import torch import torch.nn as nn from huggingface_hub import PyTorchModelHubMixin # used for model hub from streamvggt.models.aggregator import Aggregator from streamvggt.heads.camera_head import CameraHead from streamvggt.heads.dpt_head import DPTHead from streamvggt.heads.track_head import TrackHead from transformers.file_utils import ModelOutput from typing import Optional, Tuple, List, Any from dataclasses import dataclass @dataclass class StreamVGGTOutput(ModelOutput): ress: Optional[List[dict]] = None views: Optional[torch.Tensor] = None class StreamVGGT(nn.Module, PyTorchModelHubMixin): def __init__(self, img_size=518, patch_size=14, embed_dim=1024): super().__init__() self.aggregator = Aggregator(img_size=img_size, patch_size=patch_size, embed_dim=embed_dim) self.camera_head = CameraHead(dim_in=2 * embed_dim) self.point_head = DPTHead(dim_in=2 * embed_dim, output_dim=4, activation="inv_log", conf_activation="expp1") self.depth_head = DPTHead(dim_in=2 * embed_dim, output_dim=2, activation="exp", conf_activation="expp1") self.track_head = TrackHead(dim_in=2 * embed_dim, patch_size=patch_size) def forward( self, views, query_points: torch.Tensor = None, history_info: Optional[dict] = None, past_key_values=None, use_cache=False, past_frame_idx=0 ): images = torch.stack( [view["img"] for view in views], dim=0 ).permute(1, 0, 2, 3, 4) # B S C H W # If without batch dimension, add it if len(images.shape) == 4: images = images.unsqueeze(0) if query_points is not None and len(query_points.shape) == 2: query_points = query_points.unsqueeze(0) if history_info is None: history_info = {"token": None} aggregated_tokens_list, patch_start_idx = self.aggregator(images) predictions = {} with torch.cuda.amp.autocast(enabled=False): if self.camera_head is not None: pose_enc_list = self.camera_head(aggregated_tokens_list) predictions["pose_enc"] = pose_enc_list[-1] # pose encoding of the last iteration if self.depth_head is not None: depth, depth_conf = self.depth_head( aggregated_tokens_list, images=images, patch_start_idx=patch_start_idx ) predictions["depth"] = depth predictions["depth_conf"] = depth_conf if self.point_head is not None: pts3d, pts3d_conf = self.point_head( aggregated_tokens_list, images=images, patch_start_idx=patch_start_idx ) predictions["world_points"] = pts3d predictions["world_points_conf"] = pts3d_conf if self.track_head is not None and query_points is not None: track_list, vis, conf = self.track_head( aggregated_tokens_list, images=images, patch_start_idx=patch_start_idx, query_points=query_points ) predictions["track"] = track_list[-1] # track of the last iteration predictions["vis"] = vis predictions["conf"] = conf predictions["images"] = images B, S = images.shape[:2] ress = [] for s in range(S): res = { 'pts3d_in_other_view': predictions['world_points'][:, s], # [B, H, W, 3] 'conf': predictions['world_points_conf'][:, s], # [B, H, W] 'depth': predictions['depth'][:, s], # [B, H, W, 1] 'depth_conf': predictions['depth_conf'][:, s], # [B, H, W] 'camera_pose': predictions['pose_enc'][:, s, :], # [B, 9] **({'valid_mask': views[s]["valid_mask"]} if 'valid_mask' in views[s] else {}), # [B, H, W] **({'track': predictions['track'][:, s], # [B, N, 2] 'vis': predictions['vis'][:, s], # [B, N] 'track_conf': predictions['conf'][:, s]} if 'track' in predictions else {}) } ress.append(res) return StreamVGGTOutput(ress=ress, views=views) # [S] [B, C, H, W] def inference(self, frames, query_points: torch.Tensor = None, past_key_values=None): past_key_values = [None] * self.aggregator.depth past_key_values_camera = [None] * self.camera_head.trunk_depth all_ress = [] processed_frames = [] for i, frame in enumerate(frames): images = frame["img"].unsqueeze(0) aggregator_output = self.aggregator( images, past_key_values=past_key_values, use_cache=True, past_frame_idx=i ) if isinstance(aggregator_output, tuple) and len(aggregator_output) == 3: aggregated_tokens, patch_start_idx, past_key_values = aggregator_output else: aggregated_tokens, patch_start_idx = aggregator_output with torch.cuda.amp.autocast(enabled=False): if self.camera_head is not None: pose_enc, past_key_values_camera = self.camera_head(aggregated_tokens, past_key_values_camera=past_key_values_camera, use_cache=True) pose_enc = pose_enc[-1] camera_pose = pose_enc[:, 0, :] if self.depth_head is not None: depth, depth_conf = self.depth_head( aggregated_tokens, images=images, patch_start_idx=patch_start_idx ) depth = depth[:, 0] depth_conf = depth_conf[:, 0] if self.point_head is not None: pts3d, pts3d_conf = self.point_head( aggregated_tokens, images=images, patch_start_idx=patch_start_idx ) pts3d = pts3d[:, 0] pts3d_conf = pts3d_conf[:, 0] if self.track_head is not None and query_points is not None: track_list, vis, conf = self.track_head( aggregated_tokens, images=images, patch_start_idx=patch_start_idx, query_points=query_points ) track = track_list[-1][:, 0] query_points = track vis = vis[:, 0] track_conf = conf[:, 0] all_ress.append({ 'pts3d_in_other_view': pts3d, 'conf': pts3d_conf, 'depth': depth, 'depth_conf': depth_conf, 'camera_pose': camera_pose, **({'valid_mask': frame["valid_mask"]} if 'valid_mask' in frame else {}), **({'track': track, 'vis': vis, 'track_conf': track_conf} if query_points is not None else {}) }) processed_frames.append(frame) output = StreamVGGTOutput(ress=all_ress, views=processed_frames) return output