sampleCNN-pytorch/model.py at master · kyungyunlee/sampleCNN-pytorch · GitHub

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
import torch
import torch.nn as nn
import config


class SampleCNN(nn.Module):
    def __init__(self):
        super(SampleCNN, self).__init__()

        # 59049 x 1
        self.conv1 = nn.Sequential(
            nn.Conv1d(1, 128, kernel_size=3, stride=3, padding=0),
            nn.BatchNorm1d(128),
            nn.ReLU())
        # 19683 x 128
        self.conv2 = nn.Sequential(
            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.MaxPool1d(3, stride=3))
        # 6561 x 128
        self.conv3 = nn.Sequential(
            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.MaxPool1d(3,stride=3))
        # 2187 x 128
        self.conv4 = nn.Sequential(
            nn.Conv1d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.MaxPool1d(3,stride=3))
        # 729 x 256
        self.conv5 = nn.Sequential(
            nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.MaxPool1d(3,stride=3))
        # 243 x 256
        self.conv6 = nn.Sequential(
            nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.MaxPool1d(3,stride=3),
            nn.Dropout(config.DROPOUT))
        # 81 x 256
        self.conv7 = nn.Sequential(
            nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.MaxPool1d(3,stride=3))
        # 27 x 256
        self.conv8 = nn.Sequential(
            nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.MaxPool1d(3,stride=3))
        # 9 x 256
        self.conv9 = nn.Sequential(
            nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.MaxPool1d(3,stride=3))
        # 3 x 256
        self.conv10 = nn.Sequential(
            nn.Conv1d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.MaxPool1d(3,stride=3))
        # 1 x 512
        self.conv11 = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Dropout(config.DROPOUT))
        # 1 x 512
        self.fc = nn.Linear(512, 50)
        self.activation = nn.Sigmoid()

    def forward(self, x):
        # input x : 23 x 59049 x 1
        # expected conv1d input : minibatch_size x num_channel x width

        x = x.view(x.shape[0], 1,-1)
        # x : 23 x 1 x 59049

        out = self.conv1(x)
        out = self.conv2(out)
        out = self.conv3(out)
        out = self.conv4(out)
        out = self.conv5(out)
        out = self.conv6(out)
        out = self.conv7(out)
        out = self.conv8(out)
        out = self.conv9(out)
        out = self.conv10(out)
        out = self.conv11(out)

        out = out.view(x.shape[0], out.size(1) * out.size(2))
        logit = self.fc(out)

        #logit = self.activation(logit)

        return logit