Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,421 Bytes
fc6af43 |
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 |
#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
from typing import NamedTuple
import torch.nn as nn
import torch
from . import _C
def cpu_deep_copy_tuple(input_tuple):
copied_tensors = [item.cpu().clone() if isinstance(item, torch.Tensor) else item for item in input_tuple]
return tuple(copied_tensors)
def rasterize_gaussians(
means3D,
means2D,
sh,
colors_precomp,
opacities,
scales,
rotations,
cov3Ds_precomp,
raster_settings,
):
return _RasterizeGaussians.apply(
means3D,
means2D,
sh,
colors_precomp,
opacities,
scales,
rotations,
cov3Ds_precomp,
raster_settings,
)
class _RasterizeGaussians(torch.autograd.Function):
@staticmethod
def forward(
ctx,
means3D,
means2D,
sh,
colors_precomp,
opacities,
scales,
rotations,
cov3Ds_precomp,
raster_settings,
):
# Restructure arguments the way that the C++ lib expects them
args = (
raster_settings.bg,
means3D,
colors_precomp,
opacities,
scales,
rotations,
raster_settings.scale_modifier,
cov3Ds_precomp,
raster_settings.viewmatrix,
raster_settings.projmatrix,
raster_settings.tanfovx,
raster_settings.tanfovy,
raster_settings.image_height,
raster_settings.image_width,
sh,
raster_settings.sh_degree,
raster_settings.campos,
raster_settings.prefiltered,
raster_settings.debug
)
# Invoke C++/CUDA rasterizer
if raster_settings.debug:
cpu_args = cpu_deep_copy_tuple(args) # Copy them before they can be corrupted
try:
num_rendered, color, depth, radii, geomBuffer, binningBuffer, imgBuffer = _C.rasterize_gaussians(*args)
except Exception as ex:
torch.save(cpu_args, "snapshot_fw.dump")
print("\nAn error occured in forward. Please forward snapshot_fw.dump for debugging.")
raise ex
else:
num_rendered, color, depth, radii, geomBuffer, binningBuffer, imgBuffer = _C.rasterize_gaussians(*args)
# Keep relevant tensors for backward
ctx.raster_settings = raster_settings
ctx.num_rendered = num_rendered
ctx.save_for_backward(colors_precomp, means3D, scales, rotations, cov3Ds_precomp, radii, sh, geomBuffer, binningBuffer, imgBuffer)
return color, radii, depth
@staticmethod
def backward(ctx, grad_out_color, grad_radii, grad_depth):
# Restore necessary values from context
num_rendered = ctx.num_rendered
raster_settings = ctx.raster_settings
colors_precomp, means3D, scales, rotations, cov3Ds_precomp, radii, sh, geomBuffer, binningBuffer, imgBuffer = ctx.saved_tensors
# Restructure args as C++ method expects them
args = (raster_settings.bg,
means3D,
radii,
colors_precomp,
scales,
rotations,
raster_settings.scale_modifier,
cov3Ds_precomp,
raster_settings.viewmatrix,
raster_settings.projmatrix,
raster_settings.tanfovx,
raster_settings.tanfovy,
grad_out_color,
grad_depth,
sh,
raster_settings.sh_degree,
raster_settings.campos,
geomBuffer,
num_rendered,
binningBuffer,
imgBuffer,
raster_settings.debug)
# Compute gradients for relevant tensors by invoking backward method
if raster_settings.debug:
cpu_args = cpu_deep_copy_tuple(args) # Copy them before they can be corrupted
try:
grad_means2D, grad_colors_precomp, grad_opacities, grad_means3D, grad_cov3Ds_precomp, grad_sh, grad_scales, grad_rotations = _C.rasterize_gaussians_backward(*args)
except Exception as ex:
torch.save(cpu_args, "snapshot_bw.dump")
print("\nAn error occured in backward. Writing snapshot_bw.dump for debugging.\n")
raise ex
else:
grad_means2D, grad_colors_precomp, grad_opacities, grad_means3D, grad_cov3Ds_precomp, grad_sh, grad_scales, grad_rotations = _C.rasterize_gaussians_backward(*args)
grads = (
grad_means3D,
grad_means2D,
grad_sh,
grad_colors_precomp,
grad_opacities,
grad_scales,
grad_rotations,
grad_cov3Ds_precomp,
None,
)
return grads
class GaussianRasterizationSettings(NamedTuple):
image_height: int
image_width: int
tanfovx : float
tanfovy : float
bg : torch.Tensor
scale_modifier : float
viewmatrix : torch.Tensor
projmatrix : torch.Tensor
sh_degree : int
campos : torch.Tensor
prefiltered : bool
debug : bool
class GaussianRasterizer(nn.Module):
def __init__(self, raster_settings):
super().__init__()
self.raster_settings = raster_settings
def markVisible(self, positions):
# Mark visible points (based on frustum culling for camera) with a boolean
with torch.no_grad():
raster_settings = self.raster_settings
visible = _C.mark_visible(
positions,
raster_settings.viewmatrix,
raster_settings.projmatrix)
return visible
def forward(self, means3D, means2D, opacities, shs = None, colors_precomp = None, scales = None, rotations = None, cov3D_precomp = None):
raster_settings = self.raster_settings
if (shs is None and colors_precomp is None) or (shs is not None and colors_precomp is not None):
raise Exception('Please provide excatly one of either SHs or precomputed colors!')
if ((scales is None or rotations is None) and cov3D_precomp is None) or ((scales is not None or rotations is not None) and cov3D_precomp is not None):
raise Exception('Please provide exactly one of either scale/rotation pair or precomputed 3D covariance!')
if shs is None:
shs = torch.Tensor([]).cuda()
if colors_precomp is None:
colors_precomp = torch.Tensor([]).cuda()
if scales is None:
scales = torch.Tensor([]).cuda()
if rotations is None:
rotations = torch.Tensor([]).cuda()
if cov3D_precomp is None:
cov3D_precomp = torch.Tensor([]).cuda()
# Invoke C++/CUDA rasterization routine
return rasterize_gaussians(
means3D,
means2D,
shs,
colors_precomp,
opacities,
scales,
rotations,
cov3D_precomp,
raster_settings,
)
|