
    These layers will then be used to create a new model that will take input image
    and return the outputs from these intermediate layers from the VGG model. 

    Returns:
    returns a keras model that takes image inputs and outputs the style and 
      content intermediate layers. 
    """
    # Load our model. We load pretrained VGG, trained on imagenet data
    vgg = tf.keras.applications.vgg19.VGG19(include_top=False, weights='imagenet')
    vgg.trainable = False
    # Get output layers corresponding to style and content layers 
    style_outputs = [vgg.get_layer(name).output for name in style_layers]
    content_outputs = [vgg.get_layer(name).output for name in content_layers]
    model_outputs = style_outputs + content_outputs
    # Build model 
    return models.Model(vgg.input, model_outputs)

Define and create our loss functions (content and style distances)

Content Loss

Our content loss definition is actually quite simple. We’ll pass the network both the desired content image and our base input image. This will return the intermediate layer outputs (from the layers defined above) from our model. Then we simply take the euclidean distance between the two intermediate representations of those images.

More formally, content loss is a function that describes the distance of content from our output image  and our content image, . Let  be a pre-trained deep convolutional neural network. Again, in this case we use VGG19. Let  be any image, then  is the network fed by X.

We perform backpropagation in the usual way such that we minimize this content loss. We thus change the initial image until it generates a similar response in a certain layer (defined in content_layer) as the original content image.

This can be implemented quite simply. Again it will take as input the feature maps at a layer L in a network fed by x, our input image, and p, our content image, and return the content distance.

Computing content loss

We will actually add our content losses at each desired layer. This way, each iteration when we feed our input image through the model (which in eager is simply model(input_image)!) all the content losses through the model will be properly compute and because we are executing eagerly, all the gradients will be computed.

def get_content_loss(base_content, target):
    return tf.reduce_mean(tf.square(base_content - target))

Style Loss

Computing style loss is a bit more involved, but follows the same principle, this time feeding our network the base input image and the style image. However, instead of comparing the raw intermediate outputs of the base input image and the style image, we instead compare the Gram matrices of the two outputs.

Mathematically, we describe the style loss of the base input image, , and the style image, , as the distance between the style representation (the gram matrices) of these images. We describe the style representation of an image as the correlation between different filter responses given by the Gram matrix , where  is the inner product between the vectorized feature map  and  in layer . We can see that  generated over the feature map for a given image represents the correlation between feature maps  and .

To generate a style for our base input image, we perform gradient descent from the content image to transform it into an image that matches the style representation of the original image. We do so by minimizing the mean squared distance between the feature correlation map of the style image and the input image. The contribution of each layer to the total style loss is described by

where  and  are the respective style representation in layer  of  and .  describes the number of feature maps, each of size . Thus, the total style loss across each layer is $$L_{style}(a, x) = \sum_{l \in L} w_l E_l$$ where we weight the contribution of each layer’s loss by some factor . In our case, we weight each layer equally ()

def gram_matrix(input_tensor):
    # We make the image channels first 
    channels = int(input_tensor.shape[-1])
    a = tf.reshape(input_tensor, [-1, channels])
    n = tf.shape(a)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)

def get_style_loss(base_style, gram_target):
    """Expects two images of dimension h, w, c"""
    # height, width, num filters of each layer
    # We scale the loss at a given layer by the size of the feature map and the number of filters
    height, width, channels = base_style.get_shape().as_list()
    gram_style = gram_matrix(base_style)

    return tf.reduce_mean(tf.square(gram_style - gram_target))# / (4. * (channels ** 2) * (width * height) ** 2

def get_feature_representations(model, content_path, style_path):
    """Helper function to compute our content and style feature representations.

    This function will simply load and preprocess both the content and style 
    images from their path. Then it will feed them through the network to obtain
    the outputs of the intermediate layers. 

    Arguments:
    model: The model that we are using.
    content_path: The path to the content image.
    style_path: The path to the style image

    Returns:
    returns the style features and the content features. 
    """
    # Load our images in 
    content_image = load_and_process_img(content_path)
    style_image = load_and_process_img(style_path)

    # batch compute content and style features
    style_outputs = model(style_image)
    content_outputs = model(content_image)


    # Get the style and content feature representations from our model  
    style_features = [style_layer[0] for style_layer in style_outputs[:num_style_layers]]
    content_features = [content_layer[0] for content_layer in content_outputs[num_style_layers:]]
    return style_features, content_features

Computing the loss and gradients

Here we use tf.GradientTape to compute the gradient. It allows us to take advantage of the automatic differentiation available by tracing operations for computing the gradient later. It records the operations during the forward pass and then is able to compute the gradient of our loss function with respect to our input image for the backwards pass.

def compute_loss(model, loss_weights, init_image, gram_style_features, content_features):
    """This function will compute the loss total loss.

    Arguments:
    model: The model that will give us access to the intermediate layers
    loss_weights: The weights of each contribution of each loss function. 
      (style weight, content weight, and total variation weight)
    init_image: Our initial base image. This image is what we are updating with 
      our optimization process. We apply the gradients wrt the loss we are 
      calculating to this image.
    gram_style_features: Precomputed gram matrices corresponding to the 
      defined style layers of interest.
    content_features: Precomputed outputs from defined content layers of 
      interest.

