fix_md

.gitignore

@@ -48,4 +48,5 @@ config/fix_2d.yaml
 models/**/build
 models/**/dist
 
-temp_local
+temp_local
+examples/results

app.py

@@ -26,6 +26,9 @@ import logging
 from concurrent.futures import ThreadPoolExecutor
 import atexit
 import uuid
+from models.SpaTrackV2.models.vggt4track.models.vggt_moe import VGGT4Track
+from models.SpaTrackV2.models.vggt4track.utils.load_fn import preprocess_image
+from models.SpaTrackV2.models.predictor import Predictor
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -78,20 +81,15 @@ def create_user_temp_dir():
     return temp_dir
 
 from huggingface_hub import hf_hub_download
-# init the model
-os.environ["VGGT_DIR"] = hf_hub_download("Yuxihenry/SpatialTrackerCkpts", "spatrack_front.pth") #, force_download=True)
 
-if os.environ.get("VGGT_DIR", None) is not None:
-    from models.vggt.vggt.models.vggt_moe import VGGT4Track
-    from models.vggt.vggt.utils.load_fn import preprocess_image
-    vggt_model = VGGT4Track()
-    vggt_model.load_state_dict(torch.load(os.environ.get("VGGT_DIR")), strict=False)
-    vggt_model.eval()
-    vggt_model = vggt_model.to("cuda")
+vggt4track_model = VGGT4Track.from_pretrained("Yuxihenry/SpatialTrackerV2_Front")
+vggt4track_model.eval()
+vggt4track_model = vggt4track_model.to("cuda")
 
 # Global model initialization
 print("🚀 Initializing local models...")
-tracker_model, _ = get_tracker_predictor(".", vo_points=756)
+tracker_model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Offline")
+tracker_model.eval()
 predictor = get_sam_predictor()
 print("✅ Models loaded successfully!")
 
@@ -129,7 +127,8 @@ def gpu_run_tracker(tracker_model_arg, tracker_viser_arg, temp_dir, video_name,
         print("Initializing tracker models inside GPU function...")
         out_dir = os.path.join(temp_dir, "results")
         os.makedirs(out_dir, exist_ok=True)
-        tracker_model_arg, tracker_viser_arg = get_tracker_predictor(out_dir, vo_points=vo_points, tracker_model=tracker_model)
+        tracker_model_arg, tracker_viser_arg = get_tracker_predictor(out_dir, vo_points=vo_points,
+                                                                         tracker_model=tracker_model.cuda())
     
     # Setup paths
     video_path = os.path.join(temp_dir, f"{video_name}.mp4")
@@ -159,25 +158,23 @@ def gpu_run_tracker(tracker_model_arg, tracker_viser_arg, temp_dir, video_name,
     data_npz_load = {}
 
     # run vggt 
-    if os.environ.get("VGGT_DIR", None) is not None:
-        # process the image tensor
-        video_tensor = preprocess_image(video_tensor)[None]
-        with torch.no_grad():
-            with torch.cuda.amp.autocast(dtype=torch.bfloat16):
-                # Predict attributes including cameras, depth maps, and point maps.
-                predictions = vggt_model(video_tensor.cuda()/255)
-                extrinsic, intrinsic = predictions["poses_pred"], predictions["intrs"]
-                depth_map, depth_conf = predictions["points_map"][..., 2], predictions["unc_metric"]
-        
-        depth_tensor = depth_map.squeeze().cpu().numpy()
-        extrs = np.eye(4)[None].repeat(len(depth_tensor), axis=0)
-        extrs = extrinsic.squeeze().cpu().numpy()
-        intrs = intrinsic.squeeze().cpu().numpy()
-        video_tensor = video_tensor.squeeze()
-        #NOTE: 20% of the depth is not reliable
-        # threshold = depth_conf.squeeze()[0].view(-1).quantile(0.6).item()
-        unc_metric = depth_conf.squeeze().cpu().numpy() > 0.5
+    # process the image tensor
+    video_tensor = preprocess_image(video_tensor)[None]
+    with torch.no_grad():
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            # Predict attributes including cameras, depth maps, and point maps.
+            predictions = vggt4track_model(video_tensor.cuda()/255)
+            extrinsic, intrinsic = predictions["poses_pred"], predictions["intrs"]
+            depth_map, depth_conf = predictions["points_map"][..., 2], predictions["unc_metric"]
 
