144 lines
No EOL
4.7 KiB
Python
144 lines
No EOL
4.7 KiB
Python
"""
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模型定义文件 - 使用瓶颈结构 (Bottleneck) 的深度残差网络
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目标：约50层，参数量约80M
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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 Bottleneck(nn.Module):
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"""
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瓶颈残差块：1x1(降维) -> 3x3 -> 1x1(升维)
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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 = 4 # 输出通道是中间通道的4倍
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def __init__(self, in_channels, mid_channels, stride=1, downsample=None):
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"""
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:param in_channels: 输入通道数
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:param mid_channels: 中间层通道数（1x1降维后的通道数）
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:param stride: 步长，用于下采样
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:param downsample: 下采样模块（当stride≠1或通道变化时使用）
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"""
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super().__init__()
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self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(mid_channels)
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self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(mid_channels)
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self.conv3 = nn.Conv2d(mid_channels, mid_channels * self.expansion, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(mid_channels * self.expansion)
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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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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(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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"""
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基于 Bottleneck 的 ResNet 风格模型
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各阶段配置仿照 ResNet-50，适当调整宽度以达到约80M参数
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"""
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def __init__(self, num_classes=4):
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super().__init__()
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# 第一阶段：7x7卷积 + 最大池化
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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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# 残差阶段定义
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# 每个阶段的参数：[块数, 中间通道数, 步长]
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# 为了达到80M参数，我们略微加宽网络（相比标准ResNet-50）
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layers_config = [
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(3, 64, 1), # stage2: 3个瓶颈块，输出通道 64*4=256
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(4, 128, 2), # stage3: 4个瓶颈块，输出通道 128*4=512
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(14, 256, 2), # stage4: 14个瓶颈块，输出通道 256*4=1024（加深至此阶段）
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(3, 512, 2) # stage5: 3个瓶颈块，输出通道 512*4=2048
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]
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self.in_channels = 64
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self.stage2 = self._make_layer(layers_config[0])
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self.stage3 = self._make_layer(layers_config[1])
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self.stage4 = self._make_layer(layers_config[2])
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self.stage5 = self._make_layer(layers_config[3])
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# 全局池化与分类层
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(2048, num_classes)
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def _make_layer(self, config):
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"""
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构建一个残差阶段
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:param config: (块数, 中间通道数, 第一阶段步长)
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:return: nn.Sequential
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"""
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num_blocks, mid_channels, stride = config
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downsample = None
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layers = []
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# 第一个块可能需要下采样和通道匹配
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if stride != 1 or self.in_channels != mid_channels * Bottleneck.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.in_channels, mid_channels * Bottleneck.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(mid_channels * Bottleneck.expansion),
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)
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layers.append(
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Bottleneck(self.in_channels, mid_channels, stride, downsample)
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)
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self.in_channels = mid_channels * Bottleneck.expansion
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# 后续块
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for _ in range(1, num_blocks):
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layers.append(
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Bottleneck(self.in_channels, mid_channels)
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)
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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.stage2(x)
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x = self.stage3(x)
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x = self.stage4(x)
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x = self.stage5(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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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)) |