# --------------------------------------------------------
# Octree-based Sparse Convolutional Neural Networks
# Copyright (c) 2022 Peng-Shuai Wang <wangps@hotmail.com>
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng-Shuai Wang
# --------------------------------------------------------
import torch
from typing import Optional, Union
class KeyLUT:
def __init__(self):
r256 = torch.arange(256, dtype=torch.int64)
r512 = torch.arange(512, dtype=torch.int64)
zero = torch.zeros(256, dtype=torch.int64)
device = torch.device('cpu')
self._encode = {device: (self.xyz2key(r256, zero, zero, 8),
self.xyz2key(zero, r256, zero, 8),
self.xyz2key(zero, zero, r256, 8))}
self._decode = {device: self.key2xyz(r512, 9)}
def encode_lut(self, device=torch.device('cpu')):
if device not in self._encode:
cpu = torch.device('cpu')
self._encode[device] = tuple(e.to(device) for e in self._encode[cpu])
return self._encode[device]
def decode_lut(self, device=torch.device('cpu')):
if device not in self._decode:
cpu = torch.device('cpu')
self._decode[device] = tuple(e.to(device) for e in self._decode[cpu])
return self._decode[device]
def xyz2key(self, x, y, z, depth):
key = torch.zeros_like(x)
for i in range(depth):
mask = 1 << i
key = (key | ((x & mask) << (2 * i + 2)) |
((y & mask) << (2 * i + 1)) |
((z & mask) << (2 * i + 0)))
return key
def key2xyz(self, key, depth):
x = torch.zeros_like(key)
y = torch.zeros_like(key)
z = torch.zeros_like(key)
for i in range(depth):
x = x | ((key & (1 << (3 * i + 2))) >> (2 * i + 2))
y = y | ((key & (1 << (3 * i + 1))) >> (2 * i + 1))
z = z | ((key & (1 << (3 * i + 0))) >> (2 * i + 0))
return x, y, z
_key_lut = KeyLUT()
[docs]def xyz2key(x: torch.Tensor, y: torch.Tensor, z: torch.Tensor,
b: Optional[Union[torch.Tensor, int]] = None, depth: int = 16):
r'''Encodes :attr:`x`, :attr:`y`, :attr:`z` coordinates to the shuffled keys
based on pre-computed look up tables. The speed of this function is much
faster than the method based on for-loop.
Args:
x (torch.Tensor): The x coordinate.
y (torch.Tensor): The y coordinate.
z (torch.Tensor): The z coordinate.
b (torch.Tensor or int): The batch index of the coordinates, and should be
smaller than 32768. If :attr:`b` is :obj:`torch.Tensor`, the size of
:attr:`b` must be the same as :attr:`x`, :attr:`y`, and :attr:`z`.
depth (int): The depth of the shuffled key, and must be smaller than 17 (< 17).
'''
EX, EY, EZ = _key_lut.encode_lut(x.device)
x, y, z = x.long(), y.long(), z.long()
mask = 255 if depth > 8 else (1 << depth) - 1
key = EX[x & mask] | EY[y & mask] | EZ[z & mask]
if depth > 8:
mask = (1 << (depth-8)) - 1
key16 = EX[(x >> 8) & mask] | EY[(y >> 8) & mask] | EZ[(z >> 8) & mask]
key = key16 << 24 | key
if b is not None:
b = b.long()
key = b << 48 | key
return key
[docs]def key2xyz(key: torch.Tensor, depth: int = 16):
r'''Decodes the shuffled key to :attr:`x`, :attr:`y`, :attr:`z` coordinates
and the batch index based on pre-computed look up tables.
Args:
key (torch.Tensor): The shuffled key.
depth (int): The depth of the shuffled key, and must be smaller than 17 (< 17).
'''
DX, DY, DZ = _key_lut.decode_lut(key.device)
x, y, z = torch.zeros_like(key), torch.zeros_like(key), torch.zeros_like(key)
b = key >> 48
key = key & ((1 << 48) - 1)
n = (depth + 2) // 3
for i in range(n):
k = key >> (i * 9) & 511
x = x | (DX[k] << (i * 3))
y = y | (DY[k] << (i * 3))
z = z | (DZ[k] << (i * 3))
return x, y, z, b