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Neural Style Transfer with TensorFlow

  • Last Updated : 04 Aug, 2021

Neural style transfer is an optimization technique used to take two images, a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image. This technique is used by many popular android iOS apps such as Prisma, DreamScope, PicsArt.

An example of style transfer A is a content image, B is output with style image in the bottom left corner

Architecture:

The neural style transfer paper uses feature maps generated by intermediate layers of VGG-19 network to generate the output image. This architecture takes style and content images as input and stores the features extracted by convolution layers of VGG network. 

VGG-19 architecture

VGG-19 architecture

Content Loss:

To calculate the content cost, we apply the mean square difference between matrices generated by the content layer, when we pass the generated image and the original image. Let p and x be the original image and the image that is generated, and P and F are their respective feature representation in layer l. We then define the squared-error loss between the two feature representations 

L _{\text {content}}(\rho, x, L)=\frac{1}{2} \sum_{i j}\left(F_{i j}^{l}-P_{i j}^{l}\right)^{2}
 

Style Loss:

To calculate the style cost, we will first calculate the gram matrix.  The gram matrices calculation involves calculating the inner product between the vectorized feature maps of a particular layer. Here Gij (l) represents the inner product between vectorized features i,j of layer l.
G_{i j}^{l}=\sum_{k} F_{i k}^{l} F_{j k}^{l}
Now to calculate the loss from a particular, we will find the mean square difference of gram matrices calculated from the feature vectors of the style image and the generated image. This then weighted to the layer weighing factor.

Let a and x be the original image and the generated image, and Al and Gl their respective style representation (gram matrices) in layer l. The contribution of layer l to the total loss is then:
E_l = \frac{1}{4N_l^{2}M_l^{2}}\sum \left( G_{ij}^{l} - A_{ij}^{l}\right)^{2}
Therefore, total style loss will be:
L_{style} = \sum_{l=0}^{L}w_{l}E_{l}
 

Total Loss

Total loss is the linear combination of style and content loss we defined above:
L_{\text {total}}(P, a, x)=\alpha \times L_{\text {content}}+\beta \times L_{\text {style}}
 

Where α and β are the weighting factors for content and style reconstruction, respectively.

Implementation in Tensorflow: 

  • First, we import the necessary module. In this post, we use TensorFlow v2 with Keras. We will also import VGG-19 model from tf.keras API.

Code: 



Python3




# import numpy, tensorflow and matplotlib
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
 
# import VGG 19 model and keras Model API
from tensorflow.python.keras.applications.vgg19 import VGG19, preprocess_input
from tensorflow.python.keras.preprocessing.image import load_img, img_to_array
from tensorflow.python.keras.models import Model
  • Now, we import the content and style images and save them into our working directory.

Code: 

Python3




# Image Credits: Tensorflow Doc
content_path = tf.keras.utils.get_file('content.jpg',
                                       'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg')
style_path = tf.keras.utils.get_file('style.jpg',
                                     'https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')
  • Now, we initialize the VGG model with ImageNet weights, we will also remove the top layers and make it non-trainable.

Code: 

Python3




# code
# this function download the VGG model and initiliase it
model = VGG19(
    include_top=False,
    weights='imagenet'
)
# set training to False
model.trainable = False
# Print details of different layers
 
model.summary()

 
Output:

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5
80142336/80134624 [==============================] - 1s 0us/step
Model: "vgg19"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, None, None, 3)]   0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv4 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv4 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv4 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
=================================================================
Total params: 20,024,384
Trainable params: 0
Non-trainable params: 20,024,384
________________________________________________________________
  • Now, we load and process the image using Keras preprocess input in VGG 19. The expand_dims function adds a dimension to represent a number of images in the input. This preprocess_input function (used in VGG 19 ) converts the input RGB to BGR images and centre these values around 0 according to ImageNet data (no scaling).

Code: 

Python3




# code to load and process image
def load_and_process_image(image_path):
    img = load_img(image_path)
    # convert image to array
    img = img_to_array(img)
    img = preprocess_input(img)
    img = np.expand_dims(img, axis=0)
    return img
  • Now, we define the deprocess function that takes the input image and perform the inverse of preprocess_input function that we imported above. To display the unprocessed image, we also define a display function.

