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# The original code is under the following copyright:
# 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_GS.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
# The modifications of the code are under the following copyright:
# Copyright (C) 2024, University of Liege, KAUST and University of Oxford
# TELIM research group, http://www.telecom.ulg.ac.be/
# IVUL research group, https://ivul.kaust.edu.sa/
# VGG research group, https://www.robots.ox.ac.uk/~vgg/
# All rights reserved.
# The modifications are under the LICENSE.md file.
#
# For inquiries contact jan.held@uliege.be
#
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_triangles(
triangles_points,
sigma,
num_points_per_triangle,
cumsum_of_points_per_triangle,
number_of_points,
sh,
colors_precomp,
opacities,
means2D,
scaling,
density_factor,
raster_settings,
):
return _RasterizeTriangles.apply(
triangles_points,
sigma,
num_points_per_triangle,
cumsum_of_points_per_triangle,
number_of_points,
sh,
colors_precomp,
opacities,
means2D,
scaling,
density_factor,
raster_settings,
)
class _RasterizeTriangles(torch.autograd.Function):
@staticmethod
def forward(
ctx,
triangles_points,
sigma,
num_points_per_triangle,
cumsum_of_points_per_triangle,
number_of_points,
sh,
colors_precomp,
opacities,
means2D,
scaling,
density_factor,
raster_settings,
):
# Restructure arguments the way that the C++ lib expects them
args = (
raster_settings.bg,
triangles_points,
sigma,
num_points_per_triangle,
cumsum_of_points_per_triangle,
colors_precomp,
opacities,
scaling,
density_factor,
raster_settings.viewmatrix,
raster_settings.projmatrix,
number_of_points,
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, scaling, density_factor, max_blending = _C.rasterize_triangles(*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, scaling, density_factor, max_blending = _C.rasterize_triangles(*args)
# Keep relevant tensors for backward
ctx.raster_settings = raster_settings
ctx.num_rendered = num_rendered
ctx.number_of_points = number_of_points
ctx.save_for_backward(triangles_points, sigma, num_points_per_triangle, cumsum_of_points_per_triangle, colors_precomp, radii, sh, geomBuffer, binningBuffer, imgBuffer)
return color, radii, scaling, density_factor, depth, max_blending
@staticmethod
def backward(ctx, grad_out_color, _, __, ___, grad_depth, _____):
# Restore necessary values from context
num_rendered = ctx.num_rendered
raster_settings = ctx.raster_settings
number_of_points = ctx.number_of_points
triangles_points, sigma, num_points_per_triangle, cumsum_of_points_per_triangle, colors_precomp, radii, sh, geomBuffer, binningBuffer, imgBuffer = ctx.saved_tensors
# Restructure args as C++ method expects them
args = (raster_settings.bg,
triangles_points,
sigma,
num_points_per_triangle,
cumsum_of_points_per_triangle,
radii,
colors_precomp,
raster_settings.viewmatrix,
raster_settings.projmatrix,
number_of_points,
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_triangles, grad_sigma, grad_colors_precomp, grad_opacities, grad_sh, grad_means2D = _C.rasterize_triangles_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_triangles, grad_sigma, grad_colors_precomp, grad_opacities, grad_sh, grad_means2D = _C.rasterize_triangles_backward(*args)
#print(torch.max(torch.abs(grad_triangles)), torch.min(torch.abs(grad_triangles)))
#grad_triangles = grad_triangles.reshape(-1, 8, 3)
grad_triangles = grad_triangles.flatten(0)
grad_sigma = grad_sigma.view(-1, 1)
grads = (
grad_triangles,
grad_sigma,
None,
None,
None,
grad_sh,
grad_colors_precomp,
grad_opacities,
grad_means2D,
None,
None,
None
)
return grads
class TriangleRasterizationSettings(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 TriangleRasterizer(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, triangles_points, sigma, num_points_per_triangle, cumsum_of_points_per_triangle, number_of_points, opacities, means2D, scaling, density_factor, shs = None, colors_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 shs is None:
shs = torch.Tensor([])
if colors_precomp is None:
colors_precomp = torch.Tensor([])
# Invoke C++/CUDA rasterization routine
return rasterize_triangles(
triangles_points,
sigma,
num_points_per_triangle,
cumsum_of_points_per_triangle,
number_of_points,
shs,
colors_precomp,
opacities,
means2D,
scaling,
density_factor,
raster_settings,
)
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