第二次大修
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@@ -1,16 +1,25 @@
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# models/rord.py
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import torch
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import torch.nn as nn
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from torchvision import models
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class RoRD(nn.Module):
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def __init__(self):
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"""
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修复后的 RoRD 模型。
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- 实现了共享骨干网络,以提高计算效率和减少内存占用。
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- 移除了冗余的 descriptor_head_vanilla。
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"""
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super(RoRD, self).__init__()
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# 检测骨干网络:VGG-16 直到 relu5_3(层 0 到 29)
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self.backbone_det = models.vgg16(pretrained=True).features[:30]
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# 描述骨干网络:VGG-16 直到 relu4_3(层 0 到 22)
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self.backbone_desc = models.vgg16(pretrained=True).features[:23]
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# 检测头:输出关键点概率图
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vgg16_features = models.vgg16(pretrained=True).features
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# 共享骨干网络
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self.slice1 = vgg16_features[:23] # 到 relu4_3
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self.slice2 = vgg16_features[23:30] # 从 relu4_3 到 relu5_3
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# 检测头
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self.detection_head = nn.Sequential(
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nn.Conv2d(512, 256, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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@@ -18,16 +27,8 @@ class RoRD(nn.Module):
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nn.Sigmoid()
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)
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# 普通描述子头(D2-Net 风格)
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self.descriptor_head_vanilla = nn.Sequential(
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nn.Conv2d(512, 256, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, 128, kernel_size=1),
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nn.InstanceNorm2d(128)
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)
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# RoRD 描述子头(旋转鲁棒)
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self.descriptor_head_rord = nn.Sequential(
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# 描述子头
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self.descriptor_head = nn.Sequential(
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nn.Conv2d(512, 256, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, 128, kernel_size=1),
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@@ -35,13 +36,14 @@ class RoRD(nn.Module):
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)
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def forward(self, x):
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# 检测分支
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features_det = self.backbone_det(x)
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detection = self.detection_head(features_det)
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# 共享特征提取
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features_shared = self.slice1(x)
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# 描述分支
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features_desc = self.backbone_desc(x)
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desc_vanilla = self.descriptor_head_vanilla(features_desc)
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desc_rord = self.descriptor_head_rord(features_desc)
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# 描述子分支
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descriptors = self.descriptor_head(features_shared)
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return detection, desc_vanilla, desc_rord
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# 检测器分支
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features_det = self.slice2(features_shared)
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detection_map = self.detection_head(features_det)
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return detection_map, descriptors
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