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b6045d5 | 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 | # Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import torch
def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return torch.sum(x*y, -1, keepdim=True)
def reflect(x: torch.Tensor, n: torch.Tensor) -> torch.Tensor:
return 2*dot(x, n)*n - x
def length(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:
return torch.sqrt(torch.clamp(dot(x,x), min=eps)) # Clamp to avoid nan gradients because grad(sqrt(0)) = NaN
def safe_normalize(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:
return x / length(x, eps)
def to_hvec(x: torch.Tensor, w: float) -> torch.Tensor:
return torch.nn.functional.pad(x, pad=(0,1), mode='constant', value=w)
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