init das

__init__.py

@@ -1,7 +1,8 @@
 from .nodes import NODE_CLASS_MAPPINGS as NODES_CLASS, NODE_DISPLAY_NAME_MAPPINGS as NODES_DISPLAY
 from .model_loading import NODE_CLASS_MAPPINGS as MODEL_CLASS, NODE_DISPLAY_NAME_MAPPINGS as MODEL_DISPLAY
+from .das.das_nodes import NODE_CLASS_MAPPINGS as DAS_CLASS, NODE_DISPLAY_NAME_MAPPINGS as DAS_DISPLAY
 
-NODE_CLASS_MAPPINGS = {**NODES_CLASS, **MODEL_CLASS}
-NODE_DISPLAY_NAME_MAPPINGS = {**NODES_DISPLAY, **MODEL_DISPLAY}
+NODE_CLASS_MAPPINGS = {**NODES_CLASS, **MODEL_CLASS, **DAS_CLASS}
+NODE_DISPLAY_NAME_MAPPINGS = {**NODES_DISPLAY, **MODEL_DISPLAY, **DAS_DISPLAY}
 
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

custom_cogvideox_transformer_3d.py

@@ -453,6 +453,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         use_learned_positional_embeddings: bool = False,
         patch_bias: bool = True,
         attention_mode: Optional[str] = "sdpa",
+        das_transformer: bool = False,
     ):
         super().__init__()
         inner_dim = num_attention_heads * attention_head_dim
@@ -557,6 +558,41 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         else:
             #CogVideoX-5B
             self.teacache_coefficients = [-1.53880483e+03, 8.43202495e+02, -1.34363087e+02, 7.97131516e+00, -5.23162339e-02]
+
+        #das
+        # Create linear layers for combining hidden states and tracking maps
+        if das_transformer:
+            num_tracking_blocks = 18
+            self.combine_linears = nn.ModuleList(
+                [nn.Linear(inner_dim, inner_dim) for _ in range(num_tracking_blocks)]
+            )
+             # Initialize weights of combine_linears to zero
+            for linear in self.combine_linears:
+                linear.weight.data.zero_()
+                linear.bias.data.zero_()
+
+            # Create transformer blocks for processing tracking maps
+            self.transformer_blocks_copy = nn.ModuleList(
+                [
+                    CogVideoXBlock(
+                        dim=inner_dim,
+                        num_attention_heads=self.config.num_attention_heads,
+                        attention_head_dim=self.config.attention_head_dim,
+                        time_embed_dim=self.config.time_embed_dim,
+                        dropout=self.config.dropout,
+                        activation_fn=self.config.activation_fn,
+                        attention_bias=self.config.attention_bias,
+                        norm_elementwise_affine=self.config.norm_elementwise_affine,
+                        norm_eps=self.config.norm_eps,
+                    )
+                    for _ in range(num_tracking_blocks)
+                ]
+            )
+
+            # For initial combination of hidden states and tracking maps
+            self.initial_combine_linear = nn.Linear(inner_dim, inner_dim)
+            self.initial_combine_linear.weight.data.zero_()
+            self.initial_combine_linear.bias.data.zero_()
         
 
     def _set_gradient_checkpointing(self, module, value=False):
@@ -573,6 +609,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         controlnet_states: torch.Tensor = None,
         controlnet_weights: Optional[Union[float, int, list, np.ndarray, torch.FloatTensor]] = 1.0,
         video_flow_features: Optional[torch.Tensor] = None,
+        tracking_maps: Optional[torch.Tensor] = None,
         return_dict: bool = True,
     ):
         batch_size, num_frames, channels, height, width = hidden_states.shape
@@ -602,13 +639,27 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         #print("hidden_states before patch_embedding", hidden_states.shape) #torch.Size([2, 4, 16, 60, 90])
         
         hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
-        #print("hidden_states after patch_embedding", hidden_states.shape) #1.5: torch.Size([2, 2926, 3072]) #1.0: torch.Size([2, 5626, 3072])
         hidden_states = self.embedding_dropout(hidden_states)
 
-        text_seq_length = encoder_hidden_states.shape[1]
-        encoder_hidden_states = hidden_states[:, :text_seq_length]
-        hidden_states = hidden_states[:, text_seq_length:]
-        #print("hidden_states after split", hidden_states.shape) #1.5: torch.Size([2, 2700, 3072]) #1.0: torch.Size([2, 5400, 3072])
+        if tracking_maps is not None:
+            # Process tracking maps
+            prompt_embed = encoder_hidden_states.clone()
+            tracking_maps_hidden_states = self.patch_embed(prompt_embed, tracking_maps)
+            tracking_maps_hidden_states = self.embedding_dropout(tracking_maps_hidden_states)
+            del prompt_embed
+
+            text_seq_length = encoder_hidden_states.shape[1]
+            encoder_hidden_states = hidden_states[:, :text_seq_length]
+            hidden_states = hidden_states[:, text_seq_length:]
+            tracking_maps = tracking_maps_hidden_states[:, text_seq_length:]
+
+            # Combine hidden states and tracking maps initially
+            combined = hidden_states + tracking_maps
+            tracking_maps = self.initial_combine_linear(combined)
+        else:
+            text_seq_length = encoder_hidden_states.shape[1]
+            encoder_hidden_states = hidden_states[:, :text_seq_length]
+            hidden_states = hidden_states[:, text_seq_length:]
       
         if self.use_fastercache:
             self.fastercache_counter+=1
@@ -706,6 +757,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
                 if self.use_teacache:
                     ori_hidden_states = hidden_states.clone()
                     ori_encoder_hidden_states = encoder_hidden_states.clone()
+
                 for i, block in enumerate(self.transformer_blocks):
                     hidden_states, encoder_hidden_states = block(
                         hidden_states=hidden_states,
@@ -731,6 +783,18 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
                             controlnet_block_weight = controlnet_weights                    
                         hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
 
+                    #das
+                    if i < len(self.transformer_blocks_copy) and tracking_maps is not None:
+                        tracking_maps, _ = self.transformer_blocks_copy[i](
+                            hidden_states=tracking_maps,
+                            encoder_hidden_states=encoder_hidden_states,
+                            temb=emb,
+                            image_rotary_emb=image_rotary_emb,
+                        )
+                        # Combine hidden states and tracking maps
+                        tracking_maps = self.combine_linears[i](tracking_maps)
+                        hidden_states = hidden_states + tracking_maps
+
                 if self.use_teacache:
                     self.previous_residual = hidden_states - ori_hidden_states
                     self.previous_residual_encoder = encoder_hidden_states - ori_encoder_hidden_states

das/das_nodes.py

@@ -0,0 +1,146 @@
+import torch
+import comfy.model_management as mm
+from ..utils import log
+import os
+import numpy as np
+import folder_paths
+
+class CogVideoDASTrackingEncode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "vae": ("VAE",),
+                    "images": ("IMAGE", ),
+                },
+                "optional": {
+                    "enable_tiling": ("BOOLEAN", {"default": True, "tooltip": "Enable tiling for the VAE to reduce memory usage"}),
+                    "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                    "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                    "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                },
+        }
+
+    RETURN_TYPES = ("DASTRACKING",)
+    RETURN_NAMES = ("das_tracking",)
+    FUNCTION = "encode"
+    CATEGORY = "CogVideoWrapper"
+
+    def encode(self, vae, images, enable_tiling=False, strength=1.0, start_percent=0.0, end_percent=1.0):
+        device = mm.get_torch_device()
+        offload_device = mm.unet_offload_device()
+        generator = torch.Generator(device=device).manual_seed(0)
+        
+        try:
+            vae.enable_slicing()
+        except:
+            pass
+       
+        vae_scaling_factor = vae.config.scaling_factor
+        
+        if enable_tiling:
+            from ..mz_enable_vae_encode_tiling import enable_vae_encode_tiling
+            enable_vae_encode_tiling(vae)
+
+        vae.to(device)
+
+        try:
+            vae._clear_fake_context_parallel_cache()
+        except:
+            pass
+
+        tracking_maps = images.to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
+        tracking_first_frame = tracking_maps[:, :, 0:1, :, :]
+        tracking_first_frame *= 2.0 - 1.0
+        print("tracking_first_frame shape: ", tracking_first_frame.shape)
+
+        tracking_first_frame_latent = vae.encode(tracking_first_frame).latent_dist.sample(generator).permute(0, 2, 1, 3, 4)
+        tracking_first_frame_latent = tracking_first_frame_latent * vae_scaling_factor * strength
+        log.info(f"Encoded tracking first frame latents shape: {tracking_first_frame_latent.shape}")
+
+        tracking_latents = vae.encode(tracking_maps).latent_dist.sample(generator).permute(0, 2, 1, 3, 4)  # B, T, C, H, W
+
+        tracking_latents = tracking_latents * vae_scaling_factor * strength
+        
+        log.info(f"Encoded tracking latents shape: {tracking_latents.shape}")
+        vae.to(offload_device)
+        
+        return ({
+            "tracking_maps": tracking_latents,
+            "tracking_image_latents": tracking_first_frame_latent,
+            "start_percent": start_percent,
+            "end_percent": end_percent
+            }, )
+    
+class DAS_SpaTracker:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "images": ("IMAGE", ),
+            "depth_images": ("IMAGE", ),
+            "density": ("INT", {"default": 70, "min": 1, "max": 100, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("tracking_video",)
+    FUNCTION = "encode"
+    CATEGORY = "CogVideoWrapper"
+
+    def encode(self, images, depth_images, density):
+        device = mm.get_torch_device()
+        offload_device = mm.unet_offload_device()
+        generator = torch.Generator(device=device).manual_seed(0)
+
+        model_path = os.path.join(folder_paths.models_dir, 'spatracker', 'spaT_final.pth')
+        from .spatracker.predictor import SpaTrackerPredictor
+        from .spatracker.utils.visualizer import Visualizer
+        
+        if not hasattr(self, "tracker"):
+            self.tracker = SpaTrackerPredictor(
+                checkpoint=model_path,
+                interp_shape=(384, 576),
+                seq_length=12
+            ).to(device)
+
+        segm_mask = np.ones((480, 720), dtype=np.uint8)
+
+        video = images.permute(0, 3, 1, 2).to(device).unsqueeze(0)
+        video_depth = depth_images.permute(0, 3, 1, 2).to(device)
+        video_depth = video_depth[:, 0:1, :, :]
+            
+        pred_tracks, pred_visibility, T_Firsts = self.tracker(
+            video * 255, 
+            video_depth=video_depth,
+            grid_size=density,
+            backward_tracking=False,
+            depth_predictor=None,
+            grid_query_frame=0,
+            segm_mask=torch.from_numpy(segm_mask)[None, None].to(device),
+            wind_length=12,
+            progressive_tracking=False
+        )
+
+        vis = Visualizer(grayscale=False, fps=24, pad_value=0)
+        msk_query = (T_Firsts == 0)
+        pred_tracks = pred_tracks[:,:,msk_query.squeeze()]
+        pred_visibility = pred_visibility[:,:,msk_query.squeeze()]
+        
+        tracking_video = vis.visualize(video=video, tracks=pred_tracks,
+                        visibility=pred_visibility, save_video=False,
+                        filename="temp")
+        
+        tracking_video = tracking_video.squeeze(0).permute(0, 2, 3, 1) # [T, H, W, C]
+        tracking_video = (tracking_video / 255.0).float()
+
+        return (tracking_video,)
+    
+
+NODE_CLASS_MAPPINGS = {
+    "CogVideoDASTrackingEncode": CogVideoDASTrackingEncode,
+    "DAS_SpaTracker": DAS_SpaTracker,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "CogVideoDASTrackingEncode": "CogVideo DAS Tracking Encode",
+    "DAS_SpaTracker": "DAS SpaTracker",
+    }

das/spatracker/__init__.py

@@ -0,0 +1,5 @@
+# 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.

das/spatracker/models/__init__.py

@@ -0,0 +1,5 @@
+# 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.

das/spatracker/models/build_spatracker.py

@@ -0,0 +1,55 @@
+# 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
+
+from .core.spatracker.spatracker import SpaTracker
+
+
+def build_spatracker(
+    checkpoint: str,
+    seq_length: int = 8,
+):
+    model_name = checkpoint.split("/")[-1].split(".")[0]
+    return build_spatracker_from_cfg(checkpoint=checkpoint, seq_length=seq_length)
+
+
+
+# model used to produce the results in the paper
+def build_spatracker_from_cfg(checkpoint=None, seq_length=8):
+    return _build_spatracker(
+        stride=4,
+        sequence_len=seq_length,
+        checkpoint=checkpoint,
+    )
+
+
+def _build_spatracker(
+    stride,
+    sequence_len,
+    checkpoint=None,
+):
+    spatracker = SpaTracker(
+        stride=stride,
+        S=sequence_len,
+        add_space_attn=True,
+        space_depth=6,
+        time_depth=6,
+    )
+    if checkpoint is not None:
+        with open(checkpoint, "rb") as f:
+            if "safetensors" in checkpoint:
+                from safetensors.torch import load_file
+                state_dict = load_file(checkpoint)
+            else:
+                state_dict = torch.load(f, map_location="cpu", weights_only=True)
+            if "model" in state_dict:
+                model_paras = spatracker.state_dict()
+                paras_dict = {k: v for k,v in state_dict["model"].items() if k in spatracker.state_dict()}
+                model_paras.update(paras_dict)
+                state_dict = model_paras
+        spatracker.load_state_dict(state_dict)
+    return spatracker

das/spatracker/models/core/__init__.py

@@ -0,0 +1,5 @@
+# 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.

das/spatracker/models/core/embeddings.py

@@ -0,0 +1,250 @@
+# 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
+import numpy as np
+
+def get_3d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, tuple):
+        grid_size_h, grid_size_w = grid_size
+    else:
+        grid_size_h = grid_size_w = grid_size
+    grid_h = np.arange(grid_size_h, dtype=np.float32)
+    grid_w = np.arange(grid_size_w, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size_h, grid_size_w])
+    pos_embed = get_3d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate(
+            [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
+        )
+    return pos_embed
+
+
+def get_3d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 3 == 0
+
+    # use half of dimensions to encode grid_h
+    B, S, N, _ = grid.shape
+    gridx = grid[..., 0].view(B*S*N).detach().cpu().numpy()
+    gridy = grid[..., 1].view(B*S*N).detach().cpu().numpy()
+    gridz = grid[..., 2].view(B*S*N).detach().cpu().numpy()
+
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 3, gridx)  # (N, D/3)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 3, gridy)  # (N, D/3)
+    emb_z = get_1d_sincos_pos_embed_from_grid(embed_dim // 3, gridz)  # (N, D/3)
+
+
+    emb = np.concatenate([emb_h, emb_w, emb_z], axis=1)  # (N, D)
+    emb = torch.from_numpy(emb).to(grid.device)
+    return emb.view(B, S, N, embed_dim)
+
+
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, tuple):
+        grid_size_h, grid_size_w = grid_size
+    else:
+        grid_size_h = grid_size_w = grid_size
+    grid_h = np.arange(grid_size_h, dtype=np.float32)
+    grid_w = np.arange(grid_size_w, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size_h, grid_size_w])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate(
+            [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
+        )
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000 ** omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+def get_2d_embedding(xy, C, cat_coords=True):
+    B, N, D = xy.shape
+    assert D == 2
+
+    x = xy[:, :, 0:1]
+    y = xy[:, :, 1:2]
+    div_term = (
+        torch.arange(0, C, 2, device=xy.device, dtype=torch.float32) * (1000.0 / C)
+    ).reshape(1, 1, int(C / 2))
+
+    pe_x = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32)
+    pe_y = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32)
+
+    pe_x[:, :, 0::2] = torch.sin(x * div_term)
+    pe_x[:, :, 1::2] = torch.cos(x * div_term)
+
+    pe_y[:, :, 0::2] = torch.sin(y * div_term)
+    pe_y[:, :, 1::2] = torch.cos(y * div_term)
+
+    pe = torch.cat([pe_x, pe_y], dim=2)  # B, N, C*3
+    if cat_coords:
+        pe = torch.cat([xy, pe], dim=2)  # B, N, C*3+3
+    return pe
+
+
+def get_3d_embedding(xyz, C, cat_coords=True):
+    B, N, D = xyz.shape
+    assert D == 3
+
+    x = xyz[:, :, 0:1]
+    y = xyz[:, :, 1:2]
+    z = xyz[:, :, 2:3]
+    div_term = (
+        torch.arange(0, C, 2, device=xyz.device, dtype=torch.float32) * (1000.0 / C)
+    ).reshape(1, 1, int(C / 2))
+
+    pe_x = torch.zeros(B, N, C, device=xyz.device, dtype=torch.float32)
+    pe_y = torch.zeros(B, N, C, device=xyz.device, dtype=torch.float32)
+    pe_z = torch.zeros(B, N, C, device=xyz.device, dtype=torch.float32)
+
+    pe_x[:, :, 0::2] = torch.sin(x * div_term)
+    pe_x[:, :, 1::2] = torch.cos(x * div_term)
+
+    pe_y[:, :, 0::2] = torch.sin(y * div_term)
+    pe_y[:, :, 1::2] = torch.cos(y * div_term)
+
+    pe_z[:, :, 0::2] = torch.sin(z * div_term)
+    pe_z[:, :, 1::2] = torch.cos(z * div_term)
+
+    pe = torch.cat([pe_x, pe_y, pe_z], dim=2)  # B, N, C*3
+    if cat_coords:
+        pe = torch.cat([pe, xyz], dim=2)  # B, N, C*3+3
+    return pe
+
+
+def get_4d_embedding(xyzw, C, cat_coords=True):
+    B, N, D = xyzw.shape
+    assert D == 4
+
+    x = xyzw[:, :, 0:1]
+    y = xyzw[:, :, 1:2]
+    z = xyzw[:, :, 2:3]
+    w = xyzw[:, :, 3:4]
+    div_term = (
+        torch.arange(0, C, 2, device=xyzw.device, dtype=torch.float32) * (1000.0 / C)
+    ).reshape(1, 1, int(C / 2))
+
+    pe_x = torch.zeros(B, N, C, device=xyzw.device, dtype=torch.float32)
+    pe_y = torch.zeros(B, N, C, device=xyzw.device, dtype=torch.float32)
+    pe_z = torch.zeros(B, N, C, device=xyzw.device, dtype=torch.float32)
+    pe_w = torch.zeros(B, N, C, device=xyzw.device, dtype=torch.float32)
+
+    pe_x[:, :, 0::2] = torch.sin(x * div_term)
+    pe_x[:, :, 1::2] = torch.cos(x * div_term)
+
+    pe_y[:, :, 0::2] = torch.sin(y * div_term)
+    pe_y[:, :, 1::2] = torch.cos(y * div_term)
+
+    pe_z[:, :, 0::2] = torch.sin(z * div_term)
+    pe_z[:, :, 1::2] = torch.cos(z * div_term)
+
+    pe_w[:, :, 0::2] = torch.sin(w * div_term)
+    pe_w[:, :, 1::2] = torch.cos(w * div_term)
+
+    pe = torch.cat([pe_x, pe_y, pe_z, pe_w], dim=2)  # B, N, C*3
+    if cat_coords:
+        pe = torch.cat([pe, xyzw], dim=2)  # B, N, C*3+3
+    return pe
+
+import torch.nn as nn
+class Embedder_Fourier(nn.Module):
+    def __init__(self, input_dim, max_freq_log2, N_freqs,
+                 log_sampling=True, include_input=True,
+                 periodic_fns=(torch.sin, torch.cos)):
+        '''
+        :param input_dim: dimension of input to be embedded
+        :param max_freq_log2: log2 of max freq; min freq is 1 by default
+        :param N_freqs: number of frequency bands
+        :param log_sampling: if True, frequency bands are linerly sampled in log-space
+        :param include_input: if True, raw input is included in the embedding
+        :param periodic_fns: periodic functions used to embed input
+        '''
+        super(Embedder_Fourier, self).__init__()
+
+        self.input_dim = input_dim
+        self.include_input = include_input
+        self.periodic_fns = periodic_fns
+
+        self.out_dim = 0
+        if self.include_input:
+            self.out_dim += self.input_dim
+
+        self.out_dim += self.input_dim * N_freqs * len(self.periodic_fns)
+
+        if log_sampling:
+            self.freq_bands = 2. ** torch.linspace(0., max_freq_log2, N_freqs)
+        else:
+            self.freq_bands = torch.linspace(
+                2. ** 0., 2. ** max_freq_log2, N_freqs)
+
+        self.freq_bands = self.freq_bands.numpy().tolist()
+
+    def forward(self,
+                input: torch.Tensor,
+                rescale: float = 1.0):
+        '''
+        :param input: tensor of shape [..., self.input_dim]
+        :return: tensor of shape [..., self.out_dim]
+        '''
+        assert (input.shape[-1] == self.input_dim)
+        out = []
+        if self.include_input:
+            out.append(input/rescale)
+
+        for i in range(len(self.freq_bands)):
+            freq = self.freq_bands[i]
+            for p_fn in self.periodic_fns:
+                out.append(p_fn(input * freq))
+        out = torch.cat(out, dim=-1)
+
+        assert (out.shape[-1] == self.out_dim)
+        return out

das/spatracker/models/core/model_utils.py

@@ -0,0 +1,477 @@
+# 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
+import torch.nn.functional as F
+from easydict import EasyDict as edict
+from sklearn.decomposition import PCA
+import matplotlib.pyplot as plt
+
+EPS = 1e-6
+
+def nearest_sample2d(im, x, y, return_inbounds=False):
+    # x and y are each B, N
+    # output is B, C, N
+    if len(im.shape) == 5:
+        B, N, C, H, W = list(im.shape)
+    else:
+        B, C, H, W = list(im.shape)
+    N = list(x.shape)[1]
+
+    x = x.float()
+    y = y.float()
+    H_f = torch.tensor(H, dtype=torch.float32)
+    W_f = torch.tensor(W, dtype=torch.float32)
+
+    # inbound_mask = (x>-0.5).float()*(y>-0.5).float()*(x<W_f+0.5).float()*(y<H_f+0.5).float()
+
+    max_y = (H_f - 1).int()
+    max_x = (W_f - 1).int()
+
+    x0 = torch.floor(x).int()
+    x1 = x0 + 1
+    y0 = torch.floor(y).int()
+    y1 = y0 + 1
+
+    x0_clip = torch.clamp(x0, 0, max_x)
+    x1_clip = torch.clamp(x1, 0, max_x)
+    y0_clip = torch.clamp(y0, 0, max_y)
+    y1_clip = torch.clamp(y1, 0, max_y)
+    dim2 = W
+    dim1 = W * H
+
+    base = torch.arange(0, B, dtype=torch.int64, device=x.device) * dim1
+    base = torch.reshape(base, [B, 1]).repeat([1, N])
+
+    base_y0 = base + y0_clip * dim2
+    base_y1 = base + y1_clip * dim2
+
+    idx_y0_x0 = base_y0 + x0_clip
+    idx_y0_x1 = base_y0 + x1_clip
+    idx_y1_x0 = base_y1 + x0_clip
+    idx_y1_x1 = base_y1 + x1_clip
+
+    # use the indices to lookup pixels in the flat image
+    # im is B x C x H x W
+    # move C out to last dim
+    if len(im.shape) == 5:
+        im_flat = (im.permute(0, 3, 4, 1, 2)).reshape(B * H * W, N, C)
+        i_y0_x0 = torch.diagonal(im_flat[idx_y0_x0.long()], dim1=1, dim2=2).permute(
+            0, 2, 1
+        )
+        i_y0_x1 = torch.diagonal(im_flat[idx_y0_x1.long()], dim1=1, dim2=2).permute(
+            0, 2, 1
+        )
+        i_y1_x0 = torch.diagonal(im_flat[idx_y1_x0.long()], dim1=1, dim2=2).permute(
+            0, 2, 1
+        )
+        i_y1_x1 = torch.diagonal(im_flat[idx_y1_x1.long()], dim1=1, dim2=2).permute(
+            0, 2, 1
+        )
+    else:
+        im_flat = (im.permute(0, 2, 3, 1)).reshape(B * H * W, C)
+        i_y0_x0 = im_flat[idx_y0_x0.long()]
+        i_y0_x1 = im_flat[idx_y0_x1.long()]
+        i_y1_x0 = im_flat[idx_y1_x0.long()]
+        i_y1_x1 = im_flat[idx_y1_x1.long()]
+
+    # Finally calculate interpolated values.
+    x0_f = x0.float()
+    x1_f = x1.float()
+    y0_f = y0.float()
+    y1_f = y1.float()
+
+    w_y0_x0 = ((x1_f - x) * (y1_f - y)).unsqueeze(2)
+    w_y0_x1 = ((x - x0_f) * (y1_f - y)).unsqueeze(2)
+    w_y1_x0 = ((x1_f - x) * (y - y0_f)).unsqueeze(2)
+    w_y1_x1 = ((x - x0_f) * (y - y0_f)).unsqueeze(2)
+
+    # w_yi_xo is B * N * 1
+    max_idx = torch.cat([w_y0_x0, w_y0_x1, w_y1_x0, w_y1_x1], dim=-1).max(dim=-1)[1] 
+    output = torch.stack([i_y0_x0, i_y0_x1, i_y1_x0, i_y1_x1], dim=-1).gather(-1, max_idx[...,None,None].repeat(1,1,C,1)).squeeze(-1)
+
+    # output is B*N x C
+    output = output.view(B, -1, C)
+    output = output.permute(0, 2, 1)
+    # output is B x C x N
+
+    if return_inbounds:
+        x_valid = (x > -0.5).byte() & (x < float(W_f - 0.5)).byte()
+        y_valid = (y > -0.5).byte() & (y < float(H_f - 0.5)).byte()
+        inbounds = (x_valid & y_valid).float()
+        inbounds = inbounds.reshape(
+            B, N
+        )  # something seems wrong here for B>1; i'm getting an error here (or downstream if i put -1)
+        return output, inbounds
+
+    return output  # B, C, N
+
+def smart_cat(tensor1, tensor2, dim):
+    if tensor1 is None:
+        return tensor2
+    return torch.cat([tensor1, tensor2], dim=dim)
+
+
+def normalize_single(d):
+    # d is a whatever shape torch tensor
+    dmin = torch.min(d)
+    dmax = torch.max(d)
+    d = (d - dmin) / (EPS + (dmax - dmin))
+    return d
+
+
+def normalize(d):
+    # d is B x whatever. normalize within each element of the batch
+    out = torch.zeros(d.size())
+    if d.is_cuda:
+        out = out.cuda()
+    B = list(d.size())[0]
+    for b in list(range(B)):
+        out[b] = normalize_single(d[b])
+    return out
+
+
+def meshgrid2d(B, Y, X, stack=False, norm=False, device="cuda"):
+    # returns a meshgrid sized B x Y x X
+
+    grid_y = torch.linspace(0.0, Y - 1, Y, device=torch.device(device))
+    grid_y = torch.reshape(grid_y, [1, Y, 1])
+    grid_y = grid_y.repeat(B, 1, X)
+
+    grid_x = torch.linspace(0.0, X - 1, X, device=torch.device(device))
+    grid_x = torch.reshape(grid_x, [1, 1, X])
+    grid_x = grid_x.repeat(B, Y, 1)
+
+    if stack:
+        # note we stack in xy order
+        # (see https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.grid_sample)
+        grid = torch.stack([grid_x, grid_y], dim=-1)
+        return grid
+    else:
+        return grid_y, grid_x
+
+
+def reduce_masked_mean(x, mask, dim=None, keepdim=False):
+    # x and mask are the same shape, or at least broadcastably so < actually it's safer if you disallow broadcasting
+    # returns shape-1
+    # axis can be a list of axes
+    for (a, b) in zip(x.size(), mask.size()):
+        assert a == b  # some shape mismatch!
+    prod = x * mask
+    if dim is None:
+        numer = torch.sum(prod)
+        denom = EPS + torch.sum(mask)
+    else:
+        numer = torch.sum(prod, dim=dim, keepdim=keepdim)
+        denom = EPS + torch.sum(mask, dim=dim, keepdim=keepdim)
+
+    mean = numer / denom
+    return mean
+
+
+def bilinear_sample2d(im, x, y, return_inbounds=False):
+    # x and y are each B, N
+    # output is B, C, N
+    if len(im.shape) == 5:
+        B, N, C, H, W = list(im.shape)
+    else:
+        B, C, H, W = list(im.shape)
+    N = list(x.shape)[1]
+
+    x = x.float()
+    y = y.float()
+    H_f = torch.tensor(H, dtype=torch.float32)
+    W_f = torch.tensor(W, dtype=torch.float32)
+
+    # inbound_mask = (x>-0.5).float()*(y>-0.5).float()*(x<W_f+0.5).float()*(y<H_f+0.5).float()
+
+    max_y = (H_f - 1).int()
+    max_x = (W_f - 1).int()
+
+    x0 = torch.floor(x).int()
+    x1 = x0 + 1
+    y0 = torch.floor(y).int()
+    y1 = y0 + 1
+
+    x0_clip = torch.clamp(x0, 0, max_x)
+    x1_clip = torch.clamp(x1, 0, max_x)
+    y0_clip = torch.clamp(y0, 0, max_y)
+    y1_clip = torch.clamp(y1, 0, max_y)
+    dim2 = W
+    dim1 = W * H
+
+    base = torch.arange(0, B, dtype=torch.int64, device=x.device) * dim1
+    base = torch.reshape(base, [B, 1]).repeat([1, N])
+
+    base_y0 = base + y0_clip * dim2
+    base_y1 = base + y1_clip * dim2
+
+    idx_y0_x0 = base_y0 + x0_clip
+    idx_y0_x1 = base_y0 + x1_clip
+    idx_y1_x0 = base_y1 + x0_clip
+    idx_y1_x1 = base_y1 + x1_clip
+
+    # use the indices to lookup pixels in the flat image
+    # im is B x C x H x W
+    # move C out to last dim
+    if len(im.shape) == 5:
+        im_flat = (im.permute(0, 3, 4, 1, 2)).reshape(B * H * W, N, C)
+        i_y0_x0 = torch.diagonal(im_flat[idx_y0_x0.long()], dim1=1, dim2=2).permute(
+            0, 2, 1
+        )
+        i_y0_x1 = torch.diagonal(im_flat[idx_y0_x1.long()], dim1=1, dim2=2).permute(
+            0, 2, 1
+        )
+        i_y1_x0 = torch.diagonal(im_flat[idx_y1_x0.long()], dim1=1, dim2=2).permute(
+            0, 2, 1
+        )
+        i_y1_x1 = torch.diagonal(im_flat[idx_y1_x1.long()], dim1=1, dim2=2).permute(
+            0, 2, 1
+        )
+    else:
+        im_flat = (im.permute(0, 2, 3, 1)).reshape(B * H * W, C)
+        i_y0_x0 = im_flat[idx_y0_x0.long()]
+        i_y0_x1 = im_flat[idx_y0_x1.long()]
+        i_y1_x0 = im_flat[idx_y1_x0.long()]
+        i_y1_x1 = im_flat[idx_y1_x1.long()]
+
+    # Finally calculate interpolated values.
+    x0_f = x0.float()
+    x1_f = x1.float()
+    y0_f = y0.float()
+    y1_f = y1.float()
+
+    w_y0_x0 = ((x1_f - x) * (y1_f - y)).unsqueeze(2)
+    w_y0_x1 = ((x - x0_f) * (y1_f - y)).unsqueeze(2)
+    w_y1_x0 = ((x1_f - x) * (y - y0_f)).unsqueeze(2)
+    w_y1_x1 = ((x - x0_f) * (y - y0_f)).unsqueeze(2)
+
+    output = (
+        w_y0_x0 * i_y0_x0 + w_y0_x1 * i_y0_x1 + w_y1_x0 * i_y1_x0 + w_y1_x1 * i_y1_x1
+    )
+    # output is B*N x C
+    output = output.view(B, -1, C)
+    output = output.permute(0, 2, 1)
+    # output is B x C x N
+
+    if return_inbounds:
+        x_valid = (x > -0.5).byte() & (x < float(W_f - 0.5)).byte()
+        y_valid = (y > -0.5).byte() & (y < float(H_f - 0.5)).byte()
+        inbounds = (x_valid & y_valid).float()
+        inbounds = inbounds.reshape(
+            B, N
+        )  # something seems wrong here for B>1; i'm getting an error here (or downstream if i put -1)
+        return output, inbounds
+
+    return output  # B, C, N
+
+
+def procrustes_analysis(X0,X1,Weight): # [B,N,3]                             
+    # translation
+    t0 = X0.mean(dim=1,keepdim=True)
+    t1 = X1.mean(dim=1,keepdim=True)
+    X0c = X0-t0
+    X1c = X1-t1
+    # scale
+    # s0 = (X0c**2).sum(dim=-1).mean().sqrt()
+    # s1 = (X1c**2).sum(dim=-1).mean().sqrt()
+    # X0cs = X0c/s0
+    # X1cs = X1c/s1
+    # rotation (use double for SVD, float loses precision)
+    U,_,V = (X0c.t()@X1c).double().svd(some=True)
+    R = (U@V.t()).float()
+    if R.det()<0: R[2] *= -1
+    # align X1 to X0: X1to0 = (X1-t1)/@R.t()+t0
+    se3 = edict(t0=t0[0],t1=t1[0],R=R)
+
+    return se3
+
+def bilinear_sampler(input, coords, align_corners=True, padding_mode="border"):
+    r"""Sample a tensor using bilinear interpolation
+
+    `bilinear_sampler(input, coords)` samples a tensor :attr:`input` at
+    coordinates :attr:`coords` using bilinear interpolation. It is the same
+    as `torch.nn.functional.grid_sample()` but with a different coordinate
+    convention.
+
+    The input tensor is assumed to be of shape :math:`(B, C, H, W)`, where
+    :math:`B` is the batch size, :math:`C` is the number of channels,
+    :math:`H` is the height of the image, and :math:`W` is the width of the
+    image. The tensor :attr:`coords` of shape :math:`(B, H_o, W_o, 2)` is
+    interpreted as an array of 2D point coordinates :math:`(x_i,y_i)`.
+
+    Alternatively, the input tensor can be of size :math:`(B, C, T, H, W)`,
+    in which case sample points are triplets :math:`(t_i,x_i,y_i)`. Note
+    that in this case the order of the components is slightly different
+    from `grid_sample()`, which would expect :math:`(x_i,y_i,t_i)`.
+
+    If `align_corners` is `True`, the coordinate :math:`x` is assumed to be
+    in the range :math:`[0,W-1]`, with 0 corresponding to the center of the
+    left-most image pixel :math:`W-1` to the center of the right-most
+    pixel.
+
+    If `align_corners` is `False`, the coordinate :math:`x` is assumed to
+    be in the range :math:`[0,W]`, with 0 corresponding to the left edge of
+    the left-most pixel :math:`W` to the right edge of the right-most
+    pixel.
+
+    Similar conventions apply to the :math:`y` for the range
+    :math:`[0,H-1]` and :math:`[0,H]` and to :math:`t` for the range
+    :math:`[0,T-1]` and :math:`[0,T]`.
+
+    Args:
+        input (Tensor): batch of input images.
+        coords (Tensor): batch of coordinates.
+        align_corners (bool, optional): Coordinate convention. Defaults to `True`.
+        padding_mode (str, optional): Padding mode. Defaults to `"border"`.
+
+    Returns:
+        Tensor: sampled points.
+    """
+
+    sizes = input.shape[2:]
+
+    assert len(sizes) in [2, 3]
+
+    if len(sizes) == 3:
+        # t x y -> x y t to match dimensions T H W in grid_sample
+        coords = coords[..., [1, 2, 0]]
+
+    if align_corners:
+        coords = coords * torch.tensor(
+            [2 / max(size - 1, 1) for size in reversed(sizes)], device=coords.device
+        )
+    else:
+        coords = coords * torch.tensor([2 / size for size in reversed(sizes)], device=coords.device)
+
+    coords -= 1
+
+    return F.grid_sample(input, coords, align_corners=align_corners, padding_mode=padding_mode)
+
+
+def sample_features4d(input, coords):
+    r"""Sample spatial features
+
+    `sample_features4d(input, coords)` samples the spatial features
+    :attr:`input` represented by a 4D tensor :math:`(B, C, H, W)`.
+
+    The field is sampled at coordinates :attr:`coords` using bilinear
+    interpolation. :attr:`coords` is assumed to be of shape :math:`(B, R,
+    3)`, where each sample has the format :math:`(x_i, y_i)`. This uses the
+    same convention as :func:`bilinear_sampler` with `align_corners=True`.
+
+    The output tensor has one feature per point, and has shape :math:`(B,
+    R, C)`.
+
+    Args:
+        input (Tensor): spatial features.
+        coords (Tensor): points.
+
+    Returns:
+        Tensor: sampled features.
+    """
+
+    B, _, _, _ = input.shape
+
+    # B R 2 -> B R 1 2
+    coords = coords.unsqueeze(2)
+
+    # B C R 1
+    feats = bilinear_sampler(input, coords)
+
+    return feats.permute(0, 2, 1, 3).view(
+        B, -1, feats.shape[1] * feats.shape[3]
+    )  # B C R 1 -> B R C
+
+
+def sample_features5d(input, coords):
+    r"""Sample spatio-temporal features
+
+    `sample_features5d(input, coords)` works in the same way as
+    :func:`sample_features4d` but for spatio-temporal features and points:
+    :attr:`input` is a 5D tensor :math:`(B, T, C, H, W)`, :attr:`coords` is
+    a :math:`(B, R1, R2, 3)` tensor of spatio-temporal point :math:`(t_i,
+    x_i, y_i)`. The output tensor has shape :math:`(B, R1, R2, C)`.
+
+    Args:
+        input (Tensor): spatio-temporal features.
+        coords (Tensor): spatio-temporal points.
+
+    Returns:
+        Tensor: sampled features.
+    """
+
+    B, T, _, _, _ = input.shape
+
+    # B T C H W -> B C T H W
+    input = input.permute(0, 2, 1, 3, 4)
+
+    # B R1 R2 3 -> B R1 R2 1 3
+    coords = coords.unsqueeze(3)
+
+    # B C R1 R2 1
+    feats = bilinear_sampler(input, coords)
+
+    return feats.permute(0, 2, 3, 1, 4).view(
+        B, feats.shape[2], feats.shape[3], feats.shape[1]
+    )  # B C R1 R2 1 -> B R1 R2 C
+
+def vis_PCA(fmaps, save_dir):
+    """
+        visualize the PCA of the feature maps
+    args:
+        fmaps: feature maps  1 C H W
+        save_dir: the directory to save the PCA visualization
+    """
+
+    pca = PCA(n_components=3)
+    fmap_vis = fmaps[0,...]
+    fmap_vnorm = (
+        (fmap_vis-fmap_vis.min())/
+        (fmap_vis.max()-fmap_vis.min()))
+    H_vis, W_vis = fmap_vis.shape[1:]
+    fmap_vnorm = fmap_vnorm.reshape(fmap_vnorm.shape[0],
+                                        -1).permute(1,0) 
+    fmap_pca = pca.fit_transform(fmap_vnorm.detach().cpu().numpy())
+    pca = fmap_pca.reshape(H_vis,W_vis,3)
+    plt.imsave(save_dir, 
+                (
+                    (pca-pca.min())/
+                    (pca.max()-pca.min())
+                    ))
+
+
+        # debug=False
+        # if debug==True:
+        #     pcd_idx = 60
+        #     vis_PCA(fmapYZ[0,:1], "./yz.png")
+        #     vis_PCA(fmapXZ[0,:1], "./xz.png")
+        #     vis_PCA(fmaps[0,:1], "./xy.png")
+        #     vis_PCA(fmaps[0,-1:], "./xy_.png")
+        #     fxy_q = fxy[0,0,pcd_idx:pcd_idx+1, :, None, None]
+        #     fyz_q = fyz[0,0,pcd_idx:pcd_idx+1, :, None, None]
+        #     fxz_q = fxz[0,0,pcd_idx:pcd_idx+1, :, None, None]
+        #     corr_map = (fxy_q*fmaps[0,-1:]).sum(dim=1)
+        #     corr_map_yz = (fyz_q*fmapYZ[0,-1:]).sum(dim=1)
+        #     corr_map_xz = (fxz_q*fmapXZ[0,-1:]).sum(dim=1)
+        #     coord_last = coords[0,-1,pcd_idx:pcd_idx+1]
+        #     coord_last_neigh = coords[0,-1, self.neigh_indx[pcd_idx]]
+        #     depth_last = depths_dnG[-1,0]
+        #     abs_res = (depth_last-coord_last[-1,-1]).abs()
+        #     abs_res = (abs_res - abs_res.min())/(abs_res.max()-abs_res.min())
+        #     res_dp = torch.exp(-abs_res)
+        #     enhance_corr = res_dp*corr_map
+        #     plt.imsave("./res.png", res_dp.detach().cpu().numpy())
+        #     plt.imsave("./enhance_corr.png", enhance_corr[0].detach().cpu().numpy())
+        #     plt.imsave("./corr_map.png", corr_map[0].detach().cpu().numpy())
+        #     plt.imsave("./corr_map_yz.png", corr_map_yz[0].detach().cpu().numpy())
+        #     plt.imsave("./corr_map_xz.png", corr_map_xz[0].detach().cpu().numpy())
+        #     img_feat = cv2.imread("./xy.png")
+        #     cv2.circle(img_feat, (int(coord_last[0,0]), int(coord_last[0,1])), 2, (0, 0, 255), -1)
+        #     for p_i in coord_last_neigh:
+        #         cv2.circle(img_feat, (int(p_i[0]), int(p_i[1])), 1, (0, 255, 0), -1)
+        #     cv2.imwrite("./xy_coord.png", img_feat)
+        #     import ipdb; ipdb.set_trace()

das/spatracker/models/core/spatracker/__init__.py

@@ -0,0 +1,5 @@
+# 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.

