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