优化神经网络，缩减模型大小

gui/model.py

@@ -0,0 +1,72 @@
+import torch
+import torch.nn as nn
+
+
+# 轻量双通道注意力
+class LiteDualAttention(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.channel_attn = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels, in_channels // 16, 1, bias=False),
+            nn.SiLU(),
+            nn.Conv2d(in_channels // 16, in_channels, 1, bias=False),
+            nn.Sigmoid()
+        )
+
+        self.spatial_attn = nn.Sequential(
+            nn.Conv2d(2, 1, 5, padding=2, bias=False),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        # 通道加权
+        ca = self.channel_attn(x) * x
+
+        # 空间特征统计（均值+标准差）
+        stats = torch.cat([
+            x.mean(dim=1, keepdim=True),
+            x.std(dim=1, keepdim=True)
+        ], dim=1)
+
+        # 空间加权（5x5卷积生成单通道注意力）
+        sa = self.spatial_attn(stats) * ca
+        return sa
+
+
+class CNNClassifier(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 32, 3, stride=2, padding=1, bias=False),
+            LiteDualAttention(32),
+            nn.BatchNorm2d(32),
+            nn.Hardswish(inplace=True),
+
+            nn.Conv2d(32, 48, 3, stride=2, padding=1, bias=False),
+            LiteDualAttention(48),
+            nn.BatchNorm2d(48),
+            nn.Hardswish(inplace=True),
+
+            nn.Conv2d(48, 64, 3, stride=2, padding=1, bias=False),
+            LiteDualAttention(64),
+            nn.BatchNorm2d(64),
+            nn.Hardswish(inplace=True),
+
+            nn.Conv2d(64, 96, 3, padding=2, dilation=2, bias=False),
+            LiteDualAttention(96),
+            nn.BatchNorm2d(96),
+            nn.Hardswish(inplace=True)
+        )
+
+        self.classifier = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.Linear(96, 256),
+            nn.Hardswish(inplace=True),
+            nn.Dropout(0.3),
+            nn.Linear(256, num_classes)
+        )
+
+    def forward(self, x):
+        return self.classifier(self.features(x))