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/transformer/__init__.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from romatch.utils.utils import get_grid, get_autocast_params
+from .layers.block import Block
+from .layers.attention import MemEffAttention
+from .dinov2 import vit_large, vit_small
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, blocks, hidden_dim, out_dim, is_classifier = False, *args, 
+                 amp = False, pos_enc = True, learned_embeddings = False, embedding_dim = None, amp_dtype = torch.float16, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.blocks = blocks
+        self.to_out = nn.Linear(hidden_dim, out_dim)
+        self.hidden_dim = hidden_dim
+        self.out_dim = out_dim
+        self._scales = [16]
+        self.is_classifier = is_classifier
+        self.amp = amp
+        self.amp_dtype = amp_dtype
+        self.pos_enc = pos_enc
+        self.learned_embeddings = learned_embeddings
+        if self.learned_embeddings:
+            self.learned_pos_embeddings = nn.Parameter(nn.init.kaiming_normal_(torch.empty((1, hidden_dim, embedding_dim, embedding_dim))))
+
+    def scales(self):
+        return self._scales.copy()
+
+    def forward(self, gp_posterior, features, old_stuff, new_scale):
+        autocast_device, autocast_enabled, autocast_dtype = get_autocast_params(gp_posterior.device, enabled=self.amp, dtype=self.amp_dtype)
+        with torch.autocast(autocast_device, enabled=autocast_enabled, dtype = autocast_dtype):
+            B,C,H,W = gp_posterior.shape
+            x = torch.cat((gp_posterior, features), dim = 1)
+            B,C,H,W = x.shape
+            grid = get_grid(B, H, W, x.device).reshape(B,H*W,2)
+            if self.learned_embeddings:
+                pos_enc = F.interpolate(self.learned_pos_embeddings, size = (H,W), mode = 'bilinear', align_corners = False).permute(0,2,3,1).reshape(1,H*W,C)
+            else:
+                pos_enc = 0
+            tokens = x.reshape(B,C,H*W).permute(0,2,1) + pos_enc
+            z = self.blocks(tokens)
+            out = self.to_out(z)
+            out = out.permute(0,2,1).reshape(B, self.out_dim, H, W)
+            warp, certainty = out[:, :-1], out[:, -1:]
+            return warp, certainty, None
+
+