mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 10:34:29 +08:00
84024a4e73
* compound model now uses compound field, passing num_images in the config, working on passing image indices through ngp sampler * renaming cuda func, adding torch compound support * removing unused imports * getting num images from dataloader instead of config * fixing pylint and adding todo * undoing base config change * adding cone angle and near plane to compound model config
111 lines
3.5 KiB
Python
111 lines
3.5 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""CUDA Functions"""
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from typing import Callable
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import torch
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from torch.cuda.amp import custom_bwd, custom_fwd
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def _make_lazy_cuda(name: str) -> Callable:
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def call_cuda(*args, **kwargs):
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# pylint: disable=import-outside-toplevel
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from nerfactory.cuda.backend import _C
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return getattr(_C, name)(*args, **kwargs)
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return call_cuda
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packbits = _make_lazy_cuda("packbits")
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ray_aabb_intersect = _make_lazy_cuda("ray_aabb_intersect")
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morton3D = _make_lazy_cuda("morton3D")
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morton3D_invert = _make_lazy_cuda("morton3D_invert")
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raymarching = _make_lazy_cuda("raymarching")
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volumetric_rendering_forward = _make_lazy_cuda("volumetric_rendering_forward")
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volumetric_rendering_backward = _make_lazy_cuda("volumetric_rendering_backward")
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occupancy_query = _make_lazy_cuda("occupancy_query")
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get_camera_indices = _make_lazy_cuda("get_camera_indices")
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# pylint: disable=abstract-method,arguments-differ
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class VolumeRenderer(torch.autograd.Function):
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"""CUDA Volumetirc Renderer"""
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, packed_info, starts, ends, sigmas, rgbs, opacities):
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accumulated_weight, accumulated_depth, accumulated_color, mask = volumetric_rendering_forward(
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packed_info, starts, ends, sigmas, rgbs, opacities
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)
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# TODO(ruilongli): accelerate for torch.no_grad()?
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ctx.save_for_backward(
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accumulated_weight,
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accumulated_depth,
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accumulated_color,
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packed_info,
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starts,
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ends,
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sigmas,
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rgbs,
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opacities,
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)
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return accumulated_weight, accumulated_depth, accumulated_color, mask
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@staticmethod
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@custom_bwd
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def backward(ctx, grad_weight, grad_depth, grad_color, _grad_mask):
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(
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accumulated_weight,
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accumulated_depth,
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accumulated_color,
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packed_info,
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starts,
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ends,
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sigmas,
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rgbs,
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opacities,
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) = ctx.saved_tensors
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grad_sigmas, grad_rgbs = volumetric_rendering_backward(
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accumulated_weight,
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accumulated_depth,
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accumulated_color,
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grad_weight,
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grad_depth,
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grad_color,
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packed_info,
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starts,
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ends,
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sigmas,
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rgbs,
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opacities,
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)
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# corresponds to the input argument list of forward()
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return None, None, None, grad_sigmas, grad_rgbs, None
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def unpackbits(x: torch.Tensor) -> torch.Tensor:
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"""Unpack uint8 bits back to a boolen mask.
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Args:
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x: uint8 bit tensor with shape [N]
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Returns:
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unpacked boolen tensor with shape [N * 8]
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"""
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assert x.dtype == torch.uint8 and x.dim() == 1
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bits = x.element_size() * 8
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mask = 2 ** torch.arange(bits - 1, -1, -1).to(x)
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return x.unsqueeze(-1).bitwise_and(mask).ne(0).flip(-1).bool().reshape(-1)
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