Spaces:
Paused
Paused
File size: 12,376 Bytes
4700ca8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 | """
GCTBase - Base class for GCT model implementations.
Provides shared functionality:
- Prediction heads (camera, depth, point)
- Forward pass structure
- Model hub mixin (PyTorchModelHubMixin)
"""
import logging
import numpy as np
import torch
import torch.nn as nn
from abc import ABC, abstractmethod
from typing import Optional, Dict, Any, List, Union
from huggingface_hub import PyTorchModelHubMixin
from lingbot_map.heads.dpt_head import DPTHead
from lingbot_map.utils.pose_enc import pose_encoding_to_extri_intri
from lingbot_map.utils.geometry import closed_form_inverse_se3
logger = logging.getLogger(__name__)
class GCTBase(nn.Module, PyTorchModelHubMixin, ABC):
"""
Base class for GCT model implementations.
Handles shared components:
- Prediction heads (camera, depth, point)
- Forward pass structure
- Input normalization
Subclasses must implement:
- _build_aggregator(): Create mode-specific aggregator
- _build_camera_head(): Create mode-specific camera head
"""
def __init__(
self,
# Architecture parameters
img_size: int = 518,
patch_size: int = 14,
embed_dim: int = 1024,
patch_embed: str = 'dinov2_vitl14_reg',
disable_global_rope: bool = False,
# Head configuration
enable_camera: bool = True,
enable_point: bool = True,
enable_local_point: bool = False,
enable_depth: bool = True,
enable_track: bool = False,
# Camera head sliding window
enable_camera_sliding_window: bool = False,
# 3D RoPE
enable_3d_rope: bool = False,
# Context Parallelism (kept for checkpoint compatibility but not used)
enable_ulysses_cp: bool = False,
# Normalization
enable_normalize: bool = False,
# Prediction normalization
pred_normalization: bool = False,
pred_normalization_detach_scale: bool = False,
# Gradient checkpointing
use_gradient_checkpoint: bool = True,
):
super().__init__()
# Store configuration
self.img_size = img_size
self.patch_size = patch_size
self.embed_dim = embed_dim
self.patch_embed = patch_embed
self.disable_global_rope = disable_global_rope
self.enable_ulysses_cp = False # CP disabled in standalone package
self.enable_normalize = enable_normalize
self.pred_normalization = pred_normalization
self.pred_normalization_detach_scale = pred_normalization_detach_scale
self.use_gradient_checkpoint = use_gradient_checkpoint
# Head flags
self.enable_camera = enable_camera
self.enable_point = enable_point
self.enable_local_point = enable_local_point
self.enable_depth = enable_depth
self.enable_track = enable_track
self.enable_camera_sliding_window = enable_camera_sliding_window
self.enable_3d_rope = enable_3d_rope
# Build aggregator (subclass-specific)
self.aggregator = self._build_aggregator()
# Build prediction heads (subclass-specific)
self.camera_head = self._build_camera_head() if enable_camera else None
self.point_head = self._build_point_head() if enable_point else None
self.local_point_head = self._build_local_point_head() if enable_local_point else None
self.depth_head = self._build_depth_head() if enable_depth else None
@abstractmethod
def _build_aggregator(self) -> nn.Module:
pass
@abstractmethod
def _build_camera_head(self) -> nn.Module:
pass
def _build_depth_head(self) -> nn.Module:
return DPTHead(
dim_in=2 * self.embed_dim,
patch_size=self.patch_size,
output_dim=2,
activation="exp",
conf_activation="expp1"
)
def _build_point_head(self) -> nn.Module:
return DPTHead(
dim_in=2 * self.embed_dim,
patch_size=self.patch_size,
output_dim=4,
activation="inv_log",
conf_activation="expp1"
)
def _build_local_point_head(self) -> nn.Module:
return DPTHead(
dim_in=2 * self.embed_dim,
patch_size=self.patch_size,
output_dim=4,
activation="inv_log",
conf_activation="expp1"
)
def _normalize_input(self, images: torch.Tensor, query_points=None):
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)
return images, query_points
@abstractmethod
def _aggregate_features(
self,
images: torch.Tensor,
num_frame_for_scale: Optional[int] = None,
sliding_window_size: Optional[int] = None,
num_frame_per_block: int = 1,
view_graphs: Optional[torch.Tensor] = None,
causal_graphs: Optional[Union[torch.Tensor, List[np.ndarray]]] = None,
ordered_video: Optional[torch.Tensor] = None,
is_cp_sliced: bool = False,
) -> tuple:
pass
def _predict_camera(
self,
aggregated_tokens_list: list,
mask: Optional[torch.Tensor] = None,
causal_inference: bool = False,
num_frame_for_scale: Optional[int] = None,
sliding_window_size: Optional[int] = None,
num_frame_per_block: int = 1,
gather_outputs: bool = True,
) -> Dict[str, torch.Tensor]:
if self.camera_head is None:
return {}
aggregated_tokens_list_fp32 = [t.float() for t in aggregated_tokens_list]
camera_sliding_window = sliding_window_size if self.enable_camera_sliding_window else -1
with torch.amp.autocast('cuda', enabled=False):
pose_enc_list = self.camera_head(
aggregated_tokens_list_fp32,
mask=mask,
