forked from vlimant/mpi_opt
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdensenet.py
196 lines (153 loc) · 6.67 KB
/
densenet.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
186
187
188
189
190
191
192
193
194
195
196
from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import AveragePooling2D
from keras.layers.pooling import GlobalAveragePooling2D
from keras.layers import Input, merge
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras.layers import Permute
import keras.backend as K
def conv_factory(x, nb_filter, dropout_rate=None, weight_decay=1E-4):
"""Apply BatchNorm, Relu 3x3Conv2D, optional dropout
:param x: Input keras network
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: keras network with b_norm, elu and convolution2d added
:rtype: keras network
"""
x = BatchNormalization(mode=0,
axis=1,
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Activation('elu')(x)
x = Convolution2D(nb_filter, (3, 3),
init="he_uniform",
border_mode="same",
bias=False,
W_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def transition(x, nb_filter, dropout_rate=None, weight_decay=1E-4):
"""Apply BatchNorm, Relu 1x1Conv2D, optional dropout and Maxpooling2D
:param x: keras model
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: model
:rtype: keras model, after applying batch_norm, elu-conv, dropout, maxpool
"""
x = BatchNormalization(mode=0,
axis=1,
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Activation('elu')(x)
x = Convolution2D(nb_filter, (1, 1),
init="he_uniform",
border_mode="same",
bias=False,
W_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
x = AveragePooling2D((2, 2), strides=(2, 2))(x)
return x
def denseblock(x, nb_layers, nb_filter, growth_rate,
dropout_rate=None, weight_decay=1E-4):
"""Build a denseblock where the output of each
conv_factory is fed to subsequent ones
:param x: keras model
:param nb_layers: int -- the number of layers of conv_
factory to append to the model.
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: keras model with nb_layers of conv_factory appended
:rtype: keras model
"""
list_feat = [x]
if K.image_dim_ordering() == "th":
concat_axis = 1
elif K.image_dim_ordering() == "tf":
concat_axis = -1
for i in range(nb_layers):
x = conv_factory(x, growth_rate, dropout_rate, weight_decay)
list_feat.append(x)
x = merge(list_feat, mode='concat', concat_axis=concat_axis)
nb_filter += growth_rate
return x, nb_filter
def denseblock_altern(x, nb_layers, nb_filter, growth_rate,
dropout_rate=None, weight_decay=1E-4):
"""Build a denseblock where the output of each conv_factory
is fed to subsequent ones. (Alternative of a above)
:param x: keras model
:param nb_layers: int -- the number of layers of conv_
factory to append to the model.
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: keras model with nb_layers of conv_factory appended
:rtype: keras model
* The main difference between this implementation and the implementation
above is that the one above
"""
if K.image_dim_ordering() == "th":
concat_axis = 1
elif K.image_dim_ordering() == "tf":
concat_axis = -1
for i in range(nb_layers):
merge_tensor = conv_factory(x, growth_rate, dropout_rate, weight_decay)
x = merge([merge_tensor, x], mode='concat', concat_axis=concat_axis)
nb_filter += growth_rate
return x, nb_filter
def DenseNet(nb_classes, img_dim, depth, nb_dense_block, growth_rate,
nb_filter, dropout_rate=None, weight_decay=1E-4):
""" Build the DenseNet model
:param nb_classes: int -- number of classes
:param img_dim: tuple -- (channels, rows, columns)
:param depth: int -- how many layers
:param nb_dense_block: int -- number of dense blocks to add to end
:param growth_rate: int -- number of filters to add
:param nb_filter: int -- number of filters
:param dropout_rate: float -- dropout rate
:param weight_decay: float -- weight decay
:returns: keras model with nb_layers of conv_factory appended
:rtype: keras model
"""
model_input = Input(shape=img_dim)
#sa = Permute((3,1,2))(model_input)
assert (depth - 4) % 3 == 0, "Depth must be 3 N + 4"
# layers in each dense block
nb_layers = int((depth - 4) / 3)
# Initial convolution
x = Convolution2D(nb_filter, (3, 3),
init="he_uniform",
border_mode="same",
name="initial_conv2D",
bias=False,
W_regularizer=l2(weight_decay))(model_input)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
x, nb_filter = denseblock(x, nb_layers, nb_filter, growth_rate,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
# add transition
x = transition(x, nb_filter, dropout_rate=dropout_rate,
weight_decay=weight_decay)
# The last denseblock does not have a transition
x, nb_filter = denseblock(x, nb_layers, nb_filter, growth_rate,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
x = BatchNormalization(mode=0,
axis=1,
gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = Activation('elu')(x)
x = GlobalAveragePooling2D(dim_ordering="tf")(x)
x = Dense(nb_classes,
activation='softmax',
W_regularizer=l2(weight_decay),
b_regularizer=l2(weight_decay))(x)
densenet = Model(input=[model_input], output=[x], name="DenseNet")
return densenet