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c69c125 | 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 | """Shared orchestration for the CUDA-rasterizer decoders (inria, fastgs).
Both backends share identical forward/depth plumbing — flattening B×V cameras,
handling Gaussians vs GaussiansModule, optional view chunking, and reshaping the
rasterizer outputs back to (B, V). Only the rasterizer call itself differs, so
that is the single overridable hook (``_raster`` / ``_raster_depth``).
This module imports no rasterizer backend, so importing it never requires
``diff_gaussian_rasterization`` or ``diff_gaussian_rasterization_fastgs``.
"""
from typing import TypeVar
import torch
from einops import rearrange, repeat
from jaxtyping import Float
from torch import Tensor
from tqdm import tqdm
from ...dataset import DatasetCfg
from ...scene_trainer.gaussian_module import GaussiansModule
from ..types import Gaussians
from .decoder import Decoder, DecoderOutput, DepthRenderingMode
T = TypeVar("T")
class SplattingCUDADecoder(Decoder[T]):
"""Base for the inria/fastgs splatting decoders. Subclasses implement only the two
backend hooks below; everything else (camera flattening, output reshaping) is shared."""
background_color: Float[Tensor, "3"]
def __init__(self, cfg: T, dataset_cfg: DatasetCfg) -> None:
super().__init__(cfg, dataset_cfg)
self.register_buffer(
"background_color",
torch.tensor(dataset_cfg.background_color, dtype=torch.float32),
persistent=False,
)
# --- backend hooks -----------------------------------------------------
# Both receive flat (B*V) camera tensors and return flat outputs.
def _raster(self, ext, intr, near, far, image_shape, bg, means, covars, shs, opacities,
scales, rotations_wxyz, means2d_out, means2d_abs_out=None):
"""Renders, writing screen-space means into ``means2d_out`` ([bv,N,2]) so its gradient
is reachable via autograd.grad. Returns (images [bv,3,H,W], radii [bv,N]).
``means2d_abs_out`` is the optional FastGS Abs-GS leaf (cols [2:] of its screen tensor);
backends that do not produce it ignore the argument."""
raise NotImplementedError
def _raster_depth(self, ext, intr, near, far, image_shape, means, covars, opacities, mode):
"""Returns depth [bv, H, W]."""
raise NotImplementedError
def _produces_abs_grad(self) -> bool:
"""Whether the backend exposes the FastGS abs-gradient (cols [2:]) via ``means2d_abs_out``.
Only the FastGS decoder overrides this to True; gsplat/inria stay False."""
return False
# --- shared Gaussian tensor prep --------------------------------------
def _prepare_flat_gaussians(self, gaussians: Gaussians | GaussiansModule, b: int, v: int):
"""Flatten Gaussian params to (B*V) rasterizer layout. Returns
(means, shs, opacities, scales, rotations_wxyz, covars); scales/rotations are None when
``use_covariances`` (covars supplied instead) and vice-versa. Shared by forward and the
FastGS metric-counts render so they build identical inputs."""
bv = b * v
scales = rotations_wxyz = covars = None
if isinstance(gaussians, GaussiansModule):
means = repeat(gaussians.means, "g xyz -> bv g xyz", bv=bv)
shs = repeat(gaussians.harmonics, "g c d -> bv g c d", bv=bv)
opacities = repeat(gaussians.opacities, "g -> bv g", bv=bv)
if self.cfg.use_covariances:
covars = repeat(gaussians.covariances, "g i j -> bv g i j", bv=bv)
else:
scales = repeat(gaussians.scales, "g d -> bv g d", bv=bv)
# gaussians.rotations is xyzw post-normalization; the rasterizer wants wxyz.
rotations_wxyz = repeat(gaussians.rotations[:, [3, 0, 1, 2]], "g d -> bv g d", bv=bv)
elif isinstance(gaussians, Gaussians):
means = repeat(gaussians.means, "b g xyz -> (b v) g xyz", v=v)
shs = repeat(gaussians.harmonics, "b g c d -> (b v) g c d", v=v)
opacities = repeat(gaussians.opacities, "b g -> (b v) g", v=v)
if self.cfg.use_covariances:
if gaussians.covariances is None:
raise ValueError("use_covariances=true but gaussians.covariances is None.")
covars = repeat(gaussians.covariances, "b g i j -> (b v) g i j", v=v)
else:
_scales = gaussians.scales if gaussians.stores_activated else torch.exp(gaussians.scales)
scales = repeat(_scales, "b g d -> (b v) g d", v=v)
