initial commit

models/rotation_cnn.py

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+import torch
+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):
+        super().__init__()
+        self.cnn = nn.Sequential(
+            # 基础卷积层
+            nn.Conv2d(input_channels, 32, kernel_size=3, padding=1),
+            nn.ReLU(),
+            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
+        )
+        
+    def forward(self, x):
+        features = self.cnn(x)
+        return torch.flatten(features, 1)         # 展平为特征向量
+    
+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]
+    features_list = []
+    for angle in rotations:
+        rotated_img = torch.rot90(input_image, k=angle//90, dims=[2,3])
+        feat = model(rotated_img.unsqueeze(0))
+        features_list.append(feat)
+    return torch.mean(torch.stack(features_list), dim=0).detach().numpy()