-
Notifications
You must be signed in to change notification settings - Fork 10
Expand file tree
/
Copy pathmodels.py
More file actions
149 lines (112 loc) · 7.97 KB
/
models.py
File metadata and controls
149 lines (112 loc) · 7.97 KB
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
"""
Model definitions for the four networks evaluated in Baumgartner et al.,
"Real-Time Detection and Localisation of Fetal Standard Scan Planes in 2D
Freehand Ultrasound", arXiv preprint:1612.05601 (2016).
Author: Christian Baumgartner (c.f.baumgartner@gmail.com)
Last Update: 14. March 2017
"""
from lasagne.layers import InputLayer, MaxPool2DLayer, Conv2DLayer, batch_norm
from lasagne.layers import GlobalPoolLayer, NonlinearityLayer
from lasagne.nonlinearities import linear, softmax
import theano.tensor as T
def SonoNet16(input_var, image_size, num_labels):
net = {}
net['input'] = InputLayer(shape=(None, 1, image_size[0], image_size[1]), input_var=input_var)
net['conv1_1'] = batch_norm(Conv2DLayer(net['input'], 16, 3, pad=1, flip_filters=False))
net['conv1_2'] = batch_norm(Conv2DLayer(net['conv1_1'], 16, 3, pad=1, flip_filters=False))
net['pool1'] = MaxPool2DLayer(net['conv1_2'], 2)
net['conv2_1'] = batch_norm(Conv2DLayer(net['pool1'], 32, 3, pad=1, flip_filters=False))
net['conv2_2'] = batch_norm(Conv2DLayer(net['conv2_1'], 32, 3, pad=1, flip_filters=False))
net['pool2'] = MaxPool2DLayer(net['conv2_2'], 2)
net['conv3_1'] = batch_norm(Conv2DLayer(net['pool2'], 64, 3, pad=1, flip_filters=False))
net['conv3_2'] = batch_norm(Conv2DLayer(net['conv3_1'], 64, 3, pad=1, flip_filters=False))
net['conv3_3'] = batch_norm(Conv2DLayer(net['conv3_2'], 64, 3, pad=1, flip_filters=False))
net['pool3'] = MaxPool2DLayer(net['conv3_3'], 2)
net['conv4_1'] = batch_norm(Conv2DLayer(net['pool3'], 128, 3, pad=1, flip_filters=False))
net['conv4_2'] = batch_norm(Conv2DLayer(net['conv4_1'], 128, 3, pad=1, flip_filters=False))
net['conv4_3'] = batch_norm(Conv2DLayer(net['conv4_2'], 128, 3, pad=1, flip_filters=False))
net['pool4'] = MaxPool2DLayer(net['conv4_3'], 2)
net['conv5_1'] = batch_norm(Conv2DLayer(net['pool4'], 128, 3, pad=1, flip_filters=False))
net['conv5_2'] = batch_norm(Conv2DLayer(net['conv5_1'], 128, 3, pad=1, flip_filters=False))
net['conv5_3'] = batch_norm(Conv2DLayer(net['conv5_2'], 128, 3, pad=1, flip_filters=False))
net['conv5_p'] = batch_norm(Conv2DLayer(net['conv5_3'], num_filters=64, filter_size=(1, 1)))
net['conv6_p'] = batch_norm(Conv2DLayer(net['conv5_p'], num_filters=num_labels, filter_size=(1, 1), nonlinearity=linear))
net['average_pool_p'] = GlobalPoolLayer(net['conv6_p'], pool_function=T.mean)
net['softmax_p'] = NonlinearityLayer(net['average_pool_p'], nonlinearity=softmax)
net['output'] = net['softmax_p']
net['feature_maps'] = net['conv6_p']
net['last_activation'] = net['average_pool_p']
return net
def SonoNet32(input_var, image_size, num_labels):
net = {}
net['input'] = InputLayer(shape=(None, 1, image_size[0], image_size[1]), input_var=input_var)
net['conv1_1'] = batch_norm(Conv2DLayer(net['input'], 32, 3, pad=1, flip_filters=False))
net['conv1_2'] = batch_norm(Conv2DLayer(net['conv1_1'], 32, 3, pad=1, flip_filters=False))
net['pool1'] = MaxPool2DLayer(net['conv1_2'], 2)
net['conv2_1'] = batch_norm(Conv2DLayer(net['pool1'], 64, 3, pad=1, flip_filters=False))
net['conv2_2'] = batch_norm(Conv2DLayer(net['conv2_1'], 64, 3, pad=1, flip_filters=False))
net['pool2'] = MaxPool2DLayer(net['conv2_2'], 2)
net['conv3_1'] = batch_norm(Conv2DLayer(net['pool2'], 128, 3, pad=1, flip_filters=False))
net['conv3_2'] = batch_norm(Conv2DLayer(net['conv3_1'], 128, 3, pad=1, flip_filters=False))
net['conv3_3'] = batch_norm(Conv2DLayer(net['conv3_2'], 128, 3, pad=1, flip_filters=False))
net['pool3'] = MaxPool2DLayer(net['conv3_3'], 2)
net['conv4_1'] = batch_norm(Conv2DLayer(net['pool3'], 256, 3, pad=1, flip_filters=False))
net['conv4_2'] = batch_norm(Conv2DLayer(net['conv4_1'], 256, 3, pad=1, flip_filters=False))
net['conv4_3'] = batch_norm(Conv2DLayer(net['conv4_2'], 256, 3, pad=1, flip_filters=False))