Code: 

Python3




# code
def deprocess(img):
    # perform the inverse of the pre processing step
    img[:, :, 0] += 103.939
    img[:, :, 1] += 116.779
    img[:, :, 2] += 123.68
    # convert RGB to BGR
    img = img[:, :, ::-1]
 
    img = np.clip(img, 0, 255).astype('uint8')
    return img
 
 
def display_image(image):
    # remove one dimension if image has 4 dimension
    if len(image.shape) == 4:
        img = np.squeeze(image, axis=0)
 
    img = deprocess(img)
 
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])
    plt.imshow(img)
    return
  • Now, we use the above function to display the style and content images

Code: 

Python3




# load content image
content_img = load_and_process_image(content_path)
display_image(content_img)
 
# load style image
style_img = load_and_process_image(style_path)
display_image(style_img)

 
Output:
 

Content Image

Content Image

Style Image

Style Image

  • Now, we define the content and style model using Keras.Model API. The content model takes the image as input and output the feature map from “block5_conv1” from the above VGG model.

Code: 

Python3




# define content model
content_layer = 'block5_conv2'
content_model = Model(
    inputs=model.input,
    outputs=model.get_layer(content_layer).output
)
content_model.summary()

 
Output:



Model: "functional_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, None, None, 3)]   0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv4 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv4 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
=================================================================
Total params: 15,304,768
Trainable params: 0
Non-trainable params: 15,304,768
_________________________________________________________________
  • Now, we define the content and style model using Keras.Model API. The style model takes an image as input and output the feature map from “block1_conv1, block3_conv1, and block5_conv2″ from the above VGG model.

Code: 

Python3




# define style model
style_layers = [
    'block1_conv1',
    'block3_conv1',
    'block5_conv1'
]
style_models = [Model(inputs=model.input,
                      outputs=model.get_layer(layer).output) for layer in style_layers]
  • Now, we define the content loss function, it will take the feature map of generated and real images and calculate the mean square difference between them.

Code: 

Python3




# Content loss
def content_loss(content, generated):
    a_C = content_model(content)
    loss = tf.reduce_mean(tf.square(a_C - a_G))
    return loss
  • Now, we define the gram matrix and style loss function. This function also takes the real and generated images as the input of the model and calculates gram matrices of them before calculate the style loss weighted to different layers.

Code: 

Python3




# gram matrix
def gram_matrix(A):
    channels = int(A.shape[-1])
    a = tf.reshape(A, [-1, channels])
    n = tf.shape(a)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)
 
 
weight_of_layer = 1. / len(style_models)
 
 
# style loss
def style_cost(style, generated):
    J_style = 0
 
    for style_model in style_models:
        a_S = style_model(style)
        a_G = style_model(generated)
        GS = gram_matrix(a_S)
        GG = gram_matrix(a_G)
        current_cost = tf.reduce_mean(tf.square(GS - GG))
        J_style += current_cost * weight_of_layer
 
    return J_style
  • Now, we define our training function, we will train our model to 50 iterations. This model takes input images, the number of iterations as its argument.

Python3




# training function
generated_images = []
 
 
def training_loop(content_path, style_path, iterations=50, a=10, b=1000):
    # load content and style images from their respective path
    content = load_and_process_image(content_path)
    style = load_and_process_image(style_path)
    generated = tf.Variable(content, dtype=tf.float32)
 
    opt = tf.keras.optimizers.Adam(learning_rate=7)
 
    best_cost = Inf
    best_image = None
    for i in range(iterations):
        % % time
        with tf.GradientTape() as tape:
            J_content = content_cost(content, generated)
            J_style = style_cost(style, generated)
            J_total = a * J_content + b * J_style
 
        grads = tape.gradient(J_total, generated)
        opt.apply_gradients([(grads, generated)])
 
        if J_total < best_cost:
            best_cost = J_total
            best_image = generated.numpy()
 
        print("Iteration :{}".format(i))
        print('Total Loss {:e}.'.format(J_total))
        generated_images.append(generated.numpy())
 
    return best_image
  • Now, we train our model using the training function we defined above.

