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/ecgtoHR/HRnet.py
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| a | b/ecgtoHR/HRnet.py | ||
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| 1 | import torch.nn as nn |
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| 2 | import torch |
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| 3 | from torch.autograd import Variable |
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| 4 | import numpy as np |
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| 5 | |||
| 6 | class IncResBlock(nn.Module): |
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| 7 | def __init__(self, inplanes, planes, convstr=1, convsize = 15, convpadding = 7): |
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| 8 | super(IncResBlock, self).__init__() |
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| 9 | self.Inputconv1x1 = nn.Conv1d(inplanes, planes, kernel_size=1, stride = 1, bias=False) |
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| 10 | self.conv1_1 = nn.Sequential( |
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| 11 | nn.Conv1d(in_channels = inplanes,out_channels = planes//4,kernel_size = convsize,stride = convstr,padding = convpadding), |
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| 12 | nn.BatchNorm1d(planes//4)) |
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| 13 | self.conv1_2 = nn.Sequential( |
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| 14 | nn.Conv1d(inplanes, planes//4, kernel_size=1, stride = convstr, padding=0, bias=False), |
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| 15 | nn.BatchNorm1d(planes//4), |
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| 16 | nn.LeakyReLU(0.2,), |
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| 17 | nn.Conv1d(in_channels = planes//4,out_channels = planes//4,kernel_size = convsize+2,stride = convstr,padding = convpadding+1), |
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| 18 | nn.BatchNorm1d(planes//4)) |
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| 19 | self.conv1_3 = nn.Sequential( |
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| 20 | nn.Conv1d(inplanes, planes//4, kernel_size=1, stride = convstr, padding=0, bias=False), |
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| 21 | nn.BatchNorm1d(planes//4), |
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| 22 | nn.LeakyReLU(0.2,), |
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| 23 | nn.Conv1d(in_channels = planes//4,out_channels = planes//4,kernel_size = convsize+4,stride = convstr,padding = convpadding+2), |
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| 24 | nn.BatchNorm1d(planes//4)) |
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| 25 | self.conv1_4 = nn.Sequential( |
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| 26 | nn.Conv1d(inplanes, planes//4, kernel_size=1, stride = convstr, padding=0, bias=False), |
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| 27 | nn.BatchNorm1d(planes//4), |
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| 28 | nn.LeakyReLU(0.2,), |
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| 29 | nn.Conv1d(in_channels = planes//4,out_channels = planes//4,kernel_size = convsize+6,stride = convstr,padding = convpadding+3), |
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| 30 | nn.BatchNorm1d(planes//4)) |
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| 31 | self.relu = nn.ReLU() |
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| 32 | |||
| 33 | def forward(self, x): |
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| 34 | residual = self.Inputconv1x1(x) |
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| 35 | |||
| 36 | c1 = self.conv1_1(x) |
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| 37 | c2 = self.conv1_2(x) |
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| 38 | c3 = self.conv1_3(x) |
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| 39 | c4 = self.conv1_4(x) |
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| 40 | |||
| 41 | out = torch.cat([c1,c2,c3,c4],1) |
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| 42 | out += residual |
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| 43 | out = self.relu(out) |
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| 44 | |||
| 45 | return out |
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| 46 | |||
| 47 | class IncUNet (nn.Module): |
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| 48 | def __init__(self, in_shape): |
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| 49 | super(IncUNet, self).__init__() |
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| 50 | in_channels, height, width = in_shape |
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| 51 | self.e1 = nn.Sequential( |
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| 52 | nn.Conv1d(in_channels, 64, kernel_size=4, stride=2,padding=1), |
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| 53 | nn.BatchNorm1d(64), |
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| 54 | nn.LeakyReLU(0.2,), |
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| 55 | IncResBlock(64,64)) |
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| 56 | self.e2 = nn.Sequential( |
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| 57 | nn.LeakyReLU(0.2,inplace=True), |
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| 58 | nn.Conv1d(64, 128, kernel_size=4, stride=2,padding=1), |
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| 59 | nn.BatchNorm1d(128), |
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| 60 | IncResBlock(128,128)) |
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| 61 | self.e2add = nn.Sequential( |
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| 62 | nn.Conv1d(128, 128, kernel_size=3, stride=1,padding=1), |
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| 63 | nn.BatchNorm1d(128)) |
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| 64 | self.e3 = nn.Sequential( |
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| 65 | nn.LeakyReLU(0.2,inplace=True), |
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| 66 | nn.Conv1d(128, 128, kernel_size=3, stride=1,padding=1), |
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| 67 | nn.BatchNorm1d(128), |
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| 68 | nn.LeakyReLU(0.2,), |
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| 69 | nn.Conv1d(128,256, kernel_size=4, stride=2,padding=1), |
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| 70 | nn.BatchNorm1d(256), |
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| 71 | IncResBlock(256,256)) |
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| 72 | self.e4 = nn.Sequential( |
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| 73 | nn.LeakyReLU(0.2,), |
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| 74 | nn.Conv1d(256,256, kernel_size=4 , stride=1 , padding=1), |