das/spatracker/models/core/spatracker/blocks.py

@@ -0,0 +1,999 @@
+# 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
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.cuda.amp import autocast
+from einops import rearrange
+import collections
+from functools import partial
+from itertools import repeat
+import torchvision.models as tvm
+
+from .vit.encoder import ImageEncoderViT as vitEnc
+from .dpt.models import DPTEncoder
+from .loftr import LocalFeatureTransformer
+# from models.monoD.depth_anything.dpt import DPTHeadEnc, DPTHead
+
+# From PyTorch internals
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+            return tuple(x)
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    return val if exists(val) else d
+
+
+to_2tuple = _ntuple(2)
+
+
+class Mlp(nn.Module):
+    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
+
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        norm_layer=None,
+        bias=True,
+        drop=0.0,
+        use_conv=False,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        bias = to_2tuple(bias)
+        drop_probs = to_2tuple(drop)
+        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
+
+        self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop_probs[0])
+        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
+        self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
+        self.drop2 = nn.Dropout(drop_probs[1])
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = self.fc2(x)
+        x = self.drop2(x)
+        return x
+    
+class Attention(nn.Module):
+    def __init__(self, query_dim, context_dim=None,
+                  num_heads=8, dim_head=48, qkv_bias=False, flash=False):
+        super().__init__()
+        inner_dim = self.inner_dim = dim_head * num_heads
+        context_dim = default(context_dim, query_dim)
+        self.scale = dim_head**-0.5
+        self.heads = num_heads
+        self.flash = flash
+
+        self.qkv = nn.Linear(query_dim, inner_dim*3, bias=qkv_bias)
+        self.proj = nn.Linear(inner_dim, query_dim)
+
+    def forward(self, x, context=None, attn_bias=None):
+        B, N1, _ = x.shape
+        C = self.inner_dim
+        h = self.heads
+        # q = self.to_q(x).reshape(B, N1, h, C // h).permute(0, 2, 1, 3)
+        # k, v = self.to_kv(context).chunk(2, dim=-1)
+        # context = default(context, x)
+
+        qkv = self.qkv(x).reshape(B, N1, 3, h, C // h)
+        q, k, v = qkv[:,:, 0], qkv[:,:, 1], qkv[:,:, 2]
+        N2 = x.shape[1]
+
+        k = k.reshape(B, N2, h, C // h).permute(0, 2, 1, 3)
+        v = v.reshape(B, N2, h, C // h).permute(0, 2, 1, 3)
+        q = q.reshape(B, N1, h, C // h).permute(0, 2, 1, 3)
+        if self.flash==False:
+            sim = (q @ k.transpose(-2, -1)) * self.scale
+            if attn_bias is not None:
+                sim = sim + attn_bias
+            attn = sim.softmax(dim=-1)
+            x = (attn @ v).transpose(1, 2).reshape(B, N1, C)
+        else:
+            input_args = [x.half().contiguous() for x in [q, k, v]]
+            x = F.scaled_dot_product_attention(*input_args).permute(0,2,1,3).reshape(B,N1,-1)  # type: ignore
+
+        # return self.to_out(x.float())
+        return self.proj(x.float())
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_planes, planes, norm_fn="group", stride=1):
+        super(ResidualBlock, self).__init__()
+
+        self.conv1 = nn.Conv2d(
+            in_planes,
+            planes,
+            kernel_size=3,
+            padding=1,
+            stride=stride,
+            padding_mode="zeros",
+        )
+        self.conv2 = nn.Conv2d(
+            planes, planes, kernel_size=3, padding=1, padding_mode="zeros"
+        )
+        self.relu = nn.ReLU(inplace=True)
+
+        num_groups = planes // 8
+
+        if norm_fn == "group":
+            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+            if not stride == 1:
+                self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+
+        elif norm_fn == "batch":
+            self.norm1 = nn.BatchNorm2d(planes)
+            self.norm2 = nn.BatchNorm2d(planes)
+            if not stride == 1:
+                self.norm3 = nn.BatchNorm2d(planes)
+
+        elif norm_fn == "instance":
+            self.norm1 = nn.InstanceNorm2d(planes)
+            self.norm2 = nn.InstanceNorm2d(planes)
+            if not stride == 1:
+                self.norm3 = nn.InstanceNorm2d(planes)
+
+        elif norm_fn == "none":
+            self.norm1 = nn.Sequential()
+            self.norm2 = nn.Sequential()
+            if not stride == 1:
+                self.norm3 = nn.Sequential()
+
+        if stride == 1:
+            self.downsample = None
+
+        else:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3
+            )
+
+    def forward(self, x):
+        y = x
+        y = self.relu(self.norm1(self.conv1(y)))
+        y = self.relu(self.norm2(self.conv2(y)))
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+
+        return self.relu(x + y)
+
+
+class BasicEncoder(nn.Module):
+    def __init__(
+        self, input_dim=3, output_dim=128, stride=8, norm_fn="batch", dropout=0.0,
+        Embed3D=False
+    ):
+        super(BasicEncoder, self).__init__()
+        self.stride = stride
+        self.norm_fn = norm_fn
+        self.in_planes = 64
+
+        if self.norm_fn == "group":
+            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=self.in_planes)
+            self.norm2 = nn.GroupNorm(num_groups=8, num_channels=output_dim * 2)
+
+        elif self.norm_fn == "batch":
+            self.norm1 = nn.BatchNorm2d(self.in_planes)
+            self.norm2 = nn.BatchNorm2d(output_dim * 2)
+
+        elif self.norm_fn == "instance":
+            self.norm1 = nn.InstanceNorm2d(self.in_planes)
+            self.norm2 = nn.InstanceNorm2d(output_dim * 2)
+
+        elif self.norm_fn == "none":
+            self.norm1 = nn.Sequential()
+
+        self.conv1 = nn.Conv2d(
+            input_dim,
+            self.in_planes,
+            kernel_size=7,
+            stride=2,
+            padding=3,
+            padding_mode="zeros",
+        )
+        self.relu1 = nn.ReLU(inplace=True)
+
+        self.shallow = False
+        if self.shallow:
+            self.layer1 = self._make_layer(64, stride=1)
+            self.layer2 = self._make_layer(96, stride=2)
+            self.layer3 = self._make_layer(128, stride=2)
+            self.conv2 = nn.Conv2d(128 + 96 + 64, output_dim, kernel_size=1)
+        else:
+            if Embed3D:
+                self.conv_fuse = nn.Conv2d(64+63, 
+                                           self.in_planes, kernel_size=3, padding=1)
+            self.layer1 = self._make_layer(64, stride=1)
+            self.layer2 = self._make_layer(96, stride=2)
+            self.layer3 = self._make_layer(128, stride=2)
+            self.layer4 = self._make_layer(128, stride=2)
+            self.conv2 = nn.Conv2d(
+                128 + 128 + 96 + 64,
+                output_dim * 2,
+                kernel_size=3,
+                padding=1,
+                padding_mode="zeros",
+            )
+            self.relu2 = nn.ReLU(inplace=True)
+            self.conv3 = nn.Conv2d(output_dim * 2, output_dim, kernel_size=1)
+
+        self.dropout = None
+        if dropout > 0:
+            self.dropout = nn.Dropout2d(p=dropout)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode="fan_out",
+                                                 nonlinearity="relu")
+            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
+                if m.weight is not None:
+                    nn.init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, dim, stride=1):
+        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
+        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
+        layers = (layer1, layer2)
+
+        self.in_planes = dim
+        return nn.Sequential(*layers)
+
+    def forward(self, x, feat_PE=None):
+        _, _, H, W = x.shape
+
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu1(x)
+
+        if self.shallow:
+            a = self.layer1(x)
+            b = self.layer2(a)
+            c = self.layer3(b)
+            a = F.interpolate(
+                a,
+                (H // self.stride, W // self.stride),
+                mode="bilinear",
+                align_corners=True,
+            )
+            b = F.interpolate(
+                b,
+                (H // self.stride, W // self.stride),
+                mode="bilinear",
+                align_corners=True,
+            )
+            c = F.interpolate(
+                c,
+                (H // self.stride, W // self.stride),
+                mode="bilinear",
+                align_corners=True,
+            )
+            x = self.conv2(torch.cat([a, b, c], dim=1))
+        else:
+            if feat_PE is not None:
+                x = self.conv_fuse(torch.cat([x, feat_PE], dim=1))
+                a = self.layer1(x)
+            else:
+                a = self.layer1(x)
+            b = self.layer2(a)
+            c = self.layer3(b)
+            d = self.layer4(c)
+            a = F.interpolate(
+                a,
+                (H // self.stride, W // self.stride),
+                mode="bilinear",
+                align_corners=True,
+            )
+            b = F.interpolate(
+                b,
+                (H // self.stride, W // self.stride),
+                mode="bilinear",
+                align_corners=True,
+            )
+            c = F.interpolate(
+                c,
+                (H // self.stride, W // self.stride),
+                mode="bilinear",
+                align_corners=True,
+            )
+            d = F.interpolate(
+                d,
+                (H // self.stride, W // self.stride),
+                mode="bilinear",
+                align_corners=True,
+            )
+            x = self.conv2(torch.cat([a, b, c, d], dim=1))
+            x = self.norm2(x)
+            x = self.relu2(x)
+            x = self.conv3(x)
+
+        if self.training and self.dropout is not None:
+            x = self.dropout(x)
+        return x
+
+class VitEncoder(nn.Module):
+    def __init__(self, input_dim=4, output_dim=128, stride=4):
+        super(VitEncoder, self).__init__()
+        self.vit = vitEnc(img_size=512, 
+                     depth=6, num_heads=8, in_chans=input_dim,
+                     out_chans=output_dim,embed_dim=384).cuda()
+        self.stride = stride
+    def forward(self, x):
+        T, C, H, W = x.shape
+        x_resize = F.interpolate(x.view(-1, C, H, W), size=(512, 512),
+                                  mode='bilinear', align_corners=False)
+        x_resize = self.vit(x_resize)
+        x = F.interpolate(x_resize, size=(H//self.stride, W//self.stride),
+                            mode='bilinear', align_corners=False)
+        return x
+
+class DPTEnc(nn.Module):
+    def __init__(self, input_dim=3, output_dim=128, stride=2):
+        super(DPTEnc, self).__init__()
+        self.dpt = DPTEncoder()
+        self.stride = stride
+    def forward(self, x):
+        T, C, H, W = x.shape
+        x = (x-0.5)/0.5
+        x_resize = F.interpolate(x.view(-1, C, H, W), size=(384, 384),
+                                  mode='bilinear', align_corners=False)
+        x_resize = self.dpt(x_resize)
+        x = F.interpolate(x_resize, size=(H//self.stride, W//self.stride),
+                            mode='bilinear', align_corners=False)
+        return x
+
+# class DPT_DINOv2(nn.Module):
+#     def __init__(self, encoder='vits', features=64, out_channels=[48, 96, 192, 384],
+#                   use_bn=True, use_clstoken=False, localhub=True, stride=2, enc_only=True):
+#         super(DPT_DINOv2, self).__init__()
+#         self.stride = stride
+#         self.enc_only = enc_only
+#         assert encoder in ['vits', 'vitb', 'vitl']
+        
+#         if localhub:
+#             self.pretrained = torch.hub.load('models/torchhub/facebookresearch_dinov2_main', 'dinov2_{:}14'.format(encoder), source='local', pretrained=False)
+#         else:
+#             self.pretrained = torch.hub.load('facebookresearch/dinov2', 'dinov2_{:}14'.format(encoder))
+        
+#         state_dict = torch.load("models/monoD/zoeDepth/ckpts/dinov2_vits14_pretrain.pth")
+#         self.pretrained.load_state_dict(state_dict, strict=True)
+#         self.pretrained.requires_grad_(False)
+#         dim = self.pretrained.blocks[0].attn.qkv.in_features
+#         if enc_only == True:
+#             out_channels=[128, 128, 128, 128]
+        
+#         self.DPThead = DPTHeadEnc(1, dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken)
+
+        
+#     def forward(self, x):
+#         mean_ = torch.tensor([0.485, 0.456, 0.406], 
+#                              device=x.device).view(1, 3, 1, 1)
+#         std_ = torch.tensor([0.229, 0.224, 0.225],
+#                             device=x.device).view(1, 3, 1, 1)
+#         x = (x+1)/2
+#         x = (x - mean_)/std_
+#         h, w = x.shape[-2:]
+#         h_re, w_re = 560, 560
+#         x_resize = F.interpolate(x, size=(h_re, w_re),
+#                                   mode='bilinear', align_corners=False)
+#         with torch.no_grad():
+#             features = self.pretrained.get_intermediate_layers(x_resize, 4, return_class_token=True)
+#         patch_h, patch_w = h_re // 14, w_re // 14
+#         feat = self.DPThead(features, patch_h, patch_w, self.enc_only)
+#         feat = F.interpolate(feat, size=(h//self.stride, w//self.stride), mode="bilinear", align_corners=True)
+
+#         return feat
+
+
+class VGG19(nn.Module):
+    def __init__(self, pretrained=False, amp = False, amp_dtype = torch.float16) -> None:
+        super().__init__()
+        self.layers = nn.ModuleList(tvm.vgg19_bn(pretrained=pretrained).features[:40])
+        self.amp = amp
+        self.amp_dtype = amp_dtype
+
+    def forward(self, x, **kwargs):
+        with torch.autocast("cuda", enabled=self.amp, dtype = self.amp_dtype):
+            feats = {}
+            scale = 1
+            for layer in self.layers:
+                if isinstance(layer, nn.MaxPool2d):
+                    feats[scale] = x
+                    scale = scale*2
+                x = layer(x)
+            return feats
+
+class CNNandDinov2(nn.Module):
+    def __init__(self, cnn_kwargs = None, amp = True, amp_dtype = torch.float16):
+        super().__init__()
+        # in case the Internet connection is not stable, please load the DINOv2 locally
+        self.dinov2_vitl14 = torch.hub.load('models/torchhub/facebookresearch_dinov2_main',
+                                          'dinov2_{:}14'.format("vitl"), source='local', pretrained=False)
+        
+        state_dict = torch.load("models/monoD/zoeDepth/ckpts/dinov2_vitl14_pretrain.pth")
+        self.dinov2_vitl14.load_state_dict(state_dict, strict=True)
+
+
+        cnn_kwargs = cnn_kwargs if cnn_kwargs is not None else {}
+        self.cnn = VGG19(**cnn_kwargs)
+        self.amp = amp
+        self.amp_dtype = amp_dtype
+        if self.amp:
+            dinov2_vitl14 = dinov2_vitl14.to(self.amp_dtype)
+        self.dinov2_vitl14 = [dinov2_vitl14] # ugly hack to not show parameters to DDP
+    
+    
+    def train(self, mode: bool = True):
+        return self.cnn.train(mode)
+    
+    def forward(self, x, upsample = False):
+        B,C,H,W = x.shape
+        feature_pyramid = self.cnn(x)
+
+        if not upsample:
+            with torch.no_grad():
+                if self.dinov2_vitl14[0].device != x.device:
+                    self.dinov2_vitl14[0] = self.dinov2_vitl14[0].to(x.device).to(self.amp_dtype)
+                dinov2_features_16 = self.dinov2_vitl14[0].forward_features(x.to(self.amp_dtype))
+                features_16 = dinov2_features_16['x_norm_patchtokens'].permute(0,2,1).reshape(B,1024,H//14, W//14)
+                del dinov2_features_16
+                feature_pyramid[16] = features_16
+        return feature_pyramid
+
+class Dinov2(nn.Module):
+    def __init__(self, amp = True, amp_dtype = torch.float16):
+        super().__init__()
+        # in case the Internet connection is not stable, please load the DINOv2 locally
+        self.dinov2_vitl14 = torch.hub.load('models/torchhub/facebookresearch_dinov2_main',
+                                          'dinov2_{:}14'.format("vitl"), source='local', pretrained=False)
+        
+        state_dict = torch.load("models/monoD/zoeDepth/ckpts/dinov2_vitl14_pretrain.pth")
+        self.dinov2_vitl14.load_state_dict(state_dict, strict=True)
+
+        self.amp = amp
+        self.amp_dtype = amp_dtype
+        if self.amp:
+            self.dinov2_vitl14 = self.dinov2_vitl14.to(self.amp_dtype)
+    
+    def forward(self, x, upsample = False):
+        B,C,H,W = x.shape
+        mean_ = torch.tensor([0.485, 0.456, 0.406], 
+                             device=x.device).view(1, 3, 1, 1)
+        std_ = torch.tensor([0.229, 0.224, 0.225],
+                            device=x.device).view(1, 3, 1, 1)
+        x = (x+1)/2
+        x = (x - mean_)/std_
+        h_re, w_re = 560, 560
+        x_resize = F.interpolate(x, size=(h_re, w_re),
+                                  mode='bilinear', align_corners=True)
+        if not upsample:
+            with torch.no_grad():
+                dinov2_features_16 = self.dinov2_vitl14.forward_features(x_resize.to(self.amp_dtype))
+                features_16 = dinov2_features_16['x_norm_patchtokens'].permute(0,2,1).reshape(B,1024,h_re//14, w_re//14)
+                del dinov2_features_16
+        features_16 = F.interpolate(features_16, size=(H//8, W//8), mode="bilinear", align_corners=True)
+        return features_16
+
+class AttnBlock(nn.Module):
+    """
+    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0,
+                  flash=False, **block_kwargs):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.flash=flash
+
+        self.attn = Attention(
+            hidden_size, num_heads=num_heads, qkv_bias=True, flash=flash,
+            **block_kwargs
+        )        
+        
+        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(
+            in_features=hidden_size,
+            hidden_features=mlp_hidden_dim,
+            act_layer=approx_gelu,
+            drop=0,
+        )
+    def forward(self, x):
+        x = x + self.attn(self.norm1(x))
+        x = x + self.mlp(self.norm2(x))
+        return x
+    
+class CrossAttnBlock(nn.Module):
+    def __init__(self, hidden_size, context_dim, num_heads=1, mlp_ratio=4.0,
+                 flash=True, **block_kwargs):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm_context = nn.LayerNorm(hidden_size)
+
+        self.cross_attn = Attention(
+            hidden_size, context_dim=context_dim, 
+            num_heads=num_heads, qkv_bias=True, **block_kwargs, flash=flash
+
+        )
+
+        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(
+            in_features=hidden_size,
+            hidden_features=mlp_hidden_dim,
+            act_layer=approx_gelu,
+            drop=0,
+        )
+
+    def forward(self, x, context):
+        with autocast():
+            x = x + self.cross_attn(
+                self.norm1(x), self.norm_context(context)
+            )
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+def bilinear_sampler(img, coords, mode="bilinear", mask=False):
+    """Wrapper for grid_sample, uses pixel coordinates"""
+    H, W = img.shape[-2:]
+    xgrid, ygrid = coords.split([1, 1], dim=-1)
+    # go to 0,1 then 0,2 then -1,1
+    xgrid = 2 * xgrid / (W - 1) - 1
+    ygrid = 2 * ygrid / (H - 1) - 1
+
+    grid = torch.cat([xgrid, ygrid], dim=-1)
+    img = F.grid_sample(img, grid, align_corners=True)
+
+    if mask:
+        mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1)
+        return img, mask.float()
+
+    return img
+
+
+class CorrBlock:
+    def __init__(self, fmaps, num_levels=4, radius=4, depths_dnG=None):
+        B, S, C, H_prev, W_prev = fmaps.shape
+        self.S, self.C, self.H, self.W = S, C, H_prev, W_prev
+
+        self.num_levels = num_levels
+        self.radius = radius
+        self.fmaps_pyramid = []
+        self.depth_pyramid = []
+        self.fmaps_pyramid.append(fmaps)
+        if depths_dnG is not None:
+           self.depth_pyramid.append(depths_dnG) 
+        for i in range(self.num_levels - 1):
+            if depths_dnG is not None:
+                depths_dnG_ = depths_dnG.reshape(B * S, 1, H_prev, W_prev)
+                depths_dnG_ = F.avg_pool2d(depths_dnG_, 2, stride=2)
+                _, _, H, W = depths_dnG_.shape
+                depths_dnG = depths_dnG_.reshape(B, S, 1, H, W)
+                self.depth_pyramid.append(depths_dnG)
+            fmaps_ = fmaps.reshape(B * S, C, H_prev, W_prev)
+            fmaps_ = F.avg_pool2d(fmaps_, 2, stride=2)
+            _, _, H, W = fmaps_.shape
+            fmaps = fmaps_.reshape(B, S, C, H, W)
+            H_prev = H
+            W_prev = W
+            self.fmaps_pyramid.append(fmaps)
+
+    def sample(self, coords):
+        r = self.radius
+        B, S, N, D = coords.shape
+        assert D == 2
+
+        H, W = self.H, self.W
+        out_pyramid = []
+        for i in range(self.num_levels):
+            corrs = self.corrs_pyramid[i]  # B, S, N, H, W
+            _, _, _, H, W = corrs.shape
+
+            dx = torch.linspace(-r, r, 2 * r + 1)
+            dy = torch.linspace(-r, r, 2 * r + 1)
+            delta = torch.stack(torch.meshgrid(dy, dx, indexing="ij"), axis=-1).to(
+                coords.device
+            )
+            centroid_lvl = coords.reshape(B * S * N, 1, 1, 2) / 2 ** i
+            delta_lvl = delta.view(1, 2 * r + 1, 2 * r + 1, 2)
+            coords_lvl = centroid_lvl + delta_lvl
+            corrs = bilinear_sampler(corrs.reshape(B * S * N, 1, H, W), coords_lvl)
+            corrs = corrs.view(B, S, N, -1)
+            out_pyramid.append(corrs)
+
+        out = torch.cat(out_pyramid, dim=-1)  # B, S, N, LRR*2
+        return out.contiguous().float()
+
+    def corr(self, targets):
+        B, S, N, C = targets.shape
+        assert C == self.C
+        assert S == self.S
+
+        fmap1 = targets
+
+        self.corrs_pyramid = []
+        for fmaps in self.fmaps_pyramid:
+            _, _, _, H, W = fmaps.shape
+            fmap2s = fmaps.view(B, S, C, H * W)
+            corrs = torch.matmul(fmap1, fmap2s)
+            corrs = corrs.view(B, S, N, H, W)
+            corrs = corrs / torch.sqrt(torch.tensor(C).float())
+            self.corrs_pyramid.append(corrs)
+    
+    def corr_sample(self, targets, coords, coords_dp=None):
+        B, S, N, C = targets.shape
+        r = self.radius
+        Dim_c = (2*r+1)**2
+        assert C == self.C
+        assert S == self.S
+
+        out_pyramid = []
+        out_pyramid_dp = []
+        for i in range(self.num_levels): 
+            dx = torch.linspace(-r, r, 2 * r + 1)
+            dy = torch.linspace(-r, r, 2 * r + 1)
+            delta = torch.stack(torch.meshgrid(dy, dx, indexing="ij"), axis=-1).to(
+                coords.device
+            )
+            centroid_lvl = coords.reshape(B * S * N, 1, 1, 2) / 2 ** i
+            delta_lvl = delta.view(1, 2 * r + 1, 2 * r + 1, 2)
+            coords_lvl = centroid_lvl + delta_lvl
+            fmaps = self.fmaps_pyramid[i]
+            _, _, _, H, W = fmaps.shape
+            fmap2s = fmaps.view(B*S, C, H, W)
+            if len(self.depth_pyramid)>0:
+                depths_dnG_i = self.depth_pyramid[i]
+                depths_dnG_i = depths_dnG_i.view(B*S, 1, H, W)
+                dnG_sample = bilinear_sampler(depths_dnG_i, coords_lvl.view(B*S,1,N*Dim_c,2))
+                dp_corrs = (dnG_sample.view(B*S,N,-1) - coords_dp[0]).abs()/coords_dp[0]
+                out_pyramid_dp.append(dp_corrs)
+            fmap2s_sample = bilinear_sampler(fmap2s, coords_lvl.view(B*S,1,N*Dim_c,2))
+            fmap2s_sample = fmap2s_sample.permute(0, 3, 1, 2) # B*S, N*Dim_c, C, -1
+            corrs = torch.matmul(targets.reshape(B*S*N, 1, -1), fmap2s_sample.reshape(B*S*N, Dim_c, -1).permute(0, 2, 1))
+            corrs = corrs / torch.sqrt(torch.tensor(C).float())
+            corrs = corrs.view(B, S, N, -1)
+            out_pyramid.append(corrs)
+
+        out = torch.cat(out_pyramid, dim=-1)  # B, S, N, LRR*2
+        if len(self.depth_pyramid)>0:
+            out_dp = torch.cat(out_pyramid_dp, dim=-1)
+            self.fcorrD = out_dp.contiguous().float()
+        else:
+            self.fcorrD = torch.zeros_like(out).contiguous().float()
+        return out.contiguous().float()
+
+
+class EUpdateFormer(nn.Module):
+    """
+    Transformer model that updates track estimates.
+    """
+
+    def __init__(
+        self,
+        space_depth=12,
+        time_depth=12,
+        input_dim=320,
+        hidden_size=384,
+        num_heads=8,
+        output_dim=130,
+        mlp_ratio=4.0,
+        vq_depth=3,
+        add_space_attn=True,
+        add_time_attn=True,
+        flash=True
+    ):
+        super().__init__()
+        self.out_channels = 2
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.add_space_attn = add_space_attn
+        self.input_transform = torch.nn.Linear(input_dim, hidden_size, bias=True)
+        self.flash = flash
+        self.flow_head = nn.Sequential(
+            nn.Linear(hidden_size, output_dim, bias=True),
+            nn.ReLU(inplace=True),
+            nn.Linear(output_dim, output_dim, bias=True),
+            nn.ReLU(inplace=True),
+            nn.Linear(output_dim, output_dim, bias=True)
+        )
+
+        cross_attn_kwargs = {
+            "d_model": 384,
+            "nhead": 4,
+            "layer_names": ['self', 'cross'] * 3,
+        }
+        self.gnn = LocalFeatureTransformer(cross_attn_kwargs)
+        
+        # Attention Modules in the temporal dimension         
+        self.time_blocks = nn.ModuleList(
+            [
+                AttnBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, flash=flash) if add_time_attn else nn.Identity()
+                for _ in range(time_depth)
+            ]
+        )
+
+        if add_space_attn:
+            self.space_blocks = nn.ModuleList(
+                [
+                    AttnBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, flash=flash)
+                    for _ in range(space_depth)
+                ]
+            )
+            assert len(self.time_blocks) >= len(self.space_blocks)
+
+        # Placeholder for the rigid transformation
+        self.RigidProj = nn.Linear(self.hidden_size, 128, bias=True)
+        self.Proj = nn.Linear(self.hidden_size, 128, bias=True)
+
+        self.se3_dec = nn.Linear(384, 3, bias=True)
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+    def forward(self, input_tensor, se3_feature):
+        """ Updating with Transformer
+
+        Args:
+            input_tensor: B, N, T, C
+            arap_embed: B, N, T, C
+        """
+        B, N, T, C = input_tensor.shape
+        x = self.input_transform(input_tensor)
+        tokens = x
+        K = 0
+        j = 0
+        for i in range(len(self.time_blocks)):
+            tokens_time = rearrange(tokens, "b n t c -> (b n) t c", b=B, t=T, n=N+K)
+            tokens_time = self.time_blocks[i](tokens_time)
+            tokens = rearrange(tokens_time, "(b n) t c -> b n t c ", b=B, t=T, n=N+K)
+            if self.add_space_attn and (
+                i % (len(self.time_blocks) // len(self.space_blocks)) == 0
+            ):
+                tokens_space = rearrange(tokens, "b n t c -> (b t) n c ", b=B, t=T, n=N)
+                tokens_space = self.space_blocks[j](tokens_space)
+                tokens = rearrange(tokens_space, "(b t) n c -> b n t c  ", b=B, t=T, n=N)
+                j += 1
+
+        B, N, S, _ = tokens.shape
+        feat0, feat1 = self.gnn(tokens.view(B*N*S, -1)[None,...], se3_feature[None, ...])
+
+        so3 = F.tanh(self.se3_dec(feat0.view(B*N*S, -1)[None,...].view(B, N, S, -1))/100)
+        flow = self.flow_head(feat0.view(B,N,S,-1))
+
+        return flow, _, _, feat1, so3
+
+
+class FusionFormer(nn.Module):
+    """ 
+    Fuse the feature tracks info with the low rank motion tokens
+    """
+    def __init__(
+        self,
+        d_model=64,
+        nhead=8,
+        attn_iters=4,
+        mlp_ratio=4.0,
+        flash=False,
+        input_dim=35,
+        output_dim=384+3,
+    ):
+        super().__init__()
+        self.flash = flash
+        self.in_proj = nn.ModuleList(
+            [
+                nn.Linear(input_dim, d_model)
+                for _ in range(2)
+            ]
+        )
+        self.out_proj = nn.Linear(d_model, output_dim, bias=True)
+        self.time_blocks = nn.ModuleList(
+            [
+                CrossAttnBlock(d_model, d_model, nhead, mlp_ratio=mlp_ratio)
+                for _ in range(attn_iters)
+            ]
+        )
+        self.space_blocks = nn.ModuleList(
+            [
+                AttnBlock(d_model, nhead, mlp_ratio=mlp_ratio, flash=self.flash)
+                for _ in range(attn_iters)
+            ]
+        )
+    
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        self.apply(_basic_init)
+        self.out_proj.weight.data.fill_(0)
+        self.out_proj.bias.data.fill_(0)
+
+    def forward(self, x, token_cls):
+        """ Fuse the feature tracks info with the low rank motion tokens
+
+        Args:
+            x: B, S, N, C
+            Traj_whole: B T N C
+        
+        """
+        B, S, N, C = x.shape
+        _, T, _, _ = token_cls.shape
+        x = self.in_proj[0](x)
+        token_cls = self.in_proj[1](token_cls)
+        token_cls = rearrange(token_cls, 'b t n c -> (b n) t c')
+
+        for i in range(len(self.space_blocks)):
+            x = rearrange(x, 'b s n c -> (b n) s c')
+            x = self.time_blocks[i](x, token_cls)
+            x = self.space_blocks[i](x.permute(1,0,2))
+            x = rearrange(x, '(b s) n c -> b s n c', b=B, s=S, n=N)
+
+        x = self.out_proj(x)
+        delta_xyz = x[..., :3]
+        feat_traj = x[..., 3:]
+        return delta_xyz, feat_traj   
+
+class Lie():
+    """
+    Lie algebra for SO(3) and SE(3) operations in PyTorch
+    """
+
+    def so3_to_SO3(self,w): # [...,3]
+        wx = self.skew_symmetric(w)
+        theta = w.norm(dim=-1)[...,None,None]
+        I = torch.eye(3,device=w.device,dtype=torch.float32)
+        A = self.taylor_A(theta)
+        B = self.taylor_B(theta)
+        R = I+A*wx+B*wx@wx
+        return R
+
+    def SO3_to_so3(self,R,eps=1e-7): # [...,3,3]
+        trace = R[...,0,0]+R[...,1,1]+R[...,2,2]
+        theta = ((trace-1)/2).clamp(-1+eps,1-eps).acos_()[...,None,None]%np.pi # ln(R) will explode if theta==pi
+        lnR = 1/(2*self.taylor_A(theta)+1e-8)*(R-R.transpose(-2,-1)) # FIXME: wei-chiu finds it weird
+        w0,w1,w2 = lnR[...,2,1],lnR[...,0,2],lnR[...,1,0]
+        w = torch.stack([w0,w1,w2],dim=-1)
+        return w
+
+    def se3_to_SE3(self,wu): # [...,3]
+        w,u = wu.split([3,3],dim=-1)
+        wx = self.skew_symmetric(w)
+        theta = w.norm(dim=-1)[...,None,None]
+        I = torch.eye(3,device=w.device,dtype=torch.float32)
+        A = self.taylor_A(theta)
+        B = self.taylor_B(theta)
+        C = self.taylor_C(theta)
+        R = I+A*wx+B*wx@wx
+        V = I+B*wx+C*wx@wx
+        Rt = torch.cat([R,(V@u[...,None])],dim=-1)
+        return Rt
+
+    def SE3_to_se3(self,Rt,eps=1e-8): # [...,3,4]
+        R,t = Rt.split([3,1],dim=-1)
+        w = self.SO3_to_so3(R)
+        wx = self.skew_symmetric(w)
+        theta = w.norm(dim=-1)[...,None,None]
+        I = torch.eye(3,device=w.device,dtype=torch.float32)
+        A = self.taylor_A(theta)
+        B = self.taylor_B(theta)
+        invV = I-0.5*wx+(1-A/(2*B))/(theta**2+eps)*wx@wx
+        u = (invV@t)[...,0]
+        wu = torch.cat([w,u],dim=-1)
+        return wu    
+
+    def skew_symmetric(self,w):
+        w0,w1,w2 = w.unbind(dim=-1)
+        O = torch.zeros_like(w0)
+        wx = torch.stack([torch.stack([O,-w2,w1],dim=-1),
+                          torch.stack([w2,O,-w0],dim=-1),
+                          torch.stack([-w1,w0,O],dim=-1)],dim=-2)
+        return wx
+
+    def taylor_A(self,x,nth=10):
+        # Taylor expansion of sin(x)/x
+        ans = torch.zeros_like(x)
+        denom = 1.
+        for i in range(nth+1):
+            if i>0: denom *= (2*i)*(2*i+1)
+            ans = ans+(-1)**i*x**(2*i)/denom
+        return ans
+    def taylor_B(self,x,nth=10):
+        # Taylor expansion of (1-cos(x))/x**2
+        ans = torch.zeros_like(x)
+        denom = 1.
+        for i in range(nth+1):
+            denom *= (2*i+1)*(2*i+2)
+            ans = ans+(-1)**i*x**(2*i)/denom
+        return ans
+    def taylor_C(self,x,nth=10):
+        # Taylor expansion of (x-sin(x))/x**3
+        ans = torch.zeros_like(x)
+        denom = 1.
+        for i in range(nth+1):
+            denom *= (2*i+2)*(2*i+3)
+            ans = ans+(-1)**i*x**(2*i)/denom
+        return ans
+    
+
+
+def pix2cam(coords, 
+            intr):
+    """
+    Args:
+        coords: [B, T, N, 3]
+        intr: [B, T, 3, 3]
+    """
+    coords=coords.detach()
+    B, S, N, _, = coords.shape
+    xy_src = coords.reshape(B*S*N, 3)
+    intr = intr[:, :, None, ...].repeat(1, 1, N, 1, 1).reshape(B*S*N, 3, 3)
+    xy_src = torch.cat([xy_src[..., :2], torch.ones_like(xy_src[..., :1])], dim=-1)
+    xyz_src = (torch.inverse(intr)@xy_src[...,None])[...,0]
+    dp_pred = coords[..., 2]
+    xyz_src_ = (xyz_src*(dp_pred.reshape(S*N, 1)))
+    xyz_src_ = xyz_src_.reshape(B, S, N, 3)
+    return xyz_src_
+
+def cam2pix(coords,
+            intr):
+    """
+    Args:
+        coords: [B, T, N, 3]
+        intr: [B, T, 3, 3]
+    """
+    coords=coords.detach()
+    B, S, N, _, = coords.shape
+    xy_src = coords.reshape(B*S*N, 3).clone()
+    intr = intr[:, :, None, ...].repeat(1, 1, N, 1, 1).reshape(B*S*N, 3, 3)
+    xy_src = xy_src / (xy_src[..., 2:]+1e-5)
+    xyz_src = (intr@xy_src[...,None])[...,0]
+    dp_pred = coords[..., 2]
+    xyz_src[...,2] *= dp_pred.reshape(S*N) 
+    xyz_src = xyz_src.reshape(B, S, N, 3)
+    return xyz_src
+
+def edgeMat(traj3d):
+    """ 
+    Args:
+        traj3d: [B, T, N, 3]
+    """
+    B, T, N, _ = traj3d.shape
+    traj3d = traj3d
+    traj3d = traj3d.view(B, T, N, 3)
+    traj3d = traj3d[..., None, :] - traj3d[..., None, :, :] # B, T, N, N, 3
+    edgeMat = traj3d.norm(dim=-1)  # B, T, N, N 
+    return edgeMat

das/spatracker/models/core/spatracker/dpt/base_model.py

@@ -0,0 +1,16 @@
+import torch
+
+
+class BaseModel(torch.nn.Module):
+    def load(self, path):
+        """Load model from file.
+
+        Args:
+            path (str): file path
+        """
+        parameters = torch.load(path, map_location=torch.device("cpu"))
+
+        if "optimizer" in parameters:
+            parameters = parameters["model"]
+
+        self.load_state_dict(parameters)

