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import torch
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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'])
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class BallQuery(Function):
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"""Find nearby points in spherical space."""
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@staticmethod
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def forward(ctx, min_radius: float, max_radius: float, sample_num: int,
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xyz: torch.Tensor, center_xyz: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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min_radius (float): minimum radius of the balls.
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max_radius (float): maximum radius of the balls.
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sample_num (int): maximum number of features in the balls.
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xyz (Tensor): (B, N, 3) xyz coordinates of the features.
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center_xyz (Tensor): (B, npoint, 3) centers of the ball query.
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Returns:
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Tensor: (B, npoint, nsample) tensor with the indices of
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the features that form the query balls.
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"""
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assert center_xyz.is_contiguous()
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assert xyz.is_contiguous()
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assert min_radius < max_radius
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B, N, _ = xyz.size()
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npoint = center_xyz.size(1)
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idx = xyz.new_zeros(B, npoint, sample_num, dtype=torch.int)
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ext_module.ball_query_forward(
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center_xyz,
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xyz,
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idx,
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b=B,
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n=N,
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m=npoint,
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min_radius=min_radius,
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max_radius=max_radius,
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nsample=sample_num)
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if torch.__version__ != 'parrots':
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ctx.mark_non_differentiable(idx)
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return idx
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@staticmethod
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def backward(ctx, a=None):
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return None, None, None, None
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ball_query = BallQuery.apply
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