Code: 

Python3




# Train the model and get best image
final_img = training(content_path, style_path)

Output:

CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.2 µs
Iteration :0
Total Loss 5.133922e+11.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.72 µs
Iteration :1
Total Loss 3.510511e+11.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.68 µs
Iteration :2
Total Loss 2.069992e+11.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 6.2 µs
Iteration :3
Total Loss 1.669609e+11.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.44 µs
Iteration :4
Total Loss 1.575840e+11.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :5
Total Loss 1.200623e+11.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :6
Total Loss 8.824594e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.72 µs
Iteration :7
Total Loss 7.168546e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.48 µs
Iteration :8
Total Loss 6.207320e+10.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 8.34 µs
Iteration :9
Total Loss 5.390836e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.2 µs
Iteration :10
Total Loss 4.735992e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :11
Total Loss 4.301782e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.2 µs
Iteration :12
Total Loss 3.912694e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.68 µs
Iteration :13
Total Loss 3.445185e+10.
CPU times: user 0 ns, sys: 3 µs, total: 3 µs
Wall time: 6.2 µs
Iteration :14
Total Loss 2.975165e+10.
CPU times: user 2 µs, sys: 0 ns, total: 2 µs
Wall time: 5.96 µs
Iteration :15
Total Loss 2.590984e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 20 µs
Iteration :16
Total Loss 2.302116e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.72 µs
Iteration :17
Total Loss 2.082643e+10.
CPU times: user 4 µs, sys: 1e+03 ns, total: 5 µs
Wall time: 8.34 µs
Iteration :18
Total Loss 1.906701e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.25 µs
Iteration :19
Total Loss 1.759801e+10.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 6.2 µs
Iteration :20
Total Loss 1.635128e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.2 µs
Iteration :21
Total Loss 1.525327e+10.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 5.96 µs
Iteration :22
Total Loss 1.418364e+10.
CPU times: user 4 µs, sys: 1 µs, total: 5 µs
Wall time: 9.06 µs
Iteration :23
Total Loss 1.306596e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.25 µs
Iteration :24
Total Loss 1.196509e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :25
Total Loss 1.102290e+10.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :26
Total Loss 1.025539e+10.
CPU times: user 7 µs, sys: 3 µs, total: 10 µs
Wall time: 12.6 µs
Iteration :27
Total Loss 9.570500e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.72 µs
Iteration :28
Total Loss 8.917115e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :29
Total Loss 8.328761e+09.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 9.54 µs
Iteration :30
Total Loss 7.840127e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.44 µs
Iteration :31
Total Loss 7.406647e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 8.34 µs
Iteration :32
Total Loss 6.967848e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.72 µs
Iteration :33
Total Loss 6.531650e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.72 µs
Iteration :34
Total Loss 6.136975e+09.
CPU times: user 2 µs, sys: 1 µs, total: 3 µs
Wall time: 5.96 µs
Iteration :35
Total Loss 5.788804e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.72 µs
Iteration :36
Total Loss 5.476942e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.2 µs
Iteration :37
Total Loss 5.204070e+09.
CPU times: user 3 µs, sys: 1 µs, total: 4 µs
Wall time: 6.2 µs
Iteration :38
Total Loss 4.954049e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :39
Total Loss 4.708641e+09.
CPU times: user 3 µs, sys: 2 µs, total: 5 µs
Wall time: 6.2 µs
Iteration :40
Total Loss 4.487677e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :41
Total Loss 4.296946e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.96 µs
Iteration :42
Total Loss 4.107909e+09.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 6.44 µs
Iteration :43
Total Loss 3.918156e+09.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 6.2 µs
Iteration :44
Total Loss 3.747263e+09.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 8.34 µs
Iteration :45
Total Loss 3.595638e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 5.72 µs
Iteration :46
Total Loss 3.458928e+09.
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs
Wall time: 6.2 µs
Iteration :47
Total Loss 3.331772e+09.
CPU times: user 4 µs, sys: 1e+03 ns, total: 5 µs
Wall time: 9.3 µs
Iteration :48
Total Loss 3.205911e+09.
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs
Wall time: 5.96 µs
Iteration :49
Total Loss 3.089630e+09.
  • In the final step, we plot the final and intermediate results.

Code: 

Python3




# code to display best generated image and last 10 intermediate results
plt.figure(figsize=(12, 12))
 
for i in range(10):
    plt.subplot(4, 3, i + 1)
    display_image(generated_images[i+39])
plt.show()
 
# plot best result
display_image(final_img)

Output:

Last 10 Generated images

Last 10 Generated images

Best Generated image

Best Generated image
 

References: 


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