31 lines
1.3 KiB
Python
31 lines
1.3 KiB
Python
import torch
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import torch.nn as nn
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class RotationInvariantNet(nn.Module):
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"""轻量级旋转不变特征提取网络"""
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def __init__(self, input_channels=1):
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super().__init__()
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self.cnn = nn.Sequential(
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# 基础卷积层
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nn.Conv2d(input_channels, 32, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.MaxPool2d(2), # 下采样
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nn.Conv2d(32, 64, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.Conv2d(64, 64, kernel_size=3, stride=2), # 更大感受野
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nn.AdaptiveAvgPool2d((4,4)), # 全局池化获取全局特征，调整输出尺寸为4x4
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nn.Flatten(), # 展平为一维向量
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nn.Linear(64*16, 128) # 增加全连接层以降低维度到128
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)
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def forward(self, x):
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return self.cnn(x)
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def get_rotational_features(model, input_image):
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"""计算输入图像所有旋转角度的特征平均值"""
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rotations = [0, 90, 180, 270]
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features_list = []
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for angle in rotations:
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rotated_img = torch.rot90(input_image, k=angle//90, dims=[2,3])
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feat = model(rotated_img.unsqueeze(0))
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features_list.append(feat)
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return torch.mean(torch.stack(features_list), dim=0).detach().numpy() |