forked from taki0112/MUNIT-Tensorflow
-
Notifications
You must be signed in to change notification settings - Fork 0
/
vgg16.py
179 lines (134 loc) · 6.15 KB
/
vgg16.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
import inspect
import os
import numpy as np
import tensorflow as tf
import time
import h5py
VGG_MEAN = [103.939, 116.779, 123.68]
"""
Based on https://github.com/antlerros/tensorflow-fast-neuralstyle
Download weigths from:
https://mega.nz/#!YU1FWJrA!O1ywiCS2IiOlUCtCpI6HTJOMrneN-Qdv3ywQP5poecM
or from
https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
"""
class Vgg16:
def __init__(self, vgg16_npy_path, h, w, channels=3, train=False):
path = inspect.getfile( Vgg16 )
path = os.path.abspath( os.path.join( path, os.pardir ) )
path = os.path.join( path, vgg16_npy_path )
vgg16_path = path
print("Loading weights from %s ..."%vgg16_path)
self.data_dict = None
if vgg16_npy_path.split(".")[-1] == "h5":
self.h5 = True
self.data_dict = h5py.File(vgg16_path, 'r')
print( "H5 file loaded" )
else:
self.h5 = False
self.data_dict = np.load(vgg16_path, encoding='latin1').item()
print( "Npy file loaded" )
self.h = h
self.w = w
self.ch = channels
self.is_trainable = train
def build(self, rgb):
"""
load variable from npy to build the VGG
:param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
"""
start_time = time.time()
print("Build model started")
"""
rgb_scaled = rgb * 255.0
# Convert RGB to BGR
red, green, blue = tf.split(axis=3, num_or_size_splits=3, value=rgb_scaled)
assert red.get_shape().as_list()[1:] == [224, 224, 1]
assert green.get_shape().as_list()[1:] == [224, 224, 1]
assert blue.get_shape().as_list()[1:] == [224, 224, 1]
bgr = tf.concat(axis=3, values=[
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
"""
self.conv1_1 = self.conv_layer(rgb, "conv1_1")
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
self.pool2 = self.max_pool(self.conv2_2, 'pool2')
self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
self.pool3 = self.max_pool(self.conv3_3, 'pool3')
self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
self.pool4 = self.max_pool(self.conv4_3, 'pool4')
self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
self.pool5 = self.max_pool(self.conv5_3, 'pool5')
"""
# no need for fully connected layers
self.fc6 = self.fc_layer(self.pool5, "fc6")
assert self.fc6.get_shape().as_list()[1:] == [4096]
self.relu6 = tf.nn.relu(self.fc6)
self.fc7 = self.fc_layer(self.relu6, "fc7")
self.relu7 = tf.nn.relu(self.fc7)
self.fc8 = self.fc_layer(self.relu7, "fc8")
self.prob = tf.nn.softmax(self.fc8, name="prob")
"""
#self.data_dict = None
print("Build model finished: %ds" % (time.time() - start_time))
def avg_pool(self, bottom, name):
return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def max_pool(self, bottom, name):
return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def conv_layer(self, bottom, name):
with tf.variable_scope(name):
filt = self.get_conv_filter(name)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
conv_biases = self.get_bias(name)
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
return relu
def fc_layer(self, bottom, name):
with tf.variable_scope(name):
shape = bottom.get_shape().as_list()
dim = 1
for d in shape[1:]:
dim *= d
x = tf.reshape(bottom, [-1, dim])
weights = self.get_fc_weight(name)
biases = self.get_bias(name)
# Fully connected layer. Note that the '+' operation automatically
# broadcasts the biases.
fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
return fc
def from_dataset( self, name, column ):
if self.h5:
name = 'block%d_conv%d' % (int( name.split( "_" )[ 0 ][ -1 ] ), int( name[ -1 ] ))
data = list(self.data_dict[ name ].items())[ column ][ 1 ][()]
return data
data = self.data_dict[ name ][ column ]
return data
def get_conv_filter(self, name):
return tf.constant(self.from_dataset(name, 0), name="filter")
def get_bias(self, name):
return tf.constant(self.from_dataset(name, 1), name="biases")
def get_fc_weight(self, name):
return tf.constant(self.from_dataset(name, 0), name="weights")
def get_layer_tensors( self, layers, enum ):
'''
Only to get all the names of all tensors to be sure!!
vgg_layers_tensors = [ tensor.name for tensor in tf.get_default_graph().as_graph_def().node ]
'''
nl = list()
for i in range( len( layers ) ):
nl.append( layers[ i ].split( "/" ) )
nl[ i ].insert( 1, "p" + str( enum ) )
nl[ i ] = "/".join( nl[ i ] )
return [tf.get_default_graph().get_tensor_by_name( name ) for name in nl]