mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 18:44:21 +08:00
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""CUDA Functions"""
|
|
from typing import Callable
|
|
|
|
import torch
|
|
from torch.cuda.amp import custom_bwd, custom_fwd
|
|
|
|
|
|
def _make_lazy_cuda(name: str) -> Callable:
|
|
def call_cuda(*args, **kwargs):
|
|
# pylint: disable=import-outside-toplevel
|
|
from nerfactory.cuda.backend import _C
|
|
|
|
return getattr(_C, name)(*args, **kwargs)
|
|
|
|
return call_cuda
|
|
|
|
|
|
packbits = _make_lazy_cuda("packbits")
|
|
ray_aabb_intersect = _make_lazy_cuda("ray_aabb_intersect")
|
|
morton3D = _make_lazy_cuda("morton3D")
|
|
morton3D_invert = _make_lazy_cuda("morton3D_invert")
|
|
raymarching = _make_lazy_cuda("raymarching")
|
|
volumetric_rendering_forward = _make_lazy_cuda("volumetric_rendering_forward")
|
|
volumetric_rendering_backward = _make_lazy_cuda("volumetric_rendering_backward")
|
|
occupancy_query = _make_lazy_cuda("occupancy_query")
|
|
|
|
# pylint: disable=abstract-method,arguments-differ
|
|
class VolumeRenderer(torch.autograd.Function):
|
|
"""CUDA Volumetirc Renderer"""
|
|
|
|
@staticmethod
|
|
@custom_fwd(cast_inputs=torch.float32)
|
|
def forward(ctx, packed_info, starts, ends, sigmas, rgbs, opacities):
|
|
accumulated_weight, accumulated_depth, accumulated_color, mask = volumetric_rendering_forward(
|
|
packed_info, starts, ends, sigmas, rgbs, opacities
|
|
)
|
|
# TODO(ruilongli): accelerate for torch.no_grad()?
|
|
ctx.save_for_backward(
|
|
accumulated_weight,
|
|
accumulated_depth,
|
|
accumulated_color,
|
|
packed_info,
|
|
starts,
|
|
ends,
|
|
sigmas,
|
|
rgbs,
|
|
opacities,
|
|
)
|
|
return accumulated_weight, accumulated_depth, accumulated_color, mask
|
|
|
|
@staticmethod
|
|
@custom_bwd
|
|
def backward(ctx, grad_weight, grad_depth, grad_color, _grad_mask):
|
|
(
|
|
accumulated_weight,
|
|
accumulated_depth,
|
|
accumulated_color,
|
|
packed_info,
|
|
starts,
|
|
ends,
|
|
sigmas,
|
|
rgbs,
|
|
opacities,
|
|
) = ctx.saved_tensors
|
|
grad_sigmas, grad_rgbs = volumetric_rendering_backward(
|
|
accumulated_weight,
|
|
accumulated_depth,
|
|
accumulated_color,
|
|
grad_weight,
|
|
grad_depth,
|
|
grad_color,
|
|
packed_info,
|
|
starts,
|
|
ends,
|
|
sigmas,
|
|
rgbs,
|
|
opacities,
|
|
)
|
|
# corresponds to the input argument list of forward()
|
|
return None, None, None, grad_sigmas, grad_rgbs, None
|
|
|
|
|
|
def unpackbits(x: torch.Tensor) -> torch.Tensor:
|
|
"""Unpack uint8 bits back to a boolen mask.
|
|
|
|
Args:
|
|
x: uint8 bit tensor with shape [N]
|
|
|
|
Returns:
|
|
unpacked boolen tensor with shape [N * 8]
|
|
"""
|
|
assert x.dtype == torch.uint8 and x.dim() == 1
|
|
bits = x.element_size() * 8
|
|
mask = 2 ** torch.arange(bits - 1, -1, -1).to(x)
|
|
return x.unsqueeze(-1).bitwise_and(mask).ne(0).flip(-1).bool().reshape(-1)
|