das/spatracker/models/core/spatracker/dpt/blocks.py

@@ -0,0 +1,394 @@
+import torch
+import torch.nn as nn
+
+from .vit import (
+    _make_pretrained_vitb_rn50_384,
+    _make_pretrained_vitl16_384,
+    _make_pretrained_vitb16_384,
+    forward_vit,
+    _make_pretrained_vit_tiny
+)
+
+
+def _make_encoder(
+    backbone,
+    features,
+    use_pretrained,
+    groups=1,
+    expand=False,
+    exportable=True,
+    hooks=None,
+    use_vit_only=False,
+    use_readout="ignore",
+    enable_attention_hooks=False,
+):
+    if backbone == "vitl16_384":
+        pretrained = _make_pretrained_vitl16_384(
+            use_pretrained,
+            hooks=hooks,
+            use_readout=use_readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+        scratch = _make_scratch(
+            [256, 512, 1024, 1024], features, groups=groups, expand=expand
+        )  # ViT-L/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb_rn50_384":
+        pretrained = _make_pretrained_vitb_rn50_384(
+            use_pretrained,
+            hooks=hooks,
+            use_vit_only=use_vit_only,
+            use_readout=use_readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+        scratch = _make_scratch(
+            [256, 512, 768, 768], features, groups=groups, expand=expand
+        )  # ViT-H/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb16_384":
+        pretrained = _make_pretrained_vitb16_384(
+            use_pretrained,
+            hooks=hooks,
+            use_readout=use_readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+        scratch = _make_scratch(
+            [96, 192, 384, 768], features, groups=groups, expand=expand
+        )  # ViT-B/16 - 84.6% Top1 (backbone)
+    elif backbone == "resnext101_wsl":
+        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
+        scratch = _make_scratch(
+            [256, 512, 1024, 2048], features, groups=groups, expand=expand
+        )  # efficientnet_lite3
+    elif backbone == "vit_tiny_r_s16_p8_384":
+        pretrained = _make_pretrained_vit_tiny(
+            use_pretrained,
+            hooks=hooks,
+            use_readout=use_readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+        scratch = _make_scratch(
+            [96, 192, 384, 768], features, groups=groups, expand=expand
+        )
+    else:
+        print(f"Backbone '{backbone}' not implemented")
+        assert False
+
+    return pretrained, scratch
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    out_shape4 = out_shape
+    if expand == True:
+        out_shape1 = out_shape
+        out_shape2 = out_shape * 2
+        out_shape3 = out_shape * 4
+        out_shape4 = out_shape * 8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0],
+        out_shape1,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1],
+        out_shape2,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2],
+        out_shape3,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer4_rn = nn.Conv2d(
+        in_shape[3],
+        out_shape4,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+
+    return scratch
+
+
+def _make_resnet_backbone(resnet):
+    pretrained = nn.Module()
+    pretrained.layer1 = nn.Sequential(
+        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
+    )
+
+    pretrained.layer2 = resnet.layer2
+    pretrained.layer3 = resnet.layer3
+    pretrained.layer4 = resnet.layer4
+
+    return pretrained
+
+
+def _make_pretrained_resnext101_wsl(use_pretrained):
+    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
+    return _make_resnet_backbone(resnet)
+
+
+class Interpolate(nn.Module):
+    """Interpolation module."""
+
+    def __init__(self, scale_factor, mode, align_corners=False):
+        """Init.
+
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x,
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            align_corners=self.align_corners,
+        )
+
+        return x
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module."""
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        out = self.relu(x)
+        out = self.conv1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+
+        return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block."""
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.resConfUnit1 = ResidualConvUnit(features)
+        self.resConfUnit2 = ResidualConvUnit(features)
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            output += self.resConfUnit1(xs[1])
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=True
+        )
+
+        return output
+
+
+class ResidualConvUnit_custom(nn.Module):
+    """Residual convolution module."""
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups = 1
+
+        self.conv1 = nn.Conv2d(
+            features,
+            features,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=not self.bn,
+            groups=self.groups,
+        )
+
+        self.conv2 = nn.Conv2d(
+            features,
+            features,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=not self.bn,
+            groups=self.groups,
+        )
+
+        if self.bn == True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn == True:
+            out = self.bn1(out)
+
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn == True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+        # return out + x
+
+
+class FeatureFusionBlock_custom(nn.Module):
+    """Feature fusion block."""
+
+    def __init__(
+        self,
+        features,
+        activation,
+        deconv=False,
+        bn=False,
+        expand=False,
+        align_corners=True,
+    ):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock_custom, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups = 1
+
+        self.expand = expand
+        out_features = features
+        if self.expand == True:
+            out_features = features // 2
+
+        self.out_conv = nn.Conv2d(
+            features,
+            out_features,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=True,
+            groups=1,
+        )
+
+        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+            # output += res
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output

das/spatracker/models/core/spatracker/dpt/midas_net.py

@@ -0,0 +1,77 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
+
+
+class MidasNet_large(BaseModel):
+    """Network for monocular depth estimation."""
+
+    def __init__(self, path=None, features=256, non_negative=True):
+        """Init.
+
+        Args:
+            path (str, optional): Path to saved model. Defaults to None.
+            features (int, optional): Number of features. Defaults to 256.
+            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+        """
+        print("Loading weights: ", path)
+
+        super(MidasNet_large, self).__init__()
+
+        use_pretrained = False if path is None else True
+
+        self.pretrained, self.scratch = _make_encoder(
+            backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained
+        )
+
+        self.scratch.refinenet4 = FeatureFusionBlock(features)
+        self.scratch.refinenet3 = FeatureFusionBlock(features)
+        self.scratch.refinenet2 = FeatureFusionBlock(features)
+        self.scratch.refinenet1 = FeatureFusionBlock(features)
+
+        self.scratch.output_conv = nn.Sequential(
+            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear"),
+            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+        )
+
+        if path:
+            self.load(path)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input data (image)
+
+        Returns:
+            tensor: depth
+        """
+
+        layer_1 = self.pretrained.layer1(x)
+        layer_2 = self.pretrained.layer2(layer_1)
+        layer_3 = self.pretrained.layer3(layer_2)
+        layer_4 = self.pretrained.layer4(layer_3)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return torch.squeeze(out, dim=1)

das/spatracker/models/core/spatracker/dpt/models.py

@@ -0,0 +1,231 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .base_model import BaseModel
+from .blocks import (
+    FeatureFusionBlock,
+    FeatureFusionBlock_custom,
+    Interpolate,
+    _make_encoder,
+    forward_vit,
+)
+
+
+def _make_fusion_block(features, use_bn):
+    return FeatureFusionBlock_custom(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+    )
+
+
+class DPT(BaseModel):
+    def __init__(
+        self,
+        head,
+        features=256,
+        backbone="vitb_rn50_384",
+        readout="project",
+        channels_last=False,
+        use_bn=True,
+        enable_attention_hooks=False,
+    ):
+
+        super(DPT, self).__init__()
+
+        self.channels_last = channels_last
+
+        hooks = {
+            "vitb_rn50_384": [0, 1, 8, 11],
+            "vitb16_384": [2, 5, 8, 11],
+            "vitl16_384": [5, 11, 17, 23],
+            "vit_tiny_r_s16_p8_384": [0, 1, 2, 3],
+        }
+
+        # Instantiate backbone and reassemble blocks
+        self.pretrained, self.scratch = _make_encoder(
+            backbone,
+            features,
+            False,  # Set to true of you want to train from scratch, uses ImageNet weights
+            groups=1,
+            expand=False,
+            exportable=False,
+            hooks=hooks[backbone],
+            use_readout=readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+        self.scratch.output_conv = head
+        
+        self.proj_out = nn.Sequential( 
+                        nn.Conv2d(
+                                256+512+384+384,
+                                256,
+                                kernel_size=3,
+                                padding=1,
+                                padding_mode="zeros",
+                            ),
+                        nn.BatchNorm2d(128 * 2),
+                        nn.ReLU(True),
+                        nn.Conv2d(
+                                128 * 2,
+                                128,
+                                kernel_size=3,
+                                padding=1,
+                                padding_mode="zeros",
+                            )
+                        )
+        
+
+    def forward(self, x, only_enc=False):
+        if self.channels_last == True:
+            x.contiguous(memory_format=torch.channels_last)
+        if only_enc:
+            layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
+            a = (layer_1)
+            b = (
+                F.interpolate(
+                layer_2,
+                scale_factor=2,
+                mode="bilinear",
+                align_corners=True,
+                )
+            )
+            c = (
+                F.interpolate(
+                layer_3,
+                scale_factor=8,
+                mode="bilinear",
+                align_corners=True,
+                )
+            )
+            d = (
+                F.interpolate(
+                layer_4,
+                scale_factor=16,
+                mode="bilinear",
+                align_corners=True,
+                )
+            )
+            x = self.proj_out(torch.cat([a, b, c, d], dim=1))
+            return x
+        else:
+            layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        _,_,H_out,W_out = path_1.size()
+        path_2_up = F.interpolate(path_2, size=(H_out,W_out), mode="bilinear", align_corners=True)
+        path_3_up = F.interpolate(path_3, size=(H_out,W_out), mode="bilinear", align_corners=True)
+        path_4_up = F.interpolate(path_4, size=(H_out,W_out), mode="bilinear", align_corners=True)
+
+        out = self.scratch.output_conv(path_1+path_2_up+path_3_up+path_4_up)
+
+        return out
+
+
+class DPTDepthModel(DPT):
+    def __init__(
+        self, path=None, non_negative=True, scale=1.0, shift=0.0, invert=False, **kwargs
+    ):
+        features = kwargs["features"] if "features" in kwargs else 256
+
+        self.scale = scale
+        self.shift = shift
+        self.invert = invert
+
+        head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+            nn.Identity(),
+        )
+
+        super().__init__(head, **kwargs)
+
+        if path is not None:
+            self.load(path)
+
+    def forward(self, x):
+        inv_depth = super().forward(x).squeeze(dim=1)
+
+        if self.invert:
+            depth = self.scale * inv_depth + self.shift
+            depth[depth < 1e-8] = 1e-8
+            depth = 1.0 / depth
+            return depth
+        else:
+            return inv_depth
+
+class DPTEncoder(DPT):
+    def __init__(
+        self, path=None, non_negative=True, scale=1.0, shift=0.0, invert=False, **kwargs
+    ):
+        features = kwargs["features"] if "features" in kwargs else 256
+
+        self.scale = scale
+        self.shift = shift
+
+        head = nn.Sequential(
+            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
+        )
+
+        super().__init__(head, **kwargs)
+
+        if path is not None:
+            self.load(path)
+
+    def forward(self, x):
+        features = super().forward(x, only_enc=True).squeeze(dim=1)
+
+        return features
+
+
+class DPTSegmentationModel(DPT):
+    def __init__(self, num_classes, path=None, **kwargs):
+
+        features = kwargs["features"] if "features" in kwargs else 256
+
+        kwargs["use_bn"] = True
+
+        head = nn.Sequential(
+            nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(features),
+            nn.ReLU(True),
+            nn.Dropout(0.1, False),
+            nn.Conv2d(features, num_classes, kernel_size=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+        )
+
+        super().__init__(head, **kwargs)
+
+        self.auxlayer = nn.Sequential(
+            nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(features),
+            nn.ReLU(True),
+            nn.Dropout(0.1, False),
+            nn.Conv2d(features, num_classes, kernel_size=1),
+        )
+
+        if path is not None:
+            self.load(path)

das/spatracker/models/core/spatracker/dpt/transforms.py

@@ -0,0 +1,231 @@
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(
+        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+    )
+
+    sample["disparity"] = cv2.resize(
+        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+    )
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height)."""
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+            sample["mask"] = cv2.resize(
+                sample["mask"].astype(np.float32),
+                (width, height),
+                interpolation=cv2.INTER_NEAREST,
+            )
+            sample["mask"] = sample["mask"].astype(bool)
+
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std."""
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input."""
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+
+        if "disparity" in sample:
+            disparity = sample["disparity"].astype(np.float32)
+            sample["disparity"] = np.ascontiguousarray(disparity)
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+
+        return sample

das/spatracker/models/core/spatracker/dpt/vit.py

@@ -0,0 +1,596 @@
+import torch
+import torch.nn as nn
+import timm
+import types
+import math
+import torch.nn.functional as F
+
+
+activations = {}
+
+
+def get_activation(name):
+    def hook(model, input, output):
+        activations[name] = output
+
+    return hook
+
+
+attention = {}
+
+
+def get_attention(name):
+    def hook(module, input, output):
+        x = input[0]
+        B, N, C = x.shape
+        qkv = (
+            module.qkv(x)
+            .reshape(B, N, 3, module.num_heads, C // module.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = (
+            qkv[0],
+            qkv[1],
+            qkv[2],
+        )  # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * module.scale
+
+        attn = attn.softmax(dim=-1)  # [:,:,1,1:]
+        attention[name] = attn
+
+    return hook
+
+
+def get_mean_attention_map(attn, token, shape):
+    attn = attn[:, :, token, 1:]
+    attn = attn.unflatten(2, torch.Size([shape[2] // 16, shape[3] // 16])).float()
+    attn = torch.nn.functional.interpolate(
+        attn, size=shape[2:], mode="bicubic", align_corners=False
+    ).squeeze(0)
+
+    all_attn = torch.mean(attn, 0)
+
+    return all_attn
+
+
+class Slice(nn.Module):
+    def __init__(self, start_index=1):
+        super(Slice, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        return x[:, self.start_index :]
+
+
+class AddReadout(nn.Module):
+    def __init__(self, start_index=1):
+        super(AddReadout, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        if self.start_index == 2:
+            readout = (x[:, 0] + x[:, 1]) / 2
+        else:
+            readout = x[:, 0]
+        return x[:, self.start_index :] + readout.unsqueeze(1)
+
+
+class ProjectReadout(nn.Module):
+    def __init__(self, in_features, start_index=1):
+        super(ProjectReadout, self).__init__()
+        self.start_index = start_index
+
+        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
+
+    def forward(self, x):
+        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
+        features = torch.cat((x[:, self.start_index :], readout), -1)
+
+        return self.project(features)
+
+
+class Transpose(nn.Module):
+    def __init__(self, dim0, dim1):
+        super(Transpose, self).__init__()
+        self.dim0 = dim0
+        self.dim1 = dim1
+
+    def forward(self, x):
+        x = x.transpose(self.dim0, self.dim1)
+        return x
+
+
+def forward_vit(pretrained, x):
+    b, c, h, w = x.shape
+
+    glob = pretrained.model.forward_flex(x)
+
+    layer_1 = pretrained.activations["1"]
+    layer_2 = pretrained.activations["2"]
+    layer_3 = pretrained.activations["3"]
+    layer_4 = pretrained.activations["4"]
+
+    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
+    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
+    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
+    layer_4 = pretrained.act_postprocess4[0:2](layer_4)
+
+    unflatten = nn.Sequential(
+        nn.Unflatten(
+            2,
+            torch.Size(
+                [
+                    h // pretrained.model.patch_size[1],
+                    w // pretrained.model.patch_size[0],
+                ]
+            ),
+        )
+    )
+
+    if layer_1.ndim == 3:
+        layer_1 = unflatten(layer_1)
+    if layer_2.ndim == 3:
+        layer_2 = unflatten(layer_2)
+    if layer_3.ndim == 3:
+        layer_3 = unflatten(layer_3)
+    if layer_4.ndim == 3:
+        layer_4 = unflatten(layer_4)
+
+    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
+    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
+    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
+    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
+
+    return layer_1, layer_2, layer_3, layer_4
+
+
+def _resize_pos_embed(self, posemb, gs_h, gs_w):
+    posemb_tok, posemb_grid = (
+        posemb[:, : self.start_index],
+        posemb[0, self.start_index :],
+    )
+
+    gs_old = int(math.sqrt(len(posemb_grid)))
+
+    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
+    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
+
+    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+
+    return posemb
+
+
+def forward_flex(self, x):
+    b, c, h, w = x.shape
+
+    pos_embed = self._resize_pos_embed(
+        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
+    )
+
+    B = x.shape[0]
+
+    if hasattr(self.patch_embed, "backbone"):
+        x = self.patch_embed.backbone(x)
+        if isinstance(x, (list, tuple)):
+            x = x[-1]  # last feature if backbone outputs list/tuple of features
+    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
+
+    if getattr(self, "dist_token", None) is not None:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        dist_token = self.dist_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, dist_token, x), dim=1)
+    else:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+    
+    x = x + pos_embed
+    x = self.pos_drop(x)
+
+    for blk in self.blocks:
+        x = blk(x)
+
+    x = self.norm(x)
+
+    return x
+
+
+def get_readout_oper(vit_features, features, use_readout, start_index=1):
+    if use_readout == "ignore":
+        readout_oper = [Slice(start_index)] * len(features)
+    elif use_readout == "add":
+        readout_oper = [AddReadout(start_index)] * len(features)
+    elif use_readout == "project":
+        readout_oper = [
+            ProjectReadout(vit_features, start_index) for out_feat in features
+        ]
+    else:
+        assert (
+            False
+        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
+
+    return readout_oper
+
+
+def _make_vit_b16_backbone(
+    model,
+    features=[96, 192, 384, 768],
+    size=[384, 384],
+    hooks=[2, 5, 8, 11],
+    vit_features=768,
+    use_readout="ignore",
+    start_index=1,
+    enable_attention_hooks=False,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    pretrained.activations = activations
+
+    if enable_attention_hooks:
+        pretrained.model.blocks[hooks[0]].attn.register_forward_hook(
+            get_attention("attn_1")
+        )
+        pretrained.model.blocks[hooks[1]].attn.register_forward_hook(
+            get_attention("attn_2")
+        )
+        pretrained.model.blocks[hooks[2]].attn.register_forward_hook(
+            get_attention("attn_3")
+        )
+        pretrained.model.blocks[hooks[3]].attn.register_forward_hook(
+            get_attention("attn_4")
+        )
+        pretrained.attention = attention
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    # 32, 48, 136, 384
+    pretrained.act_postprocess1 = nn.Sequential(
+        readout_oper[0],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[0],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[0],
+            out_channels=features[0],
+            kernel_size=4,
+            stride=4,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess2 = nn.Sequential(
+        readout_oper[1],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[1],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[1],
+            out_channels=features[1],
+            kernel_size=2,
+            stride=2,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_vit_b_rn50_backbone(
+    model,
+    features=[256, 512, 768, 768],
+    size=[384, 384],
+    hooks=[0, 1, 8, 11],
+    vit_features=384,
+    use_vit_only=False,
+    use_readout="ignore",
+    start_index=1,
+    enable_attention_hooks=False,
+):
+    pretrained = nn.Module()
+    pretrained.model = model
+    pretrained.model.patch_size = [32, 32]
+    ps = pretrained.model.patch_size[0]
+    if use_vit_only == True:
+        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    else:
+        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
+            get_activation("1")
+        )
+        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
+            get_activation("2")
+        )
+
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    if enable_attention_hooks:
+        pretrained.model.blocks[2].attn.register_forward_hook(get_attention("attn_1"))
+        pretrained.model.blocks[5].attn.register_forward_hook(get_attention("attn_2"))
+        pretrained.model.blocks[8].attn.register_forward_hook(get_attention("attn_3"))
+        pretrained.model.blocks[11].attn.register_forward_hook(get_attention("attn_4"))
+        pretrained.attention = attention
+
+    pretrained.activations = activations
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    if use_vit_only == True:
+        pretrained.act_postprocess1 = nn.Sequential(
+            readout_oper[0],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // ps, size[1] // ps])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[0],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[0],
+                out_channels=features[0],
+                kernel_size=4,
+                stride=4,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+
+        pretrained.act_postprocess2 = nn.Sequential(
+            readout_oper[1],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // ps, size[1] // ps])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[1],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[1],
+                out_channels=features[1],
+                kernel_size=2,
+                stride=2,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+    else:
+        pretrained.act_postprocess1 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+        pretrained.act_postprocess2 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // ps, size[1] // ps])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // ps, size[1] // ps])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [32, 32]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_pretrained_vitb_rn50_384(
+    pretrained,
+    use_readout="ignore",
+    hooks=None,
+    use_vit_only=False,
+    enable_attention_hooks=False,
+):
+    # model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
+    # model = timm.create_model("vit_tiny_r_s16_p8_384", pretrained=pretrained)
+    model = timm.create_model("vit_small_r26_s32_384", pretrained=pretrained)
+    hooks = [0, 1, 8, 11] if hooks == None else hooks
+    return _make_vit_b_rn50_backbone(
+        model,
+        features=[128, 256, 384, 384],
+        size=[384, 384],
+        hooks=hooks,
+        use_vit_only=use_vit_only,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+def _make_pretrained_vit_tiny(
+    pretrained,
+    use_readout="ignore",
+    hooks=None,
+    use_vit_only=False,
+    enable_attention_hooks=False,
+):
+    # model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
+    model = timm.create_model("vit_tiny_r_s16_p8_384", pretrained=pretrained)
+    import ipdb; ipdb.set_trace()
+    hooks = [0, 1, 8, 11] if hooks == None else hooks
+    return _make_vit_tiny_backbone(
+        model,
+        features=[256, 512, 768, 768],
+        size=[384, 384],
+        hooks=hooks,
+        use_vit_only=use_vit_only,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+def _make_pretrained_vitl16_384(
+    pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
+):
+    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
+
+    hooks = [5, 11, 17, 23] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[256, 512, 1024, 1024],
+        hooks=hooks,
+        vit_features=1024,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+
+def _make_pretrained_vitb16_384(
+    pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
+):  
+    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+
+def _make_pretrained_deitb16_384(
+    pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
+):
+    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+
+def _make_pretrained_deitb16_distil_384(
+    pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
+):
+    model = timm.create_model(
+        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
+    )
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        start_index=2,
+        enable_attention_hooks=enable_attention_hooks,
+    )

das/spatracker/models/core/spatracker/feature_net.py

@@ -0,0 +1,916 @@
+"""
+    Adapted from ConvONet
+    https://github.com/autonomousvision/convolutional_occupancy_networks/blob/838bea5b2f1314f2edbb68d05ebb0db49f1f3bd2/src/encoder/pointnet.py#L1
+"""
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+# from torch_scatter import scatter_mean, scatter_max
+from .unet import UNet
+from ..model_utils import (
+    vis_PCA
+)
+from einops import rearrange
+import numpy as np
+
+def compute_iou(occ1, occ2):
+    ''' Computes the Intersection over Union (IoU) value for two sets of
+    occupancy values.
+
+    Args:
+        occ1 (tensor): first set of occupancy values
+        occ2 (tensor): second set of occupancy values
+    '''
+    occ1 = np.asarray(occ1)
+    occ2 = np.asarray(occ2)
+
+    # Put all data in second dimension
+    # Also works for 1-dimensional data
+    if occ1.ndim >= 2:
+        occ1 = occ1.reshape(occ1.shape[0], -1)
+    if occ2.ndim >= 2:
+        occ2 = occ2.reshape(occ2.shape[0], -1)
+
+    # Convert to boolean values
+    occ1 = (occ1 >= 0.5)
+    occ2 = (occ2 >= 0.5)
+
+    # Compute IOU
+    area_union = (occ1 | occ2).astype(np.float32).sum(axis=-1)
+    area_intersect = (occ1 & occ2).astype(np.float32).sum(axis=-1)
+
+    iou = (area_intersect / area_union)
+
+    return iou
+
+
+def chamfer_distance(points1, points2, use_kdtree=True, give_id=False):
+    ''' Returns the chamfer distance for the sets of points.
+
+    Args:
+        points1 (numpy array): first point set
+        points2 (numpy array): second point set
+        use_kdtree (bool): whether to use a kdtree
+        give_id (bool): whether to return the IDs of nearest points
+    '''
+    if use_kdtree:
+        return chamfer_distance_kdtree(points1, points2, give_id=give_id)
+    else:
+        return chamfer_distance_naive(points1, points2)
+
+
+def chamfer_distance_naive(points1, points2):
+    ''' Naive implementation of the Chamfer distance.
+
+    Args:
+        points1 (numpy array): first point set
+        points2 (numpy array): second point set    
+    '''
+    assert(points1.size() == points2.size())
+    batch_size, T, _ = points1.size()
+
+    points1 = points1.view(batch_size, T, 1, 3)
+    points2 = points2.view(batch_size, 1, T, 3)
+
+    distances = (points1 - points2).pow(2).sum(-1)
+
+    chamfer1 = distances.min(dim=1)[0].mean(dim=1)
+    chamfer2 = distances.min(dim=2)[0].mean(dim=1)
+
+    chamfer = chamfer1 + chamfer2
+    return chamfer
+
+
+def chamfer_distance_kdtree(points1, points2, give_id=False):
+    ''' KD-tree based implementation of the Chamfer distance.
+
+    Args:
+        points1 (numpy array): first point set
+        points2 (numpy array): second point set
+        give_id (bool): whether to return the IDs of the nearest points
+    '''
+    # Points have size batch_size x T x 3
+    batch_size = points1.size(0)
+
+    # First convert points to numpy
+    points1_np = points1.detach().cpu().numpy()
+    points2_np = points2.detach().cpu().numpy()
+
+    # Get list of nearest neighbors indieces
+    idx_nn_12, _ = get_nearest_neighbors_indices_batch(points1_np, points2_np)
+    idx_nn_12 = torch.LongTensor(idx_nn_12).to(points1.device)
+    # Expands it as batch_size x 1 x 3
+    idx_nn_12_expand = idx_nn_12.view(batch_size, -1, 1).expand_as(points1)
+
+    # Get list of nearest neighbors indieces
+    idx_nn_21, _ = get_nearest_neighbors_indices_batch(points2_np, points1_np)
+    idx_nn_21 = torch.LongTensor(idx_nn_21).to(points1.device)
+    # Expands it as batch_size x T x 3
+    idx_nn_21_expand = idx_nn_21.view(batch_size, -1, 1).expand_as(points2)
+
+    # Compute nearest neighbors in points2 to points in points1
+    # points_12[i, j, k] = points2[i, idx_nn_12_expand[i, j, k], k]
+    points_12 = torch.gather(points2, dim=1, index=idx_nn_12_expand)
+
+    # Compute nearest neighbors in points1 to points in points2
+    # points_21[i, j, k] = points2[i, idx_nn_21_expand[i, j, k], k]
+    points_21 = torch.gather(points1, dim=1, index=idx_nn_21_expand)
+
+    # Compute chamfer distance
+    chamfer1 = (points1 - points_12).pow(2).sum(2).mean(1)
+    chamfer2 = (points2 - points_21).pow(2).sum(2).mean(1)
+
+    # Take sum
+    chamfer = chamfer1 + chamfer2
+
+    # If required, also return nearest neighbors
+    if give_id:
+        return chamfer1, chamfer2, idx_nn_12, idx_nn_21
+
+    return chamfer
+
+
+def get_nearest_neighbors_indices_batch(points_src, points_tgt, k=1):
+    ''' Returns the nearest neighbors for point sets batchwise.
+
+    Args:
+        points_src (numpy array): source points
+        points_tgt (numpy array): target points
+        k (int): number of nearest neighbors to return
+    '''
+    indices = []
+    distances = []
+
+    for (p1, p2) in zip(points_src, points_tgt):
+        raise NotImplementedError()
+        # kdtree = KDTree(p2)
+        dist, idx = kdtree.query(p1, k=k)
+        indices.append(idx)
+        distances.append(dist)
+
+    return indices, distances
+
+
+def make_3d_grid(bb_min, bb_max, shape):
+    ''' Makes a 3D grid.
+
+    Args:
+        bb_min (tuple): bounding box minimum
+        bb_max (tuple): bounding box maximum
+        shape (tuple): output shape
+    '''
+    size = shape[0] * shape[1] * shape[2]
+
+    pxs = torch.linspace(bb_min[0], bb_max[0], shape[0])
+    pys = torch.linspace(bb_min[1], bb_max[1], shape[1])
+    pzs = torch.linspace(bb_min[2], bb_max[2], shape[2])
+
+    pxs = pxs.view(-1, 1, 1).expand(*shape).contiguous().view(size)
+    pys = pys.view(1, -1, 1).expand(*shape).contiguous().view(size)
+    pzs = pzs.view(1, 1, -1).expand(*shape).contiguous().view(size)
+    p = torch.stack([pxs, pys, pzs], dim=1)
+
+    return p
+
+
+def transform_points(points, transform):
+    ''' Transforms points with regard to passed camera information.
+
+    Args:
+        points (tensor): points tensor
+        transform (tensor): transformation matrices
+    '''
+    assert(points.size(2) == 3)
+    assert(transform.size(1) == 3)
+    assert(points.size(0) == transform.size(0))
+
+    if transform.size(2) == 4:
+        R = transform[:, :, :3]
+        t = transform[:, :, 3:]
+        points_out = points @ R.transpose(1, 2) + t.transpose(1, 2)
+    elif transform.size(2) == 3:
+        K = transform
+        points_out = points @ K.transpose(1, 2)
+
+    return points_out
+
+
+def b_inv(b_mat):
+    ''' Performs batch matrix inversion.
+
+    Arguments:
+        b_mat: the batch of matrices that should be inverted
+    '''
+
+    eye = b_mat.new_ones(b_mat.size(-1)).diag().expand_as(b_mat)
+    b_inv, _ = torch.gesv(eye, b_mat)
+    return b_inv
+
+def project_to_camera(points, transform):
+    ''' Projects points to the camera plane.
+
+    Args:
+        points (tensor): points tensor
+        transform (tensor): transformation matrices
+    '''
+    p_camera = transform_points(points, transform)
+    p_camera = p_camera[..., :2] / p_camera[..., 2:]
+    return p_camera
+
+
+def fix_Rt_camera(Rt, loc, scale):
+    ''' Fixes Rt camera matrix.
+
+    Args:
+        Rt (tensor): Rt camera matrix
+        loc (tensor): location
+        scale (float): scale
+    '''
+    # Rt is B x 3 x 4
+    # loc is B x 3 and scale is B
+    batch_size = Rt.size(0)
+    R = Rt[:, :, :3]
+    t = Rt[:, :, 3:]
+
+    scale = scale.view(batch_size, 1, 1)
+    R_new = R * scale
+    t_new = t + R @ loc.unsqueeze(2)
+
+    Rt_new = torch.cat([R_new, t_new], dim=2)
+
+    assert(Rt_new.size() == (batch_size, 3, 4))
+    return Rt_new
+
+def normalize_coordinate(p, padding=0.1, plane='xz'):
+    ''' Normalize coordinate to [0, 1] for unit cube experiments
+
+    Args:
+        p (tensor): point
+        padding (float): conventional padding paramter of ONet for unit cube, so [-0.5, 0.5] -> [-0.55, 0.55]
+        plane (str): plane feature type, ['xz', 'xy', 'yz']
+    '''
+    # breakpoint()
+    if plane == 'xz':
+        xy = p[:, :, [0, 2]]
+    elif plane =='xy':
+        xy = p[:, :, [0, 1]]
+    else:
+        xy = p[:, :, [1, 2]]
+        
+    xy = torch.clamp(xy, min=1e-6, max=1. - 1e-6)
+
+    # xy_new = xy / (1 + padding + 10e-6) # (-0.5, 0.5)
+    # xy_new = xy_new + 0.5 # range (0, 1)
+
+    # # f there are outliers out of the range
+    # if xy_new.max() >= 1:
+    #     xy_new[xy_new >= 1] = 1 - 10e-6
+    # if xy_new.min() < 0:
+    #     xy_new[xy_new < 0] = 0.0
+    # xy_new = (xy + 1.) / 2.
+    return xy
+
+def normalize_3d_coordinate(p, padding=0.1):
+    ''' Normalize coordinate to [0, 1] for unit cube experiments.
+        Corresponds to our 3D model
+
+    Args:
+        p (tensor): point
+        padding (float): conventional padding paramter of ONet for unit cube, so [-0.5, 0.5] -> [-0.55, 0.55]
+    '''
+    
+    p_nor = p / (1 + padding + 10e-4) # (-0.5, 0.5)
+    p_nor = p_nor + 0.5 # range (0, 1)
+    # f there are outliers out of the range
+    if p_nor.max() >= 1:
+        p_nor[p_nor >= 1] = 1 - 10e-4
+    if p_nor.min() < 0:
+        p_nor[p_nor < 0] = 0.0
+    return p_nor
+
+def normalize_coord(p, vol_range, plane='xz'):
+    ''' Normalize coordinate to [0, 1] for sliding-window experiments
+
+    Args:
+        p (tensor): point
+        vol_range (numpy array): volume boundary
+        plane (str): feature type, ['xz', 'xy', 'yz'] - canonical planes; ['grid'] - grid volume
+    '''
+    p[:, 0] = (p[:, 0] - vol_range[0][0]) / (vol_range[1][0] - vol_range[0][0])
+    p[:, 1] = (p[:, 1] - vol_range[0][1]) / (vol_range[1][1] - vol_range[0][1])
+    p[:, 2] = (p[:, 2] - vol_range[0][2]) / (vol_range[1][2] - vol_range[0][2])
+    
+    if plane == 'xz':
+        x = p[:, [0, 2]]
+    elif plane =='xy':
+        x = p[:, [0, 1]]
+    elif plane =='yz':
+        x = p[:, [1, 2]]
+    else:
+        x = p    
+    return x
+
+def coordinate2index(x, reso, coord_type='2d'):
+    ''' Normalize coordinate to [0, 1] for unit cube experiments.
+        Corresponds to our 3D model
+
+    Args:
+        x (tensor): coordinate
+        reso (int): defined resolution
+        coord_type (str): coordinate type
+    '''
+    x = (x * reso).long()
+    if coord_type == '2d': # plane
+        index = x[:, :, 0] + reso * x[:, :, 1]
+    elif coord_type == '3d': # grid
+        index = x[:, :, 0] + reso * (x[:, :, 1] + reso * x[:, :, 2])
+    index = index[:, None, :]
+    return index
+
+def coord2index(p, vol_range, reso=None, plane='xz'):
+    ''' Normalize coordinate to [0, 1] for sliding-window experiments.
+        Corresponds to our 3D model
+
+    Args:
+        p (tensor): points
+        vol_range (numpy array): volume boundary
+        reso (int): defined resolution
+        plane (str): feature type, ['xz', 'xy', 'yz'] - canonical planes; ['grid'] - grid volume
+    '''
+    # normalize to [0, 1]
+    x = normalize_coord(p, vol_range, plane=plane)
+    
+    if isinstance(x, np.ndarray):
+        x = np.floor(x * reso).astype(int)
+    else: #* pytorch tensor
+        x = (x * reso).long()
+
+    if x.shape[1] == 2:
+        index = x[:, 0] + reso * x[:, 1]
+        index[index > reso**2] = reso**2
+    elif x.shape[1] == 3:
+        index = x[:, 0] + reso * (x[:, 1] + reso * x[:, 2])
+        index[index > reso**3] = reso**3
+    
+    return index[None]
+
+def update_reso(reso, depth):
+    ''' Update the defined resolution so that UNet can process.
+
+    Args:
+        reso (int): defined resolution
+        depth (int): U-Net number of layers
+    '''
+    base = 2**(int(depth) - 1)
+    if ~(reso / base).is_integer(): # when this is not integer, U-Net dimension error
+        for i in range(base):
+            if ((reso + i) / base).is_integer():
+                reso = reso + i
+                break    
+    return reso
+
+def decide_total_volume_range(query_vol_metric, recep_field, unit_size, unet_depth):
+    ''' Update the defined resolution so that UNet can process.
+
+    Args:
+        query_vol_metric (numpy array): query volume size
+        recep_field (int): defined the receptive field for U-Net
+        unit_size (float): the defined voxel size
+        unet_depth (int): U-Net number of layers
+    '''
+    reso = query_vol_metric / unit_size + recep_field - 1
+    reso = update_reso(int(reso), unet_depth) # make sure input reso can be processed by UNet
+    input_vol_metric = reso * unit_size
+    p_c = np.array([0.0, 0.0, 0.0]).astype(np.float32)
+    lb_input_vol, ub_input_vol = p_c - input_vol_metric/2, p_c + input_vol_metric/2
+    lb_query_vol, ub_query_vol = p_c - query_vol_metric/2, p_c + query_vol_metric/2
+    input_vol = [lb_input_vol, ub_input_vol]
+    query_vol = [lb_query_vol, ub_query_vol]
+
+    # handle the case when resolution is too large
+    if reso > 10000:
+        reso = 1
+    
+    return input_vol, query_vol, reso
+
+def add_key(base, new, base_name, new_name, device=None):
+    ''' Add new keys to the given input
+
+    Args:
+        base (tensor): inputs
+        new (tensor): new info for the inputs
+        base_name (str): name for the input
+        new_name (str): name for the new info
+        device (device): pytorch device
+    '''
+    if (new is not None) and (isinstance(new, dict)):
+        if device is not None:
+            for key in new.keys():
+                new[key] = new[key].to(device)
+        base = {base_name: base,
+                new_name: new}
+    return base
+
+class map2local(object):
+    ''' Add new keys to the given input
+
+    Args:
+        s (float): the defined voxel size
+        pos_encoding (str): method for the positional encoding, linear|sin_cos
+    '''
+    def __init__(self, s, pos_encoding='linear'):
+        super().__init__()
+        self.s = s
+        self.pe = positional_encoding(basis_function=pos_encoding)
+
+    def __call__(self, p):
+        p = torch.remainder(p, self.s) / self.s # always possitive
+        # p = torch.fmod(p, self.s) / self.s # same sign as input p!
+        p = self.pe(p)
+        return p
+
+class positional_encoding(object):
+    ''' Positional Encoding (presented in NeRF)
+
+    Args:
+        basis_function (str): basis function
+    '''
+    def __init__(self, basis_function='sin_cos'):
+        super().__init__()
+        self.func = basis_function
+
+        L = 10
+        freq_bands = 2.**(np.linspace(0, L-1, L))
+        self.freq_bands = freq_bands * math.pi
+
+    def __call__(self, p):
+        if self.func == 'sin_cos':
+            out = []
+            p = 2.0 * p - 1.0 # chagne to the range [-1, 1]
+            for freq in self.freq_bands:
+                out.append(torch.sin(freq * p))
+                out.append(torch.cos(freq * p))
+            p = torch.cat(out, dim=2)
+        return p
+
+# Resnet Blocks
+class ResnetBlockFC(nn.Module):
+    ''' Fully connected ResNet Block class.
+
+    Args:
+        size_in (int): input dimension
+        size_out (int): output dimension
+        size_h (int): hidden dimension
+    '''
+
+    def __init__(self, size_in, size_out=None, size_h=None):
+        super().__init__()
+        # Attributes
+        if size_out is None:
+            size_out = size_in
+
+        if size_h is None:
+            size_h = min(size_in, size_out)
+
+        self.size_in = size_in
+        self.size_h = size_h
+        self.size_out = size_out
+        # Submodules
+        self.fc_0 = nn.Linear(size_in, size_h)
+        self.fc_1 = nn.Linear(size_h, size_out)
+        self.actvn = nn.ReLU()
+
+        if size_in == size_out:
+            self.shortcut = None
+        else:
+            self.shortcut = nn.Linear(size_in, size_out, bias=False)
+        # Initialization
+        nn.init.zeros_(self.fc_1.weight)
+
+    def forward(self, x):
+        net = self.fc_0(self.actvn(x))
+        dx = self.fc_1(self.actvn(net))
+
+        if self.shortcut is not None:
+            x_s = self.shortcut(x)
+        else:
+            x_s = x
+
+        return x_s + dx
+    
+
+
+'''
+------------------ the key model for Pointnet ----------------------------
+'''
+
+
+class LocalSoftSplat(nn.Module):
+
+    def __init__(self, ch=128, dim=3, hidden_dim=128, scatter_type='max', 
+                 unet=True, unet_kwargs=None, unet3d=False, unet3d_kwargs=None, 
+                 hw=None, grid_resolution=None, plane_type='xz', padding=0.1,
+                 n_blocks=4, splat_func=None):
+        super().__init__()
+        c_dim = ch
+        
+        self.c_dim = c_dim
+
+        self.fc_pos = nn.Linear(dim, 2*hidden_dim)
+        self.blocks = nn.ModuleList([
+            ResnetBlockFC(2*hidden_dim, hidden_dim) for i in range(n_blocks)
+        ])
+        self.fc_c = nn.Linear(hidden_dim, c_dim)
+
+        self.actvn = nn.ReLU()
+        self.hidden_dim = hidden_dim
+
+        if unet:
+            self.unet = UNet(c_dim, in_channels=c_dim, **unet_kwargs)
+        else:
+            self.unet = None
+
+        # get splat func
+        self.splat_func = splat_func
+    def forward(self, img_feat, 
+                Fxy2xz, Fxy2yz, Dz, gridxy=None):
+        """
+        Args:
+            img_feat (tensor): image features
+            Fxy2xz (tensor): transformation matrix from xy to xz
+            Fxy2yz (tensor): transformation matrix from xy to yz
+        """
+        B, T, _, H, W = img_feat.shape
+        fea_reshp = rearrange(img_feat, 'b t c h w -> (b h w) t c',
+                               c=img_feat.shape[2], h=H, w=W)
+
+        gridyz = gridxy + Fxy2yz
+        gridxz = gridxy + Fxy2xz
+        # normalize 
+        gridyz[:, 0, ...] = (gridyz[:, 0, ...] / (H - 1) - 0.5) * 2
+        gridyz[:, 1, ...] = (gridyz[:, 1, ...] / (Dz - 1) - 0.5) * 2
+        gridxz[:, 0, ...] = (gridxz[:, 0, ...] / (W - 1) - 0.5) * 2
+        gridxz[:, 1, ...] = (gridxz[:, 1, ...] / (Dz - 1) - 0.5) * 2
+        if len(self.blocks) > 0:
+            net = self.fc_pos(fea_reshp)
+            net = self.blocks[0](net)
+            for block in self.blocks[1:]:
+                # splat and fusion
+                net_plane = rearrange(net, '(b h w) t c -> (b t) c h w', b=B, h=H, w=W)
+                
+                net_planeYZ = self.splat_func(net_plane, Fxy2yz, None,
+                                    strMode="avg", tenoutH=Dz, tenoutW=H)
+                
+                net_planeXZ = self.splat_func(net_plane, Fxy2xz, None,
+                                    strMode="avg", tenoutH=Dz, tenoutW=W)
+
+                net_plane = net_plane + (
+                    F.grid_sample(
+                    net_planeYZ, gridyz.permute(0,2,3,1), mode='bilinear', padding_mode='border') +
+                    F.grid_sample(
+                    net_planeXZ, gridxz.permute(0,2,3,1), mode='bilinear', padding_mode='border')
+                                    )
+                
+                pooled = rearrange(net_plane, 't c h w -> (h w) t c',
+                            c=net_plane.shape[1], h=H, w=W)
+                
+                net = torch.cat([net, pooled], dim=2)
+                net = block(net) 
+
+            c = self.fc_c(net)
+            net_plane = rearrange(c, '(b h w) t c -> (b t) c h w', b=B, h=H, w=W)
+        else:
+            net_plane = rearrange(img_feat, 'b t c h w -> (b t) c h w',
+                                c=img_feat.shape[2], h=H, w=W)
+        net_planeYZ = self.splat_func(net_plane, Fxy2yz, None,
+                                strMode="avg", tenoutH=Dz, tenoutW=H)
+        net_planeXZ = self.splat_func(net_plane, Fxy2xz, None,
+                                strMode="avg", tenoutH=Dz, tenoutW=W)
+        
+        return net_plane[None], net_planeYZ[None], net_planeXZ[None]
+
+
+
+class LocalPoolPointnet(nn.Module):
+    ''' PointNet-based encoder network with ResNet blocks for each point.
+        Number of input points are fixed.
+    
+    Args:
+        c_dim (int): dimension of latent code c
+        dim (int): input points dimension
+        hidden_dim (int): hidden dimension of the network
+        scatter_type (str): feature aggregation when doing local pooling
+        unet (bool): weather to use U-Net
+        unet_kwargs (str): U-Net parameters
+        unet3d (bool): weather to use 3D U-Net
+        unet3d_kwargs (str): 3D U-Net parameters
+        plane_resolution (int): defined resolution for plane feature
+        grid_resolution (int): defined resolution for grid feature 
+        plane_type (str): feature type, 'xz' - 1-plane, ['xz', 'xy', 'yz'] - 3-plane, ['grid'] - 3D grid volume
+        padding (float): conventional padding paramter of ONet for unit cube, so [-0.5, 0.5] -> [-0.55, 0.55]
+        n_blocks (int): number of blocks ResNetBlockFC layers
+    '''
+
+    def __init__(self, ch=128, dim=3, hidden_dim=128, scatter_type='max', 
+                 unet=True, unet_kwargs=None, unet3d=False, unet3d_kwargs=None, 
+                 hw=None, grid_resolution=None, plane_type='xz', padding=0.1, n_blocks=5):
+        super().__init__()
+        c_dim = ch
+        unet3d = False
+        plane_type = ['xy', 'xz', 'yz']
+        plane_resolution = hw
+        
+        self.c_dim = c_dim
+
+        self.fc_pos = nn.Linear(dim, 2*hidden_dim)
+        self.blocks = nn.ModuleList([
+            ResnetBlockFC(2*hidden_dim, hidden_dim) for i in range(n_blocks)
+        ])
+        self.fc_c = nn.Linear(hidden_dim, c_dim)
+
+        self.actvn = nn.ReLU()
+        self.hidden_dim = hidden_dim
+
+        if unet:
+            self.unet = UNet(c_dim, in_channels=c_dim, **unet_kwargs)
+        else:
+            self.unet = None
+
+        if unet3d:
+            # self.unet3d = UNet3D(**unet3d_kwargs)
+            raise NotImplementedError()
+        else:
+            self.unet3d = None
+
+        self.reso_plane = plane_resolution
+        self.reso_grid = grid_resolution
+        self.plane_type = plane_type
+        self.padding = padding
+
+        if scatter_type == 'max':
+            self.scatter = scatter_max
+        elif scatter_type == 'mean':
+            self.scatter = scatter_mean
+        else:
+            raise ValueError('incorrect scatter type')
+
+    def generate_plane_features(self, p, c, plane='xz'):
+        # acquire indices of features in plane
+        xy = normalize_coordinate(p.clone(), plane=plane, padding=self.padding) # normalize to the range of (0, 1)
+        index = coordinate2index(xy, self.reso_plane)
+
+        # scatter plane features from points
+        fea_plane = c.new_zeros(p.size(0), self.c_dim, self.reso_plane**2)
+        c = c.permute(0, 2, 1) # B x 512 x T
+        fea_plane = scatter_mean(c, index, out=fea_plane) # B x 512 x reso^2
+        fea_plane = fea_plane.reshape(p.size(0), self.c_dim, self.reso_plane, self.reso_plane) # sparce matrix (B x 512 x reso x reso)
+
+        # process the plane features with UNet
+        if self.unet is not None:
+            fea_plane = self.unet(fea_plane)
+
+        return fea_plane
+
+    def generate_grid_features(self, p, c):
+        p_nor = normalize_3d_coordinate(p.clone(), padding=self.padding)
+        index = coordinate2index(p_nor, self.reso_grid, coord_type='3d')
+        # scatter grid features from points
+        fea_grid = c.new_zeros(p.size(0), self.c_dim, self.reso_grid**3)
+        c = c.permute(0, 2, 1)
+        fea_grid = scatter_mean(c, index, out=fea_grid) # B x C x reso^3
+        fea_grid = fea_grid.reshape(p.size(0), self.c_dim, self.reso_grid, self.reso_grid, self.reso_grid) # sparce matrix (B x 512 x reso x reso)
+
+        if self.unet3d is not None:
+            fea_grid = self.unet3d(fea_grid)
+
+        return fea_grid
+
+    def pool_local(self, xy, index, c):
+        bs, fea_dim = c.size(0), c.size(2)
+        keys = xy.keys()
+
+        c_out = 0
+        for key in keys:
+            # scatter plane features from points
+            if key == 'grid':
+                fea = self.scatter(c.permute(0, 2, 1), index[key], dim_size=self.reso_grid**3)
+            else:
+                c_permute = c.permute(0, 2, 1)
+                fea = self.scatter(c_permute, index[key], dim_size=self.reso_plane**2)
+            if self.scatter == scatter_max:
+                fea = fea[0]
+            # gather feature back to points
+            fea = fea.gather(dim=2, index=index[key].expand(-1, fea_dim, -1))
+            c_out = c_out + fea
+        return c_out.permute(0, 2, 1)
+
+
+    def forward(self, p_input, img_feats=None):
+        """
+        Args:
+            p_input (tensor): input points    T 3 H W
+            img_feats (tensor): image features  T C H W
+        """
+        T, _, H, W = img_feats.size()
+        p = rearrange(p_input, 't c h w -> (h w) t c', c=3, h=H, w=W)
+        fea_reshp = rearrange(img_feats, 't c h w -> (h w) t c',
+                               c=img_feats.shape[1], h=H, w=W)
+
+        # acquire the index for each point
+        coord = {}
+        index = {}
+        if 'xz' in self.plane_type:
+            coord['xz'] = normalize_coordinate(p.clone(), plane='xz', padding=self.padding)
+            index['xz'] = coordinate2index(coord['xz'], self.reso_plane)
+        if 'xy' in self.plane_type:
+            coord['xy'] = normalize_coordinate(p.clone(), plane='xy', padding=self.padding)
+            index['xy'] = coordinate2index(coord['xy'], self.reso_plane)
+        if 'yz' in self.plane_type:
+            coord['yz'] = normalize_coordinate(p.clone(), plane='yz', padding=self.padding)
+            index['yz'] = coordinate2index(coord['yz'], self.reso_plane)
+        if 'grid' in self.plane_type:
+            coord['grid'] = normalize_3d_coordinate(p.clone(), padding=self.padding)
+            index['grid'] = coordinate2index(coord['grid'], self.reso_grid, coord_type='3d')
+        
+        net = self.fc_pos(p) + fea_reshp
+        net = self.blocks[0](net)
+        for block in self.blocks[1:]:
+            pooled = self.pool_local(coord, index, net)
+            net = torch.cat([net, pooled], dim=2)
+            net = block(net)
+
+        c = self.fc_c(net)
+
+        fea = {}
+
+        if 'grid' in self.plane_type:
+            fea['grid'] = self.generate_grid_features(p, c)
+        if 'xz' in self.plane_type:
+            fea['xz'] = self.generate_plane_features(p, c, plane='xz')
+        if 'xy' in self.plane_type:
+            fea['xy'] = self.generate_plane_features(p, c, plane='xy')
+        if 'yz' in self.plane_type:
+            fea['yz'] = self.generate_plane_features(p, c, plane='yz')
+
+        ret = torch.stack([fea['xy'], fea['xz'], fea['yz']]).permute((1, 0, 2, 3, 4))
+        return ret
+
+class PatchLocalPoolPointnet(nn.Module):
+    ''' PointNet-based encoder network with ResNet blocks.
+        First transform input points to local system based on the given voxel size.
+        Support non-fixed number of point cloud, but need to precompute the index
+    
+    Args:
+        c_dim (int): dimension of latent code c
+        dim (int): input points dimension
+        hidden_dim (int): hidden dimension of the network
+        scatter_type (str): feature aggregation when doing local pooling
+        unet (bool): weather to use U-Net
+        unet_kwargs (str): U-Net parameters
+        unet3d (bool): weather to use 3D U-Net
+        unet3d_kwargs (str): 3D U-Net parameters
+        plane_resolution (int): defined resolution for plane feature
+        grid_resolution (int): defined resolution for grid feature 
+        plane_type (str): feature type, 'xz' - 1-plane, ['xz', 'xy', 'yz'] - 3-plane, ['grid'] - 3D grid volume
+        padding (float): conventional padding paramter of ONet for unit cube, so [-0.5, 0.5] -> [-0.55, 0.55]
+        n_blocks (int): number of blocks ResNetBlockFC layers
+        local_coord (bool): whether to use local coordinate
+        pos_encoding (str): method for the positional encoding, linear|sin_cos
+        unit_size (float): defined voxel unit size for local system
+    '''
+
+    def __init__(self, c_dim=128, dim=3, hidden_dim=128, scatter_type='max', 
+                 unet=False, unet_kwargs=None, unet3d=False, unet3d_kwargs=None, 
+                 plane_resolution=None, grid_resolution=None, plane_type='xz', padding=0.1, n_blocks=5, 
+                 local_coord=False, pos_encoding='linear', unit_size=0.1):
+        super().__init__()
+        self.c_dim = c_dim
+
+        self.blocks = nn.ModuleList([
+            ResnetBlockFC(2*hidden_dim, hidden_dim) for i in range(n_blocks)
+        ])
+        self.fc_c = nn.Linear(hidden_dim, c_dim)
+
+        self.actvn = nn.ReLU()
+        self.hidden_dim = hidden_dim
+        self.reso_plane = plane_resolution
+        self.reso_grid = grid_resolution
+        self.plane_type = plane_type
+        self.padding = padding
+
+        if unet:
+            self.unet = UNet(c_dim, in_channels=c_dim, **unet_kwargs)
+        else:
+            self.unet = None
+
+        if unet3d:
+            # self.unet3d = UNet3D(**unet3d_kwargs)
+            raise NotImplementedError()
+        else:
+            self.unet3d = None
+
+        if scatter_type == 'max':
+            self.scatter = scatter_max
+        elif scatter_type == 'mean':
+            self.scatter = scatter_mean
+        else:
+            raise ValueError('incorrect scatter type')
+
+        if local_coord:
+            self.map2local = map2local(unit_size, pos_encoding=pos_encoding)
+        else:
+            self.map2local = None
+        
+        if pos_encoding == 'sin_cos':
+            self.fc_pos = nn.Linear(60, 2*hidden_dim)
+        else:
+            self.fc_pos = nn.Linear(dim, 2*hidden_dim)
+
+    def generate_plane_features(self, index, c):
+        c = c.permute(0, 2, 1) 
+        # scatter plane features from points
+        if index.max() < self.reso_plane**2:
+            fea_plane = c.new_zeros(c.size(0), self.c_dim, self.reso_plane**2)
+            fea_plane = scatter_mean(c, index, out=fea_plane) # B x c_dim x reso^2
+        else:
+            fea_plane = scatter_mean(c, index) # B x c_dim x reso^2
+            if fea_plane.shape[-1] > self.reso_plane**2: # deal with outliers
+                fea_plane = fea_plane[:, :, :-1]
+        
+        fea_plane = fea_plane.reshape(c.size(0), self.c_dim, self.reso_plane, self.reso_plane)
+
+        # process the plane features with UNet
+        if self.unet is not None:
+            fea_plane = self.unet(fea_plane)
+
+        return fea_plane
+
+    def generate_grid_features(self, index, c):
+        # scatter grid features from points        
+        c = c.permute(0, 2, 1)
+        if index.max() < self.reso_grid**3:
+            fea_grid = c.new_zeros(c.size(0), self.c_dim, self.reso_grid**3)
+            fea_grid = scatter_mean(c, index, out=fea_grid) # B x c_dim x reso^3
+        else:
+            fea_grid = scatter_mean(c, index) # B x c_dim x reso^3
+            if fea_grid.shape[-1] > self.reso_grid**3: # deal with outliers
+                fea_grid = fea_grid[:, :, :-1]
+        fea_grid = fea_grid.reshape(c.size(0), self.c_dim, self.reso_grid, self.reso_grid, self.reso_grid)
+
+        if self.unet3d is not None:
+            fea_grid = self.unet3d(fea_grid)
+
+        return fea_grid
+
+    def pool_local(self, index, c):
+        bs, fea_dim = c.size(0), c.size(2)
+        keys = index.keys()
+
+        c_out = 0
+        for key in keys:
+            # scatter plane features from points
+            if key == 'grid':
+                fea = self.scatter(c.permute(0, 2, 1), index[key])
+            else:
+                fea = self.scatter(c.permute(0, 2, 1), index[key])
+            if self.scatter == scatter_max:
+                fea = fea[0]
+            # gather feature back to points
+            fea = fea.gather(dim=2, index=index[key].expand(-1, fea_dim, -1))
+            c_out += fea
+        return c_out.permute(0, 2, 1)
+
+
+    def forward(self, inputs):
+        p = inputs['points']
+        index = inputs['index']
+    
+        batch_size, T, D = p.size()
+
+        if self.map2local:
+            pp = self.map2local(p)
+            net = self.fc_pos(pp)
+        else:
+            net = self.fc_pos(p)
+
+        net = self.blocks[0](net)
+        for block in self.blocks[1:]:
+            pooled = self.pool_local(index, net)
+            net = torch.cat([net, pooled], dim=2)
+            net = block(net)
+
+        c = self.fc_c(net)
+
+        fea = {}
+        if 'grid' in self.plane_type:
+            fea['grid'] = self.generate_grid_features(index['grid'], c)
+        if 'xz' in self.plane_type:
+            fea['xz'] = self.generate_plane_features(index['xz'], c)
+        if 'xy' in self.plane_type:
+            fea['xy'] = self.generate_plane_features(index['xy'], c)
+        if 'yz' in self.plane_type:
+            fea['yz'] = self.generate_plane_features(index['yz'], c)
+
+        return fea