causal_inference=causal_inference,
num_frame_for_scale=num_frame_for_scale if num_frame_for_scale is not None else -1,
sliding_window_size=camera_sliding_window,
num_frame_per_block=num_frame_per_block,
)
return {
"pose_enc": pose_enc_list[-1],
"pose_enc_list": pose_enc_list,
}
def _predict_depth(
self,
aggregated_tokens_list: list,
images: torch.Tensor,
patch_start_idx: int,
gather_outputs: bool = True,
) -> Dict[str, torch.Tensor]:
if self.depth_head is None:
return {}
aggregated_tokens_list_fp32 = [t.float() for t in aggregated_tokens_list]
images_fp32 = images.float()
with torch.amp.autocast('cuda', enabled=False):
depth, depth_conf = self.depth_head(
aggregated_tokens_list_fp32,
images=images_fp32,
patch_start_idx=patch_start_idx
)
return {"depth": depth, "depth_conf": depth_conf}
def _predict_points(
self,
aggregated_tokens_list: list,
images: torch.Tensor,
patch_start_idx: int,
gather_outputs: bool = True,
) -> Dict[str, torch.Tensor]:
if self.point_head is None:
return {}
aggregated_tokens_list_fp32 = [t.float() for t in aggregated_tokens_list]
images_fp32 = images.float()
with torch.amp.autocast('cuda', enabled=False):
pts3d, pts3d_conf = self.point_head(
aggregated_tokens_list_fp32,
images=images_fp32,
patch_start_idx=patch_start_idx
)
return {"world_points": pts3d, "world_points_conf": pts3d_conf}
def _predict_local_points(
self,
aggregated_tokens_list: list,
images: torch.Tensor,
patch_start_idx: int,
gather_outputs: bool = True,
) -> Dict[str, torch.Tensor]:
if self.local_point_head is None:
return {}
aggregated_tokens_list_fp32 = [t.float() for t in aggregated_tokens_list]
images_fp32 = images.float()
with torch.amp.autocast('cuda', enabled=False):
pts3d, pts3d_conf = self.local_point_head(
aggregated_tokens_list_fp32,
images=images_fp32,
patch_start_idx=patch_start_idx
)
return {"cam_points": pts3d, "cam_points_conf": pts3d_conf}
def _unproject_depth_to_world(
self,
depth: torch.Tensor,
pose_enc: torch.Tensor,
) -> torch.Tensor:
B, S, H, W, _ = depth.shape
device = depth.device
dtype = depth.dtype
image_size_hw = (H, W)
extrinsics, intrinsics = pose_encoding_to_extri_intri(
pose_enc, image_size_hw=image_size_hw, build_intrinsics=True
)
extrinsics_flat = extrinsics.view(B * S, 3, 4)
extrinsics_4x4 = torch.zeros(B * S, 4, 4, device=device, dtype=dtype)
extrinsics_4x4[:, :3, :] = extrinsics_flat
extrinsics_4x4[:, 3, 3] = 1.0
c2w = closed_form_inverse_se3(extrinsics_4x4).view(B, S, 4, 4)
y_grid, x_grid = torch.meshgrid(
torch.arange(H, device=device, dtype=dtype),
torch.arange(W, device=device, dtype=dtype),
indexing='ij'
)
pixel_coords = torch.stack([x_grid, y_grid, torch.ones_like(x_grid)], dim=-1)
intrinsics_inv = torch.inverse(intrinsics)
camera_coords = torch.einsum('bsij,hwj->bshwi', intrinsics_inv, pixel_coords)
camera_points = camera_coords * depth
ones = torch.ones_like(camera_points[..., :1])
camera_points_h = torch.cat([camera_points, ones], dim=-1)
world_points_h = torch.einsum('bsij,bshwj->bshwi', c2w, camera_points_h)
return world_points_h[..., :3]
def forward(
self,
images: torch.Tensor,
query_points: Optional[torch.Tensor] = None,
num_frame_for_scale: Optional[int] = None,
sliding_window_size: Optional[int] = None,
num_frame_per_block: int = 1,
mask: Optional[torch.Tensor] = None,
causal_inference: bool = False,
ordered_video: Optional[torch.Tensor] = None,
gather_outputs: bool = True,
point_masks: Optional[torch.Tensor] = None,
**kwargs,
) -> Dict[str, torch.Tensor]:
"""
Forward pass of the GCT model.
Args:
images: Input images [S, 3, H, W] or [B, S, 3, H, W], in range [0, 1]
query_points: Optional query points [N, 2] or [B, N, 2]
Returns:
Dictionary containing predictions:
- pose_enc: Camera pose encoding [B, S, 9]
- depth: Depth maps [B, S, H, W, 1]
- depth_conf: Depth confidence [B, S, H, W]
- world_points: 3D world coordinates [B, S, H, W, 3]
- world_points_conf: Point confidence [B, S, H, W]
"""
images, query_points = self._normalize_input(images, query_points)
aggregated_tokens_list, patch_start_idx = self._aggregate_features(
images,
num_frame_for_scale=num_frame_for_scale,
sliding_window_size=sliding_window_size,
num_frame_per_block=num_frame_per_block,
)
predictions = {}
predictions.update(self._predict_camera(
aggregated_tokens_list,
mask=ordered_video,
causal_inference=causal_inference,
num_frame_for_scale=num_frame_for_scale,
sliding_window_size=sliding_window_size,
num_frame_per_block=num_frame_per_block,
gather_outputs=gather_outputs,
))
predictions.update(self._predict_depth(
aggregated_tokens_list, images, patch_start_idx,
gather_outputs=gather_outputs,
))
predictions.update(self._predict_points(
aggregated_tokens_list, images, patch_start_idx,
gather_outputs=gather_outputs,
))
predictions.update(self._predict_local_points(
aggregated_tokens_list, images, patch_start_idx,
gather_outputs=gather_outputs,
))
if not self.training:
predictions["images"] = images
return predictions
|