+    depth_tensor = depth_map.squeeze().cpu().numpy()
+    extrs = np.eye(4)[None].repeat(len(depth_tensor), axis=0)
+    extrs = extrinsic.squeeze().cpu().numpy()
+    intrs = intrinsic.squeeze().cpu().numpy()
+    video_tensor = video_tensor.squeeze()
+    #NOTE: 20% of the depth is not reliable
+    # threshold = depth_conf.squeeze()[0].view(-1).quantile(0.6).item()
+    unc_metric = depth_conf.squeeze().cpu().numpy() > 0.5
     # Load and process mask
     if os.path.exists(mask_path):
         mask = cv2.imread(mask_path)
@@ -199,7 +196,6 @@ def gpu_run_tracker(tracker_model_arg, tracker_viser_arg, temp_dir, video_name,
     
     query_xyt = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2)[0].cpu().numpy()
     print(f"Query points shape: {query_xyt.shape}")
-    
     # Run model inference
     with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
         (
@@ -210,8 +206,8 @@ def gpu_run_tracker(tracker_model_arg, tracker_viser_arg, temp_dir, video_name,
                             queries=query_xyt,
                             fps=1, full_point=False, iters_track=4,
                             query_no_BA=True, fixed_cam=False, stage=1, unc_metric=unc_metric,
-                            support_frame=len(video_tensor)-1, replace_ratio=0.2) 
-        
+                            support_frame=len(video_tensor)-1, replace_ratio=0.2)
+
         # Resize results to avoid large I/O
         max_size = 224
         h, w = video.shape[2:]

inference.py

@@ -17,16 +17,14 @@ import glob
 from rich import print
 import argparse
 import decord
-from models.vggt.vggt.models.vggt_moe import VGGT4Track
-from models.vggt.vggt.utils.load_fn import preprocess_image
-from models.vggt.vggt.utils.pose_enc import pose_encoding_to_extri_intri
-from huggingface_hub import hf_hub_download
+from models.SpaTrackV2.models.vggt4track.models.vggt_moe import VGGT4Track
+from models.SpaTrackV2.models.vggt4track.utils.load_fn import preprocess_image
+from models.SpaTrackV2.models.vggt4track.utils.pose_enc import pose_encoding_to_extri_intri
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--cfg_dir", type=str, default="config/magic_infer_offline.yaml")
+    parser.add_argument("--track_mode", type=str, default="offline")
     parser.add_argument("--data_type", type=str, default="RGBD")
-    parser.add_argument("--VGGT", action="store_true")
     parser.add_argument("--data_dir", type=str, default="assets/example0")
     parser.add_argument("--video_name", type=str, default="snowboard")
     parser.add_argument("--grid_size", type=int, default=10)
@@ -36,20 +34,14 @@ def parse_args():
 
 if __name__ == "__main__":
     args = parse_args()
-    cfg_dir = args.cfg_dir
     out_dir = args.data_dir + "/results"
     # fps
     fps = int(args.fps)
     mask_dir = args.data_dir + f"/{args.video_name}.png"
     
