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
• Random Walk Sampling from, e.g., Grover and Leskovec: node2vec: Scalable Feature Learning for Networks (KDD 2016)

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

We provide pip wheels for all major OS/PyTorch/CUDA combinations, see here.

To install the binaries for PyTorch 1.7.0, simply run

pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html

where ${CUDA} should be replaced by either cpu, cu92, cu101, cu102, or cu110 depending on your PyTorch installation.

To install the binaries for PyTorch 1.6.0, simply run

pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.6.0+${CUDA}.html

where ${CUDA} should be replaced by either cpu, cu92, cu101 or cu102 depending on your PyTorch installation.

Note: Binaries of older versions are also provided for PyTorch 1.4.0 and PyTorch 1.5.0 (following the same procedure).

Ensure that at least PyTorch 1.4.0 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__)"
>>> 1.4.0

$ 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

When running in a docker container without NVIDIA driver, PyTorch needs to evaluate the compute capabilities and may fail. In this case, ensure that the compute capabilities are set via TORCH_CUDA_ARCH_LIST, e.g.:

export TORCH_CUDA_ARCH_LIST = "6.0 6.1 7.2+PTX 7.5+PTX"

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(index)
tensor([0, 3])

Computes graph edges to the nearest k points.

Args:

• x (Tensor): Node feature matrix of shape [N, F].
• r (float): The radius.
• batch (LongTensor, optional): Batch vector of shape [N], which assigns each node to a specific example. batch needs to be sorted. (default: None)
• loop (bool, optional): If True, the graph will contain self-loops. (default: False)
• flow (string, optional): The flow direction when using in combination with message passing ("source_to_target" or "target_to_source"). (default: "source_to_target")
• cosine (boolean, optional): If True, will use the Cosine distance instead of Euclidean distance to find nearest neighbors. (default: False)
• num_workers (int): Number of workers to use for computation. Has no effect in case batch is not None, or the input lies on the GPU. (default: 1)

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.

Args:

• x (Tensor): Node feature matrix of shape [N, F].
• r (float): The radius.
• batch (LongTensor, optional): Batch vector of shape [N], which assigns each node to a specific example. batch needs to be sorted. (default: None)
• loop (bool, optional): If True, the graph will contain self-loops. (default: False)
• max_num_neighbors (int, optional): The maximum number of neighbors to return for each element. (default: 32)
• flow (string, optional): The flow direction when using in combination with message passing ("source_to_target" or "target_to_source"). (default: "source_to_target")
• num_workers (int): Number of workers to use for computation. Has no effect in case batch is not None, or the input lies on the GPU. (default: 1)

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])

Samples random walks of length walk_length from all node indices in start in the graph given by (row, col).

import torch
from torch_cluster import random_walk

row = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 4, 4])
col = torch.tensor([1, 0, 2, 3, 1, 4, 1, 4, 2, 3])
start = torch.tensor([0, 1, 2, 3, 4])

walk = random_walk(row, col, start, walk_length=3)

print(walk)
tensor([[0, 1, 2, 4],
        [1, 3, 4, 2],
        [2, 4, 2, 1],
        [3, 4, 2, 4],
        [4, 3, 1, 0]])

python setup.py test

torch-cluster also offers a C++ API that contains C++ equivalent of python models.

mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install