Source code for torch_cluster.fps

import torch
import torch_cluster.fps_cpu

if torch.cuda.is_available():
    import torch_cluster.fps_cuda

[docs]def fps(x, batch=None, ratio=0.5, random_start=True): r""""A sampling algorithm from the `"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" <>`_ paper, which iteratively samples the most distant point with regard to the rest points. Args: x (Tensor): Node feature matrix :math:`\mathbf{X} \in \mathbb{R}^{N \times F}`. batch (LongTensor, optional): Batch vector :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each node to a specific example. (default: :obj:`None`) ratio (float, optional): Sampling ratio. (default: :obj:`0.5`) random_start (bool, optional): If set to :obj:`False`, use the first node in :math:`\mathbf{X}` as starting node. (default: obj:`True`) :rtype: :class:`LongTensor` .. testsetup:: import torch from torch_cluster import fps .. testcode:: >>> x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]]) >>> batch = torch.tensor([0, 0, 0, 0]) >>> index = fps(x, batch, ratio=0.5) """ if batch is None: batch = x.new_zeros(x.size(0), dtype=torch.long) x = x.view(-1, 1) if x.dim() == 1 else x assert x.dim() == 2 and batch.dim() == 1 assert x.size(0) == batch.size(0) assert ratio > 0 and ratio < 1 if x.is_cuda: return torch_cluster.fps_cuda.fps(x, batch, ratio, random_start) else: return torch_cluster.fps_cpu.fps(x, batch, ratio, random_start)