    Returns:
    returns the total loss, style loss, content loss, and total variational loss
    """
    style_weight, content_weight = loss_weights

    # Feed our init image through our model. This will give us the content and 
    # style representations at our desired layers. Since we're using eager
    # our model is callable just like any other function!
    model_outputs = model(init_image)

    style_output_features = model_outputs[:num_style_layers]
    content_output_features = model_outputs[num_style_layers:]

    style_score = 0
    content_score = 0

    # Accumulate style losses from all layers
    # Here, we equally weight each contribution of each loss layer
    weight_per_style_layer = 1.0 / float(num_style_layers)
    for target_style, comb_style in zip(gram_style_features, style_output_features):
        style_score += weight_per_style_layer * get_style_loss(comb_style[0], target_style)

    # Accumulate content losses from all layers 
    weight_per_content_layer = 1.0 / float(num_content_layers)
    for target_content, comb_content in zip(content_features, content_output_features):
        content_score += weight_per_content_layer* get_content_loss(comb_content[0], target_content)

    style_score *= style_weight
    content_score *= content_weight

    # Get total loss
    loss = style_score + content_score 
    return loss, style_score, content_score

def compute_grads(cfg):
    with tf.GradientTape() as tape: 
        all_loss = compute_loss(**cfg)
    # Compute gradients wrt input image
    total_loss = all_loss[0]
    return tape.gradient(total_loss, cfg['init_image']), all_loss

Optimization the loss function and improving the style transfer result

import IPython.display

def run_style_transfer(content_path, 
                       style_path,
                       num_iterations=1000,
                       content_weight=1e3, 
                       style_weight=1e-2): 
    # We don't need to (or want to) train any layers of our model, so we set their
    # trainable to false. 
    model = get_model() 
    for layer in model.layers:
        layer.trainable = False
  
    # Get the style and content feature representations (from our specified intermediate layers) 
    style_features, content_features = get_feature_representations(model, content_path, style_path)
    gram_style_features = [gram_matrix(style_feature) for style_feature in style_features]
  
    # Set initial image
    init_image = load_and_process_img(content_path)
    init_image = tf.Variable(init_image, dtype=tf.float32)
    # Create our optimizer
    opt = tf.train.AdamOptimizer(learning_rate=5, beta1=0.99, epsilon=1e-1)

    # For displaying intermediate images 
    iter_count = 1

    # Store our best result
    best_loss, best_img = float('inf'), None

    # Create a nice config 
    loss_weights = (style_weight, content_weight)
    cfg = {
    'model': model,
    'loss_weights': loss_weights,
    'init_image': init_image,
    'gram_style_features': gram_style_features,
    'content_features': content_features
    }

    # For displaying
    num_rows = 2
    num_cols = 5
    display_interval = num_iterations/(num_rows*num_cols)
    start_time = time.time()
    global_start = time.time()

    norm_means = np.array([103.939, 116.779, 123.68])
    min_vals = -norm_means
    max_vals = 255 - norm_means   

    imgs = []
    for i in range(num_iterations):
        grads, all_loss = compute_grads(cfg)
        loss, style_score, content_score = all_loss
        opt.apply_gradients([(grads, init_image)])
        clipped = tf.clip_by_value(init_image, min_vals, max_vals)
        init_image.assign(clipped)
        end_time = time.time() 
    
    if loss < best_loss:
      # Update best loss and best image from total loss. 
        best_loss = loss
        best_img = deprocess_img(init_image.numpy())

    if i % display_interval== 0:
        start_time = time.time()

        # Use the .numpy() method to get the concrete numpy array
        plot_img = init_image.numpy()
        plot_img = deprocess_img(plot_img)
        imgs.append(plot_img)
        IPython.display.clear_output(wait=True)
        IPython.display.display_png(Image.fromarray(plot_img))
        print('Iteration: {}'.format(i))        
        print('Total loss: {:.4e}, ' 
        'style loss: {:.4e}, '
        'content loss: {:.4e}, '
        'time: {:.4f}s'.format(loss, style_score, content_score, time.time() - start_time))
    print('Total time: {:.4f}s'.format(time.time() - global_start))
    IPython.display.clear_output(wait=True)
    plt.figure(figsize=(14,4))
    for i,img in enumerate(imgs):
        plt.subplot(num_rows,num_cols,i+1)
        plt.imshow(img)
        plt.xticks([])
        plt.yticks([])

    return best_img, best_loss

Visualize outputs

We "deprocess" the output image in order to remove the processing that was applied to it.

def show_results(best_img, content_path, style_path, show_large_final=True):
    plt.figure(figsize=(10, 5))
    content = load_img(content_path) 
    style = load_img(style_path)

    plt.subplot(1, 2, 1)
    imshow(content, 'Content Image')

    plt.subplot(1, 2, 2)
    imshow(style, 'Style Image')

    if show_large_final: 
        plt.figure(figsize=(10, 10))

        plt.imshow(best_img)
        plt.title('Output Image')
        plt.show()

Finally, running the style transfer

best, best_loss = run_style_transfer(content_path, style_path, num_iterations=10)

Full code walkthrough can be found here under the bonus section of the notebook.

Hope you liked this post, you can share this for learning purpose and please feel free to add any constructive feedback and thoughts!