-
Notifications
You must be signed in to change notification settings - Fork 0
/
models.py
244 lines (191 loc) · 11.6 KB
/
models.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
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
import numpy as np
from keras.models import Model
from keras.layers import merge, Flatten, Dense, Input, Dropout, Activation, Reshape
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, UpSampling2D
from keras.layers import BatchNormalization
from keras.layers.noise import GaussianNoise
import h5py
np.random.seed(4)
# download .h5 weights from https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3
def Unet(img_rows, img_cols, custom_loss , optimizer, custom_metrics, fc_size = 8192, channels = 3):
filter_size = 5
filter_size_2 = 11
dropout_a = 0.5
dropout_b = 0.5
dropout_c = 0.5
gaussian_noise_std = 0.025
inputs = Input((channels, img_rows, img_cols))
input_with_noise = GaussianNoise(gaussian_noise_std)(inputs)
conv1 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(input_with_noise)
conv1 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv1)
conv1 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling2D((2, 2), strides=(2, 2))(conv1)
pool1 = GaussianNoise(gaussian_noise_std)(pool1)
conv2 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(pool1)
conv2 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(conv2)
conv2 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling2D((2, 2), strides=(2, 2))(conv2)
pool2 = GaussianNoise(gaussian_noise_std)(pool2)
conv3 = Convolution2D(128, filter_size, filter_size, activation='relu', border_mode='same')(pool2)
conv3 = Convolution2D(128, filter_size, filter_size, activation='relu', border_mode='same')(conv3)
conv3 = Convolution2D(128, filter_size, filter_size, activation='relu', border_mode='same')(conv3)
pool3 = MaxPooling2D((2, 2), strides=(2, 2))(conv3)
pool3 = Dropout(dropout_a)(pool3)
fc = Flatten()(pool3)
fc = Dense(fc_size, activation='relu')(fc) # 1 to fc_size
fc = Dropout(dropout_b)(fc)
n = img_rows/2/2/2 # img_rows / 8 e.g. 128/8 = 16
fc = Dense(128*n*n, activation='relu')(fc) # fc_size to 128*16*16
fc = GaussianNoise(gaussian_noise_std)(fc)
fc = Reshape((128,n,n))(fc) # 128*16*16 to 128,16,16
up1 = merge([UpSampling2D(size=(2, 2))(fc), conv3], mode='concat', concat_axis=1)
up1 = Dropout(dropout_c)(up1)
conv4 = Convolution2D(128, filter_size_2, filter_size_2, activation='relu', border_mode='same')(up1)
conv4 = Convolution2D(128, filter_size, filter_size, activation='relu', border_mode='same')(conv4)
conv4 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(conv4)
up2 = merge([UpSampling2D(size=(2, 2))(conv4), conv2], mode='concat', concat_axis=1)
up2 = Dropout(dropout_c)(up2)
conv5 = Convolution2D(64, filter_size_2, filter_size_2, activation='relu', border_mode='same')(up2)
conv5 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(conv5)
conv5 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv5)
up3 = merge([UpSampling2D(size=(2, 2))(conv5), conv1], mode='concat', concat_axis=1)
up3 = Dropout(dropout_c)(up3)
conv6 = Convolution2D(32, filter_size_2, filter_size_2, activation='relu', border_mode='same')(up3)
conv6 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv6)
conv6 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv6)
conv7 = Convolution2D(1, 1, 1, activation='sigmoid', name = 'conv7')(conv6) # 128*128*1
model = Model(input = inputs, output = conv7) # dimention of output: 128*128*1+3
model.compile(optimizer = optimizer, loss = custom_loss, metrics = custom_metrics)
return model
def VGG16(img_rows, img_cols, pretrained, freeze_pretrained, custom_loss , optimizer, custom_metrics, channels=3):
inputs = Input((channels, img_rows, img_cols))
pad1 = ZeroPadding2D((1, 1), input_shape=(channels, img_rows, img_cols))(inputs)
conv1 = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')(pad1)
conv1 = ZeroPadding2D((1, 1))(conv1)
conv1 = Convolution2D(64, 3, 3, activation='relu', name='conv1_2')(conv1)
pool1 = MaxPooling2D((2, 2), strides=(2, 2))(conv1)
pad2 = ZeroPadding2D((1, 1))(pool1)
conv2 = Convolution2D(128, 3, 3, activation='relu', name='conv2_1')(pad2)
conv2 = ZeroPadding2D((1, 1))(conv2)
conv2 = Convolution2D(128, 3, 3, activation='relu', name='conv2_2')(conv2)
pool2 = MaxPooling2D((2, 2), strides=(2, 2))(conv2)
pad3 = ZeroPadding2D((1, 1))(pool2)
conv3 = Convolution2D(256, 3, 3, activation='relu', name='conv3_1')(pad3)
conv3 = ZeroPadding2D((1, 1))(conv3)
conv3 = Convolution2D(256, 3, 3, activation='relu', name='conv3_2')(conv3)
conv3 = ZeroPadding2D((1, 1))(conv3)
conv3 = Convolution2D(256, 3, 3, activation='relu', name='conv3_3')(conv3)
pool3 = MaxPooling2D((2, 2), strides=(2, 2))(conv3)
pad4 = ZeroPadding2D((1, 1))(pool3)
conv4 = Convolution2D(512, 3, 3, activation='relu', name='conv4_1')(pad4)
conv4 = ZeroPadding2D((1, 1))(conv4)
conv4 = Convolution2D(512, 3, 3, activation='relu', name='conv4_2')(conv4)
conv4 = ZeroPadding2D((1, 1))(conv4)
conv4 = Convolution2D(512, 3, 3, activation='relu', name='conv4_3')(conv4)
pool4 = MaxPooling2D((2, 2), strides=(2, 2))(conv4)
pad5 = ZeroPadding2D((1, 1))(pool4)
conv5 = Convolution2D(512, 3, 3, activation='relu', name='conv5_1')(pad5)
conv5 = ZeroPadding2D((1, 1))(conv5)
conv5 = Convolution2D(512, 3, 3, activation='relu', name='conv5_2')(conv5)
conv5 = ZeroPadding2D((1, 1))(conv5)
conv5 = Convolution2D(512, 3, 3, activation='relu', name='conv5_3')(conv5)
model = Model(input=inputs, output=conv5)
# load weights
if pretrained:
weights_path = pretrained
f = h5py.File(weights_path)
for k in range(f.attrs['nb_layers']):
if k >= (len(model.layers) - 1):
# ignore the last layers in the savefile
break
g = f['layer_{}'.format(k)]
weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
model.layers[k+1].set_weights(weights)
f.close()
print('ImageNet pre-trained weights loaded.')
