Initial import: World-UAV prepro

Add dataloaders (v1/v2), analysis scripts, and documentation for working with UAV-GeoLoc (World-UAV).

Co-authored-by: Cursor <cursoragent@cursor.com>

GeoLoc-UAV-main/models/aggregators/multiconvap.py

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+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+from models.aggregators.LPN import get_part_pool
+
+class L2Norm(nn.Module):
+    def __init__(self, dim=1):
+        super().__init__()
+        self.dim = dim
+    def forward(self, x):
+        return F.normalize(x, p=2, dim=self.dim)
+
+class GeMPool(nn.Module):
+    """Implementation of GeM as in https://github.com/filipradenovic/cnnimageretrieval-pytorch
+    we add flatten and norm so that we can use it as one aggregation layer.
+    """
+    def __init__(self, p=3, eps=1e-6):
+        super().__init__()
+        self.p = nn.Parameter(torch.ones(1)*p)
+        self.eps = eps
+
+    def forward(self, x):
+        x = F.avg_pool2d(x.clamp(min=self.eps).pow(self.p), (x.size(-2), x.size(-1))).pow(1./self.p)
+        x = x.flatten(1)
+        return F.normalize(x, p=2, dim=1)
+
+class MulConvAP(nn.Module):
+    """Implementation of ConvAP as of https://arxiv.org/pdf/2210.10239.pdf
+
+    Args:
+        in_channels (int): number of channels in the input of ConvAP
+        out_channels (int, optional): number of channels that ConvAP outputs. Defaults to 512.
+        s1 (int, optional): spatial height of the adaptive average pooling. Defaults to 2.
+        s2 (int, optional): spatial width of the adaptive average pooling. Defaults to 2.
+    """
+    def __init__(self, in_channels, out_channels=512, s1=2, s2=2, LPN=False):
+        super(MulConvAP, self).__init__()
+        self.out_channels = out_channels
+        self.channel_pool_1 = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=1, bias=True)
+        self.channel_pool_3 = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, padding=1,bias=True)
+        self.channel_pool_5 = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=5, padding=2,bias=True)
+      
+        # self.AAP = nn.AdaptiveAvgPool2d((s1, s2))
+        self.AAP = nn.Sequential(L2Norm(), GeMPool())
+
+        # using LPN
+        if LPN == True:
+            self.LPN = True
+        else:
+            self.LPN = False
+    def forward(self, x):
+
+        if self.LPN == False:
+            # x, t = x #dinov2专属
+            x1 = self.channel_pool_1(x)
+            x3 = self.channel_pool_3(x)
+            x5 = self.channel_pool_5(x)
+
+            x1 = self.AAP(x1)
+            x3 = self.AAP(x3)
+            x5 = self.AAP(x5)
+
+            x = [i for i in [x1, x3, x5]]
+            x = torch.cat(x,dim=1)
+
+            # x = self.AAP(x)
+            x = F.normalize(x.flatten(1), p=2, dim=1)
+            return x
+        else:
+            partition_feature = get_part_pool(x)
+            partition_feature_list = []
+            for one_feature in partition_feature:
+                x1 = self.channel_pool_1(one_feature)
+                x3 = self.channel_pool_3(one_feature)
+                x5 = self.channel_pool_5(one_feature)
+
+                x1 = self.AAP(x1)
+                x3 = self.AAP(x3)
+                x5 = self.AAP(x5)
+
+                x = [i for i in [x1, x3, x5]]
+                x = torch.cat(x,dim=1)
+
+                x = F.normalize(x.flatten(1), p=2, dim=1)
+                partition_feature_list.append(x)
+            # partition_feature_tensor = torch.stack(partition_feature_list, dim=2).reshape(x.shape[0], -1)
+            
+            return partition_feature_list
+
+
+if __name__ == '__main__':
+    x = torch.randn(4, 2048, 10, 10)
+    # m = ConvAP(2048, 512)
+    # r = m(x)
+    # print(r.shape)