das/spatracker/models/core/spatracker/loftr/__init__.py

@@ -0,0 +1 @@
+from .transformer import LocalFeatureTransformer

das/spatracker/models/core/spatracker/loftr/linear_attention.py

@@ -0,0 +1,81 @@
+"""
+Linear Transformer proposed in "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention"
+Modified from: https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py
+"""
+
+import torch
+from torch.nn import Module, Dropout
+
+
+def elu_feature_map(x):
+    return torch.nn.functional.elu(x) + 1
+
+
+class LinearAttention(Module):
+    def __init__(self, eps=1e-6):
+        super().__init__()
+        self.feature_map = elu_feature_map
+        self.eps = eps
+
+    def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
+        """ Multi-Head linear attention proposed in "Transformers are RNNs"
+        Args:
+            queries: [N, L, H, D]
+            keys: [N, S, H, D]
+            values: [N, S, H, D]
+            q_mask: [N, L]
+            kv_mask: [N, S]
+        Returns:
+            queried_values: (N, L, H, D)
+        """
+        Q = self.feature_map(queries)
+        K = self.feature_map(keys)
+
+        # set padded position to zero
+        if q_mask is not None:
+            Q = Q * q_mask[:, :, None, None]
+        if kv_mask is not None:
+            K = K * kv_mask[:, :, None, None]
+            values = values * kv_mask[:, :, None, None]
+
+        v_length = values.size(1)
+        values = values / v_length  # prevent fp16 overflow
+        KV = torch.einsum("nshd,nshv->nhdv", K, values)  # (S,D)' @ S,V
+        Z = 1 / (torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1)) + self.eps)
+        queried_values = torch.einsum("nlhd,nhdv,nlh->nlhv", Q, KV, Z) * v_length
+
+        return queried_values.contiguous()
+
+
+class FullAttention(Module):
+    def __init__(self, use_dropout=False, attention_dropout=0.1):
+        super().__init__()
+        self.use_dropout = use_dropout
+        self.dropout = Dropout(attention_dropout)
+
+    def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
+        """ Multi-head scaled dot-product attention, a.k.a full attention.
+        Args:
+            queries: [N, L, H, D]
+            keys: [N, S, H, D]
+            values: [N, S, H, D]
+            q_mask: [N, L]
+            kv_mask: [N, S]
+        Returns:
+            queried_values: (N, L, H, D)
+        """
+
+        # Compute the unnormalized attention and apply the masks
+        QK = torch.einsum("nlhd,nshd->nlsh", queries, keys)
+        if kv_mask is not None:
+            QK.masked_fill_(~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]), float('-inf'))
+
+        # Compute the attention and the weighted average
+        softmax_temp = 1. / queries.size(3)**.5  # sqrt(D)
+        A = torch.softmax(softmax_temp * QK, dim=2)
+        if self.use_dropout:
+            A = self.dropout(A)
+
+        queried_values = torch.einsum("nlsh,nshd->nlhd", A, values)
+
+        return queried_values.contiguous()

das/spatracker/models/core/spatracker/loftr/transformer.py

@@ -0,0 +1,142 @@
+'''
+modified from
+https://github.com/zju3dv/LoFTR/blob/master/src/loftr/loftr_module/transformer.py
+'''
+import torch
+from torch.nn import Module, Dropout
+import copy
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def elu_feature_map(x):
+    return torch.nn.functional.elu(x) + 1
+
+class FullAttention(Module):
+    def __init__(self, use_dropout=False, attention_dropout=0.1):
+        super().__init__()
+        self.use_dropout = use_dropout
+        self.dropout = Dropout(attention_dropout)
+
+    def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
+        """ Multi-head scaled dot-product attention, a.k.a full attention.
+        Args:
+            queries: [N, L, H, D]
+            keys: [N, S, H, D]
+            values: [N, S, H, D]
+            q_mask: [N, L]
+            kv_mask: [N, S]
+        Returns:
+            queried_values: (N, L, H, D)
+        """
+
+        # Compute the unnormalized attention and apply the masks
+        # QK = torch.einsum("nlhd,nshd->nlsh", queries, keys)
+        # if kv_mask is not None:
+        #     QK.masked_fill_(~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]), float(-1e12))
+        # softmax_temp = 1. / queries.size(3)**.5  # sqrt(D)
+        # A = torch.softmax(softmax_temp * QK, dim=2)
+        # if self.use_dropout:
+        #     A = self.dropout(A)
+        # queried_values_ = torch.einsum("nlsh,nshd->nlhd", A, values)
+
+        # Compute the attention and the weighted average
+        input_args = [x.half().contiguous() for x in [queries.permute(0,2,1,3), keys.permute(0,2,1,3), values.permute(0,2,1,3)]]
+        queried_values = F.scaled_dot_product_attention(*input_args).permute(0,2,1,3).float()  # type: ignore
+
+
+        return queried_values.contiguous()
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(self,
+                 d_model,
+                 nhead,):
+        super(TransformerEncoderLayer, self).__init__()
+
+        self.dim = d_model // nhead
+        self.nhead = nhead
+
+        # multi-head attention
+        self.q_proj = nn.Linear(d_model, d_model, bias=False)
+        self.k_proj = nn.Linear(d_model, d_model, bias=False)
+        self.v_proj = nn.Linear(d_model, d_model, bias=False)
+        self.attention = FullAttention()
+        self.merge = nn.Linear(d_model, d_model, bias=False)
+
+        # feed-forward network
+        self.mlp = nn.Sequential(
+            nn.Linear(d_model*2, d_model*2, bias=False),
+            nn.ReLU(True),
+            nn.Linear(d_model*2, d_model, bias=False),
+        )
+
+        # norm and dropout
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+
+    def forward(self, x, source, x_mask=None, source_mask=None):
+        """
+        Args:
+            x (torch.Tensor): [N, L, C]
+            source (torch.Tensor): [N, S, C]
+            x_mask (torch.Tensor): [N, L] (optional)
+            source_mask (torch.Tensor): [N, S] (optional)
+        """
+        bs = x.size(0)
+        query, key, value = x, source, source
+
+        # multi-head attention
+        query = self.q_proj(query).view(bs, -1, self.nhead, self.dim)  # [N, L, (H, D)]
+        key = self.k_proj(key).view(bs, -1, self.nhead, self.dim)  # [N, S, (H, D)]
+        value = self.v_proj(value).view(bs, -1, self.nhead, self.dim)
+        message = self.attention(query, key, value, q_mask=x_mask, kv_mask=source_mask)  # [N, L, (H, D)]
+        message = self.merge(message.view(bs, -1, self.nhead*self.dim))  # [N, L, C]
+        message = self.norm1(message)
+
+        # feed-forward network
+        message = self.mlp(torch.cat([x, message], dim=2))
+        message = self.norm2(message)
+
+        return x + message
+
+class LocalFeatureTransformer(nn.Module):
+    """A Local Feature Transformer module."""
+
+    def __init__(self, config):
+        super(LocalFeatureTransformer, self).__init__()
+
+        self.config = config
+        self.d_model = config['d_model']
+        self.nhead = config['nhead']
+        self.layer_names = config['layer_names']
+        encoder_layer = TransformerEncoderLayer(config['d_model'], config['nhead'])
+        self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))])
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def forward(self, feat0, feat1, mask0=None, mask1=None):
+        """
+        Args:
+            feat0 (torch.Tensor): [N, L, C]
+            feat1 (torch.Tensor): [N, S, C]
+            mask0 (torch.Tensor): [N, L] (optional)
+            mask1 (torch.Tensor): [N, S] (optional)
+        """
+
+        assert self.d_model == feat0.size(2), "the feature number of src and transformer must be equal"
+
+        for layer, name in zip(self.layers, self.layer_names):
+            if name == 'self':
+                feat0 = layer(feat0, feat0, mask0, mask0)
+                feat1 = layer(feat1, feat1, mask1, mask1)
+            elif name == 'cross':
+                feat0 = layer(feat0, feat1, mask0, mask1)
+                feat1 = layer(feat1, feat0, mask1, mask0)
+            else:
+                raise KeyError
+
+        return feat0, feat1

das/spatracker/models/core/spatracker/losses.py

@@ -0,0 +1,90 @@
+# 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
+import torch.nn.functional as F
+from ..model_utils import reduce_masked_mean
+from .blocks import (
+        pix2cam
+)
+from ..model_utils import (
+    bilinear_sample2d
+)
+
+EPS = 1e-6
+import torchvision.transforms.functional as TF
+
+sigma = 3
+x_grid = torch.arange(-7,8,1)
+y_grid = torch.arange(-7,8,1)
+x_grid, y_grid = torch.meshgrid(x_grid, y_grid)
+gridxy = torch.stack([x_grid, y_grid], dim=-1).float()
+gs_kernel = torch.exp(-torch.sum(gridxy**2, dim=-1)/(2*sigma**2))
+
+
+def balanced_ce_loss(pred, gt, valid=None):
+    total_balanced_loss = 0.0
+    for j in range(len(gt)):
+        B, S, N = gt[j].shape
+        # pred and gt are the same shape
+        for (a, b) in zip(pred[j].size(), gt[j].size()):
+            assert a == b  # some shape mismatch!
+        # if valid is not None:
+        for (a, b) in zip(pred[j].size(), valid[j].size()):
+            assert a == b  # some shape mismatch!
+
+        pos = (gt[j] > 0.95).float()
+        neg = (gt[j] < 0.05).float()
+
+        label = pos * 2.0 - 1.0
+        a = -label * pred[j]
+        b = F.relu(a)
+        loss = b + torch.log(torch.exp(-b) + torch.exp(a - b))
+
+        pos_loss = reduce_masked_mean(loss, pos * valid[j])
+        neg_loss = reduce_masked_mean(loss, neg * valid[j])
+        balanced_loss = pos_loss + neg_loss
+        total_balanced_loss += balanced_loss / float(N)
+    import ipdb; ipdb.set_trace()
+    return total_balanced_loss
+
+
+def sequence_loss(flow_preds, flow_gt, vis, valids, gamma=0.8,
+                  intr=None, trajs_g_all=None):
+    """Loss function defined over sequence of flow predictions"""
+    total_flow_loss = 0.0
+
+    for j in range(len(flow_gt)):
+        B, S, N, D = flow_gt[j].shape
+        # assert D == 3
+        B, S1, N = vis[j].shape
+        B, S2, N = valids[j].shape
+        assert S == S1
+        assert S == S2
+        n_predictions = len(flow_preds[j])
+        if intr is not None:
+            intr_i = intr[j]
+        flow_loss = 0.0  
+        for i in range(n_predictions):
+            i_weight = gamma ** (n_predictions - i - 1)
+            flow_pred = flow_preds[j][i][..., -N:, :D]
+            flow_gt_j = flow_gt[j].clone()
+            if intr is not None:
+                xyz_j_gt = pix2cam(flow_gt_j, intr_i)
+            try:
+                i_loss = (flow_pred - flow_gt_j).abs()  # B, S, N, 3
+            except:
+                import ipdb; ipdb.set_trace()
+            if D==3:
+                i_loss[...,2]*=30
+            i_loss = torch.mean(i_loss, dim=3)  # B, S, N
+            flow_loss += i_weight * (reduce_masked_mean(i_loss, valids[j])) 
+        
+        flow_loss = flow_loss / n_predictions
+        total_flow_loss += flow_loss / float(N)
+        
+    
+    return total_flow_loss

das/spatracker/models/core/spatracker/softsplat.py

@@ -0,0 +1,539 @@
+#!/usr/bin/env python
+
+"""The code of softsplat function is modified from: 
+https://github.com/sniklaus/softmax-splatting/blob/master/softsplat.py
+
+"""
+
+
+import collections
+import cupy
+import os
+import re
+import torch
+import typing
+
+
+##########################################################
+
+
+objCudacache = {}
+
+
+def cuda_int32(intIn:int):
+    return cupy.int32(intIn)
+# end
+
+
+def cuda_float32(fltIn:float):
+    return cupy.float32(fltIn)
+# end
+
+
+def cuda_kernel(strFunction:str, strKernel:str, objVariables:typing.Dict):
+    if 'device' not in objCudacache:
+        objCudacache['device'] = torch.cuda.get_device_name()
+    # end
+
+    strKey = strFunction
+
+    for strVariable in objVariables:
+        objValue = objVariables[strVariable]
+
+        strKey += strVariable
+
+        if objValue is None:
+            continue
+
+        elif type(objValue) == int:
+            strKey += str(objValue)
+
+        elif type(objValue) == float:
+            strKey += str(objValue)
+
+        elif type(objValue) == bool:
+            strKey += str(objValue)
+
+        elif type(objValue) == str:
+            strKey += objValue
+
+        elif type(objValue) == torch.Tensor:
+            strKey += str(objValue.dtype)
+            strKey += str(objValue.shape)
+            strKey += str(objValue.stride())
+
+        elif True:
+            print(strVariable, type(objValue))
+            assert(False)
+
+        # end
+    # end
+
+    strKey += objCudacache['device']
+
+    if strKey not in objCudacache:
+        for strVariable in objVariables:
+            objValue = objVariables[strVariable]
+
+            if objValue is None:
+                continue
+
+            elif type(objValue) == int:
+                strKernel = strKernel.replace('{{' + strVariable + '}}', str(objValue))
+
+            elif type(objValue) == float:
+                strKernel = strKernel.replace('{{' + strVariable + '}}', str(objValue))
+
+            elif type(objValue) == bool:
+                strKernel = strKernel.replace('{{' + strVariable + '}}', str(objValue))
+
+            elif type(objValue) == str:
+                strKernel = strKernel.replace('{{' + strVariable + '}}', objValue)
+
+            elif type(objValue) == torch.Tensor and objValue.dtype == torch.uint8:
+                strKernel = strKernel.replace('{{type}}', 'unsigned char')
+
+            elif type(objValue) == torch.Tensor and objValue.dtype == torch.float16:
+                strKernel = strKernel.replace('{{type}}', 'half')
+
+            elif type(objValue) == torch.Tensor and objValue.dtype == torch.float32:
+                strKernel = strKernel.replace('{{type}}', 'float')
+
+            elif type(objValue) == torch.Tensor and objValue.dtype == torch.float64:
+                strKernel = strKernel.replace('{{type}}', 'double')
+
+            elif type(objValue) == torch.Tensor and objValue.dtype == torch.int32:
+                strKernel = strKernel.replace('{{type}}', 'int')
+
+            elif type(objValue) == torch.Tensor and objValue.dtype == torch.int64:
+                strKernel = strKernel.replace('{{type}}', 'long')
+
+            elif type(objValue) == torch.Tensor:
+                print(strVariable, objValue.dtype)
+                assert(False)
+
+            elif True:
+                print(strVariable, type(objValue))
+                assert(False)
+
+            # end
+        # end
+
+        while True:
+            objMatch = re.search('(SIZE_)([0-4])(\()([^\)]*)(\))', strKernel)
+
+            if objMatch is None:
+                break
+            # end
+
+            intArg = int(objMatch.group(2))
+
+            strTensor = objMatch.group(4)
+            intSizes = objVariables[strTensor].size()
+
+            strKernel = strKernel.replace(objMatch.group(), str(intSizes[intArg] if torch.is_tensor(intSizes[intArg]) == False else intSizes[intArg].item()))
+        # end
+
+        while True:
+            objMatch = re.search('(OFFSET_)([0-4])(\()', strKernel)
+
+            if objMatch is None:
+                break
+            # end
+
+            intStart = objMatch.span()[1]
+            intStop = objMatch.span()[1]
+            intParentheses = 1
+
+            while True:
+                intParentheses += 1 if strKernel[intStop] == '(' else 0
+                intParentheses -= 1 if strKernel[intStop] == ')' else 0
+
+                if intParentheses == 0:
+                    break
+                # end
+
+                intStop += 1
+            # end
+
+            intArgs = int(objMatch.group(2))
+            strArgs = strKernel[intStart:intStop].split(',')
+
+            assert(intArgs == len(strArgs) - 1)
+
+            strTensor = strArgs[0]
+            intStrides = objVariables[strTensor].stride()
+
+            strIndex = []
+
+            for intArg in range(intArgs):
+                strIndex.append('((' + strArgs[intArg + 1].replace('{', '(').replace('}', ')').strip() + ')*' + str(intStrides[intArg] if torch.is_tensor(intStrides[intArg]) == False else intStrides[intArg].item()) + ')')
+            # end
+
+            strKernel = strKernel.replace('OFFSET_' + str(intArgs) + '(' + strKernel[intStart:intStop] + ')', '(' + str.join('+', strIndex) + ')')
+        # end
+
+        while True:
+            objMatch = re.search('(VALUE_)([0-4])(\()', strKernel)
+
+            if objMatch is None:
+                break
+            # end
+
+            intStart = objMatch.span()[1]
+            intStop = objMatch.span()[1]
+            intParentheses = 1
+
+            while True:
+                intParentheses += 1 if strKernel[intStop] == '(' else 0
+                intParentheses -= 1 if strKernel[intStop] == ')' else 0
+
+                if intParentheses == 0:
+                    break
+                # end
+
+                intStop += 1
+            # end
+
+            intArgs = int(objMatch.group(2))
+            strArgs = strKernel[intStart:intStop].split(',')
+
+            assert(intArgs == len(strArgs) - 1)
+
+            strTensor = strArgs[0]
+            intStrides = objVariables[strTensor].stride()
+
+            strIndex = []
+
+            for intArg in range(intArgs):
+                strIndex.append('((' + strArgs[intArg + 1].replace('{', '(').replace('}', ')').strip() + ')*' + str(intStrides[intArg] if torch.is_tensor(intStrides[intArg]) == False else intStrides[intArg].item()) + ')')
+            # end
+
+            strKernel = strKernel.replace('VALUE_' + str(intArgs) + '(' + strKernel[intStart:intStop] + ')', strTensor + '[' + str.join('+', strIndex) + ']')
+        # end
+
+        objCudacache[strKey] = {
+            'strFunction': strFunction,
+            'strKernel': strKernel
+        }
+    # end
+
+    return strKey
+# end
+
+
+@cupy.memoize(for_each_device=True)
+def cuda_launch(strKey:str):
+    if 'CUDA_HOME' not in os.environ:
+        os.environ['CUDA_HOME'] = cupy.cuda.get_cuda_path()
+    # end
+
+    return cupy.RawKernel(objCudacache[strKey]['strKernel'], objCudacache[strKey]['strFunction'])
+# end
+
+
+##########################################################
+
+
+def softsplat(tenIn:torch.Tensor, tenFlow:torch.Tensor, 
+              tenMetric:torch.Tensor, strMode:str, tenoutH=None, tenoutW=None):
+    assert(strMode.split('-')[0] in ['sum', 'avg', 'linear', 'soft'])
+
+    if strMode == 'sum': assert(tenMetric is None)
+    if strMode == 'avg': assert(tenMetric is None)
+    if strMode.split('-')[0] == 'linear': assert(tenMetric is not None)
+    if strMode.split('-')[0] == 'soft': assert(tenMetric is not None)
+
+    if strMode == 'avg':
+        tenIn = torch.cat([tenIn, tenIn.new_ones([tenIn.shape[0], 1, tenIn.shape[2], tenIn.shape[3]])], 1)
+
+    elif strMode.split('-')[0] == 'linear':
+        tenIn = torch.cat([tenIn * tenMetric, tenMetric], 1)
+
+    elif strMode.split('-')[0] == 'soft':
+        tenIn = torch.cat([tenIn * tenMetric.exp(), tenMetric.exp()], 1)
+
+    # end
+
+    tenOut = softsplat_func.apply(tenIn, tenFlow, tenoutH, tenoutW)
+
+    if strMode.split('-')[0] in ['avg', 'linear', 'soft']:
+        tenNormalize = tenOut[:, -1:, :, :]
+
+        if len(strMode.split('-')) == 1:
+            tenNormalize = tenNormalize + 0.0000001
+
+        elif strMode.split('-')[1] == 'addeps':
+            tenNormalize = tenNormalize + 0.0000001
+
+        elif strMode.split('-')[1] == 'zeroeps':
+            tenNormalize[tenNormalize == 0.0] = 1.0
+
+        elif strMode.split('-')[1] == 'clipeps':
+            tenNormalize = tenNormalize.clip(0.0000001, None)
+
+        # end
+        tenOut = tenOut[:, :-1, :, :] / tenNormalize
+    # end
+
+    return tenOut
+# end
+
+
+class softsplat_func(torch.autograd.Function):
+    @staticmethod
+    @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
+    def forward(self, tenIn, tenFlow, H=None, W=None):
+        if H is None:
+            tenOut = tenIn.new_zeros([tenIn.shape[0], tenIn.shape[1], tenIn.shape[2], tenIn.shape[3]])
+        else:
+            tenOut = tenIn.new_zeros([tenIn.shape[0], tenIn.shape[1], H, W])
+
+        if tenIn.is_cuda == True:
+            cuda_launch(cuda_kernel('softsplat_out', '''
+                extern "C" __global__ void __launch_bounds__(512) softsplat_out(
+                    const int n,
+                    const {{type}}* __restrict__ tenIn,
+                    const {{type}}* __restrict__ tenFlow,
+                    {{type}}* __restrict__ tenOut
+                ) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
+                    const int intN = ( intIndex / SIZE_3(tenIn) / SIZE_2(tenIn) / SIZE_1(tenIn) ) % SIZE_0(tenIn);
+                    const int intC = ( intIndex / SIZE_3(tenIn) / SIZE_2(tenIn)                  ) % SIZE_1(tenIn);
+                    const int intY = ( intIndex / SIZE_3(tenIn)                                   ) % SIZE_2(tenIn);
+                    const int intX = ( intIndex                                                    ) % SIZE_3(tenIn);
+
+                    assert(SIZE_1(tenFlow) == 2);
+
+                    {{type}} fltX = ({{type}}) (intX) + VALUE_4(tenFlow, intN, 0, intY, intX);
+                    {{type}} fltY = ({{type}}) (intY) + VALUE_4(tenFlow, intN, 1, intY, intX);
+
+                    if (isfinite(fltX) == false) { return; }
+                    if (isfinite(fltY) == false) { return; }
+
+                    {{type}} fltIn = VALUE_4(tenIn, intN, intC, intY, intX);
+
+                    int intNorthwestX = (int) (floor(fltX));
+                    int intNorthwestY = (int) (floor(fltY));
+                    int intNortheastX = intNorthwestX + 1;
+                    int intNortheastY = intNorthwestY;
+                    int intSouthwestX = intNorthwestX;
+                    int intSouthwestY = intNorthwestY + 1;
+                    int intSoutheastX = intNorthwestX + 1;
+                    int intSoutheastY = intNorthwestY + 1;
+
+                    {{type}} fltNorthwest = (({{type}}) (intSoutheastX) - fltX) * (({{type}}) (intSoutheastY) - fltY);
+                    {{type}} fltNortheast = (fltX - ({{type}}) (intSouthwestX)) * (({{type}}) (intSouthwestY) - fltY);
+                    {{type}} fltSouthwest = (({{type}}) (intNortheastX) - fltX) * (fltY - ({{type}}) (intNortheastY));
+                    {{type}} fltSoutheast = (fltX - ({{type}}) (intNorthwestX)) * (fltY - ({{type}}) (intNorthwestY));
+
+                    if ((intNorthwestX >= 0) && (intNorthwestX < SIZE_3(tenOut)) && (intNorthwestY >= 0) && (intNorthwestY < SIZE_2(tenOut))) {
+                        atomicAdd(&tenOut[OFFSET_4(tenOut, intN, intC, intNorthwestY, intNorthwestX)], fltIn * fltNorthwest);
+                    }
+
+                    if ((intNortheastX >= 0) && (intNortheastX < SIZE_3(tenOut)) && (intNortheastY >= 0) && (intNortheastY < SIZE_2(tenOut))) {
+                        atomicAdd(&tenOut[OFFSET_4(tenOut, intN, intC, intNortheastY, intNortheastX)], fltIn * fltNortheast);
+                    }
+
+                    if ((intSouthwestX >= 0) && (intSouthwestX < SIZE_3(tenOut)) && (intSouthwestY >= 0) && (intSouthwestY < SIZE_2(tenOut))) {
+                        atomicAdd(&tenOut[OFFSET_4(tenOut, intN, intC, intSouthwestY, intSouthwestX)], fltIn * fltSouthwest);
+                    }
+
+                    if ((intSoutheastX >= 0) && (intSoutheastX < SIZE_3(tenOut)) && (intSoutheastY >= 0) && (intSoutheastY < SIZE_2(tenOut))) {
+                        atomicAdd(&tenOut[OFFSET_4(tenOut, intN, intC, intSoutheastY, intSoutheastX)], fltIn * fltSoutheast);
+                    }
+                } }
+            ''', {
+                'tenIn': tenIn,
+                'tenFlow': tenFlow,
+                'tenOut': tenOut
+            }))(
+                grid=tuple([int((tenIn.nelement() + 512 - 1) / 512), 1, 1]),
+                block=tuple([512, 1, 1]),
+                args=[cuda_int32(tenOut.nelement()), tenIn.data_ptr(), tenFlow.data_ptr(), tenOut.data_ptr()],
+                stream=collections.namedtuple('Stream', 'ptr')(torch.cuda.current_stream().cuda_stream)
+            )
+
+        elif tenIn.is_cuda != True:
+            assert(False)
+
+        # end
+
+        self.save_for_backward(tenIn, tenFlow)
+
+        return tenOut
+    # end
+
+    @staticmethod
+    @torch.cuda.amp.custom_bwd
+    def backward(self, tenOutgrad):
+        tenIn, tenFlow = self.saved_tensors
+
+        tenOutgrad = tenOutgrad.contiguous(); assert(tenOutgrad.is_cuda == True)
+
+        tenIngrad = tenIn.new_zeros([tenIn.shape[0], tenIn.shape[1], tenIn.shape[2], tenIn.shape[3]]) if self.needs_input_grad[0] == True else None
+        tenFlowgrad = tenFlow.new_zeros([tenFlow.shape[0], tenFlow.shape[1], tenFlow.shape[2], tenFlow.shape[3]]) if self.needs_input_grad[1] == True else None
+        Hgrad = None
+        Wgrad = None
+
+        if tenIngrad is not None:
+            cuda_launch(cuda_kernel('softsplat_ingrad', '''
+                extern "C" __global__ void __launch_bounds__(512) softsplat_ingrad(
+                    const int n,
+                    const {{type}}* __restrict__ tenIn,
+                    const {{type}}* __restrict__ tenFlow,
+                    const {{type}}* __restrict__ tenOutgrad,
+                    {{type}}* __restrict__ tenIngrad,
+                    {{type}}* __restrict__ tenFlowgrad
+                ) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
+                    const int intN = ( intIndex / SIZE_3(tenIngrad) / SIZE_2(tenIngrad) / SIZE_1(tenIngrad) ) % SIZE_0(tenIngrad);
+                    const int intC = ( intIndex / SIZE_3(tenIngrad) / SIZE_2(tenIngrad)                     ) % SIZE_1(tenIngrad);
+                    const int intY = ( intIndex / SIZE_3(tenIngrad)                                         ) % SIZE_2(tenIngrad);
+                    const int intX = ( intIndex                                                             ) % SIZE_3(tenIngrad);
+
+                    assert(SIZE_1(tenFlow) == 2);
+
+                    {{type}} fltIngrad = 0.0f;
+
+                    {{type}} fltX = ({{type}}) (intX) + VALUE_4(tenFlow, intN, 0, intY, intX);
+                    {{type}} fltY = ({{type}}) (intY) + VALUE_4(tenFlow, intN, 1, intY, intX);
+
+                    if (isfinite(fltX) == false) { return; }
+                    if (isfinite(fltY) == false) { return; }
+
+                    int intNorthwestX = (int) (floor(fltX));
+                    int intNorthwestY = (int) (floor(fltY));
+                    int intNortheastX = intNorthwestX + 1;
+                    int intNortheastY = intNorthwestY;
+                    int intSouthwestX = intNorthwestX;
+                    int intSouthwestY = intNorthwestY + 1;
+                    int intSoutheastX = intNorthwestX + 1;
+                    int intSoutheastY = intNorthwestY + 1;
+
+                    {{type}} fltNorthwest = (({{type}}) (intSoutheastX) - fltX) * (({{type}}) (intSoutheastY) - fltY);
+                    {{type}} fltNortheast = (fltX - ({{type}}) (intSouthwestX)) * (({{type}}) (intSouthwestY) - fltY);
+                    {{type}} fltSouthwest = (({{type}}) (intNortheastX) - fltX) * (fltY - ({{type}}) (intNortheastY));
+                    {{type}} fltSoutheast = (fltX - ({{type}}) (intNorthwestX)) * (fltY - ({{type}}) (intNorthwestY));
+
+                    if ((intNorthwestX >= 0) && (intNorthwestX < SIZE_3(tenOutgrad)) && (intNorthwestY >= 0) && (intNorthwestY < SIZE_2(tenOutgrad))) {
+                        fltIngrad += VALUE_4(tenOutgrad, intN, intC, intNorthwestY, intNorthwestX) * fltNorthwest;
+                    }
+
+                    if ((intNortheastX >= 0) && (intNortheastX < SIZE_3(tenOutgrad)) && (intNortheastY >= 0) && (intNortheastY < SIZE_2(tenOutgrad))) {
+                        fltIngrad += VALUE_4(tenOutgrad, intN, intC, intNortheastY, intNortheastX) * fltNortheast;
+                    }
+
+                    if ((intSouthwestX >= 0) && (intSouthwestX < SIZE_3(tenOutgrad)) && (intSouthwestY >= 0) && (intSouthwestY < SIZE_2(tenOutgrad))) {
+                        fltIngrad += VALUE_4(tenOutgrad, intN, intC, intSouthwestY, intSouthwestX) * fltSouthwest;
+                    }
+
+                    if ((intSoutheastX >= 0) && (intSoutheastX < SIZE_3(tenOutgrad)) && (intSoutheastY >= 0) && (intSoutheastY < SIZE_2(tenOutgrad))) {
+                        fltIngrad += VALUE_4(tenOutgrad, intN, intC, intSoutheastY, intSoutheastX) * fltSoutheast;
+                    }
+
+                    tenIngrad[intIndex] = fltIngrad;
+                } }
+            ''', {
+                'tenIn': tenIn,
+                'tenFlow': tenFlow,
+                'tenOutgrad': tenOutgrad,
+                'tenIngrad': tenIngrad,
+                'tenFlowgrad': tenFlowgrad
+            }))(
+                grid=tuple([int((tenIngrad.nelement() + 512 - 1) / 512), 1, 1]),
+                block=tuple([512, 1, 1]),
+                args=[cuda_int32(tenIngrad.nelement()), tenIn.data_ptr(), tenFlow.data_ptr(), tenOutgrad.data_ptr(), tenIngrad.data_ptr(), None],
+                stream=collections.namedtuple('Stream', 'ptr')(torch.cuda.current_stream().cuda_stream)
+            )
+        # end
+
+        if tenFlowgrad is not None:
+            cuda_launch(cuda_kernel('softsplat_flowgrad', '''
+                extern "C" __global__ void __launch_bounds__(512) softsplat_flowgrad(
+                    const int n,
+                    const {{type}}* __restrict__ tenIn,
+                    const {{type}}* __restrict__ tenFlow,
+                    const {{type}}* __restrict__ tenOutgrad,
+                    {{type}}* __restrict__ tenIngrad,
+                    {{type}}* __restrict__ tenFlowgrad
+                ) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
+                    const int intN = ( intIndex / SIZE_3(tenFlowgrad) / SIZE_2(tenFlowgrad) / SIZE_1(tenFlowgrad) ) % SIZE_0(tenFlowgrad);
+                    const int intC = ( intIndex / SIZE_3(tenFlowgrad) / SIZE_2(tenFlowgrad)                       ) % SIZE_1(tenFlowgrad);
+                    const int intY = ( intIndex / SIZE_3(tenFlowgrad)                                             ) % SIZE_2(tenFlowgrad);
+                    const int intX = ( intIndex                                                                   ) % SIZE_3(tenFlowgrad);
+
+                    assert(SIZE_1(tenFlow) == 2);
+
+                    {{type}} fltFlowgrad = 0.0f;
+
+                    {{type}} fltX = ({{type}}) (intX) + VALUE_4(tenFlow, intN, 0, intY, intX);
+                    {{type}} fltY = ({{type}}) (intY) + VALUE_4(tenFlow, intN, 1, intY, intX);
+
+                    if (isfinite(fltX) == false) { return; }
+                    if (isfinite(fltY) == false) { return; }
+
+                    int intNorthwestX = (int) (floor(fltX));
+                    int intNorthwestY = (int) (floor(fltY));
+                    int intNortheastX = intNorthwestX + 1;
+                    int intNortheastY = intNorthwestY;
+                    int intSouthwestX = intNorthwestX;
+                    int intSouthwestY = intNorthwestY + 1;
+                    int intSoutheastX = intNorthwestX + 1;
+                    int intSoutheastY = intNorthwestY + 1;
+
+                    {{type}} fltNorthwest = 0.0f;
+                    {{type}} fltNortheast = 0.0f;
+                    {{type}} fltSouthwest = 0.0f;
+                    {{type}} fltSoutheast = 0.0f;
+
+                    if (intC == 0) {
+                        fltNorthwest = (({{type}}) (-1.0f)) * (({{type}}) (intSoutheastY) - fltY);
+                        fltNortheast = (({{type}}) (+1.0f)) * (({{type}}) (intSouthwestY) - fltY);
+                        fltSouthwest = (({{type}}) (-1.0f)) * (fltY - ({{type}}) (intNortheastY));
+                        fltSoutheast = (({{type}}) (+1.0f)) * (fltY - ({{type}}) (intNorthwestY));
+
+                    } else if (intC == 1) {
+                        fltNorthwest = (({{type}}) (intSoutheastX) - fltX) * (({{type}}) (-1.0f));
+                        fltNortheast = (fltX - ({{type}}) (intSouthwestX)) * (({{type}}) (-1.0f));
+                        fltSouthwest = (({{type}}) (intNortheastX) - fltX) * (({{type}}) (+1.0f));
+                        fltSoutheast = (fltX - ({{type}}) (intNorthwestX)) * (({{type}}) (+1.0f));
+
+                    }
+
+                    for (int intChannel = 0; intChannel < SIZE_1(tenOutgrad); intChannel += 1) {
+                        {{type}} fltIn = VALUE_4(tenIn, intN, intChannel, intY, intX);
+
+                        if ((intNorthwestX >= 0) && (intNorthwestX < SIZE_3(tenOutgrad)) && (intNorthwestY >= 0) && (intNorthwestY < SIZE_2(tenOutgrad))) {
+                            fltFlowgrad += VALUE_4(tenOutgrad, intN, intChannel, intNorthwestY, intNorthwestX) * fltIn * fltNorthwest;
+                        }
+
+                        if ((intNortheastX >= 0) && (intNortheastX < SIZE_3(tenOutgrad)) && (intNortheastY >= 0) && (intNortheastY < SIZE_2(tenOutgrad))) {
+                            fltFlowgrad += VALUE_4(tenOutgrad, intN, intChannel, intNortheastY, intNortheastX) * fltIn * fltNortheast;
+                        }
+
+                        if ((intSouthwestX >= 0) && (intSouthwestX < SIZE_3(tenOutgrad)) && (intSouthwestY >= 0) && (intSouthwestY < SIZE_2(tenOutgrad))) {
+                            fltFlowgrad += VALUE_4(tenOutgrad, intN, intChannel, intSouthwestY, intSouthwestX) * fltIn * fltSouthwest;
+                        }
+
+                        if ((intSoutheastX >= 0) && (intSoutheastX < SIZE_3(tenOutgrad)) && (intSoutheastY >= 0) && (intSoutheastY < SIZE_2(tenOutgrad))) {
+                            fltFlowgrad += VALUE_4(tenOutgrad, intN, intChannel, intSoutheastY, intSoutheastX) * fltIn * fltSoutheast;
+                        }
+                    }
+
+                    tenFlowgrad[intIndex] = fltFlowgrad;
+                } }
+            ''', {
+                'tenIn': tenIn,
+                'tenFlow': tenFlow,
+                'tenOutgrad': tenOutgrad,
+                'tenIngrad': tenIngrad,
+                'tenFlowgrad': tenFlowgrad
+            }))(
+                grid=tuple([int((tenFlowgrad.nelement() + 512 - 1) / 512), 1, 1]),
+                block=tuple([512, 1, 1]),
+                args=[cuda_int32(tenFlowgrad.nelement()), tenIn.data_ptr(), tenFlow.data_ptr(), tenOutgrad.data_ptr(), None, tenFlowgrad.data_ptr()],
+                stream=collections.namedtuple('Stream', 'ptr')(torch.cuda.current_stream().cuda_stream)
+            )
+        # end
+        return tenIngrad, tenFlowgrad, Hgrad, Wgrad
+    # end
+# end

