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11e9a40 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | """
Net1D: 1D CNN with Squeeze-and-Excitation for ECG classification.
From PKUDigitalHealth/ECGFounder (MIT License).
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class MyConv1dPadSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride, groups=groups)
def forward(self, x):
in_dim = x.shape[-1]
out_dim = (in_dim + self.stride - 1) // self.stride
p = max(0, (out_dim - 1) * self.stride + self.kernel_size - in_dim)
pad_left = p // 2
pad_right = p - pad_left
x = F.pad(x, (pad_left, pad_right), "constant", 0)
return self.conv(x)
class MyMaxPool1dPadSame(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.kernel_size = kernel_size
self.max_pool = nn.MaxPool1d(kernel_size=kernel_size)
def forward(self, x):
p = max(0, self.kernel_size - 1)
pad_left = p // 2
pad_right = p - pad_left
x = F.pad(x, (pad_left, pad_right), "constant", 0)
return self.max_pool(x)
class Swish(nn.Module):
def forward(self, x):
return x * torch.sigmoid(x)
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, ratio, kernel_size, stride,
groups, downsample, is_first_block=False, use_bn=True, use_do=True):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.downsample = downsample
self.stride = stride if downsample else 1
self.is_first_block = is_first_block
self.use_bn = use_bn
self.use_do = use_do
middle = int(out_channels * ratio)
self.bn1 = nn.BatchNorm1d(in_channels)
self.activation1 = Swish()
self.do1 = nn.Dropout(p=0.5)
self.conv1 = MyConv1dPadSame(in_channels, middle, 1, 1, 1)
self.bn2 = nn.BatchNorm1d(middle)
self.activation2 = Swish()
self.do2 = nn.Dropout(p=0.5)
self.conv2 = MyConv1dPadSame(middle, middle, kernel_size, self.stride, groups)
self.bn3 = nn.BatchNorm1d(middle)
self.activation3 = Swish()
self.do3 = nn.Dropout(p=0.5)
self.conv3 = MyConv1dPadSame(middle, out_channels, 1, 1, 1)
# Squeeze-and-Excitation
r = 2
self.se_fc1 = nn.Linear(out_channels, out_channels // r)
self.se_fc2 = nn.Linear(out_channels // r, out_channels)
self.se_activation = Swish()
if self.downsample:
self.max_pool = MyMaxPool1dPadSame(kernel_size=self.stride)
def forward(self, x):
identity = x
out = x
if not self.is_first_block:
if self.use_bn:
out = self.bn1(out)
out = self.activation1(out)
if self.use_do:
out = self.do1(out)
out = self.conv1(out)
if self.use_bn:
out = self.bn2(out)
out = self.activation2(out)
if self.use_do:
out = self.do2(out)
out = self.conv2(out)
if self.use_bn:
out = self.bn3(out)
out = self.activation3(out)
if self.use_do:
out = self.do3(out)
out = self.conv3(out)
# SE attention
se = out.mean(-1)
se = self.se_fc1(se)
se = self.se_activation(se)
se = self.se_fc2(se)
se = torch.sigmoid(se)
out = torch.einsum('abc,ab->abc', out, se)
if self.downsample:
identity = self.max_pool(identity)
if self.out_channels != self.in_channels:
identity = identity.transpose(-1, -2)
ch1 = (self.out_channels - self.in_channels) // 2
ch2 = self.out_channels - self.in_channels - ch1
identity = F.pad(identity, (ch1, ch2), "constant", 0)
identity = identity.transpose(-1, -2)
out += identity
return out
class BasicStage(nn.Module):
def __init__(self, in_channels, out_channels, ratio, kernel_size, stride,
groups, i_stage, m_blocks, use_bn=True, use_do=True):
super().__init__()
self.block_list = nn.ModuleList()
for i_block in range(m_blocks):
is_first = (i_stage == 0 and i_block == 0)
if i_block == 0:
tmp_block = BasicBlock(
in_channels, out_channels, ratio, kernel_size,
stride, groups, downsample=True,
is_first_block=is_first, use_bn=use_bn, use_do=use_do)
else:
tmp_block = BasicBlock(
out_channels, out_channels, ratio, kernel_size,
1, groups, downsample=False,
is_first_block=False, use_bn=use_bn, use_do=use_do)
self.block_list.append(tmp_block)
def forward(self, x):
for block in self.block_list:
x = block(x)
return x
class Net1D(nn.Module):
"""
1D CNN for ECG classification.
Input: (batch, in_channels, length)
Output: (batch, n_classes)
"""
def __init__(self, in_channels, base_filters, ratio, filter_list,
m_blocks_list, kernel_size, stride, groups_width,
n_classes, use_bn=True, use_do=True, verbose=False):
super().__init__()
self.n_stages = len(filter_list)
self.use_bn = use_bn
self.first_conv = MyConv1dPadSame(in_channels, base_filters, kernel_size, stride=2)
self.first_bn = nn.BatchNorm1d(base_filters)
self.first_activation = Swish()
self.stage_list = nn.ModuleList()
in_ch = base_filters
for i_stage in range(self.n_stages):
out_ch = filter_list[i_stage]
self.stage_list.append(BasicStage(
in_ch, out_ch, ratio, kernel_size, stride,
out_ch // groups_width, i_stage, m_blocks_list[i_stage],
use_bn=use_bn, use_do=use_do))
in_ch = out_ch
self.dense = nn.Linear(in_ch, n_classes)
def forward(self, x):
out = self.first_conv(x)
if self.use_bn:
out = self.first_bn(out)
out = self.first_activation(out)
for stage in self.stage_list:
out = stage(out)
features = out.mean(-1) # Global Average Pooling
out = self.dense(features)
return out
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