"""Modules that transforms between graphs and between graph and tensors."""
import torch.nn as nn
from ...transforms import knn_graph, radius_graph, segmented_knn_graph
def pairwise_squared_distance(x):
"""
x : (n_samples, n_points, dims)
return : (n_samples, n_points, n_points)
"""
x2s = (x * x).sum(-1, keepdim=True)
return x2s + x2s.transpose(-1, -2) - 2 * x @ x.transpose(-1, -2)
[docs]
class KNNGraph(nn.Module):
r"""Layer that transforms one point set into a graph, or a batch of
point sets with the same number of points into a batched union of those graphs.
The KNNGraph is implemented in the following steps:
1. Compute an NxN matrix of pairwise distance for all points.
2. Pick the k points with the smallest distance for each point as their k-nearest neighbors.
3. Construct a graph with edges to each point as a node from its k-nearest neighbors.
The overall computational complexity is :math:`O(N^2(logN + D)`.
If a batch of point sets is provided, the point :math:`j` in point
set :math:`i` is mapped to graph node ID: :math:`i \times M + j`, where
:math:`M` is the number of nodes in each point set.
The predecessors of each node are the k-nearest neighbors of the
corresponding point.
Parameters
----------
k : int
The number of neighbors.
Notes
-----
The nearest neighbors found for a node include the node itself.
Examples
--------
The following example uses PyTorch backend.
>>> import torch
>>> from dgl.nn.pytorch.factory import KNNGraph
>>>
>>> kg = KNNGraph(2)
>>> x = torch.tensor([[0,1],
[1,2],
[1,3],
[100, 101],
[101, 102],
[50, 50]])
>>> g = kg(x)
>>> print(g.edges())
(tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 5]),
tensor([0, 0, 1, 2, 1, 2, 5, 3, 4, 3, 4, 5]))
"""
def __init__(self, k):
super(KNNGraph, self).__init__()
self.k = k
# pylint: disable=invalid-name
[docs]
def forward(
self,
x,
algorithm="bruteforce-blas",
dist="euclidean",
exclude_self=False,
):
r"""
Forward computation.
Parameters
----------
x : Tensor
:math:`(M, D)` or :math:`(N, M, D)` where :math:`N` means the
number of point sets, :math:`M` means the number of points in
each point set, and :math:`D` means the size of features.
algorithm : str, optional
Algorithm used to compute the k-nearest neighbors.
* 'bruteforce-blas' will first compute the distance matrix
using BLAS matrix multiplication operation provided by
backend frameworks. Then use topk algorithm to get
k-nearest neighbors. This method is fast when the point
set is small but has :math:`O(N^2)` memory complexity where
:math:`N` is the number of points.
* 'bruteforce' will compute distances pair by pair and
directly select the k-nearest neighbors during distance
computation. This method is slower than 'bruteforce-blas'
but has less memory overhead (i.e., :math:`O(Nk)` where :math:`N`
is the number of points, :math:`k` is the number of nearest
neighbors per node) since we do not need to store all distances.
* 'bruteforce-sharemem' (CUDA only) is similar to 'bruteforce'
but use shared memory in CUDA devices for buffer. This method is
faster than 'bruteforce' when the dimension of input points
is not large. This method is only available on CUDA device.
* 'kd-tree' will use the kd-tree algorithm (CPU only).
This method is suitable for low-dimensional data (e.g. 3D
point clouds)
* 'nn-descent' is a approximate approach from paper
`Efficient k-nearest neighbor graph construction for generic similarity
measures <https://www.cs.princeton.edu/cass/papers/www11.pdf>`_. This method
will search for nearest neighbor candidates in "neighbors' neighbors".
(default: 'bruteforce-blas')
dist : str, optional
The distance metric used to compute distance between points. It can be the following
metrics:
* 'euclidean': Use Euclidean distance (L2 norm)
:math:`\sqrt{\sum_{i} (x_{i} - y_{i})^{2}}`.
* 'cosine': Use cosine distance.