das/spatracker/models/core/spatracker/spatracker.py

@@ -0,0 +1,736 @@
+# 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
+import torch.nn as nn
+from easydict import EasyDict as edict
+from einops import rearrange
+from sklearn.cluster import SpectralClustering
+from .blocks import Lie
+import matplotlib.pyplot as plt
+import cv2
+
+import torch.nn.functional as F
+from .blocks import (
+    BasicEncoder,
+    CorrBlock,
+    EUpdateFormer,
+    FusionFormer,
+    pix2cam,
+    cam2pix,
+    edgeMat,
+    VitEncoder,
+    DPTEnc,
+    Dinov2
+)
+
+from .feature_net import (
+    LocalSoftSplat
+)
+
+from ..model_utils import (
+    meshgrid2d, bilinear_sample2d, smart_cat, sample_features5d, vis_PCA
+)
+from ..embeddings import (
+    get_2d_embedding,
+    get_3d_embedding,
+    get_1d_sincos_pos_embed_from_grid,
+    get_2d_sincos_pos_embed,
+    get_3d_sincos_pos_embed_from_grid,
+    Embedder_Fourier,
+)
+import numpy as np
+from .softsplat import softsplat 
+
+torch.manual_seed(0)
+from comfy.utils import ProgressBar
+from tqdm import tqdm
+
+def get_points_on_a_grid(grid_size, interp_shape,
+                          grid_center=(0, 0), device="cuda"):
+    if grid_size == 1:
+        return torch.tensor([interp_shape[1] / 2, 
+                             interp_shape[0] / 2], device=device)[
+            None, None
+        ]
+
+    grid_y, grid_x = meshgrid2d(
+        1, grid_size, grid_size, stack=False, norm=False, device=device
+    )
+    step = interp_shape[1] // 64
+    if grid_center[0] != 0 or grid_center[1] != 0:
+        grid_y = grid_y - grid_size / 2.0
+        grid_x = grid_x - grid_size / 2.0
+    grid_y = step + grid_y.reshape(1, -1) / float(grid_size - 1) * (
+        interp_shape[0] - step * 2
+    )
+    grid_x = step + grid_x.reshape(1, -1) / float(grid_size - 1) * (
+        interp_shape[1] - step * 2
+    )
+
+    grid_y = grid_y + grid_center[0]
+    grid_x = grid_x + grid_center[1]
+    xy = torch.stack([grid_x, grid_y], dim=-1).to(device)
+    return xy
+
+
+def sample_pos_embed(grid_size, embed_dim, coords):
+    if coords.shape[-1] == 2:
+        pos_embed = get_2d_sincos_pos_embed(embed_dim=embed_dim,
+                                             grid_size=grid_size)
+        pos_embed = (
+            torch.from_numpy(pos_embed)
+            .reshape(grid_size[0], grid_size[1], embed_dim)
+            .float()
+            .unsqueeze(0)
+            .to(coords.device)
+        )
+        sampled_pos_embed = bilinear_sample2d(
+            pos_embed.permute(0, 3, 1, 2), 
+            coords[:, 0, :, 0], coords[:, 0, :, 1]
+        )
+    elif coords.shape[-1] == 3:
+        sampled_pos_embed = get_3d_sincos_pos_embed_from_grid(
+            embed_dim, coords[:, :1, ...]
+        ).float()[:,0,...].permute(0, 2, 1)
+
+    return sampled_pos_embed
+
+
+class SpaTracker(nn.Module):
+    def __init__(
+        self,
+        S=8,
+        stride=8,
+        add_space_attn=True,
+        num_heads=8,
+        hidden_size=384,
+        space_depth=12,
+        time_depth=12,
+        args=edict({})
+    ):
+        super(SpaTracker, self).__init__()
+
+        # step1: config the arch of the model
+        self.args=args
+        # step1.1: config the default value of the model
+        if getattr(args, "depth_color", None) == None:
+            self.args.depth_color = False
+        if getattr(args, "if_ARAP", None) == None:
+            self.args.if_ARAP = True
+        if getattr(args, "flash_attn", None) == None:
+            self.args.flash_attn = True
+        if getattr(args, "backbone", None) == None:
+            self.args.backbone = "CNN"
+        if getattr(args, "Nblock", None) == None:
+            self.args.Nblock = 0  
+        if getattr(args, "Embed3D", None) == None:
+            self.args.Embed3D = True
+
+        # step1.2: config the model parameters
+        self.S = S
+        self.stride = stride
+        self.hidden_dim = 256
+        self.latent_dim = latent_dim = 128
+        self.b_latent_dim = self.latent_dim//3
+        self.corr_levels = 4
+        self.corr_radius = 3
+        self.add_space_attn = add_space_attn
+        self.lie = Lie()
+
+        # step2: config the model components
+        # @Encoder
+        self.fnet = BasicEncoder(input_dim=3,
+            output_dim=self.latent_dim, norm_fn="instance", dropout=0, 
+            stride=stride, Embed3D=False
+        )
+
+        # conv head for the tri-plane features
+        self.headyz = nn.Sequential(
+            nn.Conv2d(self.latent_dim, self.latent_dim, 3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(self.latent_dim, self.latent_dim, 3, padding=1))
+        
+        self.headxz = nn.Sequential(
+            nn.Conv2d(self.latent_dim, self.latent_dim, 3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(self.latent_dim, self.latent_dim, 3, padding=1))
+
+        # @UpdateFormer
+        self.updateformer = EUpdateFormer(
+            space_depth=space_depth,
+            time_depth=time_depth,
+            input_dim=456, 
+            hidden_size=hidden_size,
+            num_heads=num_heads,
+            output_dim=latent_dim + 3,
+            mlp_ratio=4.0,
+            add_space_attn=add_space_attn,
+            flash=getattr(self.args, "flash_attn", True)
+        )
+        self.support_features = torch.zeros(100, 384).to("cuda") + 0.1
+
+        self.norm = nn.GroupNorm(1, self.latent_dim)
+       
+        self.ffeat_updater = nn.Sequential(
+            nn.Linear(self.latent_dim, self.latent_dim),
+            nn.GELU(),
+        )
+        self.ffeatyz_updater = nn.Sequential(
+            nn.Linear(self.latent_dim, self.latent_dim),
+            nn.GELU(),
+        )
+        self.ffeatxz_updater = nn.Sequential(
+            nn.Linear(self.latent_dim, self.latent_dim),
+            nn.GELU(),
+        )
+
+        #TODO @NeuralArap: optimize the arap
+        self.embed_traj = Embedder_Fourier(
+            input_dim=5, max_freq_log2=5.0, N_freqs=3, include_input=True
+        )
+        self.embed3d = Embedder_Fourier(
+            input_dim=3, max_freq_log2=10.0, N_freqs=10, include_input=True
+        )
+        self.embedConv = nn.Conv2d(self.latent_dim+63,
+                            self.latent_dim, 3, padding=1)
+        
+        # @Vis_predictor
+        self.vis_predictor = nn.Sequential(
+            nn.Linear(128, 1),
+        )
+
+        self.embedProj = nn.Linear(63, 456)
+        self.zeroMLPflow = nn.Linear(195, 130)
+
+    def prepare_track(self, rgbds, queries):
+        """
+        NOTE:
+        Normalized the rgbs and sorted the queries via their first appeared time
+        Args: 
+            rgbds: the input rgbd images (B T 4 H W) 
+            queries: the input queries (B N 4)
+        Return:
+            rgbds: the normalized rgbds (B T 4 H W)
+            queries: the sorted queries (B N 4)
+            track_mask:         
+        """
+        assert (rgbds.shape[2]==4) and (queries.shape[2]==4)
+        #Step1: normalize the rgbs input
+        device = rgbds.device
+        rgbds[:, :, :3, ...] = 2 * (rgbds[:, :, :3, ...] / 255.0) - 1.0
+        B, T, C, H, W = rgbds.shape
+        B, N, __ = queries.shape
+        self.traj_e = torch.zeros((B, T, N, 3), device=device)
+        self.vis_e = torch.zeros((B, T, N), device=device)
+
+        #Step2: sort the points via their first appeared time
+        first_positive_inds = queries[0, :, 0].long()
+        __, sort_inds = torch.sort(first_positive_inds, dim=0, descending=False)
+        inv_sort_inds = torch.argsort(sort_inds, dim=0)
+        first_positive_sorted_inds = first_positive_inds[sort_inds]
+        # check if can be inverse
+        assert torch.allclose(
+            first_positive_inds, first_positive_inds[sort_inds][inv_sort_inds]
+        )
+
+        # filter those points never appear points during 1 - T
+        ind_array = torch.arange(T, device=device)
+        ind_array = ind_array[None, :, None].repeat(B, 1, N)
+        track_mask = (ind_array >= 
+                      first_positive_inds[None, None, :]).unsqueeze(-1)
+        
+        # scale the coords_init 
+        coords_init = queries[:, :, 1:].reshape(B, 1, N, 3).repeat(
+            1, self.S, 1, 1
+        ) 
+        coords_init[..., :2] /= float(self.stride)
+
+        #Step3: initial the regular grid   
+        gridx = torch.linspace(0, W//self.stride - 1, W//self.stride)
+        gridy = torch.linspace(0, H//self.stride - 1, H//self.stride)
+        gridx, gridy = torch.meshgrid(gridx, gridy)
+        gridxy = torch.stack([gridx, gridy], dim=-1).to(rgbds.device).permute(
+            2, 1, 0
+        )
+        vis_init = torch.ones((B, self.S, N, 1), device=device).float() * 10
+
+        # Step4: initial traj for neural arap
+        T_series = torch.linspace(0, 5, T).reshape(1, T, 1 , 1).cuda() # 1 T 1 1
+        T_series = T_series.repeat(B, 1, N, 1)
+        # get the 3d traj in the camera coordinates
+        intr_init = self.intrs[:,queries[0,:,0].long()]
+        Traj_series = pix2cam(queries[:,:,None,1:].double(), intr_init.double())
+        #torch.inverse(intr_init.double())@queries[:,:,1:,None].double() # B N 3 1
+        Traj_series = Traj_series.repeat(1, 1, T, 1).permute(0, 2, 1, 3).float()
+        Traj_series = torch.cat([T_series, Traj_series], dim=-1)
+        # get the indicator for the neural arap
+        Traj_mask = -1e2*torch.ones_like(T_series)
+        Traj_series = torch.cat([Traj_series, Traj_mask], dim=-1)
+
+        return (
+            rgbds, 
+            first_positive_inds, 
+            first_positive_sorted_inds,
+            sort_inds, inv_sort_inds, 
+            track_mask, gridxy, coords_init[..., sort_inds, :].clone(),
+            vis_init, Traj_series[..., sort_inds, :].clone()
+            )
+
+    def sample_trifeat(self, t, 
+                       coords, 
+                       featMapxy,
+                       featMapyz,
+                       featMapxz):
+        """
+        Sample the features from the 5D triplane feature map 3*(B S C H W)
+        Args:
+            t: the time index
+            coords: the coordinates of the points B S N 3
+            featMapxy: the feature map B S C Hx Wy
+            featMapyz: the feature map B S C Hy Wz
+            featMapxz: the feature map B S C Hx Wz
+        """
+        # get xy_t yz_t xz_t
+        queried_t = t.reshape(1, 1, -1, 1)
+        xy_t = torch.cat(
+            [queried_t, coords[..., [0,1]]],
+            dim=-1
+            )
+        yz_t = torch.cat(
+            [queried_t, coords[..., [1, 2]]],
+            dim=-1
+            ) 
+        xz_t = torch.cat(
+            [queried_t, coords[..., [0, 2]]],
+            dim=-1
+            )
+        featxy_init = sample_features5d(featMapxy, xy_t)
+    
+        featyz_init = sample_features5d(featMapyz, yz_t)
+        featxz_init = sample_features5d(featMapxz, xz_t)
+        
+        featxy_init = featxy_init.repeat(1, self.S, 1, 1)
+        featyz_init = featyz_init.repeat(1, self.S, 1, 1)
+        featxz_init = featxz_init.repeat(1, self.S, 1, 1)
+
+        return featxy_init, featyz_init, featxz_init
+
+    def neural_arap(self, coords, Traj_arap, intrs_S, T_mark):
+        """ calculate the ARAP embedding and offset
+        Args:
+            coords: the coordinates of the current points   1 S N' 3
+            Traj_arap: the trajectory of the points   1 T N' 5
+            intrs_S: the camera intrinsics B S 3 3
+        
+        """
+        coords_out = coords.clone()
+        coords_out[..., :2] *= float(self.stride)
+        coords_out[..., 2] = coords_out[..., 2]/self.Dz
+        coords_out[..., 2] = coords_out[..., 2]*(self.d_far-self.d_near) + self.d_near
+        intrs_S = intrs_S[:, :, None, ...].repeat(1, 1, coords_out.shape[2], 1, 1)
+        B, S, N, D = coords_out.shape
+        if S != intrs_S.shape[1]:
+            intrs_S = torch.cat(
+                [intrs_S, intrs_S[:, -1:].repeat(1, S - intrs_S.shape[1],1,1,1)], dim=1
+            )
+            T_mark = torch.cat(
+                [T_mark, T_mark[:, -1:].repeat(1, S - T_mark.shape[1],1)], dim=1
+            )
+        xyz_ = pix2cam(coords_out.double(), intrs_S.double()[:,:,0])
+        xyz_ = xyz_.float()
+        xyz_embed = torch.cat([T_mark[...,None], xyz_,
+                               torch.zeros_like(T_mark[...,None])], dim=-1)
+
+        xyz_embed = self.embed_traj(xyz_embed)
+        Traj_arap_embed = self.embed_traj(Traj_arap)
+        d_xyz,traj_feat = self.arapFormer(xyz_embed, Traj_arap_embed)
+        # update in camera coordinate
+        xyz_ = xyz_ + d_xyz.clamp(-5, 5)
+        # project back to the image plane
+        coords_out = cam2pix(xyz_.double(), intrs_S[:,:,0].double()).float()
+        # resize back
+        coords_out[..., :2] /= float(self.stride)
+        coords_out[..., 2] = (coords_out[..., 2] - self.d_near)/(self.d_far-self.d_near)
+        coords_out[..., 2] *= self.Dz
+
+        return xyz_, coords_out, traj_feat
+    
+    def gradient_arap(self, coords, aff_avg=None, aff_std=None, aff_f_sg=None,
+                      iter=0, iter_num=4, neigh_idx=None, intr=None, msk_track=None):
+        with torch.enable_grad():
+            coords.requires_grad_(True)
+            y = self.ARAP_ln(coords, aff_f_sg=aff_f_sg, neigh_idx=neigh_idx,
+                              iter=iter, iter_num=iter_num, intr=intr,msk_track=msk_track)
+            d_output = torch.ones_like(y, requires_grad=False, device=y.device)
+            gradients = torch.autograd.grad(
+                outputs=y,
+                inputs=coords,
+                grad_outputs=d_output,
+                create_graph=True,
+                retain_graph=True,
+                only_inputs=True, allow_unused=True)[0]
+
+        return gradients.detach()
+            
+    def forward_iteration(
+        self,
+        fmapXY,
+        fmapYZ,
+        fmapXZ,
+        coords_init,
+        feat_init=None,
+        vis_init=None,
+        track_mask=None,
+        iters=4,
+        intrs_S=None,
+    ):
+        B, S_init, N, D = coords_init.shape
+        assert D == 3
+        assert B == 1
+        B, S, __, H8, W8 = fmapXY.shape
+        device = fmapXY.device
+
+        if S_init < S:
+            coords = torch.cat(
+                [coords_init, coords_init[:, -1].repeat(1, S - S_init, 1, 1)],
+                dim=1
+            )
+            vis_init = torch.cat(
+                [vis_init, vis_init[:, -1].repeat(1, S - S_init, 1, 1)], dim=1
+            )
+            intrs_S = torch.cat(
+                [intrs_S, intrs_S[:, -1].repeat(1, S - S_init, 1, 1)], dim=1
+            )
+        else:
+            coords = coords_init.clone()
+
+        fcorr_fnXY = CorrBlock(
+            fmapXY, num_levels=self.corr_levels, radius=self.corr_radius
+        )
+        fcorr_fnYZ = CorrBlock(
+            fmapYZ, num_levels=self.corr_levels, radius=self.corr_radius
+        )
+        fcorr_fnXZ = CorrBlock(
+            fmapXZ, num_levels=self.corr_levels, radius=self.corr_radius
+        )
+        
+        ffeats = torch.split(feat_init.clone(), dim=-1, split_size_or_sections=1)
+        ffeats = [f.squeeze(-1) for f in ffeats]
+
+        times_ = torch.linspace(0, S - 1, S).reshape(1, S, 1)
+        pos_embed = sample_pos_embed(
+            grid_size=(H8, W8),
+            embed_dim=456,
+            coords=coords[..., :2],
+        )
+        pos_embed = rearrange(pos_embed, "b e n -> (b n) e").unsqueeze(1)
+        
+        times_embed = (
+            torch.from_numpy(get_1d_sincos_pos_embed_from_grid(456, times_[0]))[None]
+            .repeat(B, 1, 1)
+            .float()
+            .to(device)
+        )
+        coord_predictions = []
+        attn_predictions = []
+        Rot_ln = 0
+        support_feat = self.support_features
+
+        comfy_pbar = ProgressBar(iters)
+
+        for __ in tqdm(range(iters), desc="Processing iterations", leave=True):
+            coords = coords.detach()
+            # if self.args.if_ARAP == True:
+            #     # refine the track with arap
+            #     xyz_pred, coords, flows_cat0 = self.neural_arap(coords.detach(),
+            #                                                    Traj_arap.detach(),
+            #                                                    intrs_S, T_mark)
+            with torch.no_grad():
+                fcorrsXY = fcorr_fnXY.corr_sample(ffeats[0], coords[..., :2])
+                fcorrsYZ = fcorr_fnYZ.corr_sample(ffeats[1], coords[..., [1,2]])
+                fcorrsXZ = fcorr_fnXZ.corr_sample(ffeats[2], coords[..., [0,2]])
+            # fcorrs = fcorrsXY 
+            fcorrs = fcorrsXY + fcorrsYZ + fcorrsXZ           
+            LRR = fcorrs.shape[3]
+            fcorrs_ = fcorrs.permute(0, 2, 1, 3).reshape(B * N, S, LRR)
+
+            flows_ = (coords - coords[:, 0:1]).permute(0, 2, 1, 3).reshape(B * N, S, 3)
+            flows_cat = get_3d_embedding(flows_, 64, cat_coords=True)
+            flows_cat =  self.zeroMLPflow(flows_cat)
+            
+
+            ffeats_xy = ffeats[0].permute(0, 
+                                          2, 1, 3).reshape(B * N, S, self.latent_dim)
+            ffeats_yz = ffeats[1].permute(0, 
+                                          2, 1, 3).reshape(B * N, S, self.latent_dim)
+            ffeats_xz = ffeats[2].permute(0, 
+                                          2, 1, 3).reshape(B * N, S, self.latent_dim)
+            ffeats_ = ffeats_xy + ffeats_yz + ffeats_xz
+            
+            if track_mask.shape[1] < vis_init.shape[1]:
+                track_mask = torch.cat(
+                    [
+                        track_mask,
+                        torch.zeros_like(track_mask[:, 0]).repeat(
+                            1, vis_init.shape[1] - track_mask.shape[1], 1, 1
+                        ),
+                    ],
+                    dim=1,
+                )
+            concat = (
+                torch.cat([track_mask, vis_init], dim=2)
+                .permute(0, 2, 1, 3)
+                .reshape(B * N, S, 2)
+            )
+
+            transformer_input = torch.cat([flows_cat, fcorrs_, ffeats_, concat], dim=2)
+
+            if transformer_input.shape[-1] < pos_embed.shape[-1]:
+            # padding the transformer_input to the same dimension as pos_embed
+                transformer_input = F.pad(
+                    transformer_input, (0, pos_embed.shape[-1] - transformer_input.shape[-1]),
+                    "constant", 0
+                )
+
+            x = transformer_input + pos_embed + times_embed
+            x = rearrange(x, "(b n) t d -> b n t d", b=B)
+
+            delta, AttnMap, so3_dist, delta_se3F, so3 = self.updateformer(x, support_feat)
+            support_feat = support_feat + delta_se3F[0]/100
+            delta = rearrange(delta, " b n t d -> (b n) t d")
+            d_coord = delta[:, :, :3]
+            d_feats = delta[:, :, 3:]
+            
+            ffeats_xy = self.ffeat_updater(self.norm(d_feats.view(-1, self.latent_dim))) + ffeats_xy.reshape(-1, self.latent_dim)
+            ffeats_yz = self.ffeatyz_updater(self.norm(d_feats.view(-1, self.latent_dim))) + ffeats_yz.reshape(-1, self.latent_dim)
+            ffeats_xz = self.ffeatxz_updater(self.norm(d_feats.view(-1, self.latent_dim))) + ffeats_xz.reshape(-1, self.latent_dim)
+            ffeats[0] = ffeats_xy.reshape(B, N, S, self.latent_dim).permute(
+                0, 2, 1, 3
+            )  # B,S,N,C
+            ffeats[1] = ffeats_yz.reshape(B, N, S, self.latent_dim).permute(
+                0, 2, 1, 3
+            )  # B,S,N,C
+            ffeats[2] = ffeats_xz.reshape(B, N, S, self.latent_dim).permute(
+                0, 2, 1, 3
+            )  # B,S,N,C
+            coords = coords + d_coord.reshape(B, N, S, 3).permute(0, 2, 1, 3)
+            if torch.isnan(coords).any():
+                import ipdb; ipdb.set_trace()
+
+            coords_out = coords.clone()
+            coords_out[..., :2] *= float(self.stride)
+            
+            coords_out[..., 2] = coords_out[..., 2]/self.Dz
+            coords_out[..., 2] = coords_out[..., 2]*(self.d_far-self.d_near) + self.d_near
+
+            coord_predictions.append(coords_out)
+            attn_predictions.append(AttnMap)
+            comfy_pbar.update(1)
+
+        ffeats_f = ffeats[0] + ffeats[1] + ffeats[2]
+        vis_e = self.vis_predictor(ffeats_f.reshape(B * S * N, self.latent_dim)).reshape(
+            B, S, N
+        )
+        self.support_features = support_feat.detach()
+        return coord_predictions, attn_predictions, vis_e, feat_init, Rot_ln
+
+
+    def forward(self, rgbds, queries, iters=4, feat_init=None,
+                is_train=False, intrs=None, wind_S=None):
+        self.support_features = torch.zeros(100, 384).to("cuda") + 0.1
+        self.is_train=is_train
+        B, T, C, H, W = rgbds.shape
+        # set the intrinsic or simply initialized
+        if intrs is None:
+            intrs = torch.from_numpy(np.array([[W, 0.0, W//2],
+                                              [0.0, W, H//2],
+                                              [0.0, 0.0, 1.0]]))
+            intrs = intrs[None,
+                         None,...].repeat(B, T, 1, 1).float().to(rgbds.device)
+        self.intrs = intrs
+
+        # prepare the input for tracking
+        (
+          rgbds, 
+          first_positive_inds, 
+          first_positive_sorted_inds, sort_inds, 
+          inv_sort_inds, track_mask, gridxy,
+          coords_init, vis_init, Traj_arap
+          ) = self.prepare_track(rgbds.clone(), queries)
+        coords_init_ = coords_init.clone()
+        vis_init_ = vis_init[:, :, sort_inds].clone()
+        
+        depth_all = rgbds[:, :, 3,...]
+        d_near = self.d_near = depth_all[depth_all>0.01].min().item()
+        d_far = self.d_far = depth_all[depth_all>0.01].max().item()
+        
+        if wind_S is not None:
+            self.S = wind_S
+
+        B, N, __ = queries.shape
+        self.Dz = Dz = W//self.stride
+        w_idx_start = 0
+        p_idx_end = 0
+        p_idx_start = 0
+        fmaps_ = None
+        vis_predictions = []
+        coord_predictions = []
+        attn_predictions = []
+        p_idx_end_list = []
+        Rigid_ln_total = 0
+        while w_idx_start < T - self.S // 2:
+            curr_wind_points = torch.nonzero(
+                first_positive_sorted_inds < w_idx_start + self.S)
+            if curr_wind_points.shape[0] == 0:
+                w_idx_start = w_idx_start + self.S // 2
+                continue
+            p_idx_end = curr_wind_points[-1] + 1
+            p_idx_end_list.append(p_idx_end)
+            # the T may not be divided by self.S
+            rgbds_seq = rgbds[:, w_idx_start:w_idx_start + self.S].clone()
+            S = S_local = rgbds_seq.shape[1]
+            if S < self.S:
+                rgbds_seq = torch.cat(
+                    [rgbds_seq, 
+                     rgbds_seq[:, -1, None].repeat(1, self.S - S, 1, 1, 1)],
+                    dim=1,
+                )
+                S = rgbds_seq.shape[1]
+            
+            rgbs_ = rgbds_seq.reshape(B * S, C, H, W)[:, :3]
+            depths = rgbds_seq.reshape(B * S, C, H, W)[:, 3:].clone()
+            # open the mask 
+            # Traj_arap[:, w_idx_start:w_idx_start + self.S, :p_idx_end, -1] = 0
+            #step1: normalize the depth map
+
+            depths = (depths - d_near)/(d_far-d_near)            
+            depths_dn = nn.functional.interpolate(
+                    depths, scale_factor=1.0 / self.stride, mode="nearest")
+            depths_dnG = depths_dn*Dz
+            
+            #step2: normalize the coordinate
+            coords_init_[:, :, p_idx_start:p_idx_end, 2] = (
+                coords_init[:, :, p_idx_start:p_idx_end, 2] - d_near
+                )/(d_far-d_near)
+            coords_init_[:, :, p_idx_start:p_idx_end, 2] *= Dz
+
+            # efficient triplane splatting 
+            gridxyz = torch.cat([gridxy[None,...].repeat(
+                                depths_dn.shape[0],1,1,1), depths_dnG], dim=1)
+            Fxy2yz = gridxyz[:,[1, 2], ...] - gridxyz[:,:2]
+            Fxy2xz = gridxyz[:,[0, 2], ...] - gridxyz[:,:2]
+            if getattr(self.args, "Embed3D", None) == True:
+                gridxyz_nm = gridxyz.clone()
+                gridxyz_nm[:,0,...] = (gridxyz_nm[:,0,...]-gridxyz_nm[:,0,...].min())/(gridxyz_nm[:,0,...].max()-gridxyz_nm[:,0,...].min())
+                gridxyz_nm[:,1,...] = (gridxyz_nm[:,1,...]-gridxyz_nm[:,1,...].min())/(gridxyz_nm[:,1,...].max()-gridxyz_nm[:,1,...].min())
+                gridxyz_nm[:,2,...] = (gridxyz_nm[:,2,...]-gridxyz_nm[:,2,...].min())/(gridxyz_nm[:,2,...].max()-gridxyz_nm[:,2,...].min())
+                gridxyz_nm = 2*(gridxyz_nm-0.5)
+                _,_,h4,w4 = gridxyz_nm.shape
+                gridxyz_nm = gridxyz_nm.permute(0,2,3,1).reshape(S*h4*w4, 3)
+                featPE = self.embed3d(gridxyz_nm).view(S, h4, w4, -1).permute(0,3,1,2)
+                if fmaps_ is None:
+                    fmaps_ = torch.cat([self.fnet(rgbs_),featPE], dim=1) 
+                    fmaps_ = self.embedConv(fmaps_)
+                else:
+                    fmaps_new = torch.cat([self.fnet(rgbs_[self.S // 2 :]),featPE[self.S // 2 :]], dim=1) 
+                    fmaps_new = self.embedConv(fmaps_new)
+                    fmaps_ = torch.cat(
+                        [fmaps_[self.S // 2 :], fmaps_new], dim=0
+                    )
+            else:        
+                if fmaps_ is None:
+                    fmaps_ = self.fnet(rgbs_)
+                else:
+                    fmaps_ = torch.cat(
+                    [fmaps_[self.S // 2 :], self.fnet(rgbs_[self.S // 2 :])], dim=0
+                    )
+
+            fmapXY = fmaps_[:, :self.latent_dim].reshape(
+                B, S, self.latent_dim, H // self.stride, W // self.stride
+            )
+
+            fmapYZ = softsplat(fmapXY[0], Fxy2yz, None,
+                            strMode="avg", tenoutH=self.Dz, tenoutW=H//self.stride)
+            fmapXZ = softsplat(fmapXY[0], Fxy2xz, None,
+                                strMode="avg", tenoutH=self.Dz, tenoutW=W//self.stride)
+
+            fmapYZ = self.headyz(fmapYZ)[None, ...]
+            fmapXZ = self.headxz(fmapXZ)[None, ...]
+
+            if p_idx_end - p_idx_start > 0:
+                queried_t = (first_positive_sorted_inds[p_idx_start:p_idx_end]
+                                                        - w_idx_start)
+                (featxy_init,
+                 featyz_init,
+                 featxz_init) = self.sample_trifeat(
+                     t=queried_t,featMapxy=fmapXY,
+                     featMapyz=fmapYZ,featMapxz=fmapXZ,
+                     coords=coords_init_[:, :1, p_idx_start:p_idx_end]
+                     )
+                # T, S, N, C, 3
+                feat_init_curr = torch.stack([featxy_init, 
+                                              featyz_init, featxz_init], dim=-1)
+                feat_init = smart_cat(feat_init, feat_init_curr, dim=2)
+            
+            if p_idx_start > 0:
+                # preprocess the coordinates of last windows
+                last_coords = coords[-1][:, self.S // 2 :].clone()
+                last_coords[..., :2] /= float(self.stride)
+                last_coords[..., 2:] = (last_coords[..., 2:]-d_near)/(d_far-d_near)
+                last_coords[..., 2:] = last_coords[..., 2:]*Dz                        
+                
+                coords_init_[:, : self.S // 2, :p_idx_start] = last_coords
+                coords_init_[:, self.S // 2 :, :p_idx_start] = last_coords[
+                    :, -1
+                ].repeat(1, self.S // 2, 1, 1)
+                
+                last_vis = vis[:, self.S // 2 :].unsqueeze(-1)
+                vis_init_[:, : self.S // 2, :p_idx_start] = last_vis
+                vis_init_[:, self.S // 2 :, :p_idx_start] = last_vis[:, -1].repeat(
+                    1, self.S // 2, 1, 1
+                )
+
+            coords, attns, vis, __, Rigid_ln = self.forward_iteration(
+                fmapXY=fmapXY,
+                fmapYZ=fmapYZ,
+                fmapXZ=fmapXZ,
+                coords_init=coords_init_[:, :, :p_idx_end],
+                feat_init=feat_init[:, :, :p_idx_end],
+                vis_init=vis_init_[:, :, :p_idx_end],
+                track_mask=track_mask[:, w_idx_start : w_idx_start + self.S, :p_idx_end],
+                iters=iters,
+                intrs_S=self.intrs[:, w_idx_start : w_idx_start + self.S],
+                )
+            
+            Rigid_ln_total+=Rigid_ln
+            
+            if is_train:
+                vis_predictions.append(torch.sigmoid(vis[:, :S_local]))
+                coord_predictions.append([coord[:, :S_local] for coord in coords])
+                attn_predictions.append(attns)
+
+            self.traj_e[:, w_idx_start:w_idx_start+self.S, :p_idx_end] = coords[-1][:, :S_local]
+            self.vis_e[:, w_idx_start:w_idx_start+self.S, :p_idx_end] = vis[:, :S_local]
+
+            track_mask[:, : w_idx_start + self.S, :p_idx_end] = 0.0
+            w_idx_start = w_idx_start + self.S // 2
+
+            p_idx_start = p_idx_end
+        
+        self.traj_e = self.traj_e[:, :, inv_sort_inds]
+        self.vis_e = self.vis_e[:, :, inv_sort_inds]
+
+        self.vis_e = torch.sigmoid(self.vis_e)
+        train_data = (
+            (vis_predictions, coord_predictions, attn_predictions,
+             p_idx_end_list, sort_inds, Rigid_ln_total)
+        )
+        if self.is_train:
+            return self.traj_e, feat_init, self.vis_e, train_data
+        else:
+            return self.traj_e, feat_init, self.vis_e
+

