add torch.hub support

cotracker/models/build_cotracker.py

@@ -12,6 +12,8 @@ from cotracker.models.core.cotracker.cotracker import CoTracker
 def build_cotracker(
     checkpoint: str,
 ):
+    if checkpoint is None:
+        return build_cotracker_stride_4_wind_8()
     model_name = checkpoint.split("/")[-1].split(".")[0]
     if model_name == "cotracker_stride_4_wind_8":
         return build_cotracker_stride_4_wind_8(checkpoint=checkpoint)

cotracker/predictor.py

@@ -25,7 +25,6 @@ class CoTrackerPredictor(torch.nn.Module):
         model = build_cotracker(checkpoint)
 
         self.model = model
-        self.model.to("cuda")
         self.model.eval()
 
     @torch.no_grad()
@@ -72,7 +71,7 @@ class CoTrackerPredictor(torch.nn.Module):
         grid_width = W // grid_step
         grid_height = H // grid_step
         tracks = visibilities = None
-        grid_pts = torch.zeros((1, grid_width * grid_height, 3)).to("cuda")
+        grid_pts = torch.zeros((1, grid_width * grid_height, 3)).to(video.device)
         grid_pts[0, :, 0] = grid_query_frame
         for offset in tqdm(range(grid_step * grid_step)):
             ox = offset % grid_step
@@ -107,10 +106,8 @@ class CoTrackerPredictor(torch.nn.Module):
         assert B == 1
 
         video = video.reshape(B * T, C, H, W)
-        video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear").cuda()
+        video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear")
-        video = video.reshape(
+        video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1])
-            B, T, 3, self.interp_shape[0], self.interp_shape[1]
-        ).cuda()
 
         if queries is not None:
             queries = queries.clone()

demo.py

@@ -55,6 +55,11 @@ if __name__ == "__main__":
     segm_mask = torch.from_numpy(segm_mask)[None, None]
 
     model = CoTrackerPredictor(checkpoint=args.checkpoint)
+    if torch.cuda.is_available():
+        model = model.cuda()
+        video = video.cuda()
+    else:
+        print("CUDA is not available!")
 
     pred_tracks, pred_visibility = model(
         video,

hubconf.py

@@ -0,0 +1,32 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+dependencies = ["torch", "einops", "timm", "tqdm"]
+
+_COTRACKER_URL = (
+    "https://dl.fbaipublicfiles.com/cotracker/cotracker_stride_4_wind_8.pth"
+)
+
+
+def _make_cotracker_predictor(*, pretrained: bool = True, **kwargs):
+    from cotracker.predictor import CoTrackerPredictor
+
+    predictor = CoTrackerPredictor(checkpoint=None)
+    if pretrained:
+        state_dict = torch.hub.load_state_dict_from_url(
+            _COTRACKER_URL, map_location="cpu"
+        )
+        predictor.model.load_state_dict(state_dict)
+    return predictor
+
+
+def cotracker_w8(*, pretrained: bool = True, **kwargs):
+    """
+    CoTracker model with stride 4 and window length 8. (The main model from the paper)
+    """
+    return _make_cotracker_predictor(pretrained=pretrained, **kwargs)