Source code for torch_geometric.utils.geodesic

import torch
import numpy as np
import multiprocessing as mp

try:
    import gdist
except ImportError:
    gdist = None


[docs]def geodesic_distance(pos, face, src=None, dest=None, norm=True, max_distance=None, num_workers=0): r"""Computes (normalized) geodesic distances of a mesh given by :obj:`pos` and :obj:`face`. If :obj:`src` and :obj:`dest` are given, this method only computes the geodesic distances for the respective source and target node-pairs. .. note:: This function requires the :obj:`gdist` package. To install, run :obj:`pip install cython && pip install gdist`. Args: pos (Tensor): The node positions. face (LongTensor): The face indices. src (LongTensor, optional): If given, only compute geodesic distances for the specified source indices. (default: :obj:`None`) dest (LongTensor, optional): If given, only compute geodesic distances for the specified target indices. (default: :obj:`None`) norm (bool, optional): Normalizes geodesic distances by :math:`\sqrt{\textrm{area}(\mathcal{M})}`. (default: :obj:`True`) max_distance (float, optional): If given, only yields results for geodesic distances less than :obj:`max_distance`. This will speed up runtime dramatically. (default: :obj:`None`) num_workers (int, optional): How many subprocesses to use for calculating geodesic distances. :obj:`0` means that computation takes place in the main process. :obj:`-1` means that the available amount of CPU cores is used. (default: :obj:`0`) :rtype: Tensor """ if gdist is None: raise ImportError('Package `gdist` could not be found.') max_distance = float('inf') if max_distance is None else max_distance if norm: area = (pos[face[1]] - pos[face[0]]).cross(pos[face[2]] - pos[face[0]]) norm = (area.norm(p=2, dim=1) / 2).sum().sqrt().item() else: norm = 1.0 dtype = pos.dtype pos = pos.detach().cpu().to(torch.double).numpy() face = face.detach().t().cpu().to(torch.int).numpy() if src is None and dest is None: out = gdist.local_gdist_matrix(pos, face, max_distance * norm).toarray() / norm return torch.from_numpy(out).to(dtype) if src is None: src = np.arange(pos.shape[0], dtype=np.int32) else: src = src.detach().cpu().to(torch.int).numpy() dest = None if dest is None else dest.detach().cpu().to(torch.int).numpy() num_workers = mp.cpu_count() if num_workers < -1 else num_workers if num_workers > 0: with mp.Pool(num_workers) as pool: outs = pool.map( _parallel_loop, [(pos, face, src, dest, max_distance, norm, i, dtype) for i in range(len(src))]) else: outs = [ _parallel_loop(pos, face, src, dest, max_distance, norm, i, dtype) for i in range(len(src)) ] out = torch.cat(outs, dim=0) if dest is None: out = out.view(-1, pos.shape[0]) return out
def _parallel_loop(pos, face, src, dest, max_distance, norm, i, dtype): s = src[i:i + 1] d = None if dest is None else dest[i:i + 1] out = gdist.compute_gdist(pos, face, s, d, max_distance * norm) / norm return torch.from_numpy(out).to(dtype)