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