das/spatracker/models/core/spatracker/unet.py

@@ -0,0 +1,258 @@
+'''
+Codes are from:
+https://github.com/jaxony/unet-pytorch/blob/master/model.py
+'''
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from collections import OrderedDict
+from torch.nn import init
+import numpy as np
+
+def conv3x3(in_channels, out_channels, stride=1, 
+            padding=1, bias=True, groups=1):    
+    return nn.Conv2d(
+        in_channels,
+        out_channels,
+        kernel_size=3,
+        stride=stride,
+        padding=padding,
+        bias=bias,
+        groups=groups)
+
+def upconv2x2(in_channels, out_channels, mode='transpose'):
+    if mode == 'transpose':
+        return nn.ConvTranspose2d(
+            in_channels,
+            out_channels,
+            kernel_size=2,
+            stride=2)
+    else:
+        # out_channels is always going to be the same
+        # as in_channels
+        return nn.Sequential(
+            nn.Upsample(mode='bilinear', scale_factor=2),
+            conv1x1(in_channels, out_channels))
+
+def conv1x1(in_channels, out_channels, groups=1):
+    return nn.Conv2d(
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        groups=groups,
+        stride=1)
+
+
+class DownConv(nn.Module):
+    """
+    A helper Module that performs 2 convolutions and 1 MaxPool.
+    A ReLU activation follows each convolution.
+    """
+    def __init__(self, in_channels, out_channels, pooling=True):
+        super(DownConv, self).__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.pooling = pooling
+
+        self.conv1 = conv3x3(self.in_channels, self.out_channels)
+        self.conv2 = conv3x3(self.out_channels, self.out_channels)
+
+        if self.pooling:
+            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        before_pool = x
+        if self.pooling:
+            x = self.pool(x)
+        return x, before_pool
+
+
+class UpConv(nn.Module):
+    """
+    A helper Module that performs 2 convolutions and 1 UpConvolution.
+    A ReLU activation follows each convolution.
+    """
+    def __init__(self, in_channels, out_channels, 
+                 merge_mode='concat', up_mode='transpose'):
+        super(UpConv, self).__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.merge_mode = merge_mode
+        self.up_mode = up_mode
+
+        self.upconv = upconv2x2(self.in_channels, self.out_channels, 
+            mode=self.up_mode)
+
+        if self.merge_mode == 'concat':
+            self.conv1 = conv3x3(
+                2*self.out_channels, self.out_channels)
+        else:
+            # num of input channels to conv2 is same
+            self.conv1 = conv3x3(self.out_channels, self.out_channels)
+        self.conv2 = conv3x3(self.out_channels, self.out_channels)
+
+
+    def forward(self, from_down, from_up):
+        """ Forward pass
+        Arguments:
+            from_down: tensor from the encoder pathway
+            from_up: upconv'd tensor from the decoder pathway
+        """
+        from_up = self.upconv(from_up)
+        if self.merge_mode == 'concat':
+            x = torch.cat((from_up, from_down), 1)
+        else:
+            x = from_up + from_down
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        return x
+
+
+class UNet(nn.Module):
+    """ `UNet` class is based on https://arxiv.org/abs/1505.04597
+
+    The U-Net is a convolutional encoder-decoder neural network.
+    Contextual spatial information (from the decoding,
+    expansive pathway) about an input tensor is merged with
+    information representing the localization of details
+    (from the encoding, compressive pathway).
+
+    Modifications to the original paper:
+    (1) padding is used in 3x3 convolutions to prevent loss
+        of border pixels
+    (2) merging outputs does not require cropping due to (1)
+    (3) residual connections can be used by specifying
+        UNet(merge_mode='add')
+    (4) if non-parametric upsampling is used in the decoder
+        pathway (specified by upmode='upsample'), then an
+        additional 1x1 2d convolution occurs after upsampling
+        to reduce channel dimensionality by a factor of 2.
+        This channel halving happens with the convolution in
+        the tranpose convolution (specified by upmode='transpose')
+    """
+
+    def __init__(self, num_classes, in_channels=3, depth=5, 
+                 start_filts=64, up_mode='transpose', 
+                 merge_mode='concat', **kwargs):
+        """
+        Arguments:
+            in_channels: int, number of channels in the input tensor.
+                Default is 3 for RGB images.
+            depth: int, number of MaxPools in the U-Net.
+            start_filts: int, number of convolutional filters for the 
+                first conv.
+            up_mode: string, type of upconvolution. Choices: 'transpose'
+                for transpose convolution or 'upsample' for nearest neighbour
+                upsampling.
+        """
+        super(UNet, self).__init__()
+
+        if up_mode in ('transpose', 'upsample'):
+            self.up_mode = up_mode
+        else:
+            raise ValueError("\"{}\" is not a valid mode for "
+                             "upsampling. Only \"transpose\" and "
+                             "\"upsample\" are allowed.".format(up_mode))
+    
+        if merge_mode in ('concat', 'add'):
+            self.merge_mode = merge_mode
+        else:
+            raise ValueError("\"{}\" is not a valid mode for"
+                             "merging up and down paths. "
+                             "Only \"concat\" and "
+                             "\"add\" are allowed.".format(up_mode))
+
+        # NOTE: up_mode 'upsample' is incompatible with merge_mode 'add'
+        if self.up_mode == 'upsample' and self.merge_mode == 'add':
+            raise ValueError("up_mode \"upsample\" is incompatible "
+                             "with merge_mode \"add\" at the moment "
+                             "because it doesn't make sense to use "
+                             "nearest neighbour to reduce "
+                             "depth channels (by half).")
+
+        self.num_classes = num_classes
+        self.in_channels = in_channels
+        self.start_filts = start_filts
+        self.depth = depth
+
+        self.down_convs = []
+        self.up_convs = []
+
+        # create the encoder pathway and add to a list
+        for i in range(depth):
+            ins = self.in_channels if i == 0 else outs
+            outs = self.start_filts*(2**i)
+            pooling = True if i < depth-1 else False
+
+            down_conv = DownConv(ins, outs, pooling=pooling)
+            self.down_convs.append(down_conv)
+
+        # create the decoder pathway and add to a list
+        # - careful! decoding only requires depth-1 blocks
+        for i in range(depth-1):
+            ins = outs
+            outs = ins // 2
+            up_conv = UpConv(ins, outs, up_mode=up_mode,
+                merge_mode=merge_mode)
+            self.up_convs.append(up_conv)
+
+        # add the list of modules to current module
+        self.down_convs = nn.ModuleList(self.down_convs)
+        self.up_convs = nn.ModuleList(self.up_convs)
+
+        self.conv_final = conv1x1(outs, self.num_classes)
+
+        self.reset_params()
+
+    @staticmethod
+    def weight_init(m):
+        if isinstance(m, nn.Conv2d):
+            init.xavier_normal_(m.weight)
+            init.constant_(m.bias, 0)
+
+
+    def reset_params(self):
+        for i, m in enumerate(self.modules()):
+            self.weight_init(m)
+
+
+    def forward(self, x):
+        encoder_outs = []
+        # encoder pathway, save outputs for merging
+        for i, module in enumerate(self.down_convs):
+            x, before_pool = module(x)
+            encoder_outs.append(before_pool)
+        for i, module in enumerate(self.up_convs):
+            before_pool = encoder_outs[-(i+2)]
+            x = module(before_pool, x)
+        
+        # No softmax is used. This means you need to use
+        # nn.CrossEntropyLoss is your training script,
+        # as this module includes a softmax already.
+        x = self.conv_final(x)
+        return x
+
+if __name__ == "__main__":
+    """
+    testing
+    """
+    model = UNet(1, depth=5, merge_mode='concat', in_channels=1, start_filts=32)
+    print(model)
+    print(sum(p.numel() for p in model.parameters()))
+
+    reso = 176
+    x = np.zeros((1, 1, reso, reso))
+    x[:,:,int(reso/2-1), int(reso/2-1)] = np.nan
+    x = torch.FloatTensor(x)
+
+    out = model(x)
+    print('%f'%(torch.sum(torch.isnan(out)).detach().cpu().numpy()/(reso*reso)))
+    
+    # loss = torch.sum(out)
+    # loss.backward()

das/spatracker/models/core/spatracker/vit/common.py

@@ -0,0 +1,43 @@
+# 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
+import torch.nn as nn
+
+from typing import Type
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_dim: int,
+        act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x

das/spatracker/models/core/spatracker/vit/encoder.py

@@ -0,0 +1,397 @@
+# 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
+import torch.nn as nn
+import torch.nn.functional as F
+
+from typing import Optional, Tuple, Type
+
+from .common import (
+    LayerNorm2d, MLPBlock
+)
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed 
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+
+        return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(
+    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
+) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (
+        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+    ).view(B, q_h * q_w, k_h * k_w)
+
+    return attn
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        kernel_size: Tuple[int, int] = (16, 16),
+        stride: Tuple[int, int] = (16, 16),
+        padding: Tuple[int, int] = (0, 0),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x

das/spatracker/predictor.py

@@ -0,0 +1,288 @@
+# 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
+import torch.nn.functional as F
+import time
+
+from tqdm import tqdm
+from .models.core.spatracker.spatracker import get_points_on_a_grid
+from .models.core.model_utils import smart_cat
+from .models.build_spatracker import (
+    build_spatracker,
+)
+from .models.core.model_utils import (
+    meshgrid2d, bilinear_sample2d, smart_cat
+)
+
+from comfy.utils import ProgressBar
+
+class SpaTrackerPredictor(torch.nn.Module):
+    def __init__(
+        self, checkpoint="cotracker/checkpoints/cotracker_stride_4_wind_8.pth",
+        interp_shape=(384, 512),
+        seq_length=16
+    ):
+        super().__init__()
+        self.interp_shape = interp_shape
+        self.support_grid_size = 6
+        model = build_spatracker(checkpoint, seq_length=seq_length)
+
+        self.model = model
+        self.model.eval()
+
+    @torch.no_grad()
+    def forward(
+        self,
+        video,  # (1, T, 3, H, W)
+        video_depth = None, # (T, 1, H, W)
+        # input prompt types:
+        # - None. Dense tracks are computed in this case. You can adjust *query_frame* to compute tracks starting from a specific frame.
+        # *backward_tracking=True* will compute tracks in both directions.
+        # - queries. Queried points of shape (1, N, 3) in format (t, x, y) for frame index and pixel coordinates.
+        # - grid_size. Grid of N*N points from the first frame. if segm_mask is provided, then computed only for the mask.
+        # You can adjust *query_frame* and *backward_tracking* for the regular grid in the same way as for dense tracks.
+        queries: torch.Tensor = None,
+        segm_mask: torch.Tensor = None,  # Segmentation mask of shape (B, 1, H, W)
+        grid_size: int = 0,
+        grid_query_frame: int = 0,  # only for dense and regular grid tracks
+        backward_tracking: bool = False,
+        depth_predictor=None,
+        wind_length: int = 8,
+        progressive_tracking: bool = False,
+    ):
+        if queries is None and grid_size == 0:
+            tracks, visibilities, T_Firsts = self._compute_dense_tracks(
+                video,
+                grid_query_frame=grid_query_frame,
+                backward_tracking=backward_tracking,
+                video_depth=video_depth,
+                depth_predictor=depth_predictor,
+                wind_length=wind_length,
+            )
+        else:
+            tracks, visibilities, T_Firsts = self._compute_sparse_tracks(
+                video,
+                queries,
+                segm_mask,
+                grid_size,
+                add_support_grid=False, #(grid_size == 0 or segm_mask is not None),
+                grid_query_frame=grid_query_frame,
+                backward_tracking=backward_tracking,
+                video_depth=video_depth,
+                depth_predictor=depth_predictor,
+                wind_length=wind_length,
+            )
+        
+        return tracks, visibilities, T_Firsts
+
+    def _compute_dense_tracks(
+        self, video, grid_query_frame, grid_size=30, backward_tracking=False,
+        depth_predictor=None, video_depth=None, wind_length=8
+    ):
+        *_, H, W = video.shape
+        grid_step = W // grid_size
+        grid_width = W // grid_step
+        grid_height = H // grid_step
+        tracks = visibilities = T_Firsts = None
+        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
+            oy = offset // grid_step
+            grid_pts[0, :, 1] = (
+                torch.arange(grid_width).repeat(grid_height) * grid_step + ox
+            )
+            grid_pts[0, :, 2] = (
+                torch.arange(grid_height).repeat_interleave(grid_width) * grid_step + oy
+            )
+            tracks_step, visibilities_step, T_First_step = self._compute_sparse_tracks(
+                video=video,
+                queries=grid_pts,
+                backward_tracking=backward_tracking,
+                wind_length=wind_length,
+                video_depth=video_depth,
+                depth_predictor=depth_predictor,
+            )
+            tracks = smart_cat(tracks, tracks_step, dim=2)
+            visibilities = smart_cat(visibilities, visibilities_step, dim=2)
+            T_Firsts = smart_cat(T_Firsts, T_First_step, dim=1)
+
+
+        return tracks, visibilities, T_Firsts
+
+    def _compute_sparse_tracks(
+        self,
+        video,
+        queries,
+        segm_mask=None,
+        grid_size=0,
+        add_support_grid=False,
+        grid_query_frame=0,
+        backward_tracking=False,
+        depth_predictor=None,
+        video_depth=None,
+        wind_length=8,
+    ):
+        B, T, C, H, W = video.shape
+        assert B == 1
+
+        video = video.reshape(B * T, C, H, W)
+        video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear")
+        video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1])
+
+        if queries is not None:
+            queries = queries.clone()
+            B, N, D = queries.shape
+            assert D == 3
+            queries[:, :, 1] *= self.interp_shape[1] / W
+            queries[:, :, 2] *= self.interp_shape[0] / H
+        elif grid_size > 0:
+            grid_pts = get_points_on_a_grid(grid_size, self.interp_shape, device=video.device)
+            if segm_mask is not None:
+                segm_mask = F.interpolate(
+                    segm_mask, tuple(self.interp_shape), mode="nearest"
+                )
+                point_mask = segm_mask[0, 0][
+                    (grid_pts[0, :, 1]).round().long().cpu(),
+                    (grid_pts[0, :, 0]).round().long().cpu(),
+                ].bool()
+                grid_pts_extra = grid_pts[:, point_mask]
+            else:
+                grid_pts_extra = None
+            if grid_pts_extra is not None:
+                total_num = int(grid_pts_extra.shape[1])
+                total_num = min(800, total_num)
+                pick_idx = torch.randperm(grid_pts_extra.shape[1])[:total_num]
+                grid_pts_extra = grid_pts_extra[:, pick_idx]
+                queries_extra = torch.cat(
+                    [
+                        torch.ones_like(grid_pts_extra[:, :, :1]) * grid_query_frame,
+                        grid_pts_extra,
+                    ],
+                    dim=2,
+                )
+
+            queries = torch.cat(
+                [torch.zeros_like(grid_pts[:, :, :1]), grid_pts],
+                dim=2,
+            )
+
+        if add_support_grid:
+            grid_pts = get_points_on_a_grid(self.support_grid_size, self.interp_shape, device=video.device)
+            grid_pts = torch.cat(
+                [torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2
+            )
+            queries = torch.cat([queries, grid_pts], dim=1)
+
+        ## ----------- estimate the video depth -----------##
+        if video_depth is None:
+            with torch.no_grad():
+                if video[0].shape[0]>30:
+                    vidDepths = [] 
+                    for i in range(video[0].shape[0]//30+1):
+                        if (i+1)*30 > video[0].shape[0]:
+                            end_idx = video[0].shape[0]
+                        else:
+                            end_idx = (i+1)*30
+                        if end_idx == i*30:
+                            break
+                        video_ = video[0][i*30:end_idx]
+                        vidDepths.append(depth_predictor.infer(video_/255))
+
+                    video_depth = torch.cat(vidDepths, dim=0)
+
+                else:
+                    video_depth = depth_predictor.infer(video[0]/255)
+        video_depth = F.interpolate(video_depth,
+                                     tuple(self.interp_shape), mode="nearest")
+        
+        # from PIL import Image
+        # import numpy
+        # depth_frame = video_depth[0].detach().cpu()
+        # depth_frame = depth_frame.squeeze(0)
+        # print(depth_frame)
+        # print(depth_frame.min(), depth_frame.max())
+        # depth_img = (depth_frame * 255).numpy().astype(numpy.uint8)
+        # depth_img = Image.fromarray(depth_img, mode='L')
+        # depth_img.save('outputs/depth_map.png')
+
+        # frame = video[0, 0].detach().cpu()
+        # frame = frame.permute(1, 2, 0)
+        # frame = (frame * 255).numpy().astype(numpy.uint8)
+        # frame = Image.fromarray(frame, mode='RGB')
+        # frame.save('outputs/frame.png')
+
+        depths = video_depth
+        rgbds = torch.cat([video, depths[None,...]], dim=2)
+        # get the 3D queries
+        comfy_pbar = ProgressBar(queries.shape[1])
+        depth_interp=[]
+        for i in tqdm(range(queries.shape[1]), desc="Processing queries"):
+            depth_interp_i = bilinear_sample2d(video_depth[queries[:, i:i+1, 0].long()], 
+                                queries[:, i:i+1, 1], queries[:, i:i+1, 2])
+            depth_interp.append(depth_interp_i)
+            comfy_pbar.update(1)
+
+        depth_interp = torch.cat(depth_interp, dim=1)
+        queries = smart_cat(queries, depth_interp,dim=-1)
+
+        #NOTE: free the memory of depth_predictor
+        del depth_predictor
+        torch.cuda.empty_cache()
+        t0 = time.time()
+        tracks, __, visibilities = self.model(rgbds=rgbds, queries=queries, iters=6, wind_S=wind_length)
+        print("Time taken for inference: ", time.time()-t0)
+
+        if backward_tracking:
+            tracks, visibilities = self._compute_backward_tracks(
+                rgbds, queries, tracks, visibilities
+            )
+            if add_support_grid:
+                queries[:, -self.support_grid_size ** 2 :, 0] = T - 1
+        if add_support_grid:
+            tracks = tracks[:, :, : -self.support_grid_size ** 2]
+            visibilities = visibilities[:, :, : -self.support_grid_size ** 2]
+        thr = 0.9
+        visibilities = visibilities > thr
+
+        # correct query-point predictions
+        # see https://github.com/facebookresearch/co-tracker/issues/28
+
+        # TODO: batchify
+        
+        for i in tqdm(range(len(queries)), desc="Processing queries", leave=False):
+            queries_t = queries[i, :tracks.size(2), 0].to(torch.int64)
+            arange = torch.arange(0, len(queries_t))
+
+            # overwrite the predictions with the query points
+            tracks[i, queries_t, arange] = queries[i, :tracks.size(2), 1:]
+
+            # correct visibilities, the query points should be visible
+            visibilities[i, queries_t, arange] = True
+            
+        T_First = queries[..., :tracks.size(2), 0].to(torch.uint8)
+        tracks[:, :, :, 0] *= W / float(self.interp_shape[1])
+        tracks[:, :, :, 1] *= H / float(self.interp_shape[0])
+        return tracks, visibilities, T_First
+
+    def _compute_backward_tracks(self, video, queries, tracks, visibilities):
+        inv_video = video.flip(1).clone()
+        inv_queries = queries.clone()
+        inv_queries[:, :, 0] = inv_video.shape[1] - inv_queries[:, :, 0] - 1
+
+        inv_tracks, __, inv_visibilities = self.model(
+            rgbds=inv_video, queries=queries, iters=6
+        )
+
+        inv_tracks = inv_tracks.flip(1)
+        inv_visibilities = inv_visibilities.flip(1)
+
+        mask = tracks == 0
+
+        tracks[mask] = inv_tracks[mask]
+        visibilities[mask[:, :, :, 0]] = inv_visibilities[mask[:, :, :, 0]]
+        return tracks, visibilities

das/spatracker/utils/__init__.py

@@ -0,0 +1,5 @@
+# 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.

das/spatracker/utils/basic.py

@@ -0,0 +1,397 @@
+import os
+import numpy as np
+from os.path import isfile
+import torch
+import torch.nn.functional as F
+EPS = 1e-6
+import copy
+
+def sub2ind(height, width, y, x):
+    return y*width + x
+
+def ind2sub(height, width, ind):
+    y = ind // width
+    x = ind % width
+    return y, x
+    
+def get_lr_str(lr):
+    lrn = "%.1e" % lr # e.g., 5.0e-04
+    lrn = lrn[0] + lrn[3:5] + lrn[-1] # e.g., 5e-4
+    return lrn
+    
+def strnum(x):
+    s = '%g' % x
+    if '.' in s:
+        if x < 1.0:
+            s = s[s.index('.'):]
+        s = s[:min(len(s),4)]
+    return s
+
+def assert_same_shape(t1, t2):
+    for (x, y) in zip(list(t1.shape), list(t2.shape)):
+        assert(x==y)
+
+def print_stats(name, tensor):
+    shape = tensor.shape
+    tensor = tensor.detach().cpu().numpy()
+    print('%s (%s) min = %.2f, mean = %.2f, max = %.2f' % (name, tensor.dtype, np.min(tensor), np.mean(tensor), np.max(tensor)), shape)
+
+def print_stats_py(name, tensor):
+    shape = tensor.shape
+    print('%s (%s) min = %.2f, mean = %.2f, max = %.2f' % (name, tensor.dtype, np.min(tensor), np.mean(tensor), np.max(tensor)), shape)
+
+def print_(name, tensor):
+    tensor = tensor.detach().cpu().numpy()
+    print(name, tensor, tensor.shape)
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+def normalize_single(d):
+    # d is a whatever shape torch tensor
+    dmin = torch.min(d)
+    dmax = torch.max(d)
+    d = (d-dmin)/(EPS+(dmax-dmin))
+    return d
+
+def normalize(d):
+    # d is B x whatever. normalize within each element of the batch
+    out = torch.zeros(d.size())
+    if d.is_cuda:
+        out = out.cuda()
+    B = list(d.size())[0]
+    for b in list(range(B)):
+        out[b] = normalize_single(d[b])
+    return out
+
+def hard_argmax2d(tensor):
+    B, C, Y, X = list(tensor.shape)
+    assert(C==1)
+
+    # flatten the Tensor along the height and width axes
+    flat_tensor = tensor.reshape(B, -1)
+    # argmax of the flat tensor
+    argmax = torch.argmax(flat_tensor, dim=1)
+
+    # convert the indices into 2d coordinates
+    argmax_y = torch.floor(argmax / X) # row
+    argmax_x = argmax % X # col
+
+    argmax_y = argmax_y.reshape(B)
+    argmax_x = argmax_x.reshape(B)
+    return argmax_y, argmax_x
+
+def argmax2d(heat, hard=True):
+    B, C, Y, X = list(heat.shape)
+    assert(C==1)
+
+    if hard:
+        # hard argmax
+        loc_y, loc_x = hard_argmax2d(heat)
+        loc_y = loc_y.float()
+        loc_x = loc_x.float()
+    else:
+        heat = heat.reshape(B, Y*X)
+        prob = torch.nn.functional.softmax(heat, dim=1)
+
+        grid_y, grid_x = meshgrid2d(B, Y, X)
+
+        grid_y = grid_y.reshape(B, -1)
+        grid_x = grid_x.reshape(B, -1)
+        
+        loc_y = torch.sum(grid_y*prob, dim=1)
+        loc_x = torch.sum(grid_x*prob, dim=1)
+        # these are B
+        
+    return loc_y, loc_x
+
+def reduce_masked_mean(x, mask, dim=None, keepdim=False):
+    # x and mask are the same shape, or at least broadcastably so < actually it's safer if you disallow broadcasting
+    # returns shape-1
+    # axis can be a list of axes
+    for (a,b) in zip(x.size(), mask.size()):
+        # if not b==1: 
+        assert(a==b) # some shape mismatch!
+    # assert(x.size() == mask.size())
+    prod = x*mask
+    if dim is None:
+        numer = torch.sum(prod)
+        denom = EPS+torch.sum(mask)
+    else:
+        numer = torch.sum(prod, dim=dim, keepdim=keepdim)
+        denom = EPS+torch.sum(mask, dim=dim, keepdim=keepdim)
+        
+    mean = numer/denom
+    return mean
+
+def reduce_masked_median(x, mask, keep_batch=False):
+    # x and mask are the same shape
+    assert(x.size() == mask.size())
+    device = x.device
+
+    B = list(x.shape)[0]
+    x = x.detach().cpu().numpy()
+    mask = mask.detach().cpu().numpy()
+
+    if keep_batch:
+        x = np.reshape(x, [B, -1])
+        mask = np.reshape(mask, [B, -1])
+        meds = np.zeros([B], np.float32)
+        for b in list(range(B)):
+            xb = x[b]
+            mb = mask[b]
+            if np.sum(mb) > 0:
+                xb = xb[mb > 0]
+                meds[b] = np.median(xb)
+            else:
+                meds[b] = np.nan
+        meds = torch.from_numpy(meds).to(device)
+        return meds.float()
+    else:
+        x = np.reshape(x, [-1])
+        mask = np.reshape(mask, [-1])
+        if np.sum(mask) > 0:
+            x = x[mask > 0]
+            med = np.median(x)
+        else:
+            med = np.nan
+        med = np.array([med], np.float32)
+        med = torch.from_numpy(med).to(device)
+        return med.float()
+
+def pack_seqdim(tensor, B):
+    shapelist = list(tensor.shape)
+    B_, S = shapelist[:2]
+    assert(B==B_)
+    otherdims = shapelist[2:]
+    tensor = torch.reshape(tensor, [B*S]+otherdims)
+    return tensor
+
+def unpack_seqdim(tensor, B):
+    shapelist = list(tensor.shape)
+    BS = shapelist[0]
+    assert(BS%B==0)
+    otherdims = shapelist[1:]
+    S = int(BS/B)
+    tensor = torch.reshape(tensor, [B,S]+otherdims)
+    return tensor
+
+def meshgrid2d(B, Y, X, stack=False, norm=False, device='cuda', on_chans=False):
+    # returns a meshgrid sized B x Y x X
+
+    grid_y = torch.linspace(0.0, Y-1, Y, device=torch.device(device))
+    grid_y = torch.reshape(grid_y, [1, Y, 1])
+    grid_y = grid_y.repeat(B, 1, X)
+
+    grid_x = torch.linspace(0.0, X-1, X, device=torch.device(device))
+    grid_x = torch.reshape(grid_x, [1, 1, X])
+    grid_x = grid_x.repeat(B, Y, 1)
+
+    if norm:
+        grid_y, grid_x = normalize_grid2d(
+            grid_y, grid_x, Y, X)
+
+    if stack:
+        # note we stack in xy order
+        # (see https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.grid_sample)
+        if on_chans:
+            grid = torch.stack([grid_x, grid_y], dim=1)
+        else:
+            grid = torch.stack([grid_x, grid_y], dim=-1)
+        return grid
+    else:
+        return grid_y, grid_x
+
+def meshgrid3d(B, Z, Y, X, stack=False, norm=False, device='cuda'):
+    # returns a meshgrid sized B x Z x Y x X
+    
+    grid_z = torch.linspace(0.0, Z-1, Z, device=device)
+    grid_z = torch.reshape(grid_z, [1, Z, 1, 1])
+    grid_z = grid_z.repeat(B, 1, Y, X)
+
+    grid_y = torch.linspace(0.0, Y-1, Y, device=device)
+    grid_y = torch.reshape(grid_y, [1, 1, Y, 1])
+    grid_y = grid_y.repeat(B, Z, 1, X)
+
+    grid_x = torch.linspace(0.0, X-1, X, device=device)
+    grid_x = torch.reshape(grid_x, [1, 1, 1, X])
+    grid_x = grid_x.repeat(B, Z, Y, 1)
+
+    # if cuda:
+    #     grid_z = grid_z.cuda()
+    #     grid_y = grid_y.cuda()
+    #     grid_x = grid_x.cuda()
+        
+    if norm:
+        grid_z, grid_y, grid_x = normalize_grid3d(
+            grid_z, grid_y, grid_x, Z, Y, X)
+
+    if stack:
+        # note we stack in xyz order
+        # (see https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.grid_sample)
+        grid = torch.stack([grid_x, grid_y, grid_z], dim=-1)
+        return grid
+    else:
+        return grid_z, grid_y, grid_x
+    
+def normalize_grid2d(grid_y, grid_x, Y, X, clamp_extreme=True):
+    # make things in [-1,1]
+    grid_y = 2.0*(grid_y / float(Y-1)) - 1.0
+    grid_x = 2.0*(grid_x / float(X-1)) - 1.0
+    
+    if clamp_extreme:
+        grid_y = torch.clamp(grid_y, min=-2.0, max=2.0)
+        grid_x = torch.clamp(grid_x, min=-2.0, max=2.0)
+        
+    return grid_y, grid_x
+
+def normalize_grid3d(grid_z, grid_y, grid_x, Z, Y, X, clamp_extreme=True):
+    # make things in [-1,1]
+    grid_z = 2.0*(grid_z / float(Z-1)) - 1.0
+    grid_y = 2.0*(grid_y / float(Y-1)) - 1.0
+    grid_x = 2.0*(grid_x / float(X-1)) - 1.0
+    
+    if clamp_extreme:
+        grid_z = torch.clamp(grid_z, min=-2.0, max=2.0)
+        grid_y = torch.clamp(grid_y, min=-2.0, max=2.0)
+        grid_x = torch.clamp(grid_x, min=-2.0, max=2.0)
+    
+    return grid_z, grid_y, grid_x
+
+def gridcloud2d(B, Y, X, norm=False, device='cuda'):
+    # we want to sample for each location in the grid
+    grid_y, grid_x = meshgrid2d(B, Y, X, norm=norm, device=device)
+    x = torch.reshape(grid_x, [B, -1])
+    y = torch.reshape(grid_y, [B, -1])
+    # these are B x N
+    xy = torch.stack([x, y], dim=2)
+    # this is B x N x 2
+    return xy
+
+def gridcloud3d(B, Z, Y, X, norm=False, device='cuda'):
+    # we want to sample for each location in the grid
+    grid_z, grid_y, grid_x = meshgrid3d(B, Z, Y, X, norm=norm, device=device)
+    x = torch.reshape(grid_x, [B, -1])
+    y = torch.reshape(grid_y, [B, -1])
+    z = torch.reshape(grid_z, [B, -1])
+    # these are B x N
+    xyz = torch.stack([x, y, z], dim=2)
+    # this is B x N x 3
+    return xyz
+
+import re
+def readPFM(file):
+    file = open(file, 'rb')
+
+    color = None
+    width = None
+    height = None
+    scale = None
+    endian = None
+
+    header = file.readline().rstrip()
+    if header == b'PF':
+        color = True
+    elif header == b'Pf':
+        color = False
+    else:
+        raise Exception('Not a PFM file.')
+
+    dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline())
+    if dim_match:
+        width, height = map(int, dim_match.groups())
+    else:
+        raise Exception('Malformed PFM header.')
+
+    scale = float(file.readline().rstrip())
+    if scale < 0: # little-endian
+        endian = '<'
+        scale = -scale
+    else:
+        endian = '>' # big-endian
+
+    data = np.fromfile(file, endian + 'f')
+    shape = (height, width, 3) if color else (height, width)
+
+    data = np.reshape(data, shape)
+    data = np.flipud(data)
+    return data    
+
+def normalize_boxlist2d(boxlist2d, H, W):
+    boxlist2d = boxlist2d.clone()
+    ymin, xmin, ymax, xmax = torch.unbind(boxlist2d, dim=2)
+    ymin = ymin / float(H)
+    ymax = ymax / float(H)
+    xmin = xmin / float(W)
+    xmax = xmax / float(W)
+    boxlist2d = torch.stack([ymin, xmin, ymax, xmax], dim=2)
+    return boxlist2d
+
+def unnormalize_boxlist2d(boxlist2d, H, W):
+    boxlist2d = boxlist2d.clone()
+    ymin, xmin, ymax, xmax = torch.unbind(boxlist2d, dim=2)
+    ymin = ymin * float(H)
+    ymax = ymax * float(H)
+    xmin = xmin * float(W)
+    xmax = xmax * float(W)
+    boxlist2d = torch.stack([ymin, xmin, ymax, xmax], dim=2)
+    return boxlist2d
+
+def unnormalize_box2d(box2d, H, W):
+    return unnormalize_boxlist2d(box2d.unsqueeze(1), H, W).squeeze(1)
+
+def normalize_box2d(box2d, H, W):
+    return normalize_boxlist2d(box2d.unsqueeze(1), H, W).squeeze(1)
+
+def get_gaussian_kernel_2d(channels, kernel_size=3, sigma=2.0, mid_one=False):
+    C = channels
+    xy_grid = gridcloud2d(C, kernel_size, kernel_size) # C x N x 2
+
+    mean = (kernel_size - 1)/2.0
+    variance = sigma**2.0
+
+    gaussian_kernel = (1.0/(2.0*np.pi*variance)**1.5) * torch.exp(-torch.sum((xy_grid - mean)**2.0, dim=-1) / (2.0*variance)) # C X N
+    gaussian_kernel = gaussian_kernel.view(C, 1, kernel_size, kernel_size) # C x 1 x 3 x 3
+    kernel_sum = torch.sum(gaussian_kernel, dim=(2,3), keepdim=True)
+
+    gaussian_kernel = gaussian_kernel / kernel_sum # normalize
+
+    if mid_one:
+        # normalize so that the middle element is 1
+        maxval = gaussian_kernel[:,:,(kernel_size//2),(kernel_size//2)].reshape(C, 1, 1, 1)
+        gaussian_kernel = gaussian_kernel / maxval
+
+    return gaussian_kernel
+
+def gaussian_blur_2d(input, kernel_size=3, sigma=2.0, reflect_pad=False, mid_one=False):
+    B, C, Z, X = input.shape
+    kernel = get_gaussian_kernel_2d(C, kernel_size, sigma, mid_one=mid_one)
+    if reflect_pad:
+        pad = (kernel_size - 1)//2
+        out = F.pad(input, (pad, pad, pad, pad), mode='reflect')
+        out = F.conv2d(out, kernel, padding=0, groups=C)
+    else:
+        out = F.conv2d(input, kernel, padding=(kernel_size - 1)//2, groups=C)
+    return out
+
+def gradient2d(x, absolute=False, square=False, return_sum=False):
+    # x should be B x C x H x W
+    dh = x[:, :, 1:, :] - x[:, :, :-1, :]
+    dw = x[:, :, :, 1:] - x[:, :, :, :-1]
+
+    zeros = torch.zeros_like(x)
+    zero_h = zeros[:, :, 0:1, :]
+    zero_w = zeros[:, :, :, 0:1]
+    dh = torch.cat([dh, zero_h], axis=2)
+    dw = torch.cat([dw, zero_w], axis=3)
+    if absolute:
+        dh = torch.abs(dh)
+        dw = torch.abs(dw)
+    if square:
+        dh = dh ** 2
+        dw = dw ** 2
+    if return_sum:
+        return dh+dw
+    else: 
+        return dh, dw