# Normalize rotations_unnorm here (the always-present grad leaf) rather than
# using gaussians.rotations: the learned optimizer precomputes the latter under
# torch.no_grad(), so the screen-space loss must reach rotations_unnorm directly
# (else autograd.grad sees it as unused). Numerically == gaussians.rotations;
# xyzw -> wxyz for the rasterizer. Mirrors the gsplat decoder.
rot = torch.nn.functional.normalize(gaussians.rotations_unnorm, dim=-1)
rotations_wxyz = repeat(rot[..., [3, 0, 1, 2]], "b g d -> (b v) g d", v=v)
if not gaussians.stores_activated:
opacities = torch.sigmoid(opacities)
else:
raise ValueError(f"Unknown gaussians type: {type(gaussians)}")
return means, shs, opacities, scales, rotations_wxyz, covars
# --- shared forward ----------------------------------------------------
def forward(
self,
gaussians: Gaussians | GaussiansModule,
extrinsics: Float[Tensor, "batch view 4 4"],
intrinsics: Float[Tensor, "batch view 3 3"],
near: Float[Tensor, "batch view"],
far: Float[Tensor, "batch view"],
image_shape: tuple[int, int],
depth_mode: DepthRenderingMode | None = None,
return_radii: bool = False,
iter_batch_size: int = -1,
to_cpu: bool = False,
) -> DecoderOutput:
b, v, _, _ = extrinsics.shape
bv = b * v
# Flatten camera params to (B*V)
flat_ext = rearrange(extrinsics, "b v i j -> (b v) i j")
flat_int = rearrange(intrinsics, "b v i j -> (b v) i j")
flat_near = rearrange(near, "b v -> (b v)")
flat_far = rearrange(far, "b v -> (b v)")
flat_bg = repeat(self.background_color, "c -> (b v) c", b=b, v=v)
# Prepare Gaussian tensors in flat (B*V) format
means, shs, opacities, scales, rotations_wxyz, covars = self._prepare_flat_gaussians(gaussians, b, v)
# Single [B, V, N, 2] screen-space-means leaf. Each view's rasterizer call consumes a
# slice of it (via means2d_flat), so the 2D gradient is reachable as
# torch.autograd.grad(loss, out.means2d) — uniformly with the gsplat decoder, and
# without a .backward()/.grad pass. Returned as-is (no reshape, which would detach it).
n_gauss = means.shape[1]
means2d = torch.zeros((b, v, n_gauss, 2), dtype=means.dtype, device=means.device,
requires_grad=True)
means2d_flat = means2d.reshape(bv, n_gauss, 2)
# FastGS only: a second leaf for the abs-gradient (cols [2:] of its screen tensor).
means2d_abs = means2d_abs_flat = None
if self._produces_abs_grad():
means2d_abs = torch.zeros((b, v, n_gauss, 2), dtype=means.dtype, device=means.device,
requires_grad=True)
means2d_abs_flat = means2d_abs.reshape(bv, n_gauss, 2)
def _render_flat(s: slice):
return self._raster(
flat_ext[s], flat_int[s], flat_near[s], flat_far[s], image_shape, flat_bg[s],
means[s], covars[s] if covars is not None else None, shs[s], opacities[s],
scales[s] if scales is not None else None,
rotations_wxyz[s] if rotations_wxyz is not None else None,
means2d_flat[s],
means2d_abs_flat[s] if means2d_abs_flat is not None else None,
)
if iter_batch_size < 0:
imgs, radii_flat = _render_flat(slice(None))
if to_cpu:
imgs = imgs.detach().cpu()
radii_flat = radii_flat.detach().cpu()
else:
all_imgs, all_radii = [], []
for i in tqdm(range(0, bv, iter_batch_size), desc="Rendering in batches"):
s = slice(i, min(i + iter_batch_size, bv))
imgs_c, rad_c = _render_flat(s)
if to_cpu:
imgs_c = imgs_c.detach().cpu()
rad_c = rad_c.detach().cpu()
all_imgs.append(imgs_c)
all_radii.append(rad_c)
imgs = torch.cat(all_imgs, dim=0)
radii_flat = torch.cat(all_radii, dim=0)
# Reshape (B*V) → (B, V)
color = rearrange(imgs, "(b v) c h w -> b v c h w", b=b, v=v)
radii_bv = rearrange(radii_flat, "(b v) n -> b v n", b=b, v=v)
means2d_bv = means2d.detach().cpu() if to_cpu else means2d # [B, V, N, 2]
means2d_abs_bv = None
if means2d_abs is not None:
means2d_abs_bv = means2d_abs.detach().cpu() if to_cpu else means2d_abs # [B, V, N, 2]
# Expand scalar radii [B, V, N] → [B, V, N, 2] to match gsplat interface
radii_out = radii_bv.unsqueeze(-1).expand(-1, -1, -1, 2).contiguous()
visibility_filter = radii_bv > 0 # [B, V, N]
depth = (
self._render_depth(gaussians, extrinsics, intrinsics, near, far, image_shape, depth_mode)
if depth_mode is not None
else None
)
return DecoderOutput(
color=color,
depth=depth,
accumulated_alpha=None,
means2d=means2d_bv,
means2d_abs=means2d_abs_bv,
radii=radii_out,
visibility_filter=visibility_filter,
)
# --- shared depth ------------------------------------------------------
def _render_depth(
self,
gaussians: Gaussians | GaussiansModule,
extrinsics: Float[Tensor, "batch view 4 4"],
intrinsics: Float[Tensor, "batch view 3 3"],
near: Float[Tensor, "batch view"],
far: Float[Tensor, "batch view"],
image_shape: tuple[int, int],
mode: DepthRenderingMode = "depth",
) -> Float[Tensor, "batch view height width"]:
b, v, _, _ = extrinsics.shape
if isinstance(gaussians, GaussiansModule):
means = repeat(gaussians.means, "g xyz -> (b v) g xyz", b=b, v=v)
covars = repeat(gaussians.covariances, "g i j -> (b v) g i j", b=b, v=v)
opacities = repeat(gaussians.opacities, "g -> (b v) g", b=b, v=v)
else:
means = repeat(gaussians.means, "b g xyz -> (b v) g xyz", v=v)
covars = repeat(gaussians.covariances, "b g i j -> (b v) g i j", v=v)
opacities = repeat(gaussians.opacities, "b g -> (b v) g", v=v)
if not gaussians.stores_activated:
opacities = torch.sigmoid(opacities)
result = self._raster_depth(
rearrange(extrinsics, "b v i j -> (b v) i j"),
rearrange(intrinsics, "b v i j -> (b v) i j"),
rearrange(near, "b v -> (b v)"),
rearrange(far, "b v -> (b v)"),
image_shape,
means,
covars,
opacities,
mode,
)
return rearrange(result, "(b v) h w -> b v h w", b=b, v=v)
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