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| 75 | nn.BatchNorm1d(256), |
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| 76 | nn.LeakyReLU(0.2,inplace=True), |
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| 77 | nn.Conv1d(256,512, kernel_size=4, stride=2,padding=1), |
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| 78 | nn.BatchNorm1d(512), |
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| 79 | IncResBlock(512,512)) |
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| 80 | self.e4add = nn.Sequential( |
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| 81 | nn.LeakyReLU(0.2,), |
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| 82 | nn.Conv1d(512,512, kernel_size=3, stride=1,padding=1), |
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| 83 | nn.BatchNorm1d(512)) |
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| 84 | self.e5 = nn.Sequential( |
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| 85 | nn.LeakyReLU(0.2,inplace=True), |
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| 86 | nn.Conv1d(512,512, kernel_size=3, stride=1,padding=1), |
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| 87 | nn.BatchNorm1d(512), |
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| 88 | nn.LeakyReLU(0.2,), |
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| 89 | nn.Conv1d(512,512, kernel_size=4, stride=2,padding=1), |
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| 90 | nn.BatchNorm1d(512), |
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| 91 | IncResBlock(512,512)) |
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| 92 | |||
| 93 | self.e6 = nn.Sequential( |
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| 94 | nn.LeakyReLU(0.2,), |
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| 95 | nn.Conv1d(512,512, kernel_size=3, stride=1,padding=1), |
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| 96 | nn.BatchNorm1d(512), |
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| 97 | nn.LeakyReLU(0.2,inplace=True), |
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| 98 | nn.Conv1d(512,512, kernel_size=4, stride=2,padding=1), |
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| 99 | nn.BatchNorm1d(512), |
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| 100 | IncResBlock(512,512)) |
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| 101 | |||
| 102 | self.e6add = nn.Sequential( |
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| 103 | nn.Conv1d(512,512, kernel_size=3, stride=1,padding=1), |
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| 104 | nn.BatchNorm1d(512)) |
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| 105 | |||
| 106 | self.e7 = nn.Sequential( |
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| 107 | nn.LeakyReLU(0.2,inplace=True), |
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| 108 | nn.Conv1d(512,512, kernel_size=3, stride=1,padding=1), |
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| 109 | nn.BatchNorm1d(512), |
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| 110 | nn.LeakyReLU(0.2,), |
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| 111 | nn.Conv1d(512,512, kernel_size=4, stride=2,padding=1), |
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| 112 | nn.BatchNorm1d(512), |
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| 113 | IncResBlock(512,512)) |
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| 114 | |||
| 115 | self.e8 = nn.Sequential( |
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| 116 | nn.LeakyReLU(0.2,), |
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| 117 | nn.Conv1d(512,512, kernel_size=4, stride=1,padding=1), |
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| 118 | nn.BatchNorm1d(512), |
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| 119 | nn.LeakyReLU(0.2,inplace=True), |
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| 120 | nn.Conv1d(512,512, kernel_size=4, stride=2,padding=1), |
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| 121 | nn.BatchNorm1d(512)) |
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| 122 | |||
| 123 | |||
| 124 | self.d1 = nn.Sequential( |
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| 125 | nn.LeakyReLU(0.2,), |
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| 126 | nn.ConvTranspose1d(512, 512, kernel_size=4, stride=2,padding=1), |
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| 127 | nn.BatchNorm1d(512), |
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| 128 | nn.LeakyReLU(0.2,), |
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| 129 | nn.ConvTranspose1d(512, 512, kernel_size=4, stride=1,padding =1), |
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| 130 | nn.BatchNorm1d(512), |
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| 131 | IncResBlock(512,512)) |
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| 132 | |||
| 133 | self.d2 = nn.Sequential( |
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| 134 | nn.LeakyReLU(0.2,), |
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| 135 | nn.ConvTranspose1d(1024, 512, kernel_size=4, stride=2,padding=1), |
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| 136 | nn.BatchNorm1d(512), |
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| 137 | nn.LeakyReLU(0.2,), |
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| 138 | nn.ConvTranspose1d(512, 512, kernel_size=3, stride=1,padding=1), |
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| 139 | nn.BatchNorm1d(512), |
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| 140 | IncResBlock(512,512)) |
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| 141 | |||
| 142 | self.d3 = nn.Sequential( |
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| 143 | nn.LeakyReLU(0.2,), |
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| 144 | nn.ConvTranspose1d(1024, 512, kernel_size=3, stride=1,padding=1), |
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| 145 | nn.BatchNorm1d(512), |
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| 146 | nn.Dropout(p=0.5), |
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| 147 | IncResBlock(512,512)) |
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| 148 | |||
| 149 | self.d4 = nn.Sequential( |
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| 150 | nn.LeakyReLU(0.2,), |
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| 151 | nn.ConvTranspose1d(1024, 512, kernel_size=4, stride=2,padding=1), |
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| 152 | nn.BatchNorm1d(512), |
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| 153 | nn.LeakyReLU(0.2,), |
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| 154 | nn.ConvTranspose1d(512, 512, kernel_size=3, stride=1,padding=1), |
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| 155 | nn.BatchNorm1d(512), |
