initial commit

models/rord.py

@@ -0,0 +1,47 @@
+import torch
+import torch.nn as nn
+from torchvision import models
+
+class RoRD(nn.Module):
+    def __init__(self):
+        super(RoRD, self).__init__()
+        # 检测骨干网络：VGG-16 直到 relu5_3（层 0 到 29）
+        self.backbone_det = models.vgg16(pretrained=True).features[:30]
+        # 描述骨干网络：VGG-16 直到 relu4_3（层 0 到 22）
+        self.backbone_desc = models.vgg16(pretrained=True).features[:23]
+        
+        # 检测头：输出关键点概率图
+        self.detection_head = nn.Sequential(
+            nn.Conv2d(512, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 1, kernel_size=1),
+            nn.Sigmoid()
+        )
+        
+        # 普通描述子头（D2-Net 风格）
+        self.descriptor_head_vanilla = nn.Sequential(
+            nn.Conv2d(512, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 128, kernel_size=1),
+            nn.InstanceNorm2d(128)
+        )
+        
+        # RoRD 描述子头（旋转鲁棒）
+        self.descriptor_head_rord = nn.Sequential(
+            nn.Conv2d(512, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 128, kernel_size=1),
+            nn.InstanceNorm2d(128)
+        )
+
+    def forward(self, x):
+        # 检测分支
+        features_det = self.backbone_det(x)
+        detection = self.detection_head(features_det)
+        
+        # 描述分支
+        features_desc = self.backbone_desc(x)
+        desc_vanilla = self.descriptor_head_vanilla(features_desc)
+        desc_rord = self.descriptor_head_rord(features_desc)
+        
+        return detection, desc_vanilla, desc_rord