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80 lines
2.6 KiB
Python
80 lines
2.6 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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import torch
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from torch.cuda.amp import custom_bwd, custom_fwd
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from nerfactory.cuda.backend import _C
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packbits = _C.packbits
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ray_aabb_intersect = _C.ray_aabb_intersect
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morton3D = _C.morton3D
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morton3D_invert = _C.morton3D_invert
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raymarching = _C.raymarching
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volumetric_rendering_forward = _C.volumetric_rendering_forward
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volumetric_rendering_backward = _C.volumetric_rendering_backward
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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):
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accumulated_weight, accumulated_depth, accumulated_color, mask = volumetric_rendering_forward(
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packed_info, starts, ends, sigmas, rgbs
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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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)
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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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) = 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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)
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# corresponds to the input argument list of forward()
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return None, None, None, grad_sigmas, grad_rgbs
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