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| from typing import Optional, Tuple |
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| import torch |
| from torch.autograd import Function |
|
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| from ..utils import ext_loader |
|
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| ext_module = ext_loader.load_ext( |
| '_ext', ['ball_query_forward', 'stack_ball_query_forward']) |
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|
| class BallQuery(Function): |
| """Find nearby points in spherical space.""" |
|
|
| @staticmethod |
| def forward( |
| ctx, |
| min_radius: float, |
| max_radius: float, |
| sample_num: int, |
| xyz: torch.Tensor, |
| center_xyz: torch.Tensor, |
| xyz_batch_cnt: Optional[torch.Tensor] = None, |
| center_xyz_batch_cnt: Optional[torch.Tensor] = None |
| ) -> torch.Tensor: |
| """ |
| Args: |
| min_radius (float): minimum radius of the balls. |
| max_radius (float): maximum radius of the balls. |
| sample_num (int): maximum number of features in the balls. |
| xyz (torch.Tensor): (B, N, 3) xyz coordinates of the features, |
| or staked input (N1 + N2 ..., 3). |
| center_xyz (torch.Tensor): (B, npoint, 3) centers of the ball |
| query, or staked input (M1 + M2 ..., 3). |
| xyz_batch_cnt: (batch_size): Stacked input xyz coordinates nums in |
| each batch, just like (N1, N2, ...). Defaults to None. |
| New in version 1.7.0. |
| center_xyz_batch_cnt: (batch_size): Stacked centers coordinates |
| nums in each batch, just line (M1, M2, ...). Defaults to None. |
| New in version 1.7.0. |
| |
| Returns: |
| torch.Tensor: (B, npoint, nsample) tensor with the indices of the |
| features that form the query balls. |
| """ |
| assert center_xyz.is_contiguous() |
| assert xyz.is_contiguous() |
| assert min_radius < max_radius |
| if xyz_batch_cnt is not None and center_xyz_batch_cnt is not None: |
| assert xyz_batch_cnt.dtype == torch.int |
| assert center_xyz_batch_cnt.dtype == torch.int |
| idx = center_xyz.new_zeros((center_xyz.shape[0], sample_num), |
| dtype=torch.int32) |
| ext_module.stack_ball_query_forward( |
| center_xyz, |
| center_xyz_batch_cnt, |
| xyz, |
| xyz_batch_cnt, |
| idx, |
| max_radius=max_radius, |
| nsample=sample_num, |
| ) |
| else: |
| B, N, _ = xyz.size() |
| npoint = center_xyz.size(1) |
| idx = xyz.new_zeros(B, npoint, sample_num, dtype=torch.int32) |
| ext_module.ball_query_forward( |
| center_xyz, |
| xyz, |
| idx, |
| b=B, |
| n=N, |
| m=npoint, |
| min_radius=min_radius, |
| max_radius=max_radius, |
| nsample=sample_num) |
| if torch.__version__ != 'parrots': |
| ctx.mark_non_differentiable(idx) |
| return idx |
|
|
| @staticmethod |
| def backward(ctx, a=None) -> Tuple[None, None, None, None]: |
| return None, None, None, None |
|
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|
| ball_query = BallQuery.apply |
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|