(default: 'euclidean')
exclude_self : bool, optional
If True, the output graph will not contain self loop edges, and each node will not
be counted as one of its own k neighbors. If False, the output graph will contain
self loop edges, and a node will be counted as one of its own k neighbors.
Returns
-------
DGLGraph
A DGLGraph without features.
"""
return knn_graph(
x, self.k, algorithm=algorithm, dist=dist, exclude_self=exclude_self
)
[docs]
class SegmentedKNNGraph(nn.Module):
r"""Layer that transforms one point set into a graph, or a batch of
point sets with different number of points into a batched union of those graphs.
If a batch of point sets is provided, then the point :math:`j` in the point
set :math:`i` is mapped to graph node ID:
:math:`\sum_{p<i} |V_p| + j`, where :math:`|V_p|` means the number of
points in the point set :math:`p`.
The predecessors of each node are the k-nearest neighbors of the
corresponding point.
Parameters
----------
k : int
The number of neighbors.
Notes
-----
The nearest neighbors found for a node include the node itself.
Examples
--------
The following example uses PyTorch backend.
>>> import torch
>>> from dgl.nn.pytorch.factory import SegmentedKNNGraph
>>>
>>> kg = SegmentedKNNGraph(2)
>>> x = torch.tensor([[0,1],
... [1,2],
... [1,3],
... [100, 101],
... [101, 102],
... [50, 50],
... [24,25],
... [25,24]])
>>> g = kg(x, [3,3,2])
>>> print(g.edges())
(tensor([0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7]),
tensor([0, 0, 1, 2, 1, 2, 3, 4, 5, 3, 4, 5, 6, 7, 6, 7]))
>>>
"""
def __init__(self, k):
super(SegmentedKNNGraph, self).__init__()
self.k = k
# pylint: disable=invalid-name
[docs]
def forward(
self,
x,
segs,
algorithm="bruteforce-blas",
dist="euclidean",
exclude_self=False,
):
r"""Forward computation.
Parameters
----------
x : Tensor
:math:`(M, D)` where :math:`M` means the total number of points
in all point sets, and :math:`D` means the size of features.
segs : iterable of int
:math:`(N)` integers where :math:`N` means the number of point
sets. The number of elements must sum up to :math:`M`. And any
:math:`N` should :math:`\ge k`
algorithm : str, optional
Algorithm used to compute the k-nearest neighbors.
* 'bruteforce-blas' will first compute the distance matrix
using BLAS matrix multiplication operation provided by
backend frameworks. Then use topk algorithm to get
k-nearest neighbors. This method is fast when the point
set is small but has :math:`O(N^2)` memory complexity where
:math:`N` is the number of points.
* 'bruteforce' will compute distances pair by pair and
directly select the k-nearest neighbors during distance
computation. This method is slower than 'bruteforce-blas'
but has less memory overhead (i.e., :math:`O(Nk)` where :math:`N`
is the number of points, :math:`k` is the number of nearest
neighbors per node) since we do not need to store all distances.
* 'bruteforce-sharemem' (CUDA only) is similar to 'bruteforce'
but use shared memory in CUDA devices for buffer. This method is
faster than 'bruteforce' when the dimension of input points
is not large. This method is only available on CUDA device.
* 'kd-tree' will use the kd-tree algorithm (CPU only).
This method is suitable for low-dimensional data (e.g. 3D
point clouds)
* 'nn-descent' is a approximate approach from paper
`Efficient k-nearest neighbor graph construction for generic similarity
measures <https://www.cs.princeton.edu/cass/papers/www11.pdf>`_. This method
will search for nearest neighbor candidates in "neighbors' neighbors".
(default: 'bruteforce-blas')
dist : str, optional
The distance metric used to compute distance between points. It can be the following
metrics:
* 'euclidean': Use Euclidean distance (L2 norm)
:math:`\sqrt{\sum_{i} (x_{i} - y_{i})^{2}}`.
* 'cosine': Use cosine distance.
