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from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib as mpl
from tqdm import trange
steps = 100
style_weight = 1e-2
content_weight = 1e4
total_variation_weight = 1e8
mpl.rcParams['figure.figsize'] = (13, 10)
mpl.rcParams['axes.grid'] = False
content_path = 'content.jpg'
style_path = 'style.jpg'
def load_img(path_to_img):
max_dim = 600
img = tf.io.read_file(path_to_img)
img = tf.image.decode_jpeg(img)
img = tf.image.convert_image_dtype(img, tf.float32)
shape = tf.cast(tf.shape(img)[:-1], tf.float32)
long = max(shape)
scale = max_dim/long
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)
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."""
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, input):
input = input*255.0
preprocessed_input = tf.keras.applications.vgg19.preprocess_input(input)
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)
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)
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
def high_pass_x_y(image):
x_var = image[:, :, 1:, :] - image[:, :, :-1, :]
y_var = image[:, 1:, :, :] - image[:, :-1, :, :]
return x_var, y_var
def total_variation_loss(image):
x_deltas, y_deltas = high_pass_x_y(image)
return tf.reduce_mean(x_deltas**2)+tf.reduce_mean(y_deltas**2)
@tf.function()
def train_step(image):
with tf.GradientTape() as tape:
outputs = extractor(image)
loss = style_content_loss(outputs)
loss += total_variation_weight*total_variation_loss(image)
grad = tape.gradient(loss, image)
opt.apply_gradients([(grad, image)])
image.assign(clip_0_1(image))
image = tf.Variable(content_image)
for n in trange(steps):
train_step(image)
plt.imshow(image.read_value()[0])
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