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Commit 0a038334 authored by rusty1s's avatar rusty1s
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README.md
View file @ 0a038334
......@@ -90,7 +90,7 @@ tensor([0, 5, 3, 0, 1])
## FarthestPointSampling
A sampling algorithm, which iteratively samples the most distant point (in metric distance) with regard to the rest points.
A sampling algorithm, which iteratively samples the most distant point with regard to the rest points.
```python
import torch
......@@ -98,7 +98,7 @@ from torch_cluster import fps
x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
batch = torch.tensor([0, 0, 0, 0])
sample = fps(x, batch, ratio=0.5, random_start=False)
index = fps(x, batch, ratio=0.5, random_start=False)
```
```
......@@ -108,7 +108,7 @@ tensor([0, 3])
## kNN-Graph
Computes graph edges to the nearest *k* points in metric space.
Computes graph edges to the nearest *k* points.
```python
import torch
......@@ -127,7 +127,7 @@ tensor([[0, 0, 1, 1, 2, 2, 3, 3],
## Radius-Graph
Computes graph edges to all points within a given distance in metric space.
Computes graph edges to all points within a given distance.
```python
import torch
......@@ -146,17 +146,17 @@ tensor([[0, 0, 1, 1, 2, 2, 3, 3],
## Nearest
Clusters points which are nearest to a given query point in metric space.
Clusters points in *x* together which are nearest to a given query point in *y*.
```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])
query_x = torch.Tensor([[-1, 0], [1, 0]])
query_batch = torch.Tensor([0, 0])
cluster = nearest(x, query_x, batch_x, query_batch)
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)
```
```
......
torch_cluster/fps.py
View file @ 0a038334
......@@ -2,31 +2,36 @@ import torch
if torch.cuda.is_available():
import fps_cuda
""" """
def fps(x, batch=None, ratio=0.5, random_start=True):
"""Iteratively samples the most distant point (in metric distance) with
regard to the rest points.
r""""A sampling algorithm from the `"PointNet++: Deep Hierarchical Feature
Learning on Point Sets in a Metric Space"
<https://arxiv.org/abs/1706.02413>`_ paper, which iteratively samples the
most distant point with regard to the rest points.
Args:
x (Tensor): D-dimensional point features.
batch (LongTensor, optional): Vector that maps each point to its
example identifier. If :obj:`None`, all points belong to the same
example. If not :obj:`None`, points in the same example need to
have contiguous memory layout and :obj:`batch` needs to be
ascending. (default: :obj:`None`)
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
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`)
ratio (float, optional): Sampling ratio. (default: :obj:`0.5`)
random_start (bool, optional): Whether the starting node is
sampled randomly. (default: :obj:`True`)
random_start (bool, optional): If set to :obj:`False`, use the first
node in :math:`\mathbf{X}` as starting node. (default: obj:`True`)
:rtype: :class:`LongTensor`
Examples::
.. testsetup::
import torch
from torch_cluster import fps
.. testcode::
>>> x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
>>> batch = torch.tensor([0, 0, 0, 0])
>>> sample = fps(x, batch, ratio=0.5)
>>> index = fps(x, batch, ratio=0.5)
"""
if batch is None:
......
torch_cluster/knn.py
View file @ 0a038334
......@@ -6,22 +6,30 @@ if torch.cuda.is_available():
def knn(x, y, k, batch_x=None, batch_y=None):
"""Finds for each element in `y` the `k` nearest points in `x`.
r"""Finds for each element in :obj:`y` the :obj:`k` nearest points in
:obj:`x`.
Args:
x (Tensor): D-dimensional point features.
y (Tensor): D-dimensional point features.
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
y (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{M \times F}`.
k (int): The number of neighbors.
batch_x (LongTensor, optional): Vector that maps each point to its
example identifier. If :obj:`None`, all points belong to the same
example. If not :obj:`None`, points in the same example need to
have contiguous memory layout and :obj:`batch` needs to be
ascending. (default: :obj:`None`)
batch_y (LongTensor, optional): See `batch_x` (default: :obj:`None`)
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`)
:rtype: :class:`LongTensor`
Examples::
.. testsetup::
import torch
from torch_cluster import knn
.. testcode::
>>> x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
>>> batch_x = torch.tensor([0, 0, 0, 0])
......@@ -70,22 +78,26 @@ def knn(x, y, k, batch_x=None, batch_y=None):
def knn_graph(x, k, batch=None, loop=False):
"""Finds for each element in `x` the `k` nearest points.
r"""Computes graph edges to the nearest :obj:`k` points.