if freeze_pretrained:
for layer in model.layers:
layer.trainable = False
dropout_val = 0.5
up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)
up6 = Dropout(dropout_val)(up6)
conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(up6)
conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv6)
up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)
up7 = Dropout(dropout_val)(up7)
conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(up7)
conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv7)
up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)
up8 = Dropout(dropout_val)(up8)
conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up8)
conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv8)
up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)
up9 = Dropout(dropout_val)(up9)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up9)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv9)
conv10 = Convolution2D(1, 1, 1, activation='sigmoid', name = 'conv10')(conv9)
model = Model(input=inputs, output=conv10) # dimention of output: 128*128*1+3
model.compile(optimizer = optimizer, loss = custom_loss, metrics = custom_metrics)
return model
def Unet2(img_rows, img_cols, custom_loss , optimizer, custom_metrics, fc_size = 0, channels = 3):
filter_size = 5
filter_size_2 = 11
dropout_a = 0.5
dropout_b = 0.5
dropout_c = 0.5
gaussian_noise_std = 0.025
inputs = Input((channels, img_rows, img_cols))
input_with_noise = GaussianNoise(gaussian_noise_std)(inputs)
conv1 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(input_with_noise)
conv1 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv1)
conv1 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling2D((2, 2), strides=(2, 2))(conv1)
pool1 = GaussianNoise(gaussian_noise_std)(pool1)
conv2 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(pool1)
conv2 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(conv2)
conv2 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling2D((2, 2), strides=(2, 2))(conv2)
pool2 = GaussianNoise(gaussian_noise_std)(pool2)
conv3 = Convolution2D(128, filter_size, filter_size, activation='relu', border_mode='same')(pool2)
conv3 = Convolution2D(128, filter_size, filter_size, activation='relu', border_mode='same')(conv3)
conv3 = Convolution2D(128, filter_size, filter_size, activation='relu', border_mode='same')(conv3)
pool3 = MaxPooling2D((2, 2), strides=(2, 2))(conv3)
pool3 = Dropout(dropout_a)(pool3)
if fc_size>0:
fc = Flatten()(pool3)
fc = Dense(fc_size)(fc)
fc = BatchNormalization()(fc)
fc = Activation('relu')(fc)
fc = Dropout(dropout_b)(fc)
n = img_rows/2/2/2
fc = Dense(img_rows*n*n)(fc)
fc = BatchNormalization()(fc)
fc = Activation('relu')(fc)
fc = GaussianNoise(gaussian_noise_std)(fc)
fc = Reshape((128,n,n))(fc)
else:
fc = Convolution2D(256, filter_size, filter_size, activation='relu', border_mode='same')(pool3)
fc = BatchNormalization()(fc)
fc = Dropout(dropout_b)(fc)
up1 = merge([UpSampling2D(size=(2, 2))(fc), conv3], mode='concat', concat_axis=1)
up1 = BatchNormalization()(up1)
up1 = Dropout(dropout_c)(up1)
conv4 = Convolution2D(128, filter_size_2, filter_size_2, activation='relu', border_mode='same')(up1)
conv4 = Convolution2D(128, filter_size, filter_size, activation='relu', border_mode='same')(conv4)
conv4 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(conv4)
up2 = merge([UpSampling2D(size=(2, 2))(conv4), conv2], mode='concat', concat_axis=1)
up2 = BatchNormalization()(up2)
up2 = Dropout(dropout_c)(up2)
conv5 = Convolution2D(64, filter_size_2, filter_size_2, activation='relu', border_mode='same')(up2)
conv5 = Convolution2D(64, filter_size, filter_size, activation='relu', border_mode='same')(conv5)
conv5 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv5)
up3 = merge([UpSampling2D(size=(2, 2))(conv5), conv1], mode='concat', concat_axis=1)
up3 = BatchNormalization()(up3)
up3 = Dropout(dropout_c)(up3)
conv6 = Convolution2D(32, filter_size_2, filter_size_2, activation='relu', border_mode='same')(up3)
conv6 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv6)
conv6 = Convolution2D(32, filter_size, filter_size, activation='relu', border_mode='same')(conv6)
conv7 = Convolution2D(1, 1, 1, activation='sigmoid')(conv6)
model = Model(input=inputs, output=conv7)
model.compile(optimizer=optimizer, loss=custom_loss, metrics=custom_metrics)
return model