-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdenoiser_model.py
127 lines (113 loc) · 4.63 KB
/
denoiser_model.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
# @title Denoiser Architecture Pretrained Model (Baseline Wander Remover)
from keras.api.layers import Input, Conv1D, Dropout, BatchNormalization, concatenate
from keras.api.models import Model
def LANLFilter_module(x, layers):
LB0 = Conv1D(filters=int(layers / 8),
kernel_size=3,
activation='linear',
strides=1,
padding='same')(x)
LB1 = Conv1D(filters=int(layers / 8),
kernel_size=5,
activation='linear',
strides=1,
padding='same')(x)
LB2 = Conv1D(filters=int(layers / 8),
kernel_size=9,
activation='linear',
strides=1,
padding='same')(x)
LB3 = Conv1D(filters=int(layers / 8),
kernel_size=15,
activation='linear',
strides=1,
padding='same')(x)
NLB0 = Conv1D(filters=int(layers / 8),
kernel_size=3,
activation='relu',
strides=1,
padding='same')(x)
NLB1 = Conv1D(filters=int(layers / 8),
kernel_size=5,
activation='relu',
strides=1,
padding='same')(x)
NLB2 = Conv1D(filters=int(layers / 8),
kernel_size=9,
activation='relu',
strides=1,
padding='same')(x)
NLB3 = Conv1D(filters=int(layers / 8),
kernel_size=15,
activation='relu',
strides=1,
padding='same')(x)
x = concatenate([LB0, LB1, LB2, LB3, NLB0, NLB1, NLB2, NLB3])
return x
def LANLFilter_module_dilated(x, layers):
LB1 = Conv1D(filters=int(layers / 6),
kernel_size=5,
activation='linear',
dilation_rate=3,
padding='same')(x)
LB2 = Conv1D(filters=int(layers / 6),
kernel_size=9,
activation='linear',
dilation_rate=3,
padding='same')(x)
LB3 = Conv1D(filters=int(layers / 6),
kernel_size=15,
dilation_rate=3,
activation='linear',
padding='same')(x)
NLB1 = Conv1D(filters=int(layers / 6),
kernel_size=5,
activation='relu',
dilation_rate=3,
padding='same')(x)
NLB2 = Conv1D(filters=int(layers / 6),
kernel_size=9,
activation='relu',
dilation_rate=3,
padding='same')(x)
NLB3 = Conv1D(filters=int(layers / 6),
kernel_size=15,
dilation_rate=3,
activation='relu',
padding='same')(x)
x = concatenate([LB1, LB2, LB3, NLB1, NLB2, NLB3])
# x = BatchNormalization()(x)
return x
def get_denoiser(signal_size=512):
input_shape = (signal_size, 1)
# Denoiser
denoiser_input_layer = Input(shape=input_shape, dtype='float64')
denoiser_hidden_layer = LANLFilter_module(denoiser_input_layer, 64)
denoiser_hidden_layer = Dropout(0.4)(denoiser_hidden_layer)
denoiser_hidden_layer = BatchNormalization()(denoiser_hidden_layer)
denoiser_hidden_layer = LANLFilter_module_dilated(denoiser_hidden_layer, 64)
denoiser_hidden_layer = Dropout(0.4)(denoiser_hidden_layer)
denoiser_hidden_layer = BatchNormalization()(denoiser_hidden_layer)
denoiser_hidden_layer = LANLFilter_module(denoiser_hidden_layer, 32)
denoiser_hidden_layer = Dropout(0.4)(denoiser_hidden_layer)
denoiser_hidden_layer = BatchNormalization()(denoiser_hidden_layer)
denoiser_hidden_layer = LANLFilter_module_dilated(denoiser_hidden_layer, 32)
denoiser_hidden_layer = Dropout(0.4)(denoiser_hidden_layer)
denoiser_hidden_layer = BatchNormalization()(denoiser_hidden_layer)
denoiser_hidden_layer = LANLFilter_module(denoiser_hidden_layer, 16)
denoiser_hidden_layer = Dropout(0.4)(denoiser_hidden_layer)
denoiser_hidden_layer = BatchNormalization()(denoiser_hidden_layer)
denoiser_hidden_layer = LANLFilter_module_dilated(denoiser_hidden_layer, 16)
denoiser_hidden_layer = Dropout(0.4)(denoiser_hidden_layer)
denoiser_hidden_layer = BatchNormalization()(denoiser_hidden_layer)
denoiser_output_layer = Conv1D(filters=1,
kernel_size=9,
activation='linear',
strides=1,
padding='same')(denoiser_hidden_layer)
denoiser_model = Model(inputs=denoiser_input_layer,
outputs=denoiser_output_layer)
# load the weights
denoiser_model.load_weights(
'pretrain_deepfilter_mblanld_isysrg_360hz.weights.h5')
return denoiser_model