Spaces:
Running on Zero
Running on Zero
File size: 8,675 Bytes
41ff959 | 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 | from dataclasses import dataclass
from typing import Literal
import torch
import torch.nn.functional as F
from einops import rearrange
from src.model.types import BatchedViews
from src.model.encoder.depth.infinidepth.infinidepth_wrapper import InfiniDepth
from src.model.encoder.depth.infinidepth.sampling_utils import (
SparseSamplingOutput,
make_sparse_surface_samples,
)
from src.model.encoder.encoder import Encoder
from src.model.encoder.encoder_infinisplat import DinoBasicImageFeatureBranch
from src.model.encoder.gaussian.gaussian_decoder import (
GaussianDecoder,
GaussianDecoderCfg,
)
from src.model.encoder.gaussian.implicit_gs_head import ImplicitGSHead
from src.utils.gaussians import Gaussians3D, unproject_gaussians
@dataclass
class EncoderInfiniDepthQueryCfg:
name: Literal["infinisplat_infinidepth"]
sample_point_num: int
image_basic_dim: int
image_backbone_type: str
implicit_gs_query_batch_size: int
implicit_gs_hidden_list: list[int]
gaussian_decoder: GaussianDecoderCfg
class EncoderInfiniDepthQuery(Encoder[EncoderInfiniDepthQueryCfg]):
def __init__(self, cfg: EncoderInfiniDepthQueryCfg) -> None:
super().__init__(cfg)
self.depth_predictor = InfiniDepth()
self.depth_predictor.eval()
self.image_feature_branch = DinoBasicImageFeatureBranch(
backbone_type=cfg.image_backbone_type,
basic_dim=cfg.image_basic_dim,
)
self.implicit_gs_head = ImplicitGSHead(
hidden_dim=self.image_feature_branch.hidden_dim,
basic_dim=cfg.image_basic_dim,
hidden_list=list(cfg.implicit_gs_hidden_list),
)
self.gaussian_decoder = GaussianDecoder(cfg=cfg.gaussian_decoder)
def _sample_map(
self,
feature_map: torch.Tensor,
coords_yx: torch.Tensor,
) -> torch.Tensor:
sampled = F.grid_sample(
feature_map,
coords_yx.flip(-1).unsqueeze(1),
mode="bilinear",
align_corners=False,
)
return sampled[:, :, 0, :].transpose(1, 2)
def _sample_sparse_coords(
self,
dense_depthmap_flat: torch.Tensor,
intrinsics_flat: torch.Tensor,
image_flat: torch.Tensor,
) -> SparseSamplingOutput:
"""Sample sparse support coordinates from the selected dense depth surface.
Args:
dense_depthmap_flat: Dense depth with shape `[B*V, 1, H, W]`.
intrinsics_flat: Pixel-space intrinsics with shape `[B*V, 3, 3]`.
image_flat: Context RGB images with shape `[B*V, 3, H, W]`.
Returns:
Sparse sampling output whose tensors have leading shape `[B*V, N]`.
"""
sample_coords_yx_ndc = []
sample_kind = []
sample_responsibility_area_metric = []
for sample_index, (depth_hw, intrinsic, image_chw) in enumerate(
zip(dense_depthmap_flat[:, 0], intrinsics_flat, image_flat, strict=True)
):
sampling_output = make_sparse_surface_samples(
depth_hw=depth_hw,
image_chw=image_chw.detach(),
fx=float(intrinsic[0, 0].item()),
fy=float(intrinsic[1, 1].item()),
cx=float(intrinsic[0, 2].item()),
cy=float(intrinsic[1, 2].item()),
sample_point_num=int(self.cfg.sample_point_num),
coord_norm="minus_one_to_one",
)
sample_coords_yx_ndc.append(sampling_output.coords_yx_ndc)
sample_kind.append(sampling_output.sample_kind)
sample_responsibility_area_metric.append(
sampling_output.sample_responsibility_area_metric
)
return SparseSamplingOutput(
coords_yx_ndc=torch.stack(sample_coords_yx_ndc, dim=0),
sample_responsibility_area_metric=torch.stack(
sample_responsibility_area_metric,
dim=0,
),
sample_kind=torch.stack(sample_kind, dim=0),
)
def _decode_dino_gaussian_delta(
self,
features,
basic_feat: torch.Tensor,
patch_h: int,
patch_w: int,
coords_yx: torch.Tensor,
) -> torch.Tensor:
feat_map = self.implicit_gs_head.encode_feat(features, patch_h, patch_w)
query_batch_size = int(self.cfg.implicit_gs_query_batch_size)
num_queries = coords_yx.shape[1]
chunks = []
for start in range(0, num_queries, query_batch_size):
end = min(start + query_batch_size, num_queries)
chunks.append(
self.implicit_gs_head.decode_dpt(
feat_map,
basic_feat,
coords_yx[:, start:end],
)
)
return torch.cat(chunks, dim=1)
def forward(
self,
context: BatchedViews,
):
"""Encode prompt-conditioned context views with InfiniDepth.
