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vgg.py
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""" Part of this code is due to the MatConvNet team and is used to load the parameters of the pretrained VGG19 model """
import scipy.io as io
import numpy as np
import tensorflow as tf
# values for the lambda hyperparam for each layer
STYLE_LAYERS = [
('conv1_1', 0.2),
('conv2_1', 0.2),
('conv3_1', 0.2),
('conv4_1', 0.2),
('conv5_1', 0.2)
]
class CONFIG:
IMAGE_WIDTH = 800
IMAGE_HEIGHT = 600
COLOR_CHANNELS = 3
NOISE_RATIO = 0.6
MEANS = np.array([123.68, 116.779, 103.939]).reshape((1,1,1,3))
# Pick the VGG 19-layer model from the authors of the paper
# "Very Deep Convolutional Networks for Large-Scale Image Recognition".
VGG_MODEL = 'model/imagenet-vgg-verydeep-19.mat'
CONTENT_IMAGES_DIR = 'data/images/content/'
STYLE_IMAGES_DIR = 'data/images/style/'
OUTPUT_AUX_DIR = 'data/out/auxillary/'
OUTPUT_DIR = 'data/out/'
vgg = io.loadmat(CONFIG.VGG_MODEL)
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'.
"""
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')
def load_vgg_model():
"""
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
"""
# 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