| |
| import torch |
| from torch.autograd import Function |
|
|
| from ..utils import ext_loader |
|
|
| ext_module = ext_loader.load_ext('_ext', ['ball_query_forward']) |
|
|
|
|
| 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) -> 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 (Tensor): (B, N, 3) xyz coordinates of the features. |
| center_xyz (Tensor): (B, npoint, 3) centers of the ball query. |
| |
| Returns: |
| 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 |
|
|
| B, N, _ = xyz.size() |
| npoint = center_xyz.size(1) |
| idx = xyz.new_zeros(B, npoint, sample_num, dtype=torch.int) |
|
|
| 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): |
| return None, None, None, None |
|
|
|
|
| ball_query = BallQuery.apply |
|
|