-    os.environ["VGGT_DIR"] = hf_hub_download("Yuxihenry/SpatialTrackerCkpts",
-                                                         "spatrack_front.pth") #, force_download=True)
-    VGGT_DIR = os.environ["VGGT_DIR"]
-    assert os.path.exists(VGGT_DIR), f"VGGT_DIR {VGGT_DIR} does not exist"
-    front_track = VGGT4Track()
-    front_track.load_state_dict(torch.load(VGGT_DIR), strict=False)
-    front_track.eval()
-    front_track = front_track.to("cuda")
+    vggt4track_model = VGGT4Track.from_pretrained("Yuxihenry/SpatialTrackerV2_Front")
+    vggt4track_model.eval()
+    vggt4track_model = vggt4track_model.to("cuda")
 
     if args.data_type == "RGBD":
         npz_dir = args.data_dir + f"/{args.video_name}.npz"
@@ -76,7 +68,7 @@ if __name__ == "__main__":
         with torch.no_grad():
             with torch.cuda.amp.autocast(dtype=torch.bfloat16):
                 # Predict attributes including cameras, depth maps, and point maps.
-                predictions = front_track(video_tensor.cuda()/255)
+                predictions = vggt4track_model(video_tensor.cuda()/255)
                 extrinsic, intrinsic = predictions["poses_pred"], predictions["intrs"]
                 depth_map, depth_conf = predictions["points_map"][..., 2], predictions["unc_metric"]
         
@@ -103,21 +95,23 @@ if __name__ == "__main__":
     viz = True
     os.makedirs(out_dir, exist_ok=True)
         
-    with open(cfg_dir, "r") as f:
-        cfg = yaml.load(f, Loader=yaml.FullLoader)
-    cfg = easydict.EasyDict(cfg)
-    cfg.out_dir = out_dir
-    cfg.model.track_num = args.vo_points
-    print(f"Downloading model from HuggingFace: {cfg.ckpts}")
-    checkpoint_path = hf_hub_download(
-        repo_id=cfg.ckpts,
-        repo_type="model",
-        filename="SpaTrack3_offline.pth"
-    )
-    model = Predictor.from_pretrained(checkpoint_path, model_cfg=cfg["model"])
+    # with open(cfg_dir, "r") as f:
+    #     cfg = yaml.load(f, Loader=yaml.FullLoader)
+    # cfg = easydict.EasyDict(cfg)
+    # cfg.out_dir = out_dir
+    # cfg.model.track_num = args.vo_points
+    # print(f"Downloading model from HuggingFace: {cfg.ckpts}")
+    if args.track_mode == "offline":
+        model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Offline")
+    else:
+        model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Online")
+
+    # config the model; the track_num is the number of points in the grid
+    model.spatrack.track_num = args.vo_points
+    
     model.eval()
     model.to("cuda")
-    viser = Visualizer(save_dir=cfg.out_dir, grayscale=True, 
+    viser = Visualizer(save_dir=out_dir, grayscale=True, 
                      fps=10, pad_value=0, tracks_leave_trace=5)
     
     grid_size = args.grid_size

models/SpaTrackV2/models/SpaTrack.py

@@ -66,15 +66,15 @@ class SpaTrack2(nn.Module, PyTorchModelHubMixin):
 
         # Tracker model
         self.Track3D = TrackRefiner3D(Track_cfg)
-        track_base_ckpt_dir = Track_cfg.base_ckpt
+        track_base_ckpt_dir = Track_cfg["base_ckpt"]
         if os.path.exists(track_base_ckpt_dir):
             track_pretrain = torch.load(track_base_ckpt_dir)
             self.Track3D.load_state_dict(track_pretrain, strict=False)
         
         # wrap the function of make lora trainable
         self.make_paras_trainable = partial(self.make_paras_trainable,
-                                            mode=ft_cfg.mode,
-                                            paras_name=ft_cfg.paras_name)
+                                            mode=ft_cfg["mode"],
+                                            paras_name=ft_cfg["paras_name"])
         self.track_num = track_num
 