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| 156 | IncResBlock(512,512)) |
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| 157 | |||
| 158 | self.d5 = nn.Sequential( |
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| 159 | nn.LeakyReLU(0.2,), |
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| 160 | nn.ConvTranspose1d(1024, 512, kernel_size=4, stride=2,padding=1), |
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| 161 | nn.BatchNorm1d(512), |
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| 162 | nn.LeakyReLU(0.2,), |
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| 163 | nn.ConvTranspose1d(512, 512, kernel_size=3, stride=1,padding=1), |
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| 164 | nn.BatchNorm1d(512), |
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| 165 | IncResBlock(512,512)) |
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| 166 | |||
| 167 | self.d6 = nn.Sequential( |
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| 168 | nn.LeakyReLU(0.2,), |
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| 169 | nn.ConvTranspose1d(1024, 512, kernel_size=3, stride=1,padding=1), |
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| 170 | nn.BatchNorm1d(512), |
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| 171 | IncResBlock(512,512)) |
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| 172 | |||
| 173 | self.d7 = nn.Sequential( |
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| 174 | nn.LeakyReLU(0.2,), |
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| 175 | nn.ConvTranspose1d(1024, 256, kernel_size=4, stride=2,padding=1), |
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| 176 | nn.BatchNorm1d(256), |
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| 177 | nn.LeakyReLU(0.2,), |
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| 178 | nn.ConvTranspose1d(256, 256, kernel_size=4, stride=1,padding=1), |
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| 179 | nn.BatchNorm1d(256), |
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| 180 | IncResBlock(256,256)) |
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| 181 | |||
| 182 | self.d8 = nn.Sequential( |
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| 183 | nn.LeakyReLU(0.2,), |
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| 184 | nn.ConvTranspose1d(512, 128, kernel_size=4, stride=2,padding=1), |
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| 185 | nn.BatchNorm1d(128), |
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| 186 | nn.LeakyReLU(0.2,), |
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| 187 | nn.ConvTranspose1d(128, 128, kernel_size=3, stride=1,padding=1), |
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| 188 | nn.BatchNorm1d(128)) |
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| 189 | |||
| 190 | self.d9 = nn.Sequential( |
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| 191 | nn.LeakyReLU(0.2,), |
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| 192 | nn.ConvTranspose1d(256, 128, kernel_size=3, stride=1,padding=1), |
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| 193 | nn.BatchNorm1d(128)) |
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| 194 | |||
| 195 | self.d10 = nn.Sequential( |
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| 196 | nn.LeakyReLU(0.2,), |
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| 197 | nn.ConvTranspose1d(256, 64, kernel_size=4, stride=2,padding=1), |
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| 198 | nn.BatchNorm1d(64)) |
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| 199 | |||
| 200 | self.out_l = nn.Sequential( |
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| 201 | nn.LeakyReLU(0.2,), |
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| 202 | nn.ConvTranspose1d(128, in_channels, kernel_size=4, stride=2,padding=1)) |
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| 203 | |||
| 204 | |||
| 205 | def forward(self, x): |
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| 206 | |||
| 207 | en1 = self.e1(x) |
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| 208 | en2 = self.e2(en1) |
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| 209 | en2add = self.e2add(en2) |
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| 210 | en3 = self.e3(en2add) |
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| 211 | en4 = self.e4(en3) |
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| 212 | en4add = self.e4add(en4) |
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| 213 | en5 = self.e5(en4add) |
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| 214 | en6 = self.e6(en5) |
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| 215 | en6add = self.e6add(en6) |
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| 216 | en7 = self.e7(en6add) |
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| 217 | en8 = self.e8(en7) |
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| 218 | |||
| 219 | de1_ = self.d1(en8) |
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| 220 | de1 = torch.cat([en7,de1_],1) |
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| 221 | de2_ = self.d2(de1) |
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| 222 | de2 = torch.cat([en6add,de2_],1) |
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| 223 | de3_ = self.d3(de2) |
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| 224 | de3 = torch.cat([en6,de3_],1) |
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| 225 | de4_ = self.d4(de3) |
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| 226 | de4 = torch.cat([en5,de4_],1) |
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| 227 | de5_ = self.d5(de4) |
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| 228 | de5 = torch.cat([en4add,de5_],1) |
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| 229 | de6_ = self.d6(de5) |
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| 230 | de6 = torch.cat([en4,de6_],1) |
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| 231 | de7_ = self.d7(de6) |
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| 232 | de7 = torch.cat([en3,de7_],1) |
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| 233 | de8 = self.d8(de7) |
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| 234 | de8_ = self.d8(de7) |
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| 235 | de8 = torch.cat([en2add,de8_],1) |
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| 236 | de9_ = self.d9(de8) |
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| 237 | de9 = torch.cat([en2,de9_],1) |
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| 238 | de10_ = self.d10(de9) |
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| 239 | de10 = torch.cat([en1,de10_],1) |
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| 240 | out = self.out_l(de10) |
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| 241 | |||
| 242 | return out |