-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodel.py
192 lines (140 loc) · 8.88 KB
/
model.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
180
181
182
183
184
185
import tensorlayer as tl
import tensorflow as tf
import time
from tensorlayer.layers import *
def VGG19(rgb, reuse):
# rgb: image inputs pixel value range [0, 1]
VGG_MEAN = [103.939, 116.779, 123.68]
with tf.variable_scope('VGG19', reuse=reuse):
start_time = time.time()
print ("Start to build model...")
rgb_scaled = rgb *255.0
if tf.__version__ <= '0.11':
red, green, blue = tf.split(3, 3, rgb_scaled)
else: # TF 1.0
# print(rgb_scaled)
red, green, blue = tf.split(rgb_scaled, 3, 3)
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]
if tf.__version__ <= '0.11':
bgr = tf.concat(3, [
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
else:
bgr = tf.concat(
[
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
], axis=3)
assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
""" input layer """
net_in = InputLayer(bgr, name='input')
""" conv1 """
network = Conv2d(net_in, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_1')
network = Conv2d(network, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_2')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool1')
""" conv2 """
network = Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_1')
network = Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_2')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool2')
""" conv3 """
network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_1')
network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_2')
network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_3')
network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_4')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool3')
""" conv4 """
network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_1')
network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_2')
network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_3')
network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_4')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool4') # (batch_size, 14, 14, 512)
conv = network
""" conv5 """
network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_1')
network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_2')
network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_3')
network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_4')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool5') # (batch_size, 7, 7, 512)
""" fc 6~8 """
network = FlattenLayer(network, name='flatten')
network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc6')
network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc7')
network = DenseLayer(network, n_units=1000, act=tf.identity, name='fc8')
print("build model finished: %fs" % (time.time() - start_time))
return network, conv
def generator(feature_map, is_train=False, reuse=False):
w_init = tf.truncated_normal_initializer(stddev=0.02)
g_init = tf.truncated_normal_initializer(mean=1.0, stddev=0.02)
image_size = 224
s2, s4, s8, s16 = int(image_size/2), int(image_size/4), int(image_size/8), int(image_size/16),
gf_dim = 32
c_dim = 3
assert feature_map.outputs.get_shape().as_list()[1:] == [14, 14, 512]
# make sure the size matches if the size of current batch is not batch size
batch_size = feature_map.outputs.get_shape().as_list()[0]
filter_size = (5, 5)
strides = (2, 2)
with tf.variable_scope('generator', reuse=reuse):
# (14, 14, 512)
net_in = InputLayer(feature_map.outputs, name='g/in')
# (14, 14, 512)
net_h0 = BatchNormLayer(net_in, act=tf.nn.relu, is_train=is_train,
gamma_init=g_init, name='g/h0/batch_norm')
# (28, 28, 256)
net_h1 = DeConv2d(net_h0, n_filter=gf_dim*8, filter_size=filter_size, out_size=(s8, s8), strides=strides,
padding='SAME', batch_size=batch_size, W_init=w_init, name='g/h1/deconv2d')
net_h1 = BatchNormLayer(net_h1, act=tf.nn.relu, is_train=is_train,
gamma_init=g_init, name='g/h1/batch_norm')
# (56, 56, 128)
net_h2 = DeConv2d(net_h1, n_filter=gf_dim*4, filter_size=filter_size, out_size=(s4, s4), strides=strides,
padding='SAME', batch_size=batch_size, W_init=w_init, name='g/h2/deconv2d')
net_h2 = BatchNormLayer(net_h2, act=tf.nn.relu, is_train=is_train,
gamma_init=g_init, name='g/h2/batch_norm')
# (112, 112, 64)
net_h3 = DeConv2d(net_h2, n_filter=gf_dim*2, filter_size=filter_size, out_size=(s2, s2), strides=strides,
padding='SAME', batch_size=batch_size, W_init=w_init, name='g/h3/deconv2d')
net_h3 = BatchNormLayer(net_h3, act=tf.nn.relu, is_train=is_train,
gamma_init=g_init, name='g/h3/batch_norm')
# (224, 224, 3)
net_h4 = DeConv2d(net_h3, n_filter=c_dim, filter_size=filter_size, out_size=(image_size, image_size), strides=strides,
padding='SAME', batch_size=batch_size, W_init=w_init, name='g/h4/deconv2d')
logits = net_h4.outputs
net_h4.outputs = tf.nn.tanh(net_h4.outputs)
return net_h4, logits
def discriminator(inputs, is_train=True, reuse=False):
df_dim = 64
w_init = tf.truncated_normal_initializer(stddev=0.02)
gamma_init = tf.truncated_normal_initializer(mean=1.0, stddev=0.02)
filter_size = (5, 5)
strides = (2, 2)
with tf.variable_scope('discriminator', reuse=reuse):
net_in = InputLayer(inputs=inputs, name='d/in')
# (112, 112, 64)
net_h0 = Conv2d(net_in, n_filter=df_dim, filter_size=filter_size, strides=strides,
act = lambda x: tl.act.lrelu(x, 0.2), padding='SAME', W_init=w_init, name='d/h0/conv2d')
# Why not batch normalized in the first layer ???
# (56, 56, 128)
net_h1 = Conv2d(net_h0, n_filter=df_dim*2, filter_size=filter_size, strides=strides,
act=None, padding='SAME', W_init=w_init, name='d/h1/conv2d')
net_h1 = BatchNormLayer(net_h1, act=lambda x:tl.act.lrelu(x, 0.2), is_train=is_train,
gamma_init=gamma_init, name='d/h1/batch_norm')
# (28, 28, 256)
net_h2 = Conv2d(net_h1, n_filter=df_dim*4, filter_size=filter_size, strides=strides,
act=None, padding='SAME', W_init=w_init, name='d/h2/conv2')
net_h2 = BatchNormLayer(net_h2, act=lambda x:tl.act.lrelu(x, 0.2), is_train=is_train,
gamma_init=gamma_init, name='d/h2/batch_norm')
# (14, 14, 512)
net_h3 = Conv2d(net_h2, n_filter=df_dim*8, filter_size=filter_size, strides=strides,
act=None, padding='SAME', W_init=w_init, name='d/h3/conv2')
net_h3 = BatchNormLayer(net_h3, act=lambda x:tl.act.lrelu(x, 0.2), is_train=is_train,
gamma_init=gamma_init, name='d/h3/batch_norm')
net_h4 = FlattenLayer(net_h3, name='d/h4/flatten')
net_h4 = DenseLayer(net_h4, n_units=1, act=tf.identity, W_init=w_init, name='d/h4/lin_sigmoid')
logits = net_h4.outputs
net_h4.outputs = tf.nn.sigmoid(net_h4.outputs)
return net_h4, logits