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import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import \
Conv2D, AveragePooling2D
from skimage import transform
import hyperparameters as hp
class YourModel(tf.keras.Model):
""" Your own neural network model. """
def __init__(self, content_image, style_image): #normalize these images to float values
super(YourModel, self).__init__()
self.content_image = transform.resize(content_image, tf.shape(style_image), anti_aliasing=True)
self.content_image = tf.expand_dims(self.content_image, axis=0)
print(self.content_image)
#perhaps consider cropping to avoid distortion
self.style_image = transform.resize(style_image, tf.shape(style_image), anti_aliasing=True)
self.style_image = tf.expand_dims(self.style_image, axis=0)
#self.x = tf.Variable(initial_value = self.content_image.numpy().astype(np.float32), trainable=True)
self.x = tf.Variable(initial_value = np.random.rand(self.content_image.shape[0],
self.content_image.shape[1], self.content_image.shape[2], self.content_image.shape[3]).astype(np.float32), trainable=True)
self.alpha = hp.alpha
self.beta = hp.beta
self.photo_layers = None
self.art_layers = None
#(self.x.shape)
#print(self.content_image.shape, self.style_image.shape)
self.optimizer = tf.keras.optimizers.Adam(hp.learning_rate)
self.vgg16 = [
# Block 1
Conv2D(64, 3, 1, padding="same",
activation="relu", name="block1_conv1"),
Conv2D(64, 3, 1, padding="same",
activation="relu", name="block1_conv2"),
AveragePooling2D(2, name="block1_pool"),
# Block 2
Conv2D(128, 3, 1, padding="same",
activation="relu", name="block2_conv1"),
Conv2D(128, 3, 1, padding="same",
activation="relu", name="block2_conv2"),
AveragePooling2D(2, name="block2_pool"),
# Block 3
Conv2D(256, 3, 1, padding="same",
activation="relu", name="block3_conv1"),
Conv2D(256, 3, 1, padding="same",
activation="relu", name="block3_conv2"),
Conv2D(256, 3, 1, padding="same",
activation="relu", name="block3_conv3"),
AveragePooling2D(2, name="block3_pool"),
# Block 4
Conv2D(512, 3, 1, padding="same",
activation="relu", name="block4_conv1"),
Conv2D(512, 3, 1, padding="same",
activation="relu", name="block4_conv2"),
Conv2D(512, 3, 1, padding="same",
activation="relu", name="block4_conv3"),
AveragePooling2D(2, name="block4_pool"),
# Block 5
Conv2D(512, 3, 1, padding="same",
activation="relu", name="block5_conv1"),
Conv2D(512, 3, 1, padding="same",
activation="relu", name="block5_conv2"),
Conv2D(512, 3, 1, padding="same",
activation="relu", name="block5_conv3"),
AveragePooling2D(2, name="block5_pool"),
]
for layer in self.vgg16:
layer.trainable = False
self.layer_to_filters = {layer.name: layer.filters for layer in self.vgg16 if "conv" in layer.name}
self.indexed_layers = [layer for layer in self.vgg16 if "conv1" in layer.name]
self.desired = [layer.name for layer in self.vgg16 if "conv1" in layer.name]
self.vgg16 = tf.keras.Sequential(self.vgg16, name="vgg")
# create a map of the layers to their corresponding number of filters if it is a convolutional layer
def call(self, x):
layers = []
for layer in self.vgg16.layers:
# pass the x through
x = layer(x)
# print("Sotech117 is so so sus")
# save the output of each layer if it is in the desired list
if layer.name in self.desired:
layers.append(x)
return x, layers
def loss_fn(self, p, a, x):
# print(p)
if(self.photo_layers == None):
_, self.photo_layers = self.call(p)
# print(a)
if(self.art_layers == None):
_, self.art_layers = self.call(a)
# print(x)
_, input_layers = self.call(x)
content_l = self.content_loss(self.photo_layers, input_layers)
style_l = self.style_loss(self.art_layers, input_layers)
# Equation 7
print('style_loss', style_l)
print('content_loss', content_l)
return (self.alpha * content_l) + (self.beta * style_l)
def content_loss(self, photo_layers, input_layers):
L_content = tf.constant(0.0)
for i in range(len(photo_layers)):
pl = photo_layers[i]
il = input_layers[i]
L_content = tf.math.add(L_content, tf.reduce_mean(tf.square(pl - il)))
#print('content loss', L_content)
return L_content
def layer_loss(self, art_layer, input_layer):
# vectorize the art_layers
art_layer = tf.reshape(art_layer, (-1, art_layer.shape[-1]))
# # vectorize the input_layers
input_layer = tf.reshape(input_layer, (-1, input_layer.shape[-1]))
# get the gram matrices
G_l = tf.matmul(tf.transpose(input_layer), input_layer)
A_l = tf.matmul(tf.transpose(art_layer), art_layer)
# vals = []
# for i in range(input_dim):
# vals_i = []
# for j in range(input_dim):
# il = tf.reshape(input_layers[i], [-1])
# al = tf.reshape(art_layers[j], [-1])
# k = tf.reduce_sum(tf.multiply(il, al))
# vals_i.append(k)
# vals.append(tf.stack(vals_i))
# G = tf.stack(vals)
# get the loss per each lateral layer
# N depends on # of filters in the layer, M depends on hight and width of feature map
M_l = art_layer.shape[0]
N_l = art_layer.shape[1]
# layer.filters might not work
E_l = 1/4 * (M_l**(-2)) *(N_l**(-2)) * tf.reduce_sum(tf.square(G_l - A_l))
# while Sotech is botty:
# Jayson_tatum.tear_acl()
# return ("this is just another day")
#print('Layer loss', E_l)
return E_l
def style_loss(self, art_layers, input_layers):
L_style = tf.constant(0.0)
for i in range(len(art_layers)):
art_layer = art_layers[i]
input_layer = input_layers[i]
L_style = tf.math.add(L_style, self.layer_loss(art_layer, input_layer)*(1/5))
#print('style loss', L_style)
return L_style
def train_step(self):
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(self.x)
loss = self.loss_fn(self.content_image, self.style_image, self.x)
print('loss', loss)
#print('self.x', self.x)
gradients = tape.gradient(loss, [self.x])
#print('gradients', gradients)
self.optimizer.apply_gradients(zip(gradients, [self.x]))
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