     def make_paras_trainable(self, mode: str = 'fix', paras_name: List[str] = []):
@@ -149,7 +149,7 @@ class SpaTrack2(nn.Module, PyTorchModelHubMixin):
     ):  
         # step 1 allocate the query points on the grid
         T, C, H, W = video.shape
-        
+
         if annots_train is not None:
             vis_gt = annots_train["vis"]
             _, _, N = vis_gt.shape
@@ -300,39 +300,6 @@ class SpaTrack2(nn.Module, PyTorchModelHubMixin):
                                 **kwargs, annots=annots)
             if self.training:
                 loss += out["loss"].squeeze()
-            # from models.SpaTrackV2.utils.visualizer import Visualizer
-            # vis_track = Visualizer(grayscale=False,
-            #                     fps=10, pad_value=50, tracks_leave_trace=0)
-            # vis_track.visualize(video=segment,
-            #                         tracks=out["traj_est"][...,:2], 
-            #                         visibility=out["vis_est"],
-            #                         save_video=True) 
-            # # visualize 4d      
-            # import os, json
-            # import os.path as osp
-            # viser4d_dir = os.path.join("viser_4d_results")
-            # os.makedirs(viser4d_dir, exist_ok=True)
-            # depth_est = annots["depth_gt"][0]
-            # unc_metric = out["unc_metric"]
-            # mask = (unc_metric > 0.5).squeeze(1)
-            # # pose_est = out["poses_pred"].squeeze(0)
-            # pose_est = annots["traj_mat"][0]
-            # rgb_tracks = out["rgb_tracks"].squeeze(0)
-            # intrinsics = out["intrs"].squeeze(0)
-            # for i_k in range(out["depth"].shape[0]):
-            #     img_i = out["imgs_raw"][0][i_k].permute(1, 2, 0).cpu().numpy()
-            #     img_i = cv2.cvtColor(img_i, cv2.COLOR_BGR2RGB)
-            #     cv2.imwrite(osp.join(viser4d_dir, f'frame_{i_k:04d}.png'), img_i)
-            #     if stage == 1:
-            #         depth = depth_est[i_k].squeeze().cpu().numpy()
-            #         np.save(osp.join(viser4d_dir, f'frame_{i_k:04d}.npy'), depth)
-            #     else:
-            #         point_map_vis = out["points_map"][i_k].cpu().numpy()
-            #         np.save(osp.join(viser4d_dir, f'point_{i_k:04d}.npy'), point_map_vis)
-            # np.save(os.path.join(viser4d_dir, f'intrinsics.npy'), intrinsics.cpu().numpy())
-            # np.save(os.path.join(viser4d_dir, f'extrinsics.npy'), pose_est.cpu().numpy())
-            # np.save(os.path.join(viser4d_dir, f'conf.npy'), mask.float().cpu().numpy())
-            # np.save(os.path.join(viser4d_dir, f'colored_track3d.npy'), rgb_tracks.cpu().numpy())
 
             queries_len = len(queries_new)
             # update the track3d and track2d
@@ -724,40 +691,3 @@ class SpaTrack2(nn.Module, PyTorchModelHubMixin):
                     }
                 
         return ret
-
-
-
-
-# three stages of training
-
-# stage 1: 
-# gt depth and intrinsics synthetic (includes Dynamic Replica, Kubric, Pointodyssey, Vkitti, TartanAir and Indoor() ) Motion Patern (tapvid3d)
-# Tracking and Pose as well -> based on gt depth and intrinsics
-# (Finished) -> (megasam + base model) vs. tapip3d.     (use depth from megasam or pose, which keep the same setting as tapip3d.)
-
-# stage 2: fixed 3D tracking 
-# Joint depth refiner 
-# input depth from whatever + rgb -> temporal module + scale and shift token -> coarse alignment -> scale and shift 
-# estimate the 3D tracks -> 3D tracks combine with pointmap -> update for pointmap (iteratively) -> residual map B T 3 H W 
-# ongoing two days
-
-# stage 3: train multi windows by propagation 
-# 4 frames overlapped -> train on 64 -> fozen image encoder and finetuning the transformer  (learnable parameters pretty small) 
-
-# types of scenarioes:
-# 1. auto driving (waymo open dataset)
-# 2. robot
-# 3. internet ego video
-
-
-
-# Iterative Transformer -- Solver -- General Neural MegaSAM + Tracks
-# Update Variables:
-# 1. 3D tracks B T N 3  xyz.
-# 2. 2D tracks B T N 2  x y.
-# 3. Dynamic Mask B T H W.
-# 4. Camera Pose B T 4 4.
-# 5. Video Depth.
-
-# (RGB, RGBD, RGBD+Pose) x (Static, Dynamic)
-# Campatiablity by product. 

