113 lines
3.2 KiB
Python
113 lines
3.2 KiB
Python
"""
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模型定义文件 - ResNet-34
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author : yukun-hh
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date : 2026-4-10
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"""
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import torch
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from torch import nn
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from torch.nn import functional as F
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from torchsummary import summary
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class BasicBlock(nn.Module):
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"""
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ResNet-34 基础残差块：3x3 -> 3x3
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若需要下采样或通道变化，则在跳跃连接中使用 1x1 卷积
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"""
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expansion = 1
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def __init__(self, in_channels, out_channels, stride=1, downsample=None):
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super().__init__()
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(out_channels)
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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def forward(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class Net(nn.Module):
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def __init__(self, num_classes=4, dropout=0.5):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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layers_config = [
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(3, 64, 1), # layer1
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(4, 128, 2), # layer2
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(6, 256, 2), # layer3
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(3, 512, 2), # layer4
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]
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self.in_channels = 64
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self.layer1 = self._make_layer(layers_config[0])
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self.layer2 = self._make_layer(layers_config[1])
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self.layer3 = self._make_layer(layers_config[2])
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self.layer4 = self._make_layer(layers_config[3])
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.dropout = nn.Dropout(dropout)
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self.fc = nn.Linear(512, num_classes)
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def _make_layer(self, config):
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num_blocks, out_channels, stride = config
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downsample = None
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layers = []
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if stride != 1 or self.in_channels != out_channels:
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downsample = nn.Sequential(
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nn.Conv2d(self.in_channels, out_channels,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(out_channels),
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)
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layers.append(BasicBlock(self.in_channels, out_channels, stride, downsample))
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self.in_channels = out_channels
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for _ in range(1, num_blocks):
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layers.append(BasicBlock(self.in_channels, out_channels))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.dropout(x)
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x = self.fc(x)
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return x
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if __name__ == '__main__':
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model = Net(num_classes=4)
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summary(model, input_size=(3, 256, 256))
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