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import torch
import torch.nn as nn
import torch.nn.functional as F
class ResNetBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResNetBlock, self).__init__()
self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=7, stride=stride, padding=3, bias=False)
self.bn1 = nn.BatchNorm1d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=7, stride=1, padding=3, bias=False)
self.bn2 = nn.BatchNorm1d(out_channels)
self.downsample = downsample
def forward(self, x):
identity = x
if self.downsample is not None:
identity = self.downsample(x)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += identity
out = self.relu(out)
return out
class ResNet1d(nn.Module):
"""
ResNet-1D for ECG Classification.
Adapted from 'Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline' (Wang et al. 2017)
"""
def __init__(self, num_classes=5):
super(ResNet1d, self).__init__()
self.inplanes = 64
# Initial: 12 leads -> 64 channels
self.conv1 = nn.Conv1d(12, 64, kernel_size=15, stride=2, padding=7, bias=False)
self.bn1 = nn.BatchNorm1d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
# Layers
self.layer1 = self._make_layer(64, 2, stride=1)
self.layer2 = self._make_layer(128, 2, stride=2)
self.layer3 = self._make_layer(256, 2, stride=2)
self.layer4 = self._make_layer(512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Linear(512, num_classes)
def _make_layer(self, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes:
downsample = nn.Sequential(
nn.Conv1d(self.inplanes, planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm1d(planes),
)
layers = []
layers.append(ResNetBlock(self.inplanes, planes, stride, downsample))
self.inplanes = planes
for _ in range(1, blocks):
layers.append(ResNetBlock(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
if __name__ == "__main__":
# Test
model = ResNet1d(num_classes=5)
dummy = torch.randn(2, 12, 5000)
out = model(dummy)
print(f"Input: {dummy.shape}")
print(f"Output: {out.shape}")
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