models/SpaTrackV2/models/predictor.py

@@ -16,82 +16,21 @@ from typing import Union, Optional
 import cv2
 import os
 import decord
+from huggingface_hub import PyTorchModelHubMixin  # used for model hub
 
-class Predictor(torch.nn.Module):
+class Predictor(torch.nn.Module, PyTorchModelHubMixin):
     def __init__(self, args=None):
         super().__init__()
         self.args = args
         self.spatrack = SpaTrack2(loggers=[None, None, None], **args)
-        self.S_wind = args.Track_cfg.s_wind
-        self.overlap = args.Track_cfg.overlap
+        self.S_wind = args["Track_cfg"]["s_wind"]
+        self.overlap = args["Track_cfg"]["overlap"]
 
     def to(self, device: Union[str, torch.device]):
         self.spatrack.to(device)
         if self.spatrack.base_model is not None:
             self.spatrack.base_model.to(device)
 
-    @classmethod
-    def from_pretrained(
-        cls,
-        pretrained_model_name_or_path: Union[str, Path],
-        *,
-        force_download: bool = False,
-        cache_dir: Optional[str] = None,
-        device: Optional[Union[str, torch.device]] = None,
-        model_cfg: Optional[dict] = None,
-        **kwargs,
-    ) -> "SpaTrack2":
-        """
-        Load a pretrained model from a local file or a remote repository.
-
-        Args:
-            pretrained_model_name_or_path (str or Path):
-                - Path to a local model file (e.g., `./model.pth`).
-                - HuggingFace Hub model ID (e.g., `username/model-name`).
-            force_download (bool, optional):
-                Whether to force re-download even if cached. Default: False.
-            cache_dir (str, optional):
-                Custom cache directory. Default: None (use default cache).
-            device (str or torch.device, optional):
-                Target device (e.g., "cuda", "cpu"). Default: None (keep original).
-            **kwargs:
-                Additional config overrides.
-
-        Returns:
-            SpaTrack2: Loaded pretrained model.
-        """
-        # (1) check the path is local or remote
-        if isinstance(pretrained_model_name_or_path, Path):
-            model_path = str(pretrained_model_name_or_path)
-        else:
-            model_path = pretrained_model_name_or_path
-        # (2) if the path is remote, download it
-        if not os.path.exists(model_path):
-            raise NotImplementedError("Remote download not implemented yet. Use a local path.")
-        # (3) load the model weights
-
-        state_dict = torch.load(model_path, map_location="cpu")
-        # (4) initialize the model (can load config.json if exists)
-        config_path = os.path.join(os.path.dirname(model_path), "config.json")
-        config = {}
-        if os.path.exists(config_path):
-            import json
-            with open(config_path, "r") as f:
-                config.update(json.load(f))
-        config.update(kwargs)  # allow override the config
-        if model_cfg is not None:
-            config = model_cfg
-        model = cls(config)
-        if "model" in state_dict:
-            model.spatrack.load_state_dict(state_dict["model"], strict=False)
-        else:
-            model.spatrack.load_state_dict(state_dict, strict=False)
-        # (5) device management
-        if device is not None:
-            model.to(device)
-
-        return model
-
     def forward(self, video: str|torch.Tensor|np.ndarray,
                  depth: str|torch.Tensor|np.ndarray=None,
                  unc_metric: str|torch.Tensor|np.ndarray=None,
@@ -146,7 +85,6 @@ class Predictor(torch.nn.Module):
                                                 window_len=self.S_wind, overlap_len=self.overlap, track2d_gt=track2d_gt, full_point=full_point, iters_track=iters_track,
                                                 fixed_cam=fixed_cam, query_no_BA=query_no_BA, stage=stage, support_frame=support_frame, replace_ratio=replace_ratio) + (video[:T_],)
             
