import torch
import scipy.spatial
if torch.cuda.is_available():
import torch_cluster.radius_cuda
[docs]def radius(x, y, r, batch_x=None, batch_y=None, max_num_neighbors=32):
r"""Finds for each element in :obj:`y` all points in :obj:`x` within
distance :obj:`r`.
Args:
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
y (Tensor): Node feature matrix
:math:`\mathbf{Y} \in \mathbb{R}^{M \times F}`.
r (float): The radius.
batch_x (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`)
batch_y (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each
node to a specific example. (default: :obj:`None`)
max_num_neighbors (int, optional): The maximum number of neighbors to
return for each element in :obj:`y`. (default: :obj:`32`)
:rtype: :class:`LongTensor`
.. testsetup::
import torch
from torch_cluster import radius
.. testcode::
>>> x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
>>> batch_x = torch.tensor([0, 0, 0, 0])
>>> y = torch.Tensor([[-1, 0], [1, 0]])
>>> batch_y = torch.tensor([0, 0])
>>> assign_index = radius(x, y, 1.5, batch_x, batch_y)
"""
if batch_x is None:
batch_x = x.new_zeros(x.size(0), dtype=torch.long)
if batch_y is None:
batch_y = y.new_zeros(y.size(0), dtype=torch.long)
x = x.view(-1, 1) if x.dim() == 1 else x
y = y.view(-1, 1) if y.dim() == 1 else y
assert x.dim() == 2 and batch_x.dim() == 1
assert y.dim() == 2 and batch_y.dim() == 1
assert x.size(1) == y.size(1)
assert x.size(0) == batch_x.size(0)
assert y.size(0) == batch_y.size(0)
if x.is_cuda:
return torch_cluster.radius_cuda.radius(x, y, r, batch_x, batch_y,
max_num_neighbors)
x = torch.cat([x, 2 * r * batch_x.view(-1, 1).to(x.dtype)], dim=-1)
y = torch.cat([y, 2 * r * batch_y.view(-1, 1).to(y.dtype)], dim=-1)
tree = scipy.spatial.cKDTree(x.detach().numpy())
_, col = tree.query(
y.detach().numpy(), k=max_num_neighbors, distance_upper_bound=r + 1e-8)
col = [torch.from_numpy(c).to(torch.long) for c in col]
row = [torch.full_like(c, i) for i, c in enumerate(col)]
row, col = torch.cat(row, dim=0), torch.cat(col, dim=0)
mask = col < int(tree.n)
return torch.stack([row[mask], col[mask]], dim=0)
[docs]def radius_graph(x,
r,
batch=None,
loop=False,
max_num_neighbors=32,
flow='source_to_target'):
r"""Computes graph edges to all points within a given distance.
Args:
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
r (float): The radius.
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`)
loop (bool, optional): If :obj:`True`, the graph will contain
self-loops. (default: :obj:`False`)
max_num_neighbors (int, optional): The maximum number of neighbors to
return for each element in :obj:`y`. (default: :obj:`32`)
flow (string, optional): The flow direction when using in combination
with message passing (:obj:`"source_to_target"` or
:obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
:rtype: :class:`LongTensor`
.. testsetup::
import torch
from torch_cluster import radius_graph
.. testcode::
>>> x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
>>> batch = torch.tensor([0, 0, 0, 0])
>>> edge_index = radius_graph(x, r=1.5, batch=batch, loop=False)
"""
assert flow in ['source_to_target', 'target_to_source']
row, col = radius(x, x, r, batch, batch, max_num_neighbors + 1)
row, col = (col, row) if flow == 'source_to_target' else (row, col)
if not loop:
mask = row != col
row, col = row[mask], col[mask]
return torch.stack([row, col], dim=0)