Args:
x (Tensor): D-dimensional point features.
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
k (int): The number of neighbors.
batch (LongTensor, optional): Vector that maps each point to its
example identifier. If :obj:`None`, all points belong to the same
example. If not :obj:`None`, points in the same example need to
have contiguous memory layout and :obj:`batch` needs to be
ascending. (default: :obj:`None`)
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`)
:rtype: :class:`LongTensor`
Examples::
.. testsetup::
import torch
from torch_cluster import knn_graph
.. testcode::
>>> x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
>>> batch = torch.tensor([0, 0, 0, 0])
......
torch_cluster/nearest.py
View file @ 0a038334
......@@ -6,24 +6,32 @@ if torch.cuda.is_available():
def nearest(x, y, batch_x=None, batch_y=None):
"""Finds for each element in `x` its nearest point in `y`.
"""Clusters points in :obj:`x` together which are nearest to a given query
point in :obj:`y`.
Args:
x (Tensor): D-dimensional point features.
y (Tensor): D-dimensional point features.
batch_x (LongTensor, optional): Vector that maps each point to its
example identifier. If :obj:`None`, all points belong to the same
example. If not :obj:`None`, points in the same example need to
have contiguous memory layout and :obj:`batch` needs to be
ascending. (default: :obj:`None`)
batch_y (LongTensor, optional): See `batch_x` (default: :obj:`None`)
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
y (Tensor): Node feature matrix
:math:`\mathbf{X} \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. (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`)
Examples::
.. testsetup::
import torch
from torch_cluster import nearest
.. testcode::
>>> x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
>>> batch_x = torch.Tensor([0, 0, 0, 0])
>>> batch_x = torch.tensor([0, 0, 0, 0])
>>> y = torch.Tensor([[-1, 0], [1, 0]])
>>> batch_x = torch.Tensor([0, 0])
>>> batch_x = torch.tensor([0, 0])
>>> cluster = nearest(x, y, batch_x, batch_y)
"""
......
torch_cluster/radius.py
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......@@ -6,24 +6,33 @@ if torch.cuda.is_available():
def radius(x, y, r, batch_x=None, batch_y=None, max_num_neighbors=32):
"""Finds for each element in `y` all points in `x` within distance `r`.
r"""Finds for each element in :obj:`y` all points in :obj:`x` within
distance :obj:`r`.
Args:
x (Tensor): D-dimensional point features.
y (Tensor): D-dimensional point features.
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
y (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{M \times F}`.
r (float): The radius.
batch_x (LongTensor, optional): Vector that maps each point to its
example identifier. If :obj:`None`, all points belong to the same
example. If not :obj:`None`, points in the same example need to
have contiguous memory layout and :obj:`batch` needs to be
ascending. (default: :obj:`None`)
batch_y (LongTensor, optional): See `batch_x` (default: :obj:`None`)
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 `y`. (default: :obj:`32`)
return for each element in :obj:`y`. (default: :obj:`32`)
:rtype: :class:`LongTensor`
Examples::
.. 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])
......@@ -63,24 +72,28 @@ def radius(x, y, r, batch_x=None, batch_y=None, max_num_neighbors=32):
def radius_graph(x, r, batch=None, loop=False, max_num_neighbors=32):
"""Finds for each element in `x` all points in `x` within distance `r`.
r"""Computes graph edges to all points within a given distance.
Args:
x (Tensor): D-dimensional point features.
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
r (float): The radius.
batch (LongTensor, optional): Vector that maps each point to its
example identifier. If :obj:`None`, all points belong to the same
example. If not :obj:`None`, points in the same example need to
have contiguous memory layout and :obj:`batch` needs to be
ascending. (default: :obj:`None`)
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 `y`. (default: :obj:`32`)
return for each element in :obj:`y`. (default: :obj:`32`)
:rtype: :class:`LongTensor`
Examples::
.. 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])
......
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