-        
         return ret
 
     

models/SpaTrackV2/models/tracker3D/TrackRefiner.py

@@ -30,7 +30,7 @@ from models.SpaTrackV2.models.tracker3D.delta_utils.upsample_transformer import
 class TrackRefiner3D(CoTrackerThreeOffline):
 
     def __init__(self, args=None):
-        super().__init__(**args.base)
+        super().__init__(**args["base"])
         
         """
         This is 3D warpper from cotracker, which load the cotracker pretrain and
@@ -46,15 +46,7 @@ class TrackRefiner3D(CoTrackerThreeOffline):
         self.proj_xyz_embed = Mlp(in_features=1210+50, hidden_features=1110, out_features=1110)
         # get the anchor point's embedding, and init the pts refiner
         update_pts = True
-        # self.corr_transformer = nn.ModuleList([
-        #     CorrPointformer(
-        #         dim=128,
-        #         num_heads=8,
-        #         head_dim=128 // 8,
-        #         mlp_ratio=4.0,
-        #     )
-        #     for _ in range(self.corr_levels)
-        # ])
+
         self.corr_transformer = nn.ModuleList([
             CorrPointformer(
                 dim=128,
@@ -67,29 +59,11 @@ class TrackRefiner3D(CoTrackerThreeOffline):
         self.fnet = BasicEncoder(input_dim=3, 
                                  output_dim=self.latent_dim, stride=self.stride)
         self.corr3d_radius = 3
-
-        if args.stablizer:   
-            self.scale_shift_tokens = nn.Parameter(torch.randn(1, 2, self.latent_dim, requires_grad=True))
-            self.upsample_kernel_size = 5
-            self.residual_embedding = nn.Parameter(torch.randn(
-                                            self.latent_dim, self.model_resolution[0]//16, 
-                                            self.model_resolution[1]//16, requires_grad=True))
-            self.dense_mlp = nn.Conv2d(2*self.latent_dim+63, self.latent_dim, kernel_size=1, stride=1, padding=0)
-            self.upsample_factor = 4
-            self.upsample_transformer = UpsampleTransformerAlibi(
-                kernel_size=self.upsample_kernel_size, # kernel_size=3, # 
-                stride=self.stride,
-                latent_dim=self.latent_dim,
-                num_attn_blocks=2,
-                upsample_factor=4,
-            )
-        else:
-            self.update_pointmap = None
         
-        self.mode = args.mode
+        self.mode = args["mode"]
         if self.mode == "online":
-            self.s_wind = args.s_wind
-            self.overlap = args.overlap
+            self.s_wind = args["s_wind"]
+            self.overlap = args["overlap"]
 
     def upsample_with_mask(
         self, inp: torch.Tensor, mask: torch.Tensor
@@ -1061,29 +1035,7 @@ class TrackRefiner3D(CoTrackerThreeOffline):
             vis_est = (vis_est>0.5).float()
             sync_loss += (vis_est.detach()[...,None]*(coords_proj_curr - coords_proj).norm(dim=-1, keepdim=True)*(1-mask_nan[...,None].float())).mean()
             # coords_proj_curr[~mask_nan.view(B*T, N)] = coords_proj.view(B*T, N, 2)[~mask_nan.view(B*T, N)].to(coords_proj_curr.dtype)
-            # if torch.isnan(coords_proj_curr).sum()>0:
-            #     import pdb; pdb.set_trace()
 
