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from typing import Optional
import scipy.cluster
import torch
def nearest(
x: torch.Tensor,
y: torch.Tensor,
batch_x: Optional[torch.Tensor] = None,
batch_y: Optional[torch.Tensor] = None,
) -> torch.Tensor:
r"""Clusters points in :obj:`x` together which are nearest to a given query
point in :obj:`y`.
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}`.
batch_x (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. :obj:`batch_x` needs to be sorted.
(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. :obj:`batch_y` needs to be sorted.
(default: :obj:`None`)
:rtype: :class:`LongTensor`
.. code-block:: python
import torch
from torch_cluster import nearest
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])
cluster = nearest(x, y, batch_x, batch_y)
"""
x = x.view(-1, 1) if x.dim() == 1 else x
y = y.view(-1, 1) if y.dim() == 1 else y
assert x.size(1) == y.size(1)
if batch_x is not None and (batch_x[1:] - batch_x[:-1] < 0).any():
raise ValueError("'batch_x' is not sorted")
if batch_y is not None and (batch_y[1:] - batch_y[:-1] < 0).any():
raise ValueError("'batch_y' is not sorted")
if x.is_cuda:
if batch_x is not None:
assert x.size(0) == batch_x.numel()
batch_size = int(batch_x.max()) + 1
deg = x.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_x, torch.ones_like(batch_x))
ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:])
else:
ptr_x = torch.tensor([0, x.size(0)], device=x.device)
if batch_y is not None:
assert y.size(0) == batch_y.numel()
batch_size = int(batch_y.max()) + 1
deg = y.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_y, torch.ones_like(batch_y))
ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:])
else:
ptr_y = torch.tensor([0, y.size(0)], device=y.device)
# If an instance in `batch_x` is non-empty, it must be non-empty in
# `batch_y `as well:
nonempty_ptr_x = (ptr_x[1:] - ptr_x[:-1]) > 0
nonempty_ptr_y = (ptr_y[1:] - ptr_y[:-1]) > 0
if not torch.equal(nonempty_ptr_x, nonempty_ptr_y):
raise ValueError("Some batch indices occur in 'batch_x' "
"that do not occur in 'batch_y'")
return torch.ops.torch_cluster.nearest(x, y, ptr_x, ptr_y)
else:
if batch_x is None and batch_y is not None:
batch_x = x.new_zeros(x.size(0), dtype=torch.long)
if batch_y is None and batch_x is not None:
batch_y = y.new_zeros(y.size(0), dtype=torch.long)
# Translate and rescale x and y to [0, 1].
if batch_x is not None and batch_y is not None:
# If an instance in `batch_x` is non-empty, it must be non-empty in
# `batch_y `as well:
unique_batch_x = batch_x.unique_consecutive()
unique_batch_y = batch_y.unique_consecutive()
if not torch.equal(unique_batch_x, unique_batch_y):
raise ValueError("Some batch indices occur in 'batch_x' "
"that do not occur in 'batch_y'")
assert x.dim() == 2 and batch_x.dim() == 1
assert y.dim() == 2 and batch_y.dim() == 1
assert x.size(0) == batch_x.size(0)
assert y.size(0) == batch_y.size(0)
min_xy = min(x.min().item(), y.min().item())
x, y = x - min_xy, y - min_xy
max_xy = max(x.max().item(), y.max().item())
x.div_(max_xy)
y.div_(max_xy)
# Concat batch/features to ensure no cross-links between examples.
D = x.size(-1)
x = torch.cat([x, 2 * D * batch_x.view(-1, 1).to(x.dtype)], -1)
y = torch.cat([y, 2 * D * batch_y.view(-1, 1).to(y.dtype)], -1)
return torch.from_numpy(
scipy.cluster.vq.vq(x.detach().cpu(),
y.detach().cpu())[0]).to(torch.long)
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