das/spatracker/utils/geom.py

@@ -0,0 +1,547 @@
+import torch
+from . import basic as utils
+import numpy as np
+import torchvision.ops as ops
+from .basic import print_
+
+def matmul2(mat1, mat2):
+    return torch.matmul(mat1, mat2)
+
+def matmul3(mat1, mat2, mat3):
+    return torch.matmul(mat1, torch.matmul(mat2, mat3))
+
+def eye_3x3(B, device='cuda'):
+    rt = torch.eye(3, device=torch.device(device)).view(1,3,3).repeat([B, 1, 1])
+    return rt
+
+def eye_4x4(B, device='cuda'):
+    rt = torch.eye(4, device=torch.device(device)).view(1,4,4).repeat([B, 1, 1])
+    return rt
+
+def safe_inverse(a): #parallel version
+    B, _, _ = list(a.shape)
+    inv = a.clone()
+    r_transpose = a[:, :3, :3].transpose(1,2) #inverse of rotation matrix
+
+    inv[:, :3, :3] = r_transpose
+    inv[:, :3, 3:4] = -torch.matmul(r_transpose, a[:, :3, 3:4])
+
+    return inv
+
+def safe_inverse_single(a):
+    r, t = split_rt_single(a)
+    t = t.view(3,1)
+    r_transpose = r.t()
+    inv = torch.cat([r_transpose, -torch.matmul(r_transpose, t)], 1)
+    bottom_row = a[3:4, :] # this is [0, 0, 0, 1]
+    # bottom_row = torch.tensor([0.,0.,0.,1.]).view(1,4)
+    inv = torch.cat([inv, bottom_row], 0)
+    return inv
+
+def split_intrinsics(K):
+    # K is B x 3 x 3 or B x 4 x 4
+    fx = K[:,0,0]
+    fy = K[:,1,1]
+    x0 = K[:,0,2]
+    y0 = K[:,1,2]
+    return fx, fy, x0, y0
+
+def apply_pix_T_cam(pix_T_cam, xyz):
+
+    fx, fy, x0, y0 = split_intrinsics(pix_T_cam)
+    
+    # xyz is shaped B x H*W x 3
+    # returns xy, shaped B x H*W x 2
+    
+    B, N, C = list(xyz.shape)
+    assert(C==3)
+    
+    x, y, z = torch.unbind(xyz, axis=-1)
+
+    fx = torch.reshape(fx, [B, 1])
+    fy = torch.reshape(fy, [B, 1])
+    x0 = torch.reshape(x0, [B, 1])
+    y0 = torch.reshape(y0, [B, 1])
+
+    EPS = 1e-4
+    z = torch.clamp(z, min=EPS)
+    x = (x*fx)/(z)+x0
+    y = (y*fy)/(z)+y0
+    xy = torch.stack([x, y], axis=-1)
+    return xy
+
+def apply_pix_T_cam_py(pix_T_cam, xyz):
+
+    fx, fy, x0, y0 = split_intrinsics(pix_T_cam)
+    
+    # xyz is shaped B x H*W x 3
+    # returns xy, shaped B x H*W x 2
+    
+    B, N, C = list(xyz.shape)
+    assert(C==3)
+    
+    x, y, z = xyz[:,:,0], xyz[:,:,1], xyz[:,:,2]
+
+    fx = np.reshape(fx, [B, 1])
+    fy = np.reshape(fy, [B, 1])
+    x0 = np.reshape(x0, [B, 1])
+    y0 = np.reshape(y0, [B, 1])
+
+    EPS = 1e-4
+    z = np.clip(z, EPS, None)
+    x = (x*fx)/(z)+x0
+    y = (y*fy)/(z)+y0
+    xy = np.stack([x, y], axis=-1)
+    return xy
+
+def get_camM_T_camXs(origin_T_camXs, ind=0):
+    B, S = list(origin_T_camXs.shape)[0:2]
+    camM_T_camXs = torch.zeros_like(origin_T_camXs)
+    for b in list(range(B)):
+        camM_T_origin = safe_inverse_single(origin_T_camXs[b,ind])
+        for s in list(range(S)):
+            camM_T_camXs[b,s] = torch.matmul(camM_T_origin, origin_T_camXs[b,s])
+    return camM_T_camXs
+
+def apply_4x4(RT, xyz):
+    B, N, _ = list(xyz.shape)
+    ones = torch.ones_like(xyz[:,:,0:1])
+    xyz1 = torch.cat([xyz, ones], 2)
+    xyz1_t = torch.transpose(xyz1, 1, 2)
+    # this is B x 4 x N
+    xyz2_t = torch.matmul(RT, xyz1_t)
+    xyz2 = torch.transpose(xyz2_t, 1, 2)
+    xyz2 = xyz2[:,:,:3]
+    return xyz2
+
+def apply_4x4_py(RT, xyz):
+    # print('RT', RT.shape)
+    B, N, _ = list(xyz.shape)
+    ones = np.ones_like(xyz[:,:,0:1])
+    xyz1 = np.concatenate([xyz, ones], 2)
+    # print('xyz1', xyz1.shape)
+    xyz1_t = xyz1.transpose(0,2,1)
+    # print('xyz1_t', xyz1_t.shape)
+    # this is B x 4 x N
+    xyz2_t = np.matmul(RT, xyz1_t)
+    # print('xyz2_t', xyz2_t.shape)
+    xyz2 = xyz2_t.transpose(0,2,1)
+    # print('xyz2', xyz2.shape)
+    xyz2 = xyz2[:,:,:3]
+    return xyz2
+
+def apply_3x3(RT, xy):
+    B, N, _ = list(xy.shape)
+    ones = torch.ones_like(xy[:,:,0:1])
+    xy1 = torch.cat([xy, ones], 2)
+    xy1_t = torch.transpose(xy1, 1, 2)
+    # this is B x 4 x N
+    xy2_t = torch.matmul(RT, xy1_t)
+    xy2 = torch.transpose(xy2_t, 1, 2)
+    xy2 = xy2[:,:,:2]
+    return xy2
+
+def generate_polygon(ctr_x, ctr_y, avg_r, irregularity, spikiness, num_verts):
+    '''
+    Start with the center of the polygon at ctr_x, ctr_y, 
+    Then creates the polygon by sampling points on a circle around the center.
+    Random noise is added by varying the angular spacing between sequential points,
+    and by varying the radial distance of each point from the centre.
+
+    Params:
+        ctr_x, ctr_y - coordinates of the "centre" of the polygon
+        avg_r - in px, the average radius of this polygon, this roughly controls how large the polygon is, really only useful for order of magnitude.
+        irregularity - [0,1] indicating how much variance there is in the angular spacing of vertices. [0,1] will map to [0, 2pi/numberOfVerts]
+        spikiness - [0,1] indicating how much variance there is in each vertex from the circle of radius avg_r. [0,1] will map to [0, avg_r]
+pp        num_verts
+
+    Returns:
+        np.array [num_verts, 2] - CCW order.
+    '''
+    # spikiness
+    spikiness = np.clip(spikiness, 0, 1) * avg_r
+
+    # generate n angle steps
+    irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / num_verts
+    lower = (2*np.pi / num_verts) - irregularity
+    upper = (2*np.pi / num_verts) + irregularity
+
+    # angle steps
+    angle_steps = np.random.uniform(lower, upper, num_verts)
+    sc = (2 * np.pi) / angle_steps.sum()
+    angle_steps *= sc
+
+    # get all radii
+    angle = np.random.uniform(0, 2*np.pi)
+    radii = np.clip(np.random.normal(avg_r, spikiness, num_verts), 0, 2 * avg_r)
+
+    # compute all points
+    points = []
+    for i in range(num_verts):
+        x = ctr_x + radii[i] * np.cos(angle)
+        y = ctr_y + radii[i] * np.sin(angle)
+        points.append([x, y])
+        angle += angle_steps[i]
+
+    return np.array(points).astype(int)
+
+
+def get_random_affine_2d(B, rot_min=-5.0, rot_max=5.0, tx_min=-0.1, tx_max=0.1, ty_min=-0.1, ty_max=0.1, sx_min=-0.05, sx_max=0.05, sy_min=-0.05, sy_max=0.05, shx_min=-0.05, shx_max=0.05, shy_min=-0.05, shy_max=0.05):
+    '''
+    Params:
+        rot_min: rotation amount min
+        rot_max: rotation amount max
+
+        tx_min: translation x min
+        tx_max: translation x max
+
+        ty_min: translation y min
+        ty_max: translation y max
+
+        sx_min: scaling x min
+        sx_max: scaling x max
+
+        sy_min: scaling y min
+        sy_max: scaling y max
+
+        shx_min: shear x min
+        shx_max: shear x max
+
+        shy_min: shear y min
+        shy_max: shear y max
+
+    Returns:
+        transformation matrix: (B, 3, 3)
+    '''
+    # rotation
+    if rot_max - rot_min != 0:
+        rot_amount = np.random.uniform(low=rot_min, high=rot_max, size=B)
+        rot_amount = np.pi/180.0*rot_amount
+    else:
+        rot_amount = rot_min
+    rotation = np.zeros((B, 3, 3)) # B, 3, 3
+    rotation[:, 2, 2] = 1
+    rotation[:, 0, 0] = np.cos(rot_amount)
+    rotation[:, 0, 1] = -np.sin(rot_amount)
+    rotation[:, 1, 0] = np.sin(rot_amount)
+    rotation[:, 1, 1] = np.cos(rot_amount)
+
+    # translation
+    translation = np.zeros((B, 3, 3)) # B, 3, 3
+    translation[:, [0,1,2], [0,1,2]] = 1 
+    if (tx_max - tx_min) > 0:
+        trans_x = np.random.uniform(low=tx_min, high=tx_max, size=B)
+        translation[:, 0, 2] = trans_x
+    # else:
+    #     translation[:, 0, 2] = tx_max
+    if ty_max - ty_min != 0:
+        trans_y = np.random.uniform(low=ty_min, high=ty_max, size=B)
+        translation[:, 1, 2] = trans_y
+    # else:
+    #     translation[:, 1, 2] = ty_max
+
+    # scaling
+    scaling = np.zeros((B, 3, 3)) # B, 3, 3
+    scaling[:, [0,1,2], [0,1,2]] = 1 
+    if (sx_max - sx_min) > 0:
+        scale_x = 1 + np.random.uniform(low=sx_min, high=sx_max, size=B)
+        scaling[:, 0, 0] = scale_x
+    # else:
+    #     scaling[:, 0, 0] = sx_max
+    if (sy_max - sy_min) > 0:
+        scale_y = 1 + np.random.uniform(low=sy_min, high=sy_max, size=B)
+        scaling[:, 1, 1] = scale_y
+    # else:
+    #     scaling[:, 1, 1] = sy_max
+
+    # shear
+    shear = np.zeros((B, 3, 3)) # B, 3, 3
+    shear[:, [0,1,2], [0,1,2]] = 1 
+    if (shx_max - shx_min) > 0:
+        shear_x = np.random.uniform(low=shx_min, high=shx_max, size=B)
+        shear[:, 0, 1] = shear_x
+    # else:
+    #     shear[:, 0, 1] = shx_max
+    if (shy_max - shy_min) > 0:
+        shear_y = np.random.uniform(low=shy_min, high=shy_max, size=B)
+        shear[:, 1, 0] = shear_y
+    # else:
+    #     shear[:, 1, 0] = shy_max
+
+    # compose all those
+    rt = np.einsum("ijk,ikl->ijl", rotation, translation)
+    ss = np.einsum("ijk,ikl->ijl", scaling, shear)
+    trans = np.einsum("ijk,ikl->ijl", rt, ss)
+
+    return trans
+
+def get_centroid_from_box2d(box2d):
+    ymin = box2d[:,0]
+    xmin = box2d[:,1]
+    ymax = box2d[:,2]
+    xmax = box2d[:,3]
+    x = (xmin+xmax)/2.0
+    y = (ymin+ymax)/2.0
+    return y, x
+
+def normalize_boxlist2d(boxlist2d, H, W):
+    boxlist2d = boxlist2d.clone()
+    ymin, xmin, ymax, xmax = torch.unbind(boxlist2d, dim=2)
+    ymin = ymin / float(H)
+    ymax = ymax / float(H)
+    xmin = xmin / float(W)
+    xmax = xmax / float(W)
+    boxlist2d = torch.stack([ymin, xmin, ymax, xmax], dim=2)
+    return boxlist2d
+
+def unnormalize_boxlist2d(boxlist2d, H, W):
+    boxlist2d = boxlist2d.clone()
+    ymin, xmin, ymax, xmax = torch.unbind(boxlist2d, dim=2)
+    ymin = ymin * float(H)
+    ymax = ymax * float(H)
+    xmin = xmin * float(W)
+    xmax = xmax * float(W)
+    boxlist2d = torch.stack([ymin, xmin, ymax, xmax], dim=2)
+    return boxlist2d
+
+def unnormalize_box2d(box2d, H, W):
+    return unnormalize_boxlist2d(box2d.unsqueeze(1), H, W).squeeze(1)
+
+def normalize_box2d(box2d, H, W):
+    return normalize_boxlist2d(box2d.unsqueeze(1), H, W).squeeze(1)
+
+def get_size_from_box2d(box2d):
+    ymin = box2d[:,0]
+    xmin = box2d[:,1]
+    ymax = box2d[:,2]
+    xmax = box2d[:,3]
+    height = ymax-ymin
+    width = xmax-xmin
+    return height, width
+
+def crop_and_resize(im, boxlist, PH, PW, boxlist_is_normalized=False):
+    B, C, H, W = im.shape
+    B2, N, D = boxlist.shape
+    assert(B==B2)
+    assert(D==4)
+    # PH, PW is the size to resize to
+
+    # output is B,N,C,PH,PW
+
+    # pt wants xy xy, unnormalized
+    if boxlist_is_normalized:
+        boxlist_unnorm = unnormalize_boxlist2d(boxlist, H, W)
+    else:
+        boxlist_unnorm = boxlist
+        
+    ymin, xmin, ymax, xmax = boxlist_unnorm.unbind(2)
+    # boxlist_pt = torch.stack([boxlist_unnorm[:,1], boxlist_unnorm[:,0], boxlist_unnorm[:,3], boxlist_unnorm[:,2]], dim=1)
+    boxlist_pt = torch.stack([xmin, ymin, xmax, ymax], dim=2)
+    # we want a B-len list of K x 4 arrays
+
+    # print('im', im.shape)
+    # print('boxlist', boxlist.shape)
+    # print('boxlist_pt', boxlist_pt.shape)
+
+    # boxlist_pt = list(boxlist_pt.unbind(0))
+
+    crops = []
+    for b in range(B):
+        crops_b = ops.roi_align(im[b:b+1], [boxlist_pt[b]], output_size=(PH, PW))
+        crops.append(crops_b)
+    # # crops = im
+
+    # print('crops', crops.shape)
+    # crops = crops.reshape(B,N,C,PH,PW)
+
+    
+    # crops = []
+    # for b in range(B):
+    #     crop_b = ops.roi_align(im[b:b+1], [boxlist_pt[b]], output_size=(PH, PW))
+    #     print('crop_b', crop_b.shape)
+    #     crops.append(crop_b)
+    crops = torch.stack(crops, dim=0)
+        
+    # print('crops', crops.shape)
+    # boxlist_list = boxlist_pt.unbind(0)
+    # print('rgb_crop', rgb_crop.shape)
+
+    return crops
+
+
+# def get_boxlist_from_centroid_and_size(cy, cx, h, w, clip=True):
+#     # cy,cx are both B,N
+#     ymin = cy - h/2
+#     ymax = cy + h/2
+#     xmin = cx - w/2
+#     xmax = cx + w/2
+
+#     box = torch.stack([ymin, xmin, ymax, xmax], dim=-1)
+#     if clip:
+#         box = torch.clamp(box, 0, 1)
+#     return box
+
+
+def get_boxlist_from_centroid_and_size(cy, cx, h, w):#, clip=False):
+    # cy,cx are the same shape
+    ymin = cy - h/2
+    ymax = cy + h/2
+    xmin = cx - w/2
+    xmax = cx + w/2
+
+    # if clip:
+    #     ymin = torch.clamp(ymin, 0, H-1)
+    #     ymax = torch.clamp(ymax, 0, H-1)
+    #     xmin = torch.clamp(xmin, 0, W-1)
+    #     xmax = torch.clamp(xmax, 0, W-1)
+    
+    box = torch.stack([ymin, xmin, ymax, xmax], dim=-1)
+    return box
+
+
+def get_box2d_from_mask(mask, normalize=False):
+    # mask is B, 1, H, W
+
+    B, C, H, W = mask.shape
+    assert(C==1)
+    xy = utils.basic.gridcloud2d(B, H, W, norm=False, device=mask.device) # B, H*W, 2
+
+    box = torch.zeros((B, 4), dtype=torch.float32, device=mask.device)
+    for b in range(B):
+        xy_b = xy[b] # H*W, 2
+        mask_b = mask[b].reshape(H*W)
+        xy_ = xy_b[mask_b > 0]
+        x_ = xy_[:,0]
+        y_ = xy_[:,1]
+        ymin = torch.min(y_)
+        ymax = torch.max(y_)
+        xmin = torch.min(x_)
+        xmax = torch.max(x_)
+        box[b] = torch.stack([ymin, xmin, ymax, xmax], dim=0)
+    if normalize:
+        box = normalize_boxlist2d(box.unsqueeze(1), H, W).squeeze(1)
+    return box
+
+def convert_box2d_to_intrinsics(box2d, pix_T_cam, H, W, use_image_aspect_ratio=True, mult_padding=1.0):
+    # box2d is B x 4, with ymin, xmin, ymax, xmax in normalized coords
+    # ymin, xmin, ymax, xmax = torch.unbind(box2d, dim=1)
+    # H, W is the original size of the image
+    # mult_padding is relative to object size in pixels
+
+    # i assume we're rendering an image the same size as the original (H, W)
+
+    if not mult_padding==1.0:
+        y, x = get_centroid_from_box2d(box2d)
+        h, w = get_size_from_box2d(box2d)
+        box2d = get_box2d_from_centroid_and_size(
+            y, x, h*mult_padding, w*mult_padding, clip=False)
+        
+    if use_image_aspect_ratio:
+        h, w = get_size_from_box2d(box2d)
+        y, x = get_centroid_from_box2d(box2d)
+
+        # note h,w are relative right now
+        # we need to undo this, to see the real ratio
+
+        h = h*float(H)
+        w = w*float(W)
+        box_ratio = h/w
+        im_ratio = H/float(W)
+
+        # print('box_ratio:', box_ratio)
+        # print('im_ratio:', im_ratio)
+
+        if box_ratio >= im_ratio:
+            w = h/im_ratio
+            # print('setting w:', h/im_ratio)
+        else:
+            h = w*im_ratio
+            # print('setting h:', w*im_ratio)
+            
+        box2d = get_box2d_from_centroid_and_size(
+            y, x, h/float(H), w/float(W), clip=False)
+
+    assert(h > 1e-4)
+    assert(w > 1e-4)
+        
+    ymin, xmin, ymax, xmax = torch.unbind(box2d, dim=1)
+
+    fx, fy, x0, y0 = split_intrinsics(pix_T_cam)
+
+    # the topleft of the new image will now have a different offset from the center of projection
+    
+    new_x0 = x0 - xmin*W
+    new_y0 = y0 - ymin*H
+
+    pix_T_cam = pack_intrinsics(fx, fy, new_x0, new_y0)
+    # this alone will give me an image in original resolution,
+    # with its topleft at the box corner
+
+    box_h, box_w = get_size_from_box2d(box2d)
+    # these are normalized, and shaped B. (e.g., [0.4], [0.3])
+
+    # we are going to scale the image by the inverse of this,
+    # since we are zooming into this area
+
+    sy = 1./box_h
+    sx = 1./box_w
+
+    pix_T_cam = scale_intrinsics(pix_T_cam, sx, sy)
+    return pix_T_cam, box2d
+
+def pixels2camera(x,y,z,fx,fy,x0,y0):
+    # x and y are locations in pixel coordinates, z is a depth in meters
+    # they can be images or pointclouds
+    # fx, fy, x0, y0 are camera intrinsics
+    # returns xyz, sized B x N x 3
+
+    B = x.shape[0]
+    
+    fx = torch.reshape(fx, [B,1])
+    fy = torch.reshape(fy, [B,1])
+    x0 = torch.reshape(x0, [B,1])
+    y0 = torch.reshape(y0, [B,1])
+
+    x = torch.reshape(x, [B,-1])
+    y = torch.reshape(y, [B,-1])
+    z = torch.reshape(z, [B,-1])
+    
+    # unproject
+    x = (z/fx)*(x-x0)
+    y = (z/fy)*(y-y0)
+    
+    xyz = torch.stack([x,y,z], dim=2)
+    # B x N x 3
+    return xyz
+
+def camera2pixels(xyz, pix_T_cam):
+    # xyz is shaped B x H*W x 3
+    # returns xy, shaped B x H*W x 2
+    
+    fx, fy, x0, y0 = split_intrinsics(pix_T_cam)
+    x, y, z = torch.unbind(xyz, dim=-1)
+    B = list(z.shape)[0]
+
+    fx = torch.reshape(fx, [B,1])
+    fy = torch.reshape(fy, [B,1])
+    x0 = torch.reshape(x0, [B,1])
+    y0 = torch.reshape(y0, [B,1])
+    x = torch.reshape(x, [B,-1])
+    y = torch.reshape(y, [B,-1])
+    z = torch.reshape(z, [B,-1])
+
+    EPS = 1e-4
+    z = torch.clamp(z, min=EPS)
+    x = (x*fx)/z + x0
+    y = (y*fy)/z + y0
+    xy = torch.stack([x, y], dim=-1)
+    return xy
+
+def depth2pointcloud(z, pix_T_cam):
+    B, C, H, W = list(z.shape)
+    device = z.device
+    y, x = utils.basic.meshgrid2d(B, H, W, device=device)
+    z = torch.reshape(z, [B, H, W])
+    fx, fy, x0, y0 = split_intrinsics(pix_T_cam)
+    xyz = pixels2camera(x, y, z, fx, fy, x0, y0)
+    return xyz

das/spatracker/utils/misc.py

@@ -0,0 +1,166 @@
+import torch
+import numpy as np
+import math
+from prettytable import PrettyTable
+
+def count_parameters(model):
+    table = PrettyTable(["Modules", "Parameters"])
+    total_params = 0
+    for name, parameter in model.named_parameters():
+        if not parameter.requires_grad:
+            continue
+        param = parameter.numel()
+        if param > 100000:
+            table.add_row([name, param])
+        total_params+=param
+    print(table)
+    print('total params: %.2f M' % (total_params/1000000.0))
+    return total_params
+
+def posemb_sincos_2d_xy(xy, C, temperature=10000, dtype=torch.float32, cat_coords=False):
+    device = xy.device
+    dtype = xy.dtype
+    B, S, D = xy.shape
+    assert(D==2)
+    x = xy[:,:,0]
+    y = xy[:,:,1]
+    assert (C % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb'
+    omega = torch.arange(C // 4, device=device) / (C // 4 - 1)
+    omega = 1. / (temperature ** omega)
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :] 
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim=1) 
+    pe = pe.reshape(B,S,C).type(dtype)
+    if cat_coords:
+        pe = torch.cat([pe, xy], dim=2) # B,N,C+2
+    return pe
+
+class SimplePool():
+    def __init__(self, pool_size, version='pt'):
+        self.pool_size = pool_size
+        self.version = version
+        self.items = []
+        
+        if not (version=='pt' or version=='np'):
+            print('version = %s; please choose pt or np')
+            assert(False) # please choose pt or np
+            
+    def __len__(self):
+        return len(self.items)
+    
+    def mean(self, min_size=1):
+        if min_size=='half':
+            pool_size_thresh = self.pool_size/2
+        else:
+            pool_size_thresh = min_size
+            
+        if self.version=='np':
+            if len(self.items) >= pool_size_thresh:
+                return np.sum(self.items)/float(len(self.items))
+            else:
+                return np.nan
+        if self.version=='pt':
+            if len(self.items) >= pool_size_thresh:
+                return torch.sum(self.items)/float(len(self.items))
+            else:
+                return torch.from_numpy(np.nan)
+    
+    def sample(self, with_replacement=True):
+        idx = np.random.randint(len(self.items))
+        if with_replacement:
+            return self.items[idx]
+        else:
+            return self.items.pop(idx)
+    
+    def fetch(self, num=None):
+        if self.version=='pt':
+            item_array = torch.stack(self.items)
+        elif self.version=='np':
+            item_array = np.stack(self.items)
+        if num is not None:
+            # there better be some items
+            assert(len(self.items) >= num)
+                
+            # if there are not that many elements just return however many there are
+            if len(self.items) < num:
+                return item_array
+            else:
+                idxs = np.random.randint(len(self.items), size=num)
+                return item_array[idxs]
+        else:
+            return item_array
+            
+    def is_full(self):
+        full = len(self.items)==self.pool_size
+        return full
+    
+    def empty(self):
+        self.items = []
+            
+    def update(self, items):
+        for item in items:
+            if len(self.items) < self.pool_size:
+                # the pool is not full, so let's add this in
+                self.items.append(item)
+            else:
+                # the pool is full
+                # pop from the front
+                self.items.pop(0)
+                # add to the back
+                self.items.append(item)
+        return self.items
+
+def farthest_point_sample(xyz, npoint, include_ends=False, deterministic=False):
+    """
+    Input:
+        xyz: pointcloud data, [B, N, C], where C is probably 3
+        npoint: number of samples
+    Return:
+        inds: sampled pointcloud index, [B, npoint]
+    """
+    device = xyz.device
+    B, N, C = xyz.shape
+    xyz = xyz.float()
+    inds = torch.zeros(B, npoint, dtype=torch.long).to(device)
+    distance = torch.ones(B, N).to(device) * 1e10
+    if deterministic:
+        farthest = torch.randint(0, 1, (B,), dtype=torch.long).to(device)
+    else:
+        farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
+    batch_indices = torch.arange(B, dtype=torch.long).to(device)
+    for i in range(npoint):
+        if include_ends:
+            if i==0:
+                farthest = 0
+            elif i==1:
+                farthest = N-1
+        inds[:, i] = farthest
+        centroid = xyz[batch_indices, farthest, :].view(B, 1, C)
+        dist = torch.sum((xyz - centroid) ** 2, -1)
+        mask = dist < distance
+        distance[mask] = dist[mask]
+        farthest = torch.max(distance, -1)[1]
+
+        if npoint > N:
+            # if we need more samples, make them random
+            distance += torch.randn_like(distance)
+    return inds
+
+def farthest_point_sample_py(xyz, npoint):
+    N,C = xyz.shape
+    inds = np.zeros(npoint, dtype=np.int32)
+    distance = np.ones(N) * 1e10
+    farthest = np.random.randint(0, N, dtype=np.int32)
+    for i in range(npoint):
+        inds[i] = farthest
+        centroid = xyz[farthest, :].reshape(1,C)
+        dist = np.sum((xyz - centroid) ** 2, -1)
+        mask = dist < distance
+        distance[mask] = dist[mask]
+        farthest = np.argmax(distance, -1)
+        if npoint > N:
+            # if we need more samples, make them random
+            distance += np.random.randn(*distance.shape)
+    return inds
+    

das/spatracker/utils/samp.py

@@ -0,0 +1,152 @@
+import torch
+import utils.basic
+import torch.nn.functional as F
+
+def bilinear_sample2d(im, x, y, return_inbounds=False):
+    # x and y are each B, N
+    # output is B, C, N
+    B, C, H, W = list(im.shape)
+    N = list(x.shape)[1]
+
+    x = x.float()
+    y = y.float()
+    H_f = torch.tensor(H, dtype=torch.float32)
+    W_f = torch.tensor(W, dtype=torch.float32)
+    
+    # inbound_mask = (x>-0.5).float()*(y>-0.5).float()*(x<W_f+0.5).float()*(y<H_f+0.5).float()
+
+    max_y = (H_f - 1).int()
+    max_x = (W_f - 1).int()
+
+    x0 = torch.floor(x).int()
+    x1 = x0 + 1
+    y0 = torch.floor(y).int()
+    y1 = y0 + 1
+    
+    x0_clip = torch.clamp(x0, 0, max_x)
+    x1_clip = torch.clamp(x1, 0, max_x)
+    y0_clip = torch.clamp(y0, 0, max_y)
+    y1_clip = torch.clamp(y1, 0, max_y)
+    dim2 = W
+    dim1 = W * H
+
+    base = torch.arange(0, B, dtype=torch.int64, device=x.device)*dim1
+    base = torch.reshape(base, [B, 1]).repeat([1, N])
+
+    base_y0 = base + y0_clip * dim2
+    base_y1 = base + y1_clip * dim2
+
+    idx_y0_x0 = base_y0 + x0_clip
+    idx_y0_x1 = base_y0 + x1_clip
+    idx_y1_x0 = base_y1 + x0_clip
+    idx_y1_x1 = base_y1 + x1_clip
+
+    # use the indices to lookup pixels in the flat image
+    # im is B x C x H x W
+    # move C out to last dim
+    im_flat = (im.permute(0, 2, 3, 1)).reshape(B*H*W, C)
+    i_y0_x0 = im_flat[idx_y0_x0.long()]
+    i_y0_x1 = im_flat[idx_y0_x1.long()]
+    i_y1_x0 = im_flat[idx_y1_x0.long()]
+    i_y1_x1 = im_flat[idx_y1_x1.long()]
+
+    # Finally calculate interpolated values.
+    x0_f = x0.float()
+    x1_f = x1.float()
+    y0_f = y0.float()
+    y1_f = y1.float()
+
+    w_y0_x0 = ((x1_f - x) * (y1_f - y)).unsqueeze(2)
+    w_y0_x1 = ((x - x0_f) * (y1_f - y)).unsqueeze(2)
+    w_y1_x0 = ((x1_f - x) * (y - y0_f)).unsqueeze(2)
+    w_y1_x1 = ((x - x0_f) * (y - y0_f)).unsqueeze(2)
+
+    output = w_y0_x0 * i_y0_x0 + w_y0_x1 * i_y0_x1 + \
+             w_y1_x0 * i_y1_x0 + w_y1_x1 * i_y1_x1
+    # output is B*N x C
+    output = output.view(B, -1, C)
+    output = output.permute(0, 2, 1)
+    # output is B x C x N
+
+    if return_inbounds:
+        x_valid = (x > -0.5).byte() & (x < float(W_f - 0.5)).byte()
+        y_valid = (y > -0.5).byte() & (y < float(H_f - 0.5)).byte()
+        inbounds = (x_valid & y_valid).float()
+        inbounds = inbounds.reshape(B, N) # something seems wrong here for B>1; i'm getting an error here (or downstream if i put -1)
+        return output, inbounds
+
+    return output # B, C, N
+
+def paste_crop_on_canvas(crop, box2d_unnorm, H, W, fast=True, mask=None, canvas=None):
+    # this is the inverse of crop_and_resize_box2d
+    B, C, Y, X = list(crop.shape)
+    B2, D = list(box2d_unnorm.shape)
+    assert(B == B2)
+    assert(D == 4)
+
+    # here, we want to place the crop into a bigger image,
+    # at the location specified by the box2d.
+
+    if canvas is None:
+        canvas = torch.zeros((B, C, H, W), device=crop.device)
+    else:
+        B2, C2, H2, W2 = canvas.shape
+        assert(B==B2)
+        assert(C==C2)
+        assert(H==H2)
+        assert(W==W2)
+
+    # box2d_unnorm = utils.geom.unnormalize_box2d(box2d, H, W)
+
+    if fast:
+        ymin = box2d_unnorm[:, 0].long()
+        xmin = box2d_unnorm[:, 1].long()
+        ymax = box2d_unnorm[:, 2].long()
+        xmax = box2d_unnorm[:, 3].long()
+        w = (xmax - xmin).float()
+        h = (ymax - ymin).float()
+
+        grids = utils.basic.gridcloud2d(B, H, W)
+        grids_flat = grids.reshape(B, -1, 2)
+        # grids_flat[:, :, 0] = (grids_flat[:, :, 0] - xmin.float().unsqueeze(1)) / w.unsqueeze(1) * X
+        # grids_flat[:, :, 1] = (grids_flat[:, :, 1] - ymin.float().unsqueeze(1)) / h.unsqueeze(1) * Y
+
+        # for each pixel in the main image,
+        # grids_flat tells us where to sample in the crop image
+
+        # print('grids_flat', grids_flat.shape)
+        # print('crop', crop.shape)
+
+        grids_flat[:, :, 0] = (grids_flat[:, :, 0] - xmin.float().unsqueeze(1)) / w.unsqueeze(1) * 2.0 - 1.0
+        grids_flat[:, :, 1] = (grids_flat[:, :, 1] - ymin.float().unsqueeze(1)) / h.unsqueeze(1) * 2.0 - 1.0
+        
+        grid = grids_flat.reshape(B,H,W,2)
+
+        canvas = F.grid_sample(crop, grid, align_corners=False)
+        # print('canvas', canvas.shape)
+        
+        # if mask is None:
+        #     crop_resamp, inb = bilinear_sample2d(crop, grids_flat[:, :, 0], grids_flat[:, :, 1], return_inbounds=True)
+        #     crop_resamp = crop_resamp.reshape(B, C, H, W)
+        #     inb = inb.reshape(B, 1, H, W)
+        #     canvas = canvas * (1 - inb) + crop_resamp * inb
+        # else:
+        #     full_resamp = bilinear_sample2d(torch.cat([crop, mask], dim=1), grids_flat[:, :, 0], grids_flat[:, :, 1])
+        #     full_resamp = full_resamp.reshape(B, C+1, H, W)
+        #     crop_resamp = full_resamp[:,:3]
+        #     mask_resamp = full_resamp[:,3:4]
+        #     canvas = canvas * (1 - mask_resamp) + crop_resamp * mask_resamp
+    else:
+        for b in range(B):
+            ymin = box2d_unnorm[b, 0].long()
+            xmin = box2d_unnorm[b, 1].long()
+            ymax = box2d_unnorm[b, 2].long()
+            xmax = box2d_unnorm[b, 3].long()
+
+            crop_b = F.interpolate(crop[b:b + 1], (ymax - ymin, xmax - xmin)).squeeze(0)
+
+            # print('canvas[b,:,...', canvas[b,:,ymin:ymax,xmin:xmax].shape)
+            # print('crop_b', crop_b.shape)
+
+            canvas[b, :, ymin:ymax, xmin:xmax] = crop_b
+    return canvas