-            if False:
-                point_map_resize = point_map.clone().view(B, T, 3, H, W)
-                update_input = torch.cat([point_map_resize, metric_unc.view(B,T,1,H,W)], dim=2)
-                coords_append_resize = coords.clone().detach()
-                coords_append_resize[..., :2] = coords_append_resize[..., :2] * float(self.stride) 
-                update_track_input = self.norm_xyz(cam_pts_est)*5
-                update_track_input = torch.cat([update_track_input, vis_est[...,None]], dim=-1)
-                update_track_input = posenc(update_track_input, min_deg=0, max_deg=12)
-                update = self.update_pointmap.stablizer(update_input, 
-                                                        update_track_input, coords_append_resize)#, imgs=video, vis_track=viser)
-                #NOTE: update the point map
-                point_map_resize += update
-                point_map_refine_out = F.interpolate(point_map_resize.view(B*T, -1, H, W),
-                                                                size=(self.image_size[0].item(), self.image_size[1].item()), mode='nearest')
-                point_map_refine_out = rearrange(point_map_refine_out, '(b t) c h w -> b t c h w', t=T, b=B)
-                point_map_preds.append(self.denorm_xyz(point_map_refine_out))
-                point_map_org = self.denorm_xyz(point_map_refine_out).view(B*T, 3, H_, W_)
-
-            # if torch.isnan(coords).sum()>0:
-            #     import pdb; pdb.set_trace()
             #NOTE: the 2d tracking + unproject depth
             fix_cam_est = coords_append.clone()
             fix_cam_est[...,2] = depth_unproj

models/SpaTrackV2/models/vggt4track/heads/camera_head.py

@@ -11,9 +11,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.vggt.vggt.layers import Mlp
-from models.vggt.vggt.layers.block import Block
-from models.vggt.vggt.heads.head_act import activate_pose
+from models.SpaTrackV2.models.vggt4track.layers import Mlp
+from models.SpaTrackV2.models.vggt4track.layers.block import Block
+from models.SpaTrackV2.models.vggt4track.heads.head_act import activate_pose
 
 
 class CameraHead(nn.Module):

models/SpaTrackV2/models/vggt4track/heads/scale_head.py

@@ -11,9 +11,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.vggt.vggt.layers import Mlp
-from models.vggt.vggt.layers.block import Block
-from models.vggt.vggt.heads.head_act import activate_pose
+from models.SpaTrackV2.models.vggt4track.layers import Mlp
+from models.SpaTrackV2.models.vggt4track.layers.block import Block
+from models.SpaTrackV2.models.vggt4track.heads.head_act import activate_pose
 
 
 class ScaleHead(nn.Module):

models/SpaTrackV2/models/vggt4track/models/aggregator.py

@@ -10,10 +10,10 @@ import torch.nn as nn
 import torch.nn.functional as F
 from typing import Optional, Tuple, Union, List, Dict, Any
 
-from models.vggt.vggt.layers import PatchEmbed
-from models.vggt.vggt.layers.block import Block
-from models.vggt.vggt.layers.rope import RotaryPositionEmbedding2D, PositionGetter
-from models.vggt.vggt.layers.vision_transformer import vit_small, vit_base, vit_large, vit_giant2
+from models.SpaTrackV2.models.vggt4track.layers import PatchEmbed
+from models.SpaTrackV2.models.vggt4track.layers.block import Block
+from models.SpaTrackV2.models.vggt4track.layers.rope import RotaryPositionEmbedding2D, PositionGetter
+from models.SpaTrackV2.models.vggt4track.layers.vision_transformer import vit_small, vit_base, vit_large, vit_giant2
 from torch.utils.checkpoint import checkpoint
 
 logger = logging.getLogger(__name__)

models/SpaTrackV2/models/vggt4track/models/aggregator_front.py

@@ -10,10 +10,10 @@ import torch.nn as nn
 import torch.nn.functional as F
 from typing import Optional, Tuple, Union, List, Dict, Any
 
