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.

import torch
from torch_geometric.nn import knn_graph

x = torch.tensor([[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, 1.0]])
batch = torch.tensor([0, 0, 0, 0])
edge_index = knn_graph(x, k=2, batch=batch, loop=False)
Parameters:

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

  • k (int) – The number of neighbors.

  • batch (torch.Tensor, optional) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N\), which assigns each node to a specific example. (default: None)

  • loop (bool, optional) – If True, the graph will contain self-loops. (default: False)

  • flow (str, optional) – The flow direction when using in combination with message passing ("source_to_target" or "target_to_source"). (default: "source_to_target")

  • cosine (bool, optional) – If True, will use the cosine distance instead of euclidean distance to find nearest neighbors. (default: False)

  • num_workers (int, optional) – 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:

torch.Tensor