Art Generation With Neural Style Transfer
Use transfer learning to combine a content image with a s...

10
2018/07

# Art Generation With Neural Style Transfer

Use transfer learning to combine a content image with a style image

import os
import sys
import scipy.io
import scipy.misc
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from PIL import Image
from nst_utils import *
import numpy as np
import tensorflow as tf

%matplotlib inline

print(model)

imshow(content_image)

def compute_content_cost(a_C, a_G):
"""
Computes the content cost

Arguments:
a_C -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C
a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image G

Returns:
J_content -- scalar that you compute using equation 1 above.
"""

### START CODE HERE ###
# Retrieve dimensions from a_G (≈1 line)
m, n_H, n_W, n_C = a_G.get_shape().as_list()

# Reshape a_C and a_G (≈2 lines)
# unroll the two tensors
a_C_unrolled = tf.reshape(a_C,[m,-1,n_C])
a_G_unrolled = tf.reshape(a_G,[m,-1,n_C])
#print(a_G_unrolled.get_shape().as_list())

# compute the cost with tensorflow (≈1 line)
J_content = tf.reduce_sum(tf.square(a_C_unrolled - a_G_unrolled))/(4*n_H*n_W*n_C)
### END CODE HERE ###

return J_content

imshow(style_image)

def gram_matrix(A):
"""
Argument:
A -- matrix of shape (n_C, n_H*n_W)

Returns:
GA -- Gram matrix of A, of shape (n_C, n_C)
"""

### START CODE HERE ### (≈1 line)
GA = tf.matmul(A,tf.transpose(A))
### END CODE HERE ###

return GA

def compute_layer_style_cost(a_S, a_G):
"""
Arguments:
a_S -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image S
a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image G

Returns:
J_style_layer -- tensor representing a scalar value, style cost defined above by equation (2)
"""

### START CODE HERE ###
# Retrieve dimensions from a_G (≈1 line)
m, n_H, n_W, n_C = a_G.get_shape().as_list()

# Reshape the images to have them of shape (n_C, n_H*n_W) (≈2 lines)
#a_S = tf.reshape(a_S,[n_C,-1])
#a_G = tf.reshape(a_G,[n_C,-1])
a_S = tf.transpose(tf.reshape(a_S,[n_H*n_W,n_C]))
a_G = tf.transpose(tf.reshape(a_G,[n_H*n_W,n_C]))
#print(a_S.get_shape().as_list())
#print(a_G.get_shape().as_list())

# Computing gram_matrices for both images S and G (≈2 lines)
GS = gram_matrix(a_S)
GG = gram_matrix(a_G)

# Computing the loss (≈1 line)
J_style_layer = (tf.reduce_sum(tf.square(tf.subtract(GS,GG))))/(4*n_C*n_C*(n_H*n_W)*(n_H*n_W))

### END CODE HERE ###

return J_style_layer

STYLE_LAYERS = [
('conv1_1', 0.2),
('conv2_1', 0.2),
('conv3_1', 0.2),
('conv4_1', 0.2),
('conv5_1', 0.2)]

def compute_style_cost(model, STYLE_LAYERS):
"""
Computes the overall style cost from several chosen layers

Arguments:
model -- our tensorflow model
STYLE_LAYERS -- A python list containing:
- the names of the layers we would like to extract style from
- a coefficient for each of them

Returns:
J_style -- tensor representing a scalar value, style cost defined above by equation (2)
"""

# initialize the overall style cost
J_style = 0

for layer_name, coeff in STYLE_LAYERS:

# Select the output tensor of the currently selected layer
out = model[layer_name]

# Set a_S to be the hidden layer activation from the layer we have selected, by running the session on out
a_S = sess.run(out)

# Set a_G to be the hidden layer activation from same layer. Here, a_G references model[layer_name]
# and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that
# when we run the session, this will be the activations drawn from the appropriate layer, with G as input.
a_G = out

# Compute style_cost for the current layer
J_style_layer = compute_layer_style_cost(a_S, a_G)

# Add coeff * J_style_layer of this layer to overall style cost
J_style += coeff * J_style_layer

return J_style

def total_cost(J_content, J_style, alpha = 10, beta = 40):
"""
Computes the total cost function

Arguments:
J_content -- content cost coded above
J_style -- style cost coded above
alpha -- hyperparameter weighting the importance of the content cost
beta -- hyperparameter weighting the importance of the style cost

Returns:
J -- total cost as defined by the formula above.
"""

### START CODE HERE ### (≈1 line)
J = alpha*J_content + beta * J_style
### END CODE HERE ###

return J

# Reset the graph
tf.reset_default_graph()

# Start interactive session
sess = tf.InteractiveSession()

content_image = reshape_and_normalize_image(content_image)

style_image = reshape_and_normalize_image(style_image)

generated_image = generate_noise_image(content_image)
imshow(generated_image[0])

# Assign the content image to be the input of the VGG model.
sess.run(model['input'].assign(content_image))

# Select the output tensor of layer conv4_2
out = model['conv4_2']

# Set a_C to be the hidden layer activation from the layer we have selected
a_C = sess.run(out)

# Set a_G to be the hidden layer activation from same layer. Here, a_G references model['conv4_2']
# and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that
# when we run the session, this will be the activations drawn from the appropriate layer, with G as input.
a_G = out
print(a_G)
print(a_C)

# Compute the content cost
J_content = compute_content_cost(a_C, a_G)

# Assign the input of the model to be the "style" image
sess.run(model['input'].assign(style_image))

# Compute the style cost
J_style = compute_style_cost(model, STYLE_LAYERS)

J = total_cost(J_content,J_style)

# define optimizer (1 line)

# define train_step (1 line)
train_step = optimizer.minimize(J)

def model_nn(sess, input_image, num_iterations = 200):

# Initialize global variables (you need to run the session on the initializer)
### START CODE HERE ### (1 line)
sess.run(tf.global_variables_initializer())
### END CODE HERE ###

# Run the noisy input image (initial generated image) through the model. Use assign().
### START CODE HERE ### (1 line)
sess.run(model['input'].assign(input_image))
### END CODE HERE ###

for i in range(num_iterations):

# Run the session on the train_step to minimize the total cost
### START CODE HERE ### (1 line)
sess.run(train_step)
### END CODE HERE ###

# Compute the generated image by running the session on the current model['input']
### START CODE HERE ### (1 line)
generated_image = sess.run(model['input'])
### END CODE HERE ###

# Print every 20 iteration.
if i%20 == 0:
Jt, Jc, Js = sess.run([J, J_content, J_style])
print("Iteration " + str(i) + " :")
print("total cost = " + str(Jt))
print("content cost = " + str(Jc))
print("style cost = " + str(Js))

# save current generated image in the "/output" directory
save_image("output/" + str(i) + ".png", generated_image)

# save last generated image
save_image('output/generated_image.jpg', generated_image)

return generated_image

model_nn(sess, generated_image)
• The idea of using a network trained on a different task and applying it to a new task is called transfer learning. In this project we use the transfer learning and load the pre-trained VGG-19 network.
• Pay attention to the function tf.add(),tf.subtract(),tf.square(), tf.reduce_sum(), tf.reshape(X,[2,2,-1]),tf.transpose(X, perm = [0,2,1]), you can look up them in the official API docs if necessary.
• The loss consists of the J_content and the J_style, pay attention to the gram matrix and the computation of the J_style.
• Understand the two unrollments of the tensors with tf.reshape() function and compare their differences.
Last modification：March 13th, 2019 at 07:06 pm
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