[G1][Backbone] add CBAM and export scripts

models/common.py

@@ -1121,3 +1121,58 @@ class Classify(nn.Module):
         if isinstance(x, list):
             x = torch.cat(x, 1)
         return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
+
+# 以下类是我自己新加的
+
+class ChannelAttention(nn.Module):
+    def __init__(self, channels, reduction=16):
+        super().__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.max_pool = nn.AdaptiveMaxPool2d(1)
+
+        self.fc = nn.Sequential(
+            nn.Conv2d(channels, channels // reduction, 1, bias=False),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(channels // reduction, channels, 1, bias=False)
+        )
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        avg_out = self.fc(self.avg_pool(x))
+        max_out = self.fc(self.max_pool(x))
+        return self.sigmoid(avg_out + max_out)
+
+class SpatialAttention(nn.Module):
+    def __init__(self, kernel_size=7):
+        super().__init__()
+        assert kernel_size in (3, 7)
+        padding = 3 if kernel_size == 7 else 1
+
+        self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        avg_out = torch.mean(x, dim=1, keepdim=True)
+        max_out, _ = torch.max(x, dim=1, keepdim=True)
+        x = torch.cat([avg_out, max_out], dim=1)
+        return self.sigmoid(self.conv(x))
+
+class CBAM(nn.Module):
+    def __init__(self, channels, reduction=16, kernel_size=7):
+        super().__init__()
+        self.ca = ChannelAttention(channels, reduction)
+        self.sa = SpatialAttention(kernel_size)
+
+    def forward(self, x):
+        x = x * self.ca(x)
+        x = x * self.sa(x)
+        return x
+    
+class C3CBAM(C3):  
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        self.cbam = CBAM(c2)
+
+    def forward(self, x):
+        x = super().forward(x)
+        return self.cbam(x)