Args:
context: Batched context views.
Returns:
Encoder output dictionary following the InfiniSplat contract.
"""
required_keys = ("prompt_disparity", "prompt_mask")
missing_keys = [key for key in required_keys if key not in context]
if missing_keys:
raise AssertionError(
"EncoderInfiniDepthQuery requires prompt-conditioned context inputs. "
f"Missing keys: {missing_keys}"
)
b, v, _, h, w = context["image"].shape
image_flat = rearrange(context["image"], "b v c h w -> (b v) c h w")
intrinsics = context["intrinsics"].clone()
intrinsics[:, :, 0] = intrinsics[:, :, 0] * w
intrinsics[:, :, 1] = intrinsics[:, :, 1] * h
intrinsics_flat = rearrange(intrinsics, "b v i j -> (b v) i j")
with torch.no_grad():
self.depth_predictor.eval()
dense_depthmap_flat = self.depth_predictor(
{
"image": image_flat,
"prompt_disparity": rearrange(
context["prompt_disparity"],
"b v c h w -> (b v) c h w",
),
"prompt_mask": rearrange(context["prompt_mask"], "b v c h w -> (b v) c h w"),
}
)
sampling_output_flat = self._sample_sparse_coords(
dense_depthmap_flat=dense_depthmap_flat.detach(),
intrinsics_flat=intrinsics_flat,
image_flat=image_flat,
)
sample_depths_flat = self._sample_map(
dense_depthmap_flat,
sampling_output_flat.coords_yx_ndc,
)
sample_coords_yx_ndc_flat = sampling_output_flat.coords_yx_ndc
sample_kind_flat = sampling_output_flat.sample_kind
sample_responsibility_area_metric_flat = (
sampling_output_flat.sample_responsibility_area_metric
)
sampled_rgb_flat = self._sample_map(image_flat, sample_coords_yx_ndc_flat)
features, basic_feat, patch_h, patch_w = self.image_feature_branch(image_flat)
gaussian_delta_flat = self._decode_dino_gaussian_delta(
features=features,
basic_feat=basic_feat,
patch_h=patch_h,
patch_w=patch_w,
coords_yx=sample_coords_yx_ndc_flat,
)
sample_depths = rearrange(sample_depths_flat, "(b v) n c -> b v n c", b=b, v=v)
sample_coords_yx_ndc = rearrange(
sample_coords_yx_ndc_flat,
"(b v) n c -> b v n c",
b=b,
v=v,
)
sample_responsibility_area_metric = rearrange(
sample_responsibility_area_metric_flat,
"(b v) n -> b v n",
b=b,
v=v,
)
sample_kind = rearrange(sample_kind_flat, "(b v) n -> b v n", b=b, v=v)
sampled_rgb = rearrange(sampled_rgb_flat, "(b v) n c -> b v n c", b=b, v=v)
gaussian_delta = rearrange(gaussian_delta_flat, "(b v) n c -> b v n c", b=b, v=v)
gaussians_ndc: Gaussians3D = self.gaussian_decoder(
delta=gaussian_delta,
coords_yx_ndc=sample_coords_yx_ndc,
depths=sample_depths,
rgb=sampled_rgb,
intrinsics=intrinsics,
sample_kind=sample_kind,
sample_responsibility_area_metric=sample_responsibility_area_metric,
image_shape=(h, w),
)
gaussians: Gaussians3D = unproject_gaussians(
gaussians_ndc,
context["extrinsics"],
intrinsics,
(w, h),
)
return {"gaussians": gaussians}
|