增加角度显示计算，优化CNN架构

models/rotation_cnn.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 
 class RotationInvariantNet(nn.Module):
     """轻量级旋转不变特征提取网络"""
-    def __init__(self, input_channels=1, num_features=64):
+    def __init__(self, input_channels=1):
         super().__init__()
         self.cnn = nn.Sequential(
             # 基础卷积层
@@ -12,13 +12,14 @@ class RotationInvariantNet(nn.Module):
             nn.MaxPool2d(2),                     # 下采样
             nn.Conv2d(32, 64, kernel_size=3, padding=1),
             nn.ReLU(),
-            nn.AdaptiveAvgPool2d((1,1))          # 全局池化获取全局特征
+            nn.Conv2d(64, 64, kernel_size=3, stride=2),  # 更大感受野
+            nn.AdaptiveAvgPool2d((4,4)),         # 全局池化获取全局特征，调整输出尺寸为4x4
+            nn.Flatten(),                        # 展平为一维向量
+            nn.Linear(64*16, 128)                # 增加全连接层以降低维度到128
         )
-        
+
     def forward(self, x):
-        features = self.cnn(x)
-        return torch.flatten(features, 1)         # 展平为特征向量
-    
+        return self.cnn(x)
 def get_rotational_features(model, input_image):
     """计算输入图像所有旋转角度的特征平均值"""
     rotations = [0, 90, 180, 270]