-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathCNN.py
115 lines (103 loc) · 3.7 KB
/
CNN.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
from torch import nn
# Model define
def initialize_weights(model):
for m in model.modules():
if isinstance(m, nn.Conv3d) or isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight, nonlinearity='relu')
# Define 3D_CNN model class
class CNN(nn.Module):
def __init__(self, in_channels):
super(CNN, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv3d(in_channels, 32, kernel_size=(3,3,3), padding=1),
nn.ReLU(),
nn.BatchNorm3d(32),
nn.Conv3d(32, 32, kernel_size=(3,3,3), padding=1),
nn.ReLU(),
nn.Dropout3d(0.2),
nn.MaxPool3d(kernel_size=(2,2,2))
)
self.conv2 = nn.Sequential(
nn.Conv3d(32, 64, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(64),
nn.Conv3d(64, 64, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.Dropout3d(0.2),
nn.MaxPool3d(kernel_size=(2,2,2))
)
self.conv3 = nn.Sequential(
nn.Conv3d(64, 64, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(64),
nn.Conv3d(64, 64, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(64),
nn.MaxPool3d(2)
)
self.conv4 = nn.Sequential(
nn.Conv3d(64, 64, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(64),
nn.Conv3d(64, 64, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(64),
nn.Conv3d(64, 64, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(64),
nn.MaxPool3d(2)
)
self.conv5 = nn.Sequential(
nn.Conv3d(64, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96),
nn.Conv3d(96, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96),
nn.Conv3d(96, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96),
nn.MaxPool3d(2)
)
self.conv6 = nn.Sequential(
nn.Conv3d(96, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96),
nn.Conv3d(96, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96),
nn.Conv3d(96, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96),
nn.MaxPool3d(2)
)
self.conv7 = nn.Sequential(
nn.Conv3d(96, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96),
nn.Conv3d(96, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96),
nn.Conv3d(96, 96, kernel_size=(3,3,3), stride=1, padding="same"),
nn.ReLU(),
nn.BatchNorm3d(96)
)
self.flatten = nn.Flatten()
self.fc = nn.Sequential(
nn.Linear(768, 96),
nn.ReLU(),
nn.Linear(96, 32),
nn.ReLU(),
nn.Linear(32, 1)
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.conv6(x)
x = self.conv7(x)
x = self.flatten(x)
x = self.fc(x)
return x