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resnet18.py
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resnet18.py
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import torch
import torch.nn as nn
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=7, stride=1, 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.shortcut = nn.Sequential()
if stride != 1 or in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm1d(out_channels)
)
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.shortcut(residual)
out = self.relu(out)
return out
class ResNet18(nn.Module):
def __init__(self, in_channels, num_classes=2):
super(ResNet18, self).__init__()
self.in_channels = in_channels
self.conv1 = nn.Conv1d(in_channels, 64, kernel_size=7, stride=1, padding=3, bias=False)
self.bn1 = nn.BatchNorm1d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool1d(kernel_size=2)
self.in_channels = 64
self.layer1 = self._make_layer(64, 2)
self.layer2 = self._make_layer(128, 2)
self.layer3 = self._make_layer(256, 2)
self.layer4 = self._make_layer(512, 2)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Linear(512, num_classes)
def _make_layer(self, out_channels, blocks):
layers = []
layers.append(BasicBlock(self.in_channels, out_channels))
self.in_channels = out_channels
for _ in range(0, blocks):
layers.append(BasicBlock(self.in_channels, out_channels))
layers.append(nn.MaxPool1d(kernel_size=2))
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