Source code for torch_geometric.transforms.fixed_points

import math
import re

import numpy as np
import torch
from torch import Tensor

from import Data
from import functional_transform
from torch_geometric.transforms import BaseTransform

[docs]@functional_transform('fixed_points') class FixedPoints(BaseTransform): r"""Samples a fixed number of points and features from a point cloud (functional name: :obj:`fixed_points`). Args: num (int): The number of points to sample. replace (bool, optional): If set to :obj:`False`, samples points without replacement. (default: :obj:`True`) allow_duplicates (bool, optional): In case :obj:`replace` is :obj`False` and :obj:`num` is greater than the number of points, this option determines whether to add duplicated nodes to the output points or not. In case :obj:`allow_duplicates` is :obj:`False`, the number of output points might be smaller than :obj:`num`. In case :obj:`allow_duplicates` is :obj:`True`, the number of duplicated points are kept to a minimum. (default: :obj:`False`) """ def __init__( self, num: int, replace: bool = True, allow_duplicates: bool = False, ): self.num = num self.replace = replace self.allow_duplicates = allow_duplicates def forward(self, data: Data) -> Data: num_nodes = data.num_nodes assert num_nodes is not None if self.replace: choice = torch.from_numpy( np.random.choice(num_nodes, self.num, replace=True)).long() elif not self.allow_duplicates: choice = torch.randperm(num_nodes)[:self.num] else: choice =[ torch.randperm(num_nodes) for _ in range(math.ceil(self.num / num_nodes)) ], dim=0)[:self.num] for key, value in data.items(): if key == 'num_nodes': data.num_nodes = choice.size(0) elif bool('edge', key)): continue elif (isinstance(value, Tensor) and value.size(0) == num_nodes and value.size(0) != 1): data[key] = value[choice] return data def __repr__(self) -> str: return f'{self.__class__.__name__}({self.num}, replace={self.replace})'