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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
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