(default: 'euclidean')
exclude_self : bool, optional
If True, the output graph will not contain self loop edges, and each node will not
be counted as one of its own k neighbors. If False, the output graph will contain
self loop edges, and a node will be counted as one of its own k neighbors.
Returns
-------
DGLGraph
A batched DGLGraph without features.
"""
return segmented_knn_graph(
x,
self.k,
segs,
algorithm=algorithm,
dist=dist,
exclude_self=exclude_self,
)
[docs]
class RadiusGraph(nn.Module):
r"""Layer that transforms one point set into a bidirected graph with
neighbors within given distance.
The RadiusGraph is implemented in the following steps:
1. Compute an NxN matrix of pairwise distance for all points.
2. Pick the points within distance to each point as their neighbors.
3. Construct a graph with edges to each point as a node from its neighbors.
The nodes of the returned graph correspond to the points, where the neighbors
of each point are within given distance.
Parameters
----------
r : float
Radius of the neighbors.
p : float, optional
Power parameter for the Minkowski metric. When :attr:`p = 1` it is the
equivalent of Manhattan distance (L1 norm) and Euclidean distance
(L2 norm) for :attr:`p = 2`.
(default: 2)
self_loop : bool, optional
Whether the radius graph will contain self-loops.
(default: False)
compute_mode : str, optional
``use_mm_for_euclid_dist_if_necessary`` - will use matrix multiplication
approach to calculate euclidean distance (p = 2) if P > 25 or R > 25
``use_mm_for_euclid_dist`` - will always use matrix multiplication
approach to calculate euclidean distance (p = 2)
``donot_use_mm_for_euclid_dist`` - will never use matrix multiplication
approach to calculate euclidean distance (p = 2).
(default: donot_use_mm_for_euclid_dist)
Examples
--------
The following examples uses PyTorch backend.
>>> import dgl
>>> from dgl.nn.pytorch.factory import RadiusGraph
>>> x = torch.tensor([[0.0, 0.0, 1.0],
... [1.0, 0.5, 0.5],
... [0.5, 0.2, 0.2],
... [0.3, 0.2, 0.4]])
>>> rg = RadiusGraph(0.75)
>>> g = rg(x) # Each node has neighbors within 0.75 distance
>>> g.edges()
(tensor([0, 1, 2, 2, 3, 3]), tensor([3, 2, 1, 3, 0, 2]))
When :attr:`get_distances` is True, forward pass returns the radius graph and
distances for the corresponding edges.
>>> x = torch.tensor([[0.0, 0.0, 1.0],
... [1.0, 0.5, 0.5],
... [0.5, 0.2, 0.2],
... [0.3, 0.2, 0.4]])
>>> rg = RadiusGraph(0.75)
>>> g, dist = rg(x, get_distances=True)
>>> g.edges()
(tensor([0, 1, 2, 2, 3, 3]), tensor([3, 2, 1, 3, 0, 2]))
>>> dist
tensor([[0.7000],
[0.6557],
[0.6557],
[0.2828],
[0.7000],
[0.2828]])
"""
# pylint: disable=invalid-name
def __init__(
self,
r,
p=2,
self_loop=False,
compute_mode="donot_use_mm_for_euclid_dist",
):
super(RadiusGraph, self).__init__()
self.r = r
self.p = p
self.self_loop = self_loop
self.compute_mode = compute_mode
# pylint: disable=invalid-name
[docs]
def forward(self, x, get_distances=False):
r"""
Forward computation.
Parameters
----------
x : Tensor
The point coordinates. :math:`(N, D)` where :math:`N` means the
number of points in the point set, and :math:`D` means the size of
the features. It can be either on CPU or GPU. Device of the point
coordinates specifies device of the radius graph.
get_distances : bool, optional
Whether to return the distances for the corresponding edges in the
radius graph.
(default: False)
Returns
-------
DGLGraph
The constructed graph. The node IDs are in the same order as :attr:`x`.
torch.Tensor, optional
The distances for the edges in the constructed graph. The distances
are in the same order as edge IDs.
"""
return radius_graph(
x, self.r, self.p, self.self_loop, self.compute_mode, get_distances
)