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| # Copyright (c) OpenMMLab. All rights reserved. | |
| 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.""" | |
| 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 | |
| def backward(ctx, a=None): | |
| return None, None, None, None | |
| ball_query = BallQuery.apply | |