-
Notifications
You must be signed in to change notification settings - Fork 77
/
Copy pathc3d_model.py
executable file
·165 lines (150 loc) · 6.73 KB
/
c3d_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
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Convolution3D, MaxPooling3D, ZeroPadding3D
from keras.optimizers import SGD
'''
dim_ordering issue:
- 'th'-style dim_ordering: [batch, channels, depth, height, width]
- 'tf'-style dim_ordering: [batch, depth, height, width, channels]
'''
def get_model(summary=False, backend='tf'):
""" Return the Keras model of the network
"""
model = Sequential()
if backend == 'tf':
input_shape=(16, 112, 112, 3) # l, h, w, c
else:
input_shape=(3, 16, 112, 112) # c, l, h, w
model.add(Convolution3D(64, 3, 3, 3, activation='relu',
border_mode='same', name='conv1',
input_shape=input_shape))
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),
border_mode='valid', name='pool1'))
# 2nd layer group
model.add(Convolution3D(128, 3, 3, 3, activation='relu',
border_mode='same', name='conv2'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool2'))
# 3rd layer group
model.add(Convolution3D(256, 3, 3, 3, activation='relu',
border_mode='same', name='conv3a'))
model.add(Convolution3D(256, 3, 3, 3, activation='relu',
border_mode='same', name='conv3b'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool3'))
# 4th layer group
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv4a'))
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv4b'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool4'))
# 5th layer group
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv5a'))
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv5b'))
model.add(ZeroPadding3D(padding=((0, 0), (0, 1), (0, 1)), name='zeropad5'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool5'))
model.add(Flatten())
# FC layers group
model.add(Dense(4096, activation='relu', name='fc6'))
model.add(Dropout(.5))
model.add(Dense(4096, activation='relu', name='fc7'))
model.add(Dropout(.5))
model.add(Dense(487, activation='softmax', name='fc8'))
if summary:
print(model.summary())
return model
def get_int_model(model, layer, backend='tf'):
if backend == 'tf':
input_shape=(16, 112, 112, 3) # l, h, w, c
else:
input_shape=(3, 16, 112, 112) # c, l, h, w
int_model = Sequential()
int_model.add(Convolution3D(64, 3, 3, 3, activation='relu',
border_mode='same', name='conv1',
input_shape=input_shape,
weights=model.layers[0].get_weights()))
if layer == 'conv1':
return int_model
int_model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),
border_mode='valid', name='pool1'))
if layer == 'pool1':
return int_model
# 2nd layer group
int_model.add(Convolution3D(128, 3, 3, 3, activation='relu',
border_mode='same', name='conv2',
weights=model.layers[2].get_weights()))
if layer == 'conv2':
return int_model
int_model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool2'))
if layer == 'pool2':
return int_model
# 3rd layer group
int_model.add(Convolution3D(256, 3, 3, 3, activation='relu',
border_mode='same', name='conv3a',
weights=model.layers[4].get_weights()))
if layer == 'conv3a':
return int_model
int_model.add(Convolution3D(256, 3, 3, 3, activation='relu',
border_mode='same', name='conv3b',
weights=model.layers[5].get_weights()))
if layer == 'conv3b':
return int_model
int_model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool3'))
if layer == 'pool3':
return int_model
# 4th layer group
int_model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv4a',
weights=model.layers[7].get_weights()))
if layer == 'conv4a':
return int_model
int_model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv4b',
weights=model.layers[8].get_weights()))
if layer == 'conv4b':
return int_model
int_model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool4'))
if layer == 'pool4':
return int_model
# 5th layer group
int_model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv5a',
weights=model.layers[10].get_weights()))
if layer == 'conv5a':
return int_model
int_model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv5b',
weights=model.layers[11].get_weights()))
if layer == 'conv5b':
return int_model
int_model.add(ZeroPadding3D(padding=(0, 1, 1), name='zeropad'))
int_model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool5'))
if layer == 'pool5':
return int_model
int_model.add(Flatten())
# FC layers group
int_model.add(Dense(4096, activation='relu', name='fc6',
weights=model.layers[15].get_weights()))
if layer == 'fc6':
return int_model
int_model.add(Dropout(.5))
int_model.add(Dense(4096, activation='relu', name='fc7',
weights=model.layers[17].get_weights()))
if layer == 'fc7':
return int_model
int_model.add(Dropout(.5))
int_model.add(Dense(487, activation='softmax', name='fc8',
weights=model.layers[19].get_weights()))
if layer == 'fc8':
return int_model
return None
if __name__ == '__main__':
model = get_model(summary=True)