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 (PAMI 2007)
• Voxel Grid Pooling from, e.g., Simonovsky and Komodakis: Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)
• Iterative Farthest Point Sampling from, e.g. Qi et al.: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (NIPS 2017)
• k-NN and Radius graph generation
• Clustering based on Nearest points

All included operations work on varying data types and are implemented both for CPU and GPU.

Ensure that at least PyTorch 1.1.0 is installed and verify that cuda/bin and cuda/include are in your $PATH and $CPATH respectively, e.g.:

$ python -c "import torch; print(torch.__version__)"
>>> 1.1.0

$ echo $PATH
>>> /usr/local/cuda/bin:...

$ echo $CPATH
>>> /usr/local/cuda/include:...

Then run:

pip install torch-cluster

If you are running into any installation problems, please create an issue. Be sure to import torch first before using this package to resolve symbols the dynamic linker must see.

A greedy clustering algorithm of picking an unmarked vertex and matching it with one its unmarked neighbors (that maximizes its edge weight). The GPU algorithm is adapted from Fagginger Auer and Bisseling: A GPU Algorithm for Greedy Graph Matching (LNCS 2012)

import torch
from torch_cluster import graclus_cluster

row = torch.tensor([0, 1, 1, 2])
col = torch.tensor([1, 0, 2, 1])
weight = torch.Tensor([1, 1, 1, 1])  # Optional edge weights.

cluster = graclus_cluster(row, col, weight)

print(cluster)
tensor([0, 0, 1])

A clustering algorithm, which overlays a regular grid of user-defined size over a point cloud and clusters all points within a voxel.

import torch
from torch_cluster import grid_cluster

pos = torch.Tensor([[0, 0], [11, 9], [2, 8], [2, 2], [8, 3]])
size = torch.Tensor([5, 5])

cluster = grid_cluster(pos, size)

print(cluster)
tensor([0, 5, 3, 0, 1])

A sampling algorithm, which iteratively samples the most distant point with regard to the rest points.

import torch
from torch_cluster import fps

x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
batch = torch.tensor([0, 0, 0, 0])
index = fps(x, batch, ratio=0.5, random_start=False)

print(sample)
tensor([0, 3])

Computes graph edges to the nearest k points.

import torch
from torch_cluster import knn_graph

x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
batch = torch.tensor([0, 0, 0, 0])
edge_index = knn_graph(x, k=2, batch=batch, loop=False)

print(edge_index)
tensor([[1, 2, 0, 3, 0, 3, 1, 2],
        [0, 0, 1, 1, 2, 2, 3, 3]])

Computes graph edges to all points within a given distance.

import torch
from torch_cluster import radius_graph

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)

print(edge_index)
tensor([[1, 2, 0, 3, 0, 3, 1, 2],
        [0, 0, 1, 1, 2, 2, 3, 3]])

Clusters points in x together which are nearest to a given query point in y.

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)

print(cluster)
tensor([0, 0, 1, 1])

python setup.py test