topobench.transforms.data_manipulations.infere_knn_connectivity module#

InfereKNNConnectivity class definition.

class InfereKNNConnectivity(**kwargs)#

Bases: BaseTransform

Transform to infer point cloud connectivity.

The transform generates the k-nearest neighbor connectivity of the input point cloud.

Parameters:
**kwargsoptional

Parameters for the base transform.

__init__(**kwargs)#
forward(data)#

Apply the transform to the input data.

Parameters:
datatorch_geometric.data.Data

The input data.

Returns:
torch_geometric.data.Data

The transformed data.

knn_graph(x, k, batch=None, loop=False, flow='source_to_target', cosine=False, num_workers=1, batch_size=None)#

Computes graph edges to the nearest k points.

Parameters:

  • x (Tensor) – Node feature matrix \(\mathbf{X} \in \mathbb{R}^{N \times F}\).

  • k (int) – The number of neighbors.

  • batch (LongTensor, optional) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N\), 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 used 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)

  • batch_size (int, optional) – The number of examples \(B\). Automatically calculated if not given. (default: None)

Return type:

LongTensor

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)