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/convap.py

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+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+
+class ConvAP(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):
+        super(ConvAP, self).__init__()
+        self.channel_pool = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=True)
+        self.AAP = nn.AdaptiveAvgPool2d((s1, s2))
+
+    def forward(self, x):
+        #
+        x, t = x #dinov2专属
+        # x = self.channel_pool(x)
+        x = self.AAP(x)
+        x = F.normalize(x.flatten(1), p=2, dim=1)
+        return x
+    
+
+if __name__ == '__main__':
+    x = torch.randn(4, 2048, 10, 10)
+    m = ConvAP(2048, 512)
+    r = m(x)
+    print(r.shape)