Source code for torch_geometric.transforms.normalize_rotation

import torch
import torch.nn.functional as F

[docs]class NormalizeRotation(object): r"""Rotates all points so that the eigenvectors overlie the axes of the Cartesian coordinate system. If the data additionally holds normals saved in :obj:`data.norm` these will be also rotated. Args: max_points (int, optional): If set to a value greater than :obj:`0`, only a random number of :obj:`max_points` points are sampled and used to compute eigenvectors. (default: :obj:`-1`) """ def __init__(self, max_points=-1): self.max_points = max_points def __call__(self, data): pos = data.pos if self.max_points > 0 and pos.size(0) > self.max_points: perm = torch.randperm(pos.size(0)) pos = pos[perm[:self.max_points]] pos = pos - pos.mean(dim=0, keepdim=True) C = torch.matmul(pos.t(), pos) e, v = torch.eig(C, eigenvectors=True) # v[:,j] is j-th eigenvector data.pos = torch.matmul(data.pos, v) if 'norm' in data: data.norm = F.normalize(torch.matmul(data.norm, v)) return data def __repr__(self): return '{}()'.format(self.__class__.__name__)