Style_Transfer.py

import tensorflow as tf

import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (12,12)
mpl.rcParams['axes.grid'] = False

import numpy as np
import PIL.Image

def tensor_to_image(tensor):
  tensor = tensor*255
  tensor = np.array(tensor, dtype=np.uint8)
  if np.ndim(tensor)>3:
    assert tensor.shape[0] == 1
    tensor = tensor[0]
  return PIL.Image.fromarray(tensor)


content_path = 'Content.jpg'
style_path= 'Style.jpg'

def imshow(image, title=None):
  if len(image.shape) > 3:
    image = tf.squeeze(image, axis=0) 
    ### remove dim of size 1 

  plt.imshow(image)
  if title:
    plt.title(title)


def load_img(path_to_img):
  max_dim = 512
  img = tf.io.read_file(path_to_img)
  ### this one read file in the form of A Tensor of type string.
  img = tf.image.decode_image(img, channels=3)
  #Detects whether an image is a BMP, GIF, JPEG, or PNG, and performs the appropriate operation to convert the input bytes string into a Tensor of type dtype.
  img = tf.image.convert_image_dtype(img, tf.float32)

  #rescaling the image
  shape = tf.cast(tf.shape(img)[:-1], tf.float32)
  #changing the type of shape to do floting operations on them
  long_dim = max(shape)
  scale = max_dim / long_dim

  new_shape = tf.cast(shape * scale, tf.int32)

  img = tf.image.resize(img, new_shape)
  img = img[tf.newaxis, :]
  return img



content_image = load_img(content_path)
style_image = load_img(style_path)
print(content_image.shape)
print(style_image.shape)
plt.subplot(1, 2, 1)
imshow(content_image, 'Content Image')

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

content_layers = ['block5_conv2'] 

style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1', 
                'block4_conv1', 
                'block5_conv1']


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


def vgg_layers(layer_names):
  """ Creates a vgg model that returns a list of intermediate output values."""
  # Load our model. Load pretrained VGG, trained on imagenet data
  vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
  vgg.trainable = False
  
  outputs = [vgg.get_layer(name).output for name in layer_names]

  model = tf.keras.Model([vgg.input], outputs)
  return model

def gram_matrix(input_tensor):
  result = tf.linalg.einsum('bijc,bijd->bcd', input_tensor, input_tensor)
  input_shape = tf.shape(input_tensor)
  num_locations = tf.cast(input_shape[1]*input_shape[2], tf.float32)
  return result/(num_locations)


class StyleContentModel(tf.keras.models.Model):
  def __init__(self, style_layers, content_layers):
    super(StyleContentModel, self).__init__()
    self.vgg =  vgg_layers(style_layers + content_layers)
    self.style_layers = style_layers
    self.content_layers = content_layers
    self.num_style_layers = len(style_layers)
    self.vgg.trainable = False

  def call(self, inputs):
    #"Expects float input in [0,1]"
    inputs = inputs*255.0
    preprocessed_input = tf.keras.applications.vgg19.preprocess_input(inputs)
    outputs = self.vgg(preprocessed_input)
    style_outputs, content_outputs = (outputs[:self.num_style_layers], 
                                      outputs[self.num_style_layers:])

    style_outputs = [gram_matrix(style_output)
                     for style_output in style_outputs]

    content_dict = {content_name:value 
                    for content_name, value 
                    in zip(self.content_layers, content_outputs)}

    style_dict = {style_name:value
                  for style_name, value
                  in zip(self.style_layers, style_outputs)}
    
    return {'content':content_dict, 'style':style_dict}

extractor = StyleContentModel(style_layers, content_layers)

results = extractor(tf.constant(content_image))



print('Styles:')
for name, output in sorted(results['style'].items()):
  print("  ", name)
  print("    shape: ", output.numpy().shape)
  print("    min: ", output.numpy().min())
  print("    max: ", output.numpy().max())
  print("    mean: ", output.numpy().mean())
  print()

print("Contents:")
for name, output in sorted(results['content'].items()):
  print("  ", name)
  print("    shape: ", output.numpy().shape)
  print("    min: ", output.numpy().min())
  print("    max: ", output.numpy().max())
  print("    mean: ", output.numpy().mean())


style_targets = extractor(style_image)['style']
content_targets = extractor(content_image)['content']
image = tf.Variable(content_image)

def clip_0_1(image):
  return tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)


opt = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)



style_weight=1
content_weight=1e4

def style_content_loss(outputs):
    style_outputs = outputs['style']
    content_outputs = outputs['content']
    style_loss = tf.add_n([tf.reduce_mean(
        
        (style_outputs[name]-style_targets[name])**2
        
        )      for name in style_outputs.keys()])
    style_loss *= style_weight / num_style_layers

    content_loss = tf.add_n([tf.reduce_mean((content_outputs[name]-content_targets[name])**2) 
                             for name in content_outputs.keys()])
    content_loss *= content_weight / num_content_layers
    loss = style_loss + content_loss
    return loss

@tf.function()
def train_step(image):
  with tf.GradientTape() as tape:
    outputs = extractor(image)
    loss = style_content_loss(outputs)

  grad = tape.gradient(loss, image)
  opt.apply_gradients([(grad, image)])
  image.assign(clip_0_1(image))


train_step(image)
train_step(image)
train_step(image)
plt.imshow(tensor_to_image(image))
plt.show()



import time
start = time.time()

epochs = 10
steps_per_epoch = 10

step = 0
for n in range(epochs):
  for m in range(steps_per_epoch):
    step += 1
    train_step(image)
    print(".", end='')
  print("Train step: {}".format(step))
  plt.imshow(tensor_to_image(image))
  plt.show()
  
end = time.time()
print("Total time: {:.1f}".format(end-start))