-
Notifications
You must be signed in to change notification settings - Fork 0
/
deep_unet.py
154 lines (133 loc) · 6.44 KB
/
deep_unet.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
# -*- coding: utf-8 -*-
"""
Created on Sat May 29 13:03:57 2021
@author: vivin
"""
#%%
"""
This python file contains the function that returns a deep U-NET model
containing up-convolution and up-sampling. The loss function is weighted
binary-crossentropy. The UNET is built layer by layer and the returned
in the model function.
"""
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D
from keras.layers import concatenate, Conv2DTranspose, BatchNormalization, Dropout
from keras.optimizers import Adam
#from keras.utils import plot_model
from keras import backend as b
#%%
def deep_unet_model(n=5, im_sz=160, channels=8, filter_num=32,
growthfactor=2, upconvolution=True,
class_weights=[0.5, 0.3, 0.1, 0.1, 0.1]):
"""
Function to return deep-UNET model
INPUT:
n (Int):
Number of categories to identify in the image
im_size (Int):
Pixel size of input image
channels (Int):
Number of channels in the orthophotos
filter_num (Int):
The dimensionality of the output space
(i.e. the number of output filters in the convolution).
growthfactor (Int):
The scaling of the output dimensions for each layer of the UNET
upconvolution (Bool):
Avtivate or deactivate upconvolution
class_weights (Float Array of length n):
Should sum to unity else raises assertion error
OUTPUT:
model (Keras Model):
TensorFlow backend deep U-NET model
"""
droprate=0.20 #dropout rate
n_filters = filter_num
input_layer = Input((im_sz, im_sz, channels))
conv1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(input_layer)
conv1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
n_filters *= growthfactor
pool1 = BatchNormalization()(pool1)
conv2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
pool2 = Dropout(droprate)(pool2)
n_filters *= growthfactor
pool2 = BatchNormalization()(pool2)
conv3 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
pool3 = Dropout(droprate)(pool3)
n_filters *= growthfactor
pool3 = BatchNormalization()(pool3)
conv4_0 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool3)
conv4_0 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv4_0)
pool4_1 = MaxPooling2D(pool_size=(2, 2))(conv4_0)
pool4_1 = Dropout(droprate)(pool4_1)
n_filters *= growthfactor
pool4_1 = BatchNormalization()(pool4_1)
conv4_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool4_1)
conv4_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv4_1)
pool4_2 = MaxPooling2D(pool_size=(2, 2))(conv4_1)
pool4_2 = Dropout(droprate)(pool4_2)
n_filters *= growthfactor
conv5 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool4_2)
conv5 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv5)
n_filters //= growthfactor
if upconvolution:
up6_1 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2),
padding='same')(conv5), conv4_1])
else:
up6_1 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4_1])
up6_1 = BatchNormalization()(up6_1)
conv6_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up6_1)
conv6_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv6_1)
conv6_1 = Dropout(droprate)(conv6_1)
n_filters //= growthfactor
if upconvolution:
up6_2 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2),
padding='same')(conv6_1), conv4_0])
else:
up6_2 = concatenate([UpSampling2D(size=(2, 2))(conv6_1), conv4_0])
up6_2 = BatchNormalization()(up6_2)
conv6_2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up6_2)
conv6_2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv6_2)
conv6_2 = Dropout(droprate)(conv6_2)
n_filters //= growthfactor
if upconvolution:
up7 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2),
padding='same')(conv6_2), conv3])
else:
up7 = concatenate([UpSampling2D(size=(2, 2))(conv6_2), conv3])
up7 = BatchNormalization()(up7)
conv7 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv7)
conv7 = Dropout(droprate)(conv7)
n_filters //= growthfactor
if upconvolution:
up8 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2),
padding='same')(conv7), conv2])
else:
up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2])
up8 = BatchNormalization()(up8)
conv8 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv8)
conv8 = Dropout(droprate)(conv8)
n_filters //= growthfactor
if upconvolution:
up9 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2),
padding='same')(conv8), conv1])
else:
up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1])
conv9 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv9)
conv10 = Conv2D(n, (1, 1), activation='sigmoid')(conv9)
model = Model(inputs=input_layer, outputs=conv10)
def weighted_binary_crossentropy(y_true, y_pred):
class_loglosses = b.mean(b.binary_crossentropy(y_true, y_pred), axis=[0, 1, 2])
return b.sum(class_loglosses * b.constant(class_weights))
model.compile(optimizer=Adam(), loss=weighted_binary_crossentropy)
return model
#FIN