net['pool4'] = MaxPool2DLayer(net['conv4_3'], 2)
net['conv5_1'] = batch_norm(Conv2DLayer(net['pool4'], 256, 3, pad=1, flip_filters=False))
net['conv5_2'] = batch_norm(Conv2DLayer(net['conv5_1'], 256, 3, pad=1, flip_filters=False))
net['conv5_3'] = batch_norm(Conv2DLayer(net['conv5_2'], 256, 3, pad=1, flip_filters=False))
net['conv5_p'] = batch_norm(Conv2DLayer(net['conv5_3'], num_filters=128, filter_size=(1, 1)))
net['conv6_p'] = batch_norm(Conv2DLayer(net['conv5_p'], num_filters=num_labels, filter_size=(1, 1), nonlinearity=linear))
net['average_pool_p'] = GlobalPoolLayer(net['conv6_p'], pool_function=T.mean)
net['softmax_p'] = NonlinearityLayer(net['average_pool_p'], nonlinearity=softmax)
net['output'] = net['softmax_p']
net['feature_maps'] = net['conv6_p']
net['last_activation'] = net['average_pool_p']
return net
def SonoNet64(input_var, image_size, num_labels):
net = {}
net['input'] = InputLayer(shape=(None, 1, image_size[0], image_size[1]), input_var=input_var)
net['conv1_1'] = batch_norm(Conv2DLayer(net['input'], 64, 3, pad=1, flip_filters=False))
net['conv1_2'] = batch_norm(Conv2DLayer(net['conv1_1'], 64, 3, pad=1, flip_filters=False))
net['pool1'] = MaxPool2DLayer(net['conv1_2'], 2)
net['conv2_1'] = batch_norm(Conv2DLayer(net['pool1'], 128, 3, pad=1, flip_filters=False))
net['conv2_2'] = batch_norm(Conv2DLayer(net['conv2_1'], 128, 3, pad=1, flip_filters=False))
net['pool2'] = MaxPool2DLayer(net['conv2_2'], 2)
net['conv3_1'] = batch_norm(Conv2DLayer(net['pool2'], 256, 3, pad=1, flip_filters=False))
net['conv3_2'] = batch_norm(Conv2DLayer(net['conv3_1'], 256, 3, pad=1, flip_filters=False))
net['conv3_3'] = batch_norm(Conv2DLayer(net['conv3_2'], 256, 3, pad=1, flip_filters=False))
net['pool3'] = MaxPool2DLayer(net['conv3_3'], 2)
net['conv4_1'] = batch_norm(Conv2DLayer(net['pool3'], 512, 3, pad=1, flip_filters=False))
net['conv4_2'] = batch_norm(Conv2DLayer(net['conv4_1'], 512, 3, pad=1, flip_filters=False))
net['conv4_3'] = batch_norm(Conv2DLayer(net['conv4_2'], 512, 3, pad=1, flip_filters=False))
net['pool4'] = MaxPool2DLayer(net['conv4_3'], 2)
net['conv5_1'] = batch_norm(Conv2DLayer(net['pool4'], 512, 3, pad=1, flip_filters=False))
net['conv5_2'] = batch_norm(Conv2DLayer(net['conv5_1'], 512, 3, pad=1, flip_filters=False))
net['conv5_3'] = batch_norm(Conv2DLayer(net['conv5_2'], 512, 3, pad=1, flip_filters=False))
net['conv5_p'] = batch_norm(Conv2DLayer(net['conv5_3'], num_filters=256, filter_size=(1, 1)))
net['conv6_p'] = batch_norm(Conv2DLayer(net['conv5_p'], num_filters=num_labels, filter_size=(1, 1), nonlinearity=linear))
net['average_pool_p'] = GlobalPoolLayer(net['conv6_p'], pool_function=T.mean)
net['softmax_p'] = NonlinearityLayer(net['average_pool_p'], nonlinearity=softmax)
net['output'] = net['softmax_p']
net['feature_maps'] = net['conv6_p']
net['last_activation'] = net['average_pool_p']
return net
def SmallNet(input_var, image_size, num_labels):
net = {}
net['input'] = InputLayer(shape=(None, 1, image_size[0], image_size[1]), input_var=input_var)
net['conv1'] = Conv2DLayer(net['input'], num_filters=32, filter_size=(7, 7), stride=(2, 2))
net['pool1'] = MaxPool2DLayer(net['conv1'], pool_size=(2, 2))
net['conv2'] = Conv2DLayer(net['pool1'], num_filters=64, filter_size=(5, 5), stride=(2, 2))
net['pool2'] = MaxPool2DLayer(net['conv2'], pool_size=(2, 2))
net['conv3'] = Conv2DLayer(net['pool2'], num_filters=128, filter_size=(3, 3), pad=(1, 1))
net['conv4'] = Conv2DLayer(net['conv3'], num_filters=128, filter_size=(3, 3), pad=(1, 1))
net['conv5_p'] = Conv2DLayer(net['conv4'], num_filters=64, filter_size=(1, 1))
net['conv6_p'] = Conv2DLayer(net['conv5_p'], num_filters=num_labels, filter_size=(1, 1), nonlinearity=linear)
net['average_pool_p'] = GlobalPoolLayer(net['conv6_p'], pool_function=T.mean)
net['softmax_p'] = NonlinearityLayer(net['average_pool_p'], nonlinearity=softmax)
net['output'] = net['softmax_p']
net['feature_maps'] = net['conv6_p']
net['last_activation'] = net['average_pool_p']
return net