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nst_utils.py
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nst_utils.py
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import scipy.io
import scipy.misc
import numpy as np
import tensorflow as tf
from config import CONFIG
def load_vgg_model(path):
"""
Returns a model for the purpose of 'painting' the picture.
Takes only the convolution layer weights and wrap using the TensorFlow
Conv2d, Relu and AveragePooling layer. VGG actually uses maxpool but
the paper indicates that using AveragePooling yields better results.
The last few fully connected layers are not used.
Here is the detailed configuration of the VGG model:
0 is conv1_1 (3, 3, 3, 64)
1 is relu
2 is conv1_2 (3, 3, 64, 64)
3 is relu
4 is maxpool
5 is conv2_1 (3, 3, 64, 128)
6 is relu
7 is conv2_2 (3, 3, 128, 128)
8 is relu
9 is maxpool
10 is conv3_1 (3, 3, 128, 256)
11 is relu
12 is conv3_2 (3, 3, 256, 256)
13 is relu
14 is conv3_3 (3, 3, 256, 256)
15 is relu
16 is conv3_4 (3, 3, 256, 256)
17 is relu
18 is maxpool
19 is conv4_1 (3, 3, 256, 512)
20 is relu
21 is conv4_2 (3, 3, 512, 512)
22 is relu
23 is conv4_3 (3, 3, 512, 512)
24 is relu
25 is conv4_4 (3, 3, 512, 512)
26 is relu
27 is maxpool
28 is conv5_1 (3, 3, 512, 512)
29 is relu
30 is conv5_2 (3, 3, 512, 512)
31 is relu
32 is conv5_3 (3, 3, 512, 512)
33 is relu
34 is conv5_4 (3, 3, 512, 512)
35 is relu
36 is maxpool
37 is fullyconnected (7, 7, 512, 4096)
38 is relu
39 is fullyconnected (1, 1, 4096, 4096)
40 is relu
41 is fullyconnected (1, 1, 4096, 1000)
42 is softmax
"""
# Load weights in the form of key-value
vgg = scipy.io.loadmat(path)
vgg_layers = vgg['layers']
def _weights(layer, expected_layer_name):
"""
Return the weights and bias from the VGG model for a given layer.
"""
wb = vgg_layers[0][layer][0][0][2]
W = wb[0][0]
b = wb[0][1]
layer_name = vgg_layers[0][layer][0][0][0][0]
assert layer_name == expected_layer_name
return W, b
def _relu(conv2d_layer):
"""
Return the RELU function wrapped over a TensorFlow layer. Expects a
Conv2d layer input.
"""
return tf.nn.relu(conv2d_layer)
def _conv2d(prev_layer, layer, layer_name):
"""
Return the Conv2D layer using the weights, biases from the VGG
model at 'layer'. Filter being the weights
"""
W, b = _weights(layer, layer_name)
W = tf.constant(W)
b = tf.constant(np.reshape(b, b.size))
return tf.nn.conv2d(prev_layer, filter = W, strides=[1,1,1,1], padding='SAME') + b
def _conv2d_relu(prev_layer, layer, layer_name):
"""
Return the Conv2D + RELU layer using the weights, biases from the VGG
model at 'layer'.
"""
return _relu(_conv2d(prev_layer, layer, layer_name))
def _avgpool(prev_layer):
"""
Return the AveragePooling layer.
"""
return tf.nn.avg_pool(prev_layer, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# Constructs the graph model.
graph = {}
graph['input'] = tf.Variable(np.zeros((1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.COLOR_CHANNELS)),
dtype='float32')
graph['conv1_1'] = _conv2d_relu(graph['input'], 0, 'conv1_1')
graph['conv1_2'] = _conv2d_relu(graph['conv1_1'], 2, 'conv1_2')
graph['avgpool1'] = _avgpool(graph['conv1_2'])
graph['conv2_1'] = _conv2d_relu(graph['avgpool1'], 5, 'conv2_1')
graph['conv2_2'] = _conv2d_relu(graph['conv2_1'], 7, 'conv2_2')
graph['avgpool2'] = _avgpool(graph['conv2_2'])
graph['conv3_1'] = _conv2d_relu(graph['avgpool2'], 10, 'conv3_1')
graph['conv3_2'] = _conv2d_relu(graph['conv3_1'], 12, 'conv3_2')
graph['conv3_3'] = _conv2d_relu(graph['conv3_2'], 14, 'conv3_3')
graph['conv3_4'] = _conv2d_relu(graph['conv3_3'], 16, 'conv3_4')
graph['avgpool3'] = _avgpool(graph['conv3_4'])
graph['conv4_1'] = _conv2d_relu(graph['avgpool3'], 19, 'conv4_1')
graph['conv4_2'] = _conv2d_relu(graph['conv4_1'], 21, 'conv4_2')
graph['conv4_3'] = _conv2d_relu(graph['conv4_2'], 23, 'conv4_3')
graph['conv4_4'] = _conv2d_relu(graph['conv4_3'], 25, 'conv4_4')
graph['avgpool4'] = _avgpool(graph['conv4_4'])
graph['conv5_1'] = _conv2d_relu(graph['avgpool4'], 28, 'conv5_1')
graph['conv5_2'] = _conv2d_relu(graph['conv5_1'], 30, 'conv5_2')
graph['conv5_3'] = _conv2d_relu(graph['conv5_2'], 32, 'conv5_3')
graph['conv5_4'] = _conv2d_relu(graph['conv5_3'], 34, 'conv5_4')
graph['avgpool5'] = _avgpool(graph['conv5_4'])
return graph
def generate_noise_image(content_image, noise_ratio=CONFIG.NOISE_RATIO):
"""
Generates a noisy image by adding random noise to the content_image
This will help content of "generated" image more rapidly match the content of "content" image
"""
# Generate a random noise_image
noise_image = np.random.uniform(-20, 20,
(1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.COLOR_CHANNELS)).astype(
'float32')
# Set the input_image to be a weighted average of the content_image and a noise_image
input_image = noise_image * noise_ratio + content_image * (1 - noise_ratio)
return input_image
def reshape_image(image):
# Reshape image to match expected input of VGG16
image = np.reshape(image, ((1,) + image.shape))
return image
def normalize_image(image):
# Substract the mean to match the expected input of VGG16
image = image - CONFIG.MEANS
return image
def unnormalize_image(image):
# Un-normalize the image so that it looks good
image = image + CONFIG.MEANS
return image
def reshape_and_normalize_image(image):
"""
Reshape and normalize the input image (content or style)
"""
image = reshape_image(image)
image = normalize_image(image)
return image
def save_image(path, image):
image = unnormalize_image(image)
# Clip and Save the image
image = np.clip(image[0], 0, 255).astype('uint8')
scipy.misc.imsave(path, image)