1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
|
Source: pytorch-cluster
Section: science
Priority: optional
Maintainer: Debian Deep Learning Team <debian-science-maintainers@lists.alioth.debian.org>
Uploaders:
Andrius Merkys <merkys@debian.org>,
Rules-Requires-Root: no
Build-Depends:
debhelper-compat (= 13),
dh-sequence-python3,
libtorch-dev,
pybind11-dev,
python3,
python3-scipy <!nocheck>,
python3-setuptools,
python3-torch,
Testsuite: autopkgtest-pkg-pybuild
Standards-Version: 4.7.1
Homepage: https://github.com/rusty1s/pytorch_cluster
Vcs-Browser: https://salsa.debian.org/deeplearning-team/pytorch-cluster
Vcs-Git: https://salsa.debian.org/deeplearning-team/pytorch-cluster.git
Package: python3-torch-cluster
Architecture: any
Multi-Arch: foreign
Depends:
python3-torch,
${misc:Depends},
${python3:Depends},
${shlibs:Depends},
Description: PyTorch extension library of optimized graph cluster algorithms (Python 3)
This package consists of a small extension library of highly optimized graph
cluster algorithms for the use in PyTorch. The package consists of the
following clustering algorithms:
.
* Graclus from Dhillon et al.: Weighted Graph Cuts without Eigenvectors: A
Multilevel Approach
* Voxel Grid Pooling from, e.g., Simonovsky and Komodakis: Dynamic
Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
* Iterative Farthest Point Sampling from, e.g. Qi et al.: PointNet++: Deep
Hierarchical Feature Learning on Point Sets in a Metric Space
* k-NN and Radius graph generation
* Clustering based on nearest points
* Random Walk Sampling from, e.g., Grover and Leskovec: node2vec: Scalable
Feature Learning for Networks
.
All included operations work on varying data types and are implemented both
for CPU and GPU.
.
This package installs the library for Python 3.
|
