refactor: replace custom Bottleneck model with standard ResNet-34 + Dropout

Model.py

@@ -1,6 +1,5 @@
 """
-模型定义文件 - 使用瓶颈结构 (Bottleneck) 的深度残差网络
-目标：约50层，参数量约80M
+模型定义文件 - ResNet-34
 author : yukun-hh
 date : 2026-4-10
 """
@@ -10,27 +9,19 @@ from torch.nn import functional as F
 from torchsummary import summary
 
 
-class Bottleneck(nn.Module):
+class BasicBlock(nn.Module):
     """
-    瓶颈残差块：1x1(降维) -> 3x3 -> 1x1(升维)
-    若需要下采样或通道变化，则在跳跃连接中使用1x1卷积
+    ResNet-34 基础残差块：3x3 -> 3x3
+    若需要下采样或通道变化，则在跳跃连接中使用 1x1 卷积
     """
-    expansion = 4  # 输出通道是中间通道的4倍
+    expansion = 1
 
-    def __init__(self, in_channels, mid_channels, stride=1, downsample=None):
-        """
-        :param in_channels: 输入通道数
-        :param mid_channels: 中间层通道数（1x1降维后的通道数）
-        :param stride: 步长，用于下采样
-        :param downsample: 下采样模块（当stride≠1或通道变化时使用）
-        """
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
         super().__init__()
-        self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(mid_channels)
-        self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(mid_channels)
-        self.conv3 = nn.Conv2d(mid_channels, mid_channels * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(mid_channels * self.expansion)
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channels)
         self.relu = nn.ReLU(inplace=True)
         self.downsample = downsample
 
@@ -43,10 +34,6 @@ class Bottleneck(nn.Module):
 
         out = self.conv2(out)
         out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
 
         if self.downsample is not None:
             identity = self.downsample(x)
@@ -57,68 +44,49 @@ class Bottleneck(nn.Module):
 
 
 class Net(nn.Module):
-    """
-    基于 Bottleneck 的 ResNet 风格模型
-    各阶段配置仿照 ResNet-50，适当调整宽度以达到约80M参数
-    """
 
-    def __init__(self, num_classes=4):
+    def __init__(self, num_classes=4, dropout=0.5):
         super().__init__()
 
-        # 第一阶段：7x7卷积 + 最大池化
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU(inplace=True)
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 
-        # 残差阶段定义
-        # 每个阶段的参数：[块数, 中间通道数, 步长]
-        # 为了达到80M参数，我们略微加宽网络（相比标准ResNet-50）
         layers_config = [
-            (3, 64, 1),    # stage2: 3个瓶颈块，输出通道 64*4=256
-            (4, 128, 2),   # stage3: 4个瓶颈块，输出通道 128*4=512
-            (14, 256, 2),  # stage4: 14个瓶颈块，输出通道 256*4=1024（加深至此阶段）
-            (3, 512, 2)    # stage5: 3个瓶颈块，输出通道 512*4=2048
+            (3, 64, 1),    # layer1
+            (4, 128, 2),   # layer2
+            (6, 256, 2),   # layer3
+            (3, 512, 2),   # layer4
         ]
 
         self.in_channels = 64
-        self.stage2 = self._make_layer(layers_config[0])
-        self.stage3 = self._make_layer(layers_config[1])
-        self.stage4 = self._make_layer(layers_config[2])
-        self.stage5 = self._make_layer(layers_config[3])
+        self.layer1 = self._make_layer(layers_config[0])
+        self.layer2 = self._make_layer(layers_config[1])
+        self.layer3 = self._make_layer(layers_config[2])
+        self.layer4 = self._make_layer(layers_config[3])
 
-        # 全局池化与分类层
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        self.fc = nn.Linear(2048, num_classes)
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(512, num_classes)
 
     def _make_layer(self, config):
-        """
-        构建一个残差阶段
-        :param config: (块数, 中间通道数, 第一阶段步长)
-        :return: nn.Sequential
-        """
-        num_blocks, mid_channels, stride = config
+        num_blocks, out_channels, stride = config
         downsample = None
         layers = []
 
-        # 第一个块可能需要下采样和通道匹配
-        if stride != 1 or self.in_channels != mid_channels * Bottleneck.expansion:
+        if stride != 1 or self.in_channels != out_channels:
             downsample = nn.Sequential(
-                nn.Conv2d(self.in_channels, mid_channels * Bottleneck.expansion,
+                nn.Conv2d(self.in_channels, out_channels,
                           kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(mid_channels * Bottleneck.expansion),
+                nn.BatchNorm2d(out_channels),
             )
 
-        layers.append(
-            Bottleneck(self.in_channels, mid_channels, stride, downsample)
-        )
-        self.in_channels = mid_channels * Bottleneck.expansion
+        layers.append(BasicBlock(self.in_channels, out_channels, stride, downsample))
+        self.in_channels = out_channels
 
-        # 后续块
         for _ in range(1, num_blocks):
-            layers.append(
-                Bottleneck(self.in_channels, mid_channels)
-            )
+            layers.append(BasicBlock(self.in_channels, out_channels))
 
         return nn.Sequential(*layers)
 
@@ -128,17 +96,18 @@ class Net(nn.Module):
         x = self.relu(x)
         x = self.maxpool(x)
 
-        x = self.stage2(x)
-        x = self.stage3(x)
-        x = self.stage4(x)
-        x = self.stage5(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
 
         x = self.avgpool(x)
         x = torch.flatten(x, 1)
+        x = self.dropout(x)
         x = self.fc(x)
         return x
 
 
 if __name__ == '__main__':
     model = Net(num_classes=4)
-    summary(model, input_size=(3, 256, 256))
+    summary(model, input_size=(3, 256, 256))