-from models.vggt.vggt.layers import PatchEmbed
-from models.vggt.vggt.layers.block import Block
-from models.vggt.vggt.layers.rope import RotaryPositionEmbedding2D, PositionGetter
-from models.vggt.vggt.layers.vision_transformer import vit_small, vit_base, vit_large, vit_giant2
+from models.SpaTrackV2.models.vggt4track.layers import PatchEmbed
+from models.SpaTrackV2.models.vggt4track.layers.block import Block
+from models.SpaTrackV2.models.vggt4track.layers.rope import RotaryPositionEmbedding2D, PositionGetter
+from models.SpaTrackV2.models.vggt4track.layers.vision_transformer import vit_small, vit_base, vit_large, vit_giant2
 from torch.utils.checkpoint import checkpoint
 
 logger = logging.getLogger(__name__)

models/SpaTrackV2/models/vggt4track/models/tracker_front.py

@@ -9,12 +9,12 @@ import torch.nn as nn
 from torch.utils.checkpoint import checkpoint
 from huggingface_hub import PyTorchModelHubMixin  # used for model hub
 
-from models.vggt.vggt.models.aggregator_front import Aggregator
-from models.vggt.vggt.heads.camera_head import CameraHead
-from models.vggt.vggt.heads.scale_head import ScaleHead
+from models.SpaTrackV2.models.vggt4track.models.aggregator_front import Aggregator
+from models.SpaTrackV2.models.vggt4track.heads.camera_head import CameraHead
+from models.SpaTrackV2.models.vggt4track.heads.scale_head import ScaleHead
 from einops import rearrange
-from models.vggt.vggt.utils.loss import compute_loss
-from models.vggt.vggt.utils.pose_enc import pose_encoding_to_extri_intri
+from models.SpaTrackV2.utils.loss import compute_loss
+from models.SpaTrackV2.utils.pose_enc import pose_encoding_to_extri_intri
 import torch.nn.functional as F
 
 class FrontTracker(nn.Module, PyTorchModelHubMixin):

models/SpaTrackV2/models/vggt4track/models/vggt_moe.py

@@ -8,14 +8,14 @@ import torch
 import torch.nn as nn
 from huggingface_hub import PyTorchModelHubMixin  # used for model hub
 
-from models.vggt.vggt.models.aggregator import Aggregator
-from models.vggt.vggt.heads.camera_head import CameraHead
-from models.vggt.vggt.heads.dpt_head import DPTHead
-from models.vggt.vggt.heads.track_head import TrackHead
-from models.vggt.vggt.utils.loss import compute_loss
-from models.vggt.vggt.utils.pose_enc import pose_encoding_to_extri_intri
+from models.SpaTrackV2.models.vggt4track.models.aggregator import Aggregator
+from models.SpaTrackV2.models.vggt4track.heads.camera_head import CameraHead
+from models.SpaTrackV2.models.vggt4track.heads.dpt_head import DPTHead
+from models.SpaTrackV2.models.vggt4track.heads.track_head import TrackHead
+from models.SpaTrackV2.models.vggt4track.utils.loss import compute_loss
+from models.SpaTrackV2.models.vggt4track.utils.pose_enc import pose_encoding_to_extri_intri
 from models.SpaTrackV2.models.tracker3D.spatrack_modules.utils import depth_to_points_colmap, get_nth_visible_time_index
-from models.vggt.vggt.utils.load_fn import preprocess_image
+from models.SpaTrackV2.models.vggt4track.utils.load_fn import preprocess_image
 from einops import rearrange
 import torch.nn.functional as F
 

models/SpaTrackV2/models/vggt4track/utils/loss.py

@@ -15,7 +15,7 @@ from models.moge.train.losses import (
 import torch.nn.functional as F
 from models.SpaTrackV2.models.utils import pose_enc2mat, matrix_to_quaternion, get_track_points, normalize_rgb
 from models.SpaTrackV2.models.tracker3D.spatrack_modules.utils import depth_to_points_colmap, get_nth_visible_time_index
-from models.vggt.vggt.utils.pose_enc import pose_encoding_to_extri_intri, extri_intri_to_pose_encoding
+from models.SpaTrackV2.models.vggt4track.utils.pose_enc import pose_encoding_to_extri_intri, extri_intri_to_pose_encoding
 
 def compute_loss(predictions, annots):
     """

models/vggt/setup.py

@@ -1,8 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name='vggt',
-    version='0.1',
-    packages=find_packages(),
-    description='vggt local package',
-)