forked from StChenHaoGitHub/1D-deeplearning-model-pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
GoogLeNet.py
147 lines (112 loc) · 4.76 KB
/
GoogLeNet.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
import torch
from torchsummary import summary
class Inception(torch.nn.Module):
def __init__(self,in_channels=56,ch1=64,ch3_reduce=96,ch3=128,ch5_reduce=16,ch5=32,pool_proj=32):
super(Inception, self).__init__()
self.branch1 = torch.nn.Sequential(
torch.nn.Conv1d(in_channels,ch1,kernel_size=1),
torch.nn.BatchNorm1d(ch1)
)
self.branch3 = torch.nn.Sequential(
torch.nn.Conv1d(in_channels, ch3_reduce, kernel_size=1),
torch.nn.BatchNorm1d(ch3_reduce),
torch.nn.Conv1d(ch3_reduce, ch3, kernel_size=3, padding=1),
torch.nn.BatchNorm1d(ch3),
)
self.branch5 = torch.nn.Sequential(
torch.nn.Conv1d(in_channels, ch5_reduce, kernel_size=1),
torch.nn.BatchNorm1d(ch5_reduce),
torch.nn.Conv1d(ch5_reduce, ch5, kernel_size=5, padding=2),
torch.nn.BatchNorm1d(ch5),
)
self.branch_pool = torch.nn.Sequential(
torch.nn.MaxPool1d(kernel_size=3,stride=1,padding=1),
torch.nn.Conv1d(in_channels, pool_proj, kernel_size=1)
)
def forward(self,x):
return torch.cat([self.branch1(x),self.branch3(x),self.branch5(x),self.branch_pool(x)],1)
class GoogLeNet(torch.nn.Module):
def __init__(self,in_channels=2,in_sample_points=224,classes=5):
super(GoogLeNet, self).__init__()
self.features=torch.nn.Sequential(
torch.nn.Linear(in_sample_points,224),
torch.nn.Conv1d(in_channels,64,kernel_size=7,stride=2,padding=3),
torch.nn.MaxPool1d(3,2,padding=1),
torch.nn.Conv1d(64,192,3,padding=1),
torch.nn.MaxPool1d(3,2,padding=1),
Inception(192,64,96,128,16,32,32),
Inception(256,128,128,192,32,96,64),
torch.nn.MaxPool1d(3,2,padding=1),
Inception(480,192,96,208,16,48,64),
)
self.classifer_max_pool = torch.nn.MaxPool1d(5,3)
self.classifer = torch.nn.Sequential(
torch.nn.Linear(2048,1024),
torch.nn.Dropout(0.5),
torch.nn.ReLU(),
torch.nn.Linear(1024,512),
torch.nn.Dropout(0.5),
torch.nn.ReLU(),
torch.nn.Linear(512,classes),
)
self.Inception_4b = Inception(512,160,112,224,24,64,64)
self.Inception_4c = Inception(512,128,128,256,24,64,64)
self.Inception_4d = Inception(512,112,144,288,32,64,64)
self.classifer1 = torch.nn.Sequential(
torch.nn.Linear(2112,1056),
torch.nn.Dropout(0.5),
torch.nn.ReLU(),
torch.nn.Linear(1056,528),
torch.nn.Dropout(0.5),
torch.nn.ReLU(),
torch.nn.Linear(528,classes),
)
self.Inception_4e = Inception(528,256,160,320,32,128,128)
self.max_pool = torch.nn.MaxPool1d(3,2,1)
self.Inception_5a = Inception(832,256,160,320,32,128,128)
self.Inception_5b = Inception(832,384,192,384,48,128,128)
self.avg_pool = torch.nn.AvgPool1d(7,stride=1)
self.dropout = torch.nn.Dropout(0.4)
self.classifer2 = torch.nn.Sequential(
torch.nn.Linear(1024, 512),
torch.nn.Dropout(0.5),
torch.nn.ReLU(),
torch.nn.Linear(512, classes),
)
def forward(self,x):
x = self.features(x)
y = self.classifer(self.classifer_max_pool(x).view(-1,2048))
x = self.Inception_4b(x)
x = self.Inception_4c(x)
x = self.Inception_4d(x)
y1 = self.classifer1(self.classifer_max_pool(x).view(-1,2112))
x = self.Inception_4e(x)
x = self.max_pool(x)
x = self.Inception_5a(x)
x = self.Inception_5b(x)
x = self.avg_pool(x)
x = self.dropout(x)
x = x.view(-1,1024)
x = self.classifer2(x)
return x,y,y1
class GoogLeNetLoss(torch.nn.Module):
def __init__(self):
super(GoogLeNetLoss, self).__init__()
self.CrossEntropyLoss = torch.nn.CrossEntropyLoss()
def forward(self,data,label):
c2_loss = self.CrossEntropyLoss(data[0],label)
c0_loss = self.CrossEntropyLoss(data[1],label)
c1_loss = self.CrossEntropyLoss(data[2],label)
loss = c2_loss + 0.3*(c0_loss+c1_loss)
return loss
if __name__ == '__main__':
model = GoogLeNet()
input = torch.randn(size=(2,2,224))
# [c2,c0,c1] = model(input)
output = model(input)
criterion = GoogLeNetLoss()
label = torch.tensor([1,0])
print(f"损失为:{criterion(output,label)}")
print(f"输出结果为{output}")
print(model)
summary(model=model, input_size=(2, 224), device='cpu')