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import torch
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from .z_order import xyz2key as z_order_encode_
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from .z_order import key2xyz as z_order_decode_
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from .hilbert import encode as hilbert_encode_
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from .hilbert import decode as hilbert_decode_
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@torch.inference_mode()
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def encode(grid_coord, batch=None, depth=16, order="z"):
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assert order in {"z", "z-trans", "hilbert", "hilbert-trans"}
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if order == "z":
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code = z_order_encode(grid_coord, depth=depth)
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elif order == "z-trans":
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code = z_order_encode(grid_coord[:, [1, 0, 2]], depth=depth)
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elif order == "hilbert":
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code = hilbert_encode(grid_coord, depth=depth)
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elif order == "hilbert-trans":
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code = hilbert_encode(grid_coord[:, [1, 0, 2]], depth=depth)
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else:
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raise NotImplementedError
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if batch is not None:
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batch = batch.long()
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code = batch << depth * 3 | code
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return code
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@torch.inference_mode()
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def decode(code, depth=16, order="z"):
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assert order in {"z", "hilbert"}
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batch = code >> depth * 3
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code = code & ((1 << depth * 3) - 1)
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if order == "z":
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grid_coord = z_order_decode(code, depth=depth)
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elif order == "hilbert":
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grid_coord = hilbert_decode(code, depth=depth)
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else:
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raise NotImplementedError
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return grid_coord, batch
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def z_order_encode(grid_coord: torch.Tensor, depth: int = 16):
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x, y, z = grid_coord[:, 0].long(), grid_coord[:, 1].long(), grid_coord[:, 2].long()
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code = z_order_encode_(x, y, z, b=None, depth=depth)
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return code
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def z_order_decode(code: torch.Tensor, depth):
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x, y, z = z_order_decode_(code, depth=depth)
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grid_coord = torch.stack([x, y, z], dim=-1)
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return grid_coord
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def hilbert_encode(grid_coord: torch.Tensor, depth: int = 16):
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return hilbert_encode_(grid_coord, num_dims=3, num_bits=depth)
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def hilbert_decode(code: torch.Tensor, depth: int = 16):
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return hilbert_decode_(code, num_dims=3, num_bits=depth)
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