-
Notifications
You must be signed in to change notification settings - Fork 8
/
EWSNet.py
131 lines (119 loc) · 5.53 KB
/
EWSNet.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
import torch
import torch.nn as nn
# from wmodelsii8 import Sin_fast as fast
# from wmodelsii3 import Laplace_fast as fast
from wmodelsii8 import Laplace_fastv2 as fast
# from wsinc import SincConv_fast as fast
from Shrinkage import Shrinkagev3ppp2 as sage
class Mish1(nn.Module):
def __init__(self):
super(Mish1, self).__init__()
self.mish = nn.ReLU(inplace=True)
def forward(self, x):
return self.mish(x)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__() #85,42,70 #63,31,75
self.p1_0 = nn.Sequential( # nn.Conv1d(1, 50, kernel_size=18, stride=2),
# fast(out_channels=64, kernel_size=250, stride=1),
# fast1(out_channels=70, kernel_size=84, stride=1),
nn.Conv1d(1, 64, kernel_size=250, stride=1, bias=True),
nn.BatchNorm1d(64),
Mish1()
)
self.p1_1 = nn.Sequential(nn.Conv1d(64, 16, kernel_size=18, stride=2, bias=True),
# fast(out_channels=50, kernel_size=18, stride=2),
nn.BatchNorm1d(16),
Mish1()
)
self.p1_2 = nn.Sequential(nn.Conv1d(16, 10, kernel_size=10, stride=2, bias=True),
nn.BatchNorm1d(10),
Mish1()
)
self.p1_3 = nn.MaxPool1d(kernel_size=2)
self.p2_1 = nn.Sequential(nn.Conv1d(64, 32, kernel_size=6, stride=1, bias=True),
# fast(out_channels=50, kernel_size=6, stride=1),
nn.BatchNorm1d(32),
Mish1()
)
self.p2_2 = nn.Sequential(nn.Conv1d(32, 16, kernel_size=6, stride=1, bias=True),
nn.BatchNorm1d(16),
Mish1()
)
self.p2_3 = nn.MaxPool1d(kernel_size=2)
self.p2_4 = nn.Sequential(nn.Conv1d(16, 10, kernel_size=6, stride=1, bias=True),
nn.BatchNorm1d(10),
Mish1()
)
self.p2_5 = nn.Sequential(nn.Conv1d(10, 10, kernel_size=8, stride=2, bias=True),
# nn.Conv1d(10, 10, kernel_size=6, stride=2),
nn.BatchNorm1d(10),
Mish1()
) # PRelu
self.p2_6 = nn.MaxPool1d(kernel_size=2)
self.p3_0 = sage(channel=64, gap_size=1)
self.p3_1 = nn.Sequential(nn.Conv1d(64, 10, kernel_size=43, stride=4, bias=True),
nn.BatchNorm1d(10),
Mish1()
)
self.p3_2 = nn.MaxPool1d(kernel_size=2)
self.p3_3 = nn.Sequential(nn.AdaptiveAvgPool1d(1))
self.p4 = nn.Sequential(nn.Linear(10, 4))
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv1d):
if m.kernel_size == (500,):
m.weight.data = fast(out_channels=64, kernel_size=250).forward()
nn.init.constant_(m.bias.data, 0.0)
else:
nn.init.kaiming_normal_(m.weight.data)
nn.init.constant_(m.bias.data, 0.0)
elif isinstance(m, nn.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.fill_(0)
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
if m.bias is not None:
m.bias.data.fill_(1)
def forward(self, x):
x = self.p1_0(x)
p1 = self.p1_3(self.p1_2(self.p1_1(x)))
p2 = self.p2_6(self.p2_5(self.p2_4(self.p2_3(self.p2_2(self.p2_1(x))))))
x = self.p3_2(self.p3_1(x + self.p3_0(x)))
x = torch.add(x, torch.add(p1, p2))
x = self.p3_3(x).squeeze()
x = self.p4(x)
return x
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
import math
# input = torch.randn(2, 1, 1024).cuda()
# model = Net().cuda()
# # for param in model.parameters():
# # print(type(param.data), param.size())
# print("# parameters:", sum(param.numel() for param in model.parameters()))
# output = model(input)
# print(model)
##################################
# model = Net().cuda()
# for name, parameters in model.named_parameters():
# print(name, ':', parameters.size())
# model.load_state_dict(torch.load('H:\EWSNet\pre\99.53.pt'), strict=False)
# weight_t = model.state_dict()['p3_0.a'].cpu().detach().numpy()
# print(weight_t)
##################################
# model = Net().cuda()
# weight_t = model.state_dict()['p1_0.0.weight'].cpu().detach().numpy()
# y1 = weight_t[60, :, :].squeeze()
# # model.load_state_dict(torch.load('H:\EWSNet\pre\model.pt'), strict=False)
# # weight_t = model.state_dict()['p1_0.0.weight'].cpu().detach().numpy()
# # y2 = weight_t[20, :, :].squeeze()
# x = np.linspace(0, 250, 250)
# # plt.plot(x, y1, label='before')
# plt.plot(x, y1, label='Xavier_normal')
# # plt.plot(x, y2, label='after')
# plt.legend()
# plt.savefig('wahaha.tiff', format='tiff', dpi=600)
# plt.show()