das/spatracker/utils/visualizer.py

@@ -0,0 +1,409 @@
+# 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 os
+import numpy as np
+import cv2
+import torch
+import flow_vis
+
+from matplotlib import cm
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+#from moviepy.editor import ImageSequenceClip
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+
+def read_video_from_path(path):
+    cap = cv2.VideoCapture(path)
+    if not cap.isOpened():
+        print("Error opening video file")
+    else:
+        frames = []
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if ret == True:
+                frames.append(np.array(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))
+            else:
+                break
+        cap.release()
+    return np.stack(frames)
+
+
+class Visualizer:
+    def __init__(
+        self,
+        save_dir: str = "./results",
+        grayscale: bool = False,
+        pad_value: int = 0,
+        fps: int = 10,
+        mode: str = "rainbow",  # 'cool', 'optical_flow'
+        linewidth: int = 1,
+        show_first_frame: int = 10,
+        tracks_leave_trace: int = 0,  # -1 for infinite
+    ):
+        self.mode = mode
+        self.save_dir = save_dir
+        self.vtxt_path = os.path.join(save_dir, "videos.txt")
+        self.ttxt_path = os.path.join(save_dir, "trackings.txt")
+        if mode == "rainbow":
+            self.color_map = cm.get_cmap("gist_rainbow")
+        elif mode == "cool":
+            self.color_map = cm.get_cmap(mode)
+        self.show_first_frame = show_first_frame
+        self.grayscale = grayscale
+        self.tracks_leave_trace = tracks_leave_trace
+        self.pad_value = pad_value
+        self.linewidth = linewidth
+        self.fps = fps
+
+    def visualize(
+        self,
+        video: torch.Tensor,  # (B,T,C,H,W)
+        tracks: torch.Tensor,  # (B,T,N,2)
+        visibility: torch.Tensor = None,  # (B, T, N, 1) bool
+        gt_tracks: torch.Tensor = None,  # (B,T,N,2)
+        segm_mask: torch.Tensor = None,  # (B,1,H,W)
+        filename: str = "video",
+        writer=None,  # tensorboard Summary Writer, used for visualization during training
+        step: int = 0,
+        query_frame: int = 0,
+        save_video: bool = True,
+        compensate_for_camera_motion: bool = False,
+        rigid_part = None,
+        video_depth = None # (B,T,C,H,W)
+    ):
+        if compensate_for_camera_motion:
+            assert segm_mask is not None
+        if segm_mask is not None:
+            coords = tracks[0, query_frame].round().long()
+            segm_mask = segm_mask[0, query_frame][coords[:, 1], coords[:, 0]].long()
+
+        video = F.pad(
+            video,
+            (self.pad_value, self.pad_value, self.pad_value, self.pad_value),
+            "constant",
+            255,
+        )
+
+        if video_depth is not None:
+            video_depth = (video_depth*255).cpu().numpy().astype(np.uint8)
+            video_depth = ([cv2.applyColorMap(video_depth[0,i,0], cv2.COLORMAP_INFERNO) 
+                            for i in range(video_depth.shape[1])])
+            video_depth = np.stack(video_depth, axis=0)
+            video_depth = torch.from_numpy(video_depth).permute(0, 3, 1, 2)[None]
+
+        tracks = tracks + self.pad_value
+
+        if self.grayscale:
+            transform = transforms.Grayscale()
+            video = transform(video)
+            video = video.repeat(1, 1, 3, 1, 1)
+
+        tracking_video = self.draw_tracks_on_video(
+            video=video,
+            tracks=tracks,
+            visibility=visibility,
+            segm_mask=segm_mask,
+            gt_tracks=gt_tracks,
+            query_frame=query_frame,
+            compensate_for_camera_motion=compensate_for_camera_motion,
+            rigid_part=rigid_part
+        )
+
+        if save_video:
+            # import ipdb; ipdb.set_trace()
+            tracking_dir = os.path.join(self.save_dir, "tracking")
+            if not os.path.exists(tracking_dir):
+                os.makedirs(tracking_dir)
+            self.save_video(tracking_video, filename=filename+"_tracking", 
+                            savedir=tracking_dir, writer=writer, step=step)
+            # with open(self.ttxt_path, 'a') as file:
+            #     file.write(f"tracking/{filename}_tracking.mp4\n")
+
+            videos_dir = os.path.join(self.save_dir, "videos")
+            if not os.path.exists(videos_dir):
+                os.makedirs(videos_dir)
+            self.save_video(video, filename=filename, 
+                            savedir=videos_dir, writer=writer, step=step)
+            # with open(self.vtxt_path, 'a') as file:
+            #     file.write(f"videos/{filename}.mp4\n")
+            if video_depth is not None:
+                self.save_video(video_depth, filename=filename+"_depth", 
+                                savedir=os.path.join(self.save_dir, "depth"), writer=writer, step=step)
+        return tracking_video
+
+    def save_video(self, video, filename, savedir=None, writer=None, step=0):
+        if writer is not None:
+            writer.add_video(
+                f"{filename}",
+                video.to(torch.uint8),
+                global_step=step,
+                fps=self.fps,
+            )
+        else:
+            os.makedirs(self.save_dir, exist_ok=True)
+            wide_list = list(video.unbind(1))
+            wide_list = [wide[0].permute(1, 2, 0).cpu().numpy() for wide in wide_list]
+            # clip = ImageSequenceClip(wide_list[2:-1], fps=self.fps)
+            clip = ImageSequenceClip(wide_list, fps=self.fps)
+
+            # Write the video file
+            if savedir is None:
+                save_path = os.path.join(self.save_dir, f"{filename}.mp4")
+            else:
+                save_path = os.path.join(savedir, f"{filename}.mp4")
+            clip.write_videofile(save_path, codec="libx264", fps=self.fps, logger=None)
+
+            print(f"Video saved to {save_path}")
+
+    def draw_tracks_on_video(
+        self,
+        video: torch.Tensor,
+        tracks: torch.Tensor,
+        visibility: torch.Tensor = None,
+        segm_mask: torch.Tensor = None,
+        gt_tracks=None,
+        query_frame: int = 0,
+        compensate_for_camera_motion=False,
+        rigid_part=None,
+    ):
+        B, T, C, H, W = video.shape
+        _, _, N, D = tracks.shape
+
+        assert D == 3
+        assert C == 3
+        video = video[0].permute(0, 2, 3, 1).byte().detach().cpu().numpy()  # S, H, W, C
+        tracks = tracks[0].detach().cpu().numpy()  # S, N, 2
+        if gt_tracks is not None:
+            gt_tracks = gt_tracks[0].detach().cpu().numpy()
+
+        res_video = []
+
+        # process input video
+        # for rgb in video:
+        #     res_video.append(rgb.copy())
+        
+        # create a blank tensor with the same shape as the video
+        for rgb in video:
+            black_frame = np.zeros_like(rgb.copy(), dtype=rgb.dtype)
+            res_video.append(black_frame)
+
+        vector_colors = np.zeros((T, N, 3))
+
+        if self.mode == "optical_flow":
+
+            vector_colors = flow_vis.flow_to_color(tracks - tracks[query_frame][None])
+
+        elif segm_mask is None:
+            if self.mode == "rainbow":
+                x_min, x_max = tracks[0, :, 0].min(), tracks[0, :, 0].max()
+                y_min, y_max = tracks[0, :, 1].min(), tracks[0, :, 1].max()
+
+                z_inv = 1/tracks[0, :, 2]
+                z_min, z_max = np.percentile(z_inv, [2, 98])
+                
+                norm_x = plt.Normalize(x_min, x_max)
+                norm_y = plt.Normalize(y_min, y_max)
+                norm_z = plt.Normalize(z_min, z_max)
+
+                for n in range(N):
+                    r = norm_x(tracks[0, n, 0])
+                    g = norm_y(tracks[0, n, 1])
+                    # r = 0
+                    # g = 0
+                    b = norm_z(1/tracks[0, n, 2])
+                    color = np.array([r, g, b])[None] * 255
+                    vector_colors[:, n] = np.repeat(color, T, axis=0)
+            else:
+                # color changes with time
+                for t in range(T):
+                    color = np.array(self.color_map(t / T)[:3])[None] * 255
+                    vector_colors[t] = np.repeat(color, N, axis=0)
+        else:
+            if self.mode == "rainbow":
+                vector_colors[:, segm_mask <= 0, :] = 255
+
+                x_min, x_max = tracks[0, :, 0].min(), tracks[0, :, 0].max()
+                y_min, y_max = tracks[0, :, 1].min(), tracks[0, :, 1].max()
+                z_min, z_max = tracks[0, :, 2].min(), tracks[0, :, 2].max()
+
+                norm_x = plt.Normalize(x_min, x_max)
+                norm_y = plt.Normalize(y_min, y_max)
+                norm_z = plt.Normalize(z_min, z_max)
+
+                for n in range(N):
+                    r = norm_x(tracks[0, n, 0])
+                    g = norm_y(tracks[0, n, 1])
+                    b = norm_z(tracks[0, n, 2])
+                    color = np.array([r, g, b])[None] * 255
+                    vector_colors[:, n] = np.repeat(color, T, axis=0)
+
+            else:
+                # color changes with segm class
+                segm_mask = segm_mask.cpu()
+                color = np.zeros((segm_mask.shape[0], 3), dtype=np.float32)
+                color[segm_mask > 0] = np.array(self.color_map(1.0)[:3]) * 255.0
+                color[segm_mask <= 0] = np.array(self.color_map(0.0)[:3]) * 255.0
+                vector_colors = np.repeat(color[None], T, axis=0)
+
+        # Draw tracks
+        if self.tracks_leave_trace != 0:
+            for t in range(1, T):
+                first_ind = (
+                    max(0, t - self.tracks_leave_trace)
+                    if self.tracks_leave_trace >= 0
+                    else 0
+                )
+                curr_tracks = tracks[first_ind : t + 1]
+                curr_colors = vector_colors[first_ind : t + 1]
+                if compensate_for_camera_motion:
+                    diff = (
+                        tracks[first_ind : t + 1, segm_mask <= 0]
+                        - tracks[t : t + 1, segm_mask <= 0]
+                    ).mean(1)[:, None]
+
+                    curr_tracks = curr_tracks - diff
+                    curr_tracks = curr_tracks[:, segm_mask > 0]
+                    curr_colors = curr_colors[:, segm_mask > 0]
+
+                res_video[t] = self._draw_pred_tracks(
+                    res_video[t],
+                    curr_tracks,
+                    curr_colors,
+                )
+                if gt_tracks is not None:
+                    res_video[t] = self._draw_gt_tracks(
+                        res_video[t], gt_tracks[first_ind : t + 1]
+                    )
+
+        if rigid_part is not None:
+            cls_label = torch.unique(rigid_part)
+            cls_num = len(torch.unique(rigid_part))
+            # visualize the clustering results 
+            cmap = plt.get_cmap('jet')  # get the color mapping
+            colors = cmap(np.linspace(0, 1, cls_num))  
+            colors = (colors[:, :3] * 255) 
+            color_map = {lable.item(): color for lable, color in zip(cls_label, colors)}
+
+        # Draw points
+        for t in tqdm(range(T)):
+            # Create a list to store information for each point
+            points_info = []
+            for i in range(N):
+                coord = (tracks[t, i, 0], tracks[t, i, 1])
+                depth = tracks[t, i, 2]  # assume the third dimension is depth
+                visibile = True
+                if visibility is not None:
+                    visibile = visibility[0, t, i]
+                if coord[0] != 0 and coord[1] != 0:
+                    if not compensate_for_camera_motion or (
+                        compensate_for_camera_motion and segm_mask[i] > 0
+                    ):
+                        points_info.append((i, coord, depth, visibile))
+            
+            # Sort points by depth, points with smaller depth (closer) will be drawn later
+            points_info.sort(key=lambda x: x[2], reverse=True)
+            
+            for i, coord, _, visibile in points_info:
+                if rigid_part is not None:
+                    color = color_map[rigid_part.squeeze()[i].item()]
+                    cv2.circle(
+                        res_video[t],
+                        coord,
+                        int(self.linewidth * 2),
+                        color.tolist(),
+                        thickness=-1 if visibile else 2
+                        -1,
+                    )
+                else:
+                    # Determine rectangle width based on the distance between adjacent tracks in the first frame
+                    if t == 0:
+                        distances = np.linalg.norm(tracks[0] - tracks[0, i], axis=1)
+                        distances = distances[distances > 0]
+                        rect_size = int(np.min(distances))/2
+                    
+                    # Define coordinates for top-left and bottom-right corners of the rectangle
+                    top_left = (int(coord[0] - rect_size), int(coord[1] - rect_size/1.5)) # Rectangle width is 1.5x (video aspect ratio is 1.5:1)
+                    bottom_right = (int(coord[0] + rect_size), int(coord[1] + rect_size/1.5))
+
+                    # Draw rectangle
+                    cv2.rectangle(
+                        res_video[t],
+                        top_left,
+                        bottom_right,
+                        vector_colors[t, i].tolist(),
+                        thickness=-1 if visibile else 0
+                        -1,
+                    )
+
+        # Construct the final rgb sequence
+        return torch.from_numpy(np.stack(res_video)).permute(0, 3, 1, 2)[None].byte()
+
+    def _draw_pred_tracks(
+        self,
+        rgb: np.ndarray,  # H x W x 3
+        tracks: np.ndarray,  # T x 2
+        vector_colors: np.ndarray,
+        alpha: float = 0.5,
+    ):
+        T, N, _ = tracks.shape
+
+        for s in range(T - 1):
+            vector_color = vector_colors[s]
+            original = rgb.copy()
+            alpha = (s / T) ** 2
+            for i in range(N):
+                coord_y = (int(tracks[s, i, 0]), int(tracks[s, i, 1]))
+                coord_x = (int(tracks[s + 1, i, 0]), int(tracks[s + 1, i, 1]))
+                if coord_y[0] != 0 and coord_y[1] != 0:
+                    cv2.line(
+                        rgb,
+                        coord_y,
+                        coord_x,
+                        vector_color[i].tolist(),
+                        self.linewidth,
+                        cv2.LINE_AA,
+                    )
+            if self.tracks_leave_trace > 0:
+                rgb = cv2.addWeighted(rgb, alpha, original, 1 - alpha, 0)
+        return rgb
+
+    def _draw_gt_tracks(
+        self,
+        rgb: np.ndarray,  # H x W x 3,
+        gt_tracks: np.ndarray,  # T x 2
+    ):
+        T, N, _ = gt_tracks.shape
+        color = np.array((211.0, 0.0, 0.0))
+
+        for t in range(T):
+            for i in range(N):
+                gt_tracks = gt_tracks[t][i]
+                #  draw a red cross
+                if gt_tracks[0] > 0 and gt_tracks[1] > 0:
+                    length = self.linewidth * 3
+                    coord_y = (int(gt_tracks[0]) + length, int(gt_tracks[1]) + length)
+                    coord_x = (int(gt_tracks[0]) - length, int(gt_tracks[1]) - length)
+                    cv2.line(
+                        rgb,
+                        coord_y,
+                        coord_x,
+                        color,
+                        self.linewidth,
+                        cv2.LINE_AA,
+                    )
+                    coord_y = (int(gt_tracks[0]) - length, int(gt_tracks[1]) + length)
+                    coord_x = (int(gt_tracks[0]) + length, int(gt_tracks[1]) - length)
+                    cv2.line(
+                        rgb,
+                        coord_y,
+                        coord_x,
+                        color,
+                        self.linewidth,
+                        cv2.LINE_AA,
+                    )
+        return rgb

das/spatracker/utils/vox.py

@@ -0,0 +1,500 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+import utils.geom
+
+class Vox_util(object):
+    def __init__(self, Z, Y, X, scene_centroid, bounds, pad=None, assert_cube=False):
+        self.XMIN, self.XMAX, self.YMIN, self.YMAX, self.ZMIN, self.ZMAX = bounds
+        B, D = list(scene_centroid.shape)
+        self.Z, self.Y, self.X = Z, Y, X
+
+        scene_centroid = scene_centroid.detach().cpu().numpy()
+        x_centroid, y_centroid, z_centroid = scene_centroid[0]
+        self.XMIN += x_centroid
+        self.XMAX += x_centroid
+        self.YMIN += y_centroid
+        self.YMAX += y_centroid
+        self.ZMIN += z_centroid
+        self.ZMAX += z_centroid
+
+        self.default_vox_size_X = (self.XMAX-self.XMIN)/float(X)
+        self.default_vox_size_Y = (self.YMAX-self.YMIN)/float(Y)
+        self.default_vox_size_Z = (self.ZMAX-self.ZMIN)/float(Z)
+
+        if pad:
+            Z_pad, Y_pad, X_pad = pad
+            self.ZMIN -= self.default_vox_size_Z * Z_pad
+            self.ZMAX += self.default_vox_size_Z * Z_pad
+            self.YMIN -= self.default_vox_size_Y * Y_pad
+            self.YMAX += self.default_vox_size_Y * Y_pad
+            self.XMIN -= self.default_vox_size_X * X_pad
+            self.XMAX += self.default_vox_size_X * X_pad
+
+        if assert_cube:
+            # we assume cube voxels
+            if (not np.isclose(self.default_vox_size_X, self.default_vox_size_Y)) or (not np.isclose(self.default_vox_size_X, self.default_vox_size_Z)):
+                print('Z, Y, X', Z, Y, X)
+                print('bounds for this iter:',
+                      'X = %.2f to %.2f' % (self.XMIN, self.XMAX),
+                      'Y = %.2f to %.2f' % (self.YMIN, self.YMAX),
+                      'Z = %.2f to %.2f' % (self.ZMIN, self.ZMAX),
+                )
+                print('self.default_vox_size_X', self.default_vox_size_X)
+                print('self.default_vox_size_Y', self.default_vox_size_Y)
+                print('self.default_vox_size_Z', self.default_vox_size_Z)
+            assert(np.isclose(self.default_vox_size_X, self.default_vox_size_Y))
+            assert(np.isclose(self.default_vox_size_X, self.default_vox_size_Z))
+
+    def Ref2Mem(self, xyz, Z, Y, X, assert_cube=False):
+        # xyz is B x N x 3, in ref coordinates
+        # transforms ref coordinates into mem coordinates
+        B, N, C = list(xyz.shape)
+        device = xyz.device
+        assert(C==3)
+        mem_T_ref = self.get_mem_T_ref(B, Z, Y, X, assert_cube=assert_cube, device=device)
+        xyz = utils.geom.apply_4x4(mem_T_ref, xyz)
+        return xyz
+
+    def Mem2Ref(self, xyz_mem, Z, Y, X, assert_cube=False):
+        # xyz is B x N x 3, in mem coordinates
+        # transforms mem coordinates into ref coordinates
+        B, N, C = list(xyz_mem.shape)
+        ref_T_mem = self.get_ref_T_mem(B, Z, Y, X, assert_cube=assert_cube, device=xyz_mem.device)
+        xyz_ref = utils.geom.apply_4x4(ref_T_mem, xyz_mem)
+        return xyz_ref
+
+    def get_mem_T_ref(self, B, Z, Y, X, assert_cube=False, device='cuda'):
+        vox_size_X = (self.XMAX-self.XMIN)/float(X)
+        vox_size_Y = (self.YMAX-self.YMIN)/float(Y)
+        vox_size_Z = (self.ZMAX-self.ZMIN)/float(Z)
+
+        if assert_cube:
+            if (not np.isclose(vox_size_X, vox_size_Y)) or (not np.isclose(vox_size_X, vox_size_Z)):
+                print('Z, Y, X', Z, Y, X)
+                print('bounds for this iter:',
+                      'X = %.2f to %.2f' % (self.XMIN, self.XMAX),
+                      'Y = %.2f to %.2f' % (self.YMIN, self.YMAX),
+                      'Z = %.2f to %.2f' % (self.ZMIN, self.ZMAX),
+                )
+                print('vox_size_X', vox_size_X)
+                print('vox_size_Y', vox_size_Y)
+                print('vox_size_Z', vox_size_Z)
+            assert(np.isclose(vox_size_X, vox_size_Y))
+            assert(np.isclose(vox_size_X, vox_size_Z))
+
+        # translation
+        # (this makes the left edge of the leftmost voxel correspond to XMIN)
+        center_T_ref = utils.geom.eye_4x4(B, device=device)
+        center_T_ref[:,0,3] = -self.XMIN-vox_size_X/2.0
+        center_T_ref[:,1,3] = -self.YMIN-vox_size_Y/2.0
+        center_T_ref[:,2,3] = -self.ZMIN-vox_size_Z/2.0
+
+        # scaling
+        # (this makes the right edge of the rightmost voxel correspond to XMAX)
+        mem_T_center = utils.geom.eye_4x4(B, device=device)
+        mem_T_center[:,0,0] = 1./vox_size_X
+        mem_T_center[:,1,1] = 1./vox_size_Y
+        mem_T_center[:,2,2] = 1./vox_size_Z
+        mem_T_ref = utils.geom.matmul2(mem_T_center, center_T_ref)
+
+        return mem_T_ref
+
+    def get_ref_T_mem(self, B, Z, Y, X, assert_cube=False, device='cuda'):
+        mem_T_ref = self.get_mem_T_ref(B, Z, Y, X, assert_cube=assert_cube, device=device)
+        # note safe_inverse is inapplicable here,
+        # since the transform is nonrigid
+        ref_T_mem = mem_T_ref.inverse()
+        return ref_T_mem
+
+    def get_inbounds(self, xyz, Z, Y, X, already_mem=False, padding=0.0, assert_cube=False):
+        # xyz is B x N x 3
+        # padding should be 0 unless you are trying to account for some later cropping
+        if not already_mem:
+            xyz = self.Ref2Mem(xyz, Z, Y, X, assert_cube=assert_cube)
+
+        x = xyz[:,:,0]
+        y = xyz[:,:,1]
+        z = xyz[:,:,2]
+
+        x_valid = ((x-padding)>-0.5).byte() & ((x+padding)<float(X-0.5)).byte()
+        y_valid = ((y-padding)>-0.5).byte() & ((y+padding)<float(Y-0.5)).byte()
+        z_valid = ((z-padding)>-0.5).byte() & ((z+padding)<float(Z-0.5)).byte()
+        nonzero = (~(z==0.0)).byte()
+
+        inbounds = x_valid & y_valid & z_valid & nonzero
+        return inbounds.bool()
+
+    def voxelize_xyz(self, xyz_ref, Z, Y, X, already_mem=False, assert_cube=False, clean_eps=0):
+        B, N, D = list(xyz_ref.shape)
+        assert(D==3)
+        if already_mem:
+            xyz_mem = xyz_ref
+        else:
+            xyz_mem = self.Ref2Mem(xyz_ref, Z, Y, X, assert_cube=assert_cube)
+            xyz_zero = self.Ref2Mem(xyz_ref[:,0:1]*0, Z, Y, X, assert_cube=assert_cube)
+        vox = self.get_occupancy(xyz_mem, Z, Y, X, clean_eps=clean_eps, xyz_zero=xyz_zero)
+        return vox
+
+    def voxelize_xyz_and_feats(self, xyz_ref, feats, Z, Y, X, already_mem=False, assert_cube=False, clean_eps=0):
+        B, N, D = list(xyz_ref.shape)
+        B2, N2, D2 = list(feats.shape)
+        assert(D==3)
+        assert(B==B2)
+        assert(N==N2)
+        if already_mem:
+            xyz_mem = xyz_ref
+        else:
+            xyz_mem = self.Ref2Mem(xyz_ref, Z, Y, X, assert_cube=assert_cube)
+            xyz_zero = self.Ref2Mem(xyz_ref[:,0:1]*0, Z, Y, X, assert_cube=assert_cube)
+        feats = self.get_feat_occupancy(xyz_mem, feats, Z, Y, X, clean_eps=clean_eps, xyz_zero=xyz_zero)
+        return feats
+
+    def get_occupancy(self, xyz, Z, Y, X, clean_eps=0, xyz_zero=None):
+        # xyz is B x N x 3 and in mem coords
+        # we want to fill a voxel tensor with 1's at these inds
+        B, N, C = list(xyz.shape)
+        assert(C==3)
+
+        # these papers say simple 1/0 occupancy is ok:
+        #  http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_PIXOR_Real-Time_3d_CVPR_2018_paper.pdf
+        #  http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf
+        # cont fusion says they do 8-neighbor interp
+        # voxelnet does occupancy but with a bit of randomness in terms of the reflectance value i think
+
+        inbounds = self.get_inbounds(xyz, Z, Y, X, already_mem=True)
+        x, y, z = xyz[:,:,0], xyz[:,:,1], xyz[:,:,2]
+        mask = torch.zeros_like(x)
+        mask[inbounds] = 1.0
+
+        if xyz_zero is not None:
+            # only take points that are beyond a thresh of zero
+            dist = torch.norm(xyz_zero-xyz, dim=2)
+            mask[dist < 0.1] = 0
+
+        if clean_eps > 0:
+            # only take points that are already near centers
+            xyz_round = torch.round(xyz) # B, N, 3
+            dist = torch.norm(xyz_round - xyz, dim=2)
+            mask[dist > clean_eps] = 0
+
+        # set the invalid guys to zero
+        # we then need to zero out 0,0,0
+        # (this method seems a bit clumsy)
+        x = x*mask
+        y = y*mask
+        z = z*mask
+
+        x = torch.round(x)
+        y = torch.round(y)
+        z = torch.round(z)
+        x = torch.clamp(x, 0, X-1).int()
+        y = torch.clamp(y, 0, Y-1).int()
+        z = torch.clamp(z, 0, Z-1).int()
+
+        x = x.view(B*N)
+        y = y.view(B*N)
+        z = z.view(B*N)
+
+        dim3 = X
+        dim2 = X * Y
+        dim1 = X * Y * Z
+
+        base = torch.arange(0, B, dtype=torch.int32, device=xyz.device)*dim1
+        base = torch.reshape(base, [B, 1]).repeat([1, N]).view(B*N)
+
+        vox_inds = base + z * dim2 + y * dim3 + x
+        voxels = torch.zeros(B*Z*Y*X, device=xyz.device).float()
+        voxels[vox_inds.long()] = 1.0
+        # zero out the singularity
+        voxels[base.long()] = 0.0
+        voxels = voxels.reshape(B, 1, Z, Y, X)
+        # B x 1 x Z x Y x X
+        return voxels
+
+    def get_feat_occupancy(self, xyz, feat, Z, Y, X, clean_eps=0, xyz_zero=None):
+        # xyz is B x N x 3 and in mem coords
+        # feat is B x N x D
+        # we want to fill a voxel tensor with 1's at these inds
+        B, N, C = list(xyz.shape)
+        B2, N2, D2 = list(feat.shape)
+        assert(C==3)
+        assert(B==B2)
+        assert(N==N2)
+
+        # these papers say simple 1/0 occupancy is ok:
+        #  http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_PIXOR_Real-Time_3d_CVPR_2018_paper.pdf
+        #  http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf
+        # cont fusion says they do 8-neighbor interp
+        # voxelnet does occupancy but with a bit of randomness in terms of the reflectance value i think
+
+        inbounds = self.get_inbounds(xyz, Z, Y, X, already_mem=True)
+        x, y, z = xyz[:,:,0], xyz[:,:,1], xyz[:,:,2]
+        mask = torch.zeros_like(x)
+        mask[inbounds] = 1.0
+
+        if xyz_zero is not None:
+            # only take points that are beyond a thresh of zero
+            dist = torch.norm(xyz_zero-xyz, dim=2)
+            mask[dist < 0.1] = 0
+
+        if clean_eps > 0:
+            # only take points that are already near centers
+            xyz_round = torch.round(xyz) # B, N, 3
+            dist = torch.norm(xyz_round - xyz, dim=2)
+            mask[dist > clean_eps] = 0
+
+        # set the invalid guys to zero
+        # we then need to zero out 0,0,0
+        # (this method seems a bit clumsy)
+        x = x*mask # B, N
+        y = y*mask
+        z = z*mask
+        feat = feat*mask.unsqueeze(-1) # B, N, D
+
+        x = torch.round(x)
+        y = torch.round(y)
+        z = torch.round(z)
+        x = torch.clamp(x, 0, X-1).int()
+        y = torch.clamp(y, 0, Y-1).int()
+        z = torch.clamp(z, 0, Z-1).int()
+
+        # permute point orders
+        perm = torch.randperm(N)
+        x = x[:, perm]
+        y = y[:, perm]
+        z = z[:, perm]
+        feat = feat[:, perm]
+
+        x = x.view(B*N)
+        y = y.view(B*N)
+        z = z.view(B*N)
+        feat = feat.view(B*N, -1)
+
+        dim3 = X
+        dim2 = X * Y
+        dim1 = X * Y * Z
+
+        base = torch.arange(0, B, dtype=torch.int32, device=xyz.device)*dim1
+        base = torch.reshape(base, [B, 1]).repeat([1, N]).view(B*N)
+
+        vox_inds = base + z * dim2 + y * dim3 + x
+        feat_voxels = torch.zeros((B*Z*Y*X, D2), device=xyz.device).float()
+        feat_voxels[vox_inds.long()] = feat
+        # zero out the singularity
+        feat_voxels[base.long()] = 0.0
+        feat_voxels = feat_voxels.reshape(B, Z, Y, X, D2).permute(0, 4, 1, 2, 3)
+        # B x C x Z x Y x X
+        return feat_voxels
+
+    def unproject_image_to_mem(self, rgb_camB, pixB_T_camA, camB_T_camA, Z, Y, X, assert_cube=False, xyz_camA=None):
+        # rgb_camB is B x C x H x W
+        # pixB_T_camA is B x 4 x 4
+
+        # rgb lives in B pixel coords
+        # we want everything in A memory coords
+
+        # this puts each C-dim pixel in the rgb_camB
+        # along a ray in the voxelgrid
+        B, C, H, W = list(rgb_camB.shape)
+
+        if xyz_camA is None:
+            xyz_memA = utils.basic.gridcloud3d(B, Z, Y, X, norm=False, device=pixB_T_camA.device)
+            xyz_camA = self.Mem2Ref(xyz_memA, Z, Y, X, assert_cube=assert_cube)
+
+        xyz_camB = utils.geom.apply_4x4(camB_T_camA, xyz_camA)
+        z = xyz_camB[:,:,2]
+
+        xyz_pixB = utils.geom.apply_4x4(pixB_T_camA, xyz_camA)
+        normalizer = torch.unsqueeze(xyz_pixB[:,:,2], 2)
+        EPS=1e-6
+        # z = xyz_pixB[:,:,2]
+        xy_pixB = xyz_pixB[:,:,:2]/torch.clamp(normalizer, min=EPS)
+        # this is B x N x 2
+        # this is the (floating point) pixel coordinate of each voxel
+        x, y = xy_pixB[:,:,0], xy_pixB[:,:,1]
+        # these are B x N
+
+        x_valid = (x>-0.5).bool() & (x<float(W-0.5)).bool()
+        y_valid = (y>-0.5).bool() & (y<float(H-0.5)).bool()
+        z_valid = (z>0.0).bool()
+        valid_mem = (x_valid & y_valid & z_valid).reshape(B, 1, Z, Y, X).float()
+
+        if (0):
+            # handwritten version
+            values = torch.zeros([B, C, Z*Y*X], dtype=torch.float32)
+            for b in list(range(B)):
+                values[b] = utils.samp.bilinear_sample_single(rgb_camB[b], x_pixB[b], y_pixB[b])
+        else:
+            # native pytorch version
+            y_pixB, x_pixB = utils.basic.normalize_grid2d(y, x, H, W)
+            # since we want a 3d output, we need 5d tensors
+            z_pixB = torch.zeros_like(x)
+            xyz_pixB = torch.stack([x_pixB, y_pixB, z_pixB], axis=2)
+            rgb_camB = rgb_camB.unsqueeze(2)
+            xyz_pixB = torch.reshape(xyz_pixB, [B, Z, Y, X, 3])
+            values = F.grid_sample(rgb_camB, xyz_pixB, align_corners=False)
+
+        values = torch.reshape(values, (B, C, Z, Y, X))
+        values = values * valid_mem
+        return values
+
+    def warp_tiled_to_mem(self, rgb_tileB, pixB_T_camA, camB_T_camA, Z, Y, X, DMIN, DMAX, assert_cube=False):
+        # rgb_tileB is B,C,D,H,W
+        # pixB_T_camA is B,4,4
+        # camB_T_camA is B,4,4
+
+        # rgb_tileB lives in B pixel coords but it has been tiled across the Z dimension
+        # we want everything in A memory coords
+
+        # this resamples the so that each C-dim pixel in rgb_tilB
+        # is put into its correct place in the voxelgrid
+        # (using the pinhole camera model)
+        
+        B, C, D, H, W = list(rgb_tileB.shape)
+
+        xyz_memA = utils.basic.gridcloud3d(B, Z, Y, X, norm=False, device=pixB_T_camA.device)
+
+        xyz_camA = self.Mem2Ref(xyz_memA, Z, Y, X, assert_cube=assert_cube)
+
+        xyz_camB = utils.geom.apply_4x4(camB_T_camA, xyz_camA)
+        z_camB = xyz_camB[:,:,2]
+
+        # rgb_tileB has depth=DMIN in tile 0, and depth=DMAX in tile D-1
+        z_tileB = (D-1.0) * (z_camB-float(DMIN)) / float(DMAX-DMIN)
+
+        xyz_pixB = utils.geom.apply_4x4(pixB_T_camA, xyz_camA)
+        normalizer = torch.unsqueeze(xyz_pixB[:,:,2], 2)
+        EPS=1e-6
+        # z = xyz_pixB[:,:,2]
+        xy_pixB = xyz_pixB[:,:,:2]/torch.clamp(normalizer, min=EPS)
+        # this is B x N x 2
+        # this is the (floating point) pixel coordinate of each voxel
+        x, y = xy_pixB[:,:,0], xy_pixB[:,:,1]
+        # these are B x N
+
+        x_valid = (x>-0.5).bool() & (x<float(W-0.5)).bool()
+        y_valid = (y>-0.5).bool() & (y<float(H-0.5)).bool()
+        z_valid = (z_camB>0.0).bool()
+        valid_mem = (x_valid & y_valid & z_valid).reshape(B, 1, Z, Y, X).float()
+
+        z_tileB, y_pixB, x_pixB = utils.basic.normalize_grid3d(z_tileB, y, x, D, H, W)
+        xyz_pixB = torch.stack([x_pixB, y_pixB, z_tileB], axis=2)
+        xyz_pixB = torch.reshape(xyz_pixB, [B, Z, Y, X, 3])
+        values = F.grid_sample(rgb_tileB, xyz_pixB, align_corners=False)
+
+        values = torch.reshape(values, (B, C, Z, Y, X))
+        values = values * valid_mem
+        return values
+    
+
+    def apply_mem_T_ref_to_lrtlist(self, lrtlist_cam, Z, Y, X, assert_cube=False):
+        # lrtlist is B x N x 19, in cam coordinates
+        # transforms them into mem coordinates, including a scale change for the lengths
+        B, N, C = list(lrtlist_cam.shape)
+        assert(C==19)
+        mem_T_cam = self.get_mem_T_ref(B, Z, Y, X, assert_cube=assert_cube, device=lrtlist_cam.device)
+
+    def xyz2circles(self, xyz, radius, Z, Y, X, soft=True, already_mem=True, also_offset=False, grid=None):
+        # xyz is B x N x 3
+        # radius is B x N or broadcastably so
+        # output is B x N x Z x Y x X
+        B, N, D = list(xyz.shape)
+        assert(D==3)
+        if not already_mem:
+            xyz = self.Ref2Mem(xyz, Z, Y, X)
+
+        if grid is None:
+            grid_z, grid_y, grid_x = utils.basic.meshgrid3d(B, Z, Y, X, stack=False, norm=False, device=xyz.device)
+            # note the default stack is on -1
+            grid = torch.stack([grid_x, grid_y, grid_z], dim=1)
+            # this is B x 3 x Z x Y x X
+            
+        xyz = xyz.reshape(B, N, 3, 1, 1, 1)
+        grid = grid.reshape(B, 1, 3, Z, Y, X)
+        # this is B x N x Z x Y x X
+
+        # round the xyzs, so that at least one value matches the grid perfectly,
+        # and we get a value of 1 there (since exp(0)==1)
+        xyz = xyz.round()
+
+        if torch.is_tensor(radius):
+            radius = radius.clamp(min=0.01)
+
+        if soft:
+            off = grid - xyz # B,N,3,Z,Y,X
+            # interpret radius as sigma
+            dist_grid = torch.sum(off**2, dim=2, keepdim=False)
+            # this is B x N x Z x Y x X
+            if torch.is_tensor(radius):
+                radius = radius.reshape(B, N, 1, 1, 1)
+            mask = torch.exp(-dist_grid/(2*radius*radius))
+            # zero out near zero
+            mask[mask < 0.001] = 0.0
+            # h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
+            # h[h < np.finfo(h.dtype).eps * h.max()] = 0
+            # return h
+            if also_offset:
+                return mask, off
+            else:
+                return mask
+        else:
+            assert(False) # something is wrong with this. come back later to debug
+
+            dist_grid = torch.norm(grid - xyz, dim=2, keepdim=False)
+            # this is 0 at/near the xyz, and increases by 1 for each voxel away
+
+            radius = radius.reshape(B, N, 1, 1, 1)
+
+            within_radius_mask = (dist_grid < radius).float()
+            within_radius_mask = torch.sum(within_radius_mask, dim=1, keepdim=True).clamp(0, 1)
+            return within_radius_mask
+
+    def xyz2circles_bev(self, xyz, radius, Z, Y, X, already_mem=True, also_offset=False):
+        # xyz is B x N x 3
+        # radius is B x N or broadcastably so
+        # output is B x N x Z x Y x X
+        B, N, D = list(xyz.shape)
+        assert(D==3)
+        if not already_mem:
+            xyz = self.Ref2Mem(xyz, Z, Y, X)
+
+        xz = torch.stack([xyz[:,:,0], xyz[:,:,2]], dim=2)
+
+        grid_z, grid_x = utils.basic.meshgrid2d(B, Z, X, stack=False, norm=False, device=xyz.device)
+        # note the default stack is on -1
+        grid = torch.stack([grid_x, grid_z], dim=1)
+        # this is B x 2 x Z x X
+
+        xz = xz.reshape(B, N, 2, 1, 1)
+        grid = grid.reshape(B, 1, 2, Z, X)
+        # these are ready to broadcast to B x N x Z x X
+
+        # round the points, so that at least one value matches the grid perfectly,
+        # and we get a value of 1 there (since exp(0)==1)
+        xz = xz.round()
+
+        if torch.is_tensor(radius):
+            radius = radius.clamp(min=0.01)
+
+        off = grid - xz # B,N,2,Z,X
+        # interpret radius as sigma
+        dist_grid = torch.sum(off**2, dim=2, keepdim=False)
+        # this is B x N x Z x X
+        if torch.is_tensor(radius):
+            radius = radius.reshape(B, N, 1, 1, 1)
+        mask = torch.exp(-dist_grid/(2*radius*radius))
+        # zero out near zero
+        mask[mask < 0.001] = 0.0
+        
+        # add a Y dim
+        mask = mask.unsqueeze(-2)
+        off = off.unsqueeze(-2)
+        # # B,N,2,Z,1,X
+        
+        if also_offset:
+            return mask, off
+        else:
+            return mask
+        

model_loading.py

@@ -712,6 +712,7 @@ class CogVideoXModelLoader:
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         manual_offloading = True
+        das_transformer = False
         transformer_load_device = device if load_device == "main_device" else offload_device
         mm.soft_empty_cache()
 
@@ -719,9 +720,14 @@ class CogVideoXModelLoader:
 
         model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
         sd = load_torch_file(model_path, device=transformer_load_device)
+        first_key = next(iter(sd.keys()))
 
         model_type = ""
-        if sd["patch_embed.proj.weight"].shape == (3072, 33, 2, 2):
+        if first_key == "combine_linears.0.bias":
+            log.info("Detected 'Diffusion As Shader' model")
+            model_type = "I2V_5b"
+            das_transformer = True
+        elif sd["patch_embed.proj.weight"].shape == (3072, 33, 2, 2):
             model_type = "fun_5b"
         elif sd["patch_embed.proj.weight"].shape == (3072, 16, 2, 2):
             model_type = "5b"
@@ -770,7 +776,7 @@ class CogVideoXModelLoader:
                     transformer_config["sample_width"] = 300
 
         with init_empty_weights():
-            transformer = CogVideoXTransformer3DModel.from_config(transformer_config, attention_mode=attention_mode)
+            transformer = CogVideoXTransformer3DModel.from_config(transformer_config, attention_mode=attention_mode, das_transformer=das_transformer)
 
         #load weights
         #params_to_keep = {}

nodes.py

@@ -631,8 +631,9 @@ class CogVideoSampler:
                 "controlnet": ("COGVIDECONTROLNET",),
                 "tora_trajectory": ("TORAFEATURES", ),
                 "fastercache": ("FASTERCACHEARGS", ),
-                "feta_args": ("FETAARGS", ),
-                "teacache_args": ("TEACACHEARGS", ),
+                "feta_args": ("FETAARGS", {"tooltip": "Arguments for Enhance-a-video"} ),
+                "teacache_args": ("TEACACHEARGS",{"tooltip": "Arguments for TeaCache"} ),
+                "das_tracking": ("DASTRACKING", {"tooltip": "Enable tracking for Diffusion As Shader"} ),
             }
         }
 
@@ -642,17 +643,14 @@ class CogVideoSampler:
     CATEGORY = "CogVideoWrapper"
 
     def process(self, model, positive, negative, steps, cfg, seed, scheduler, num_frames, samples=None,
-                denoise_strength=1.0, image_cond_latents=None, context_options=None, controlnet=None, tora_trajectory=None, fastercache=None, feta_args=None, teacache_args=None):
+                denoise_strength=1.0, image_cond_latents=None, context_options=None, controlnet=None, tora_trajectory=None, 
+                das_tracking=None, fastercache=None, feta_args=None, teacache_args=None):
         mm.unload_all_models()
         mm.soft_empty_cache()
 
         model_name = model.get("model_name", "")
-        supports_image_conds = True if (
-        "I2V" in model_name or 
-        "interpolation" in model_name.lower() or 
-        "fun" in model_name.lower() or
-        "img2vid" in model_name.lower()
-        ) else False
+        supports_image_conds = True if model["pipe"].transformer.config.in_channels == 32 else False
+       
         if "fun" in model_name.lower() and not ("pose" in model_name.lower() or "control" in model_name.lower()) and image_cond_latents is not None:
             assert image_cond_latents["mask"] is not None, "For fun inpaint models use CogVideoImageEncodeFunInP"
             fun_mask = image_cond_latents["mask"]
@@ -771,6 +769,7 @@ class CogVideoSampler:
                 image_cond_start_percent=image_cond_start_percent if image_cond_latents is not None else 0.0,
                 image_cond_end_percent=image_cond_end_percent if image_cond_latents is not None else 1.0,
                 feta_args=feta_args,
+                das_tracking=das_tracking,
             )
         if not model["cpu_offloading"] and model["manual_offloading"]:
             pipe.transformer.to(offload_device)
@@ -1033,4 +1032,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "CogVideoImageEncodeFunInP": "CogVideo ImageEncode FunInP",
     "CogVideoEnhanceAVideo": "CogVideo Enhance-A-Video",
     "CogVideoXTeaCache": "CogVideoX TeaCache",
+    "CogVideoDASTrackingEncode": "CogVideo DAS Tracking Encode",
     }

pipeline_cogvideox.py

@@ -383,6 +383,7 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
         image_cond_start_percent: float = 0.0,
         image_cond_end_percent: float = 1.0,
         feta_args: Optional[dict] = None,
+        das_tracking: Optional[dict] = None,
         
     ):
         """
@@ -625,6 +626,24 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
         else:
             disable_enhance()
 
+        #das
+        if das_tracking is not None:
+            tracking_maps = das_tracking["tracking_maps"]
+            tracking_image_latents = das_tracking["tracking_image_latents"]
+            das_start_percent = das_tracking["start_percent"]
+            das_end_percent = das_tracking["end_percent"]
+
+            padding_shape = (
+                batch_size,
+                (latents.shape[1] - 1),
+                self.vae_latent_channels,
+                height // self.vae_scale_factor_spatial,
+                width // self.vae_scale_factor_spatial,
+            )
+
+            latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae_dtype)
+            tracking_image_latents = torch.cat([tracking_image_latents, latent_padding], dim=1)
+
         # reset TeaCache
         if hasattr(self.transformer, 'accumulated_rel_l1_distance'):
             delattr(self.transformer, 'accumulated_rel_l1_distance')
@@ -805,6 +824,15 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
                             fun_inpaint_latents = torch.cat([fun_inpaint_mask, fun_inpaint_masked_video_latents], dim=2).to(latents.dtype)
                             latent_model_input = torch.cat([latent_model_input, fun_inpaint_latents], dim=2)
 
+                    if das_tracking is not None and das_start_percent <= current_step_percentage <= das_end_percent:
+                        logger.info("DAS tracking enabled")
+                        latents_tracking_image = torch.cat([tracking_image_latents] * 2) if do_classifier_free_guidance else tracking_image_latents
+                        tracking_maps_input = torch.cat([tracking_maps] * 2) if do_classifier_free_guidance else tracking_maps
+                        tracking_maps_input = torch.cat([tracking_maps_input, latents_tracking_image], dim=2)
+                        del latents_tracking_image
+                    else:
+                        tracking_maps_input = None
+
                     # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                     timestep = t.expand(latent_model_input.shape[0])
 
@@ -836,6 +864,7 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
                         controlnet_states=controlnet_states,
                         controlnet_weights=control_weights,
                         video_flow_features=video_flow_features if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]) else None,
+                        tracking_maps=tracking_maps_input,
                     )[0]
                     noise_pred = noise_pred.float()
                     if isinstance(self.scheduler, CogVideoXDPMScheduler):