File size: 5,155 Bytes
bcf646b |
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 |
import torch
from torch import nn
class MoE_ECGFormer(nn.Module):
def __init__(self, configs, hparams):
super().__init__()
filter_sizes = [5, 9, 11]
self.conv1 = nn.Conv1d(configs.input_channels, configs.mid_channels, kernel_size=filter_sizes[0],
stride=configs.stride, bias=False, padding=(filter_sizes[0] // 2))
self.conv2 = nn.Conv1d(configs.input_channels, configs.mid_channels, kernel_size=filter_sizes[1],
stride=configs.stride, bias=False, padding=(filter_sizes[1] // 2))
self.conv3 = nn.Conv1d(configs.input_channels, configs.mid_channels, kernel_size=filter_sizes[2],
stride=configs.stride, bias=False, padding=(filter_sizes[2] // 2))
self.bn = nn.BatchNorm1d(configs.mid_channels)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool1d(kernel_size=2, stride=2, padding=1)
self.dropout = nn.Dropout(configs.dropout)
self.conv_block2 = nn.Sequential(
nn.Conv1d(configs.mid_channels, configs.mid_channels * 2, kernel_size=8, stride=1, bias=False,
padding=4),
nn.BatchNorm1d(configs.mid_channels * 2),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2, stride=2, padding=1)
)
self.conv_block3 = nn.Sequential(
nn.Conv1d(configs.mid_channels * 2, configs.final_out_channels, kernel_size=8, stride=1, bias=False,
padding=4),
nn.BatchNorm1d(configs.final_out_channels),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2, stride=2, padding=1),
)
self.inplanes = 128
self.crm = self._make_layer(SEBasicBlock, 128, 3)
# Transformer_layer
self.encoder_layer = nn.TransformerEncoderLayer(d_model=configs.trans_dim,
nhead=configs.num_heads,
batch_first=True)
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=3)
self.aap = nn.AdaptiveAvgPool1d(1)
self.clf = nn.Linear(hparams['feature_dim'], configs.num_classes)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv1d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm1d(planes * block.expansion),
)
layers = [block(self.inplanes, planes, stride, downsample)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x_in):
# Multi-scale Convolutions
x1 = self.conv1(x_in)
x2 = self.conv2(x_in)
x3 = self.conv3(x_in)
x_concat = torch.mean(torch.stack([x1, x2, x3], dim=2), dim=2)
x_concat = self.dropout(self.maxpool(self.relu(self.bn(x_concat))))
x = self.conv_block2(x_concat)
x = self.conv_block3(x)
# Channel Recalibration Module
x = self.crm(x)
# Bidirectional MoE Transformer
x1 = self.transformer_encoder(x)
x2 = self.transformer_encoder(torch.flip(x, [2]))
x = x1 + x2
x = self.aap(x)
x_flat = x.reshape(x.size(0), -1)
x_out = self.clf(x_flat)
return x_out
class SELayer(nn.Module):
def __init__(self, channel, reduction=4):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1)
return x * y.expand_as(x)
class SEBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None,
*, reduction=4):
super(SEBasicBlock, self).__init__()
self.conv1 = nn.Conv1d(inplanes, planes, stride)
self.bn1 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv1d(planes, planes, 1)
self.bn2 = nn.BatchNorm1d(planes)
self.se = SELayer(planes, reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
|