Source code for torch_geometric.utils.to_dense_adj

import torch
from torch_scatter import scatter_add

[docs]def to_dense_adj(edge_index, batch=None, edge_attr=None): r"""Converts batched sparse adjacency matrices given by edge indices and edge attributes to a single dense batched adjacency matrix. Args: edge_index (LongTensor): The edge indices. batch (LongTensor, optional): Batch vector :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each node to a specific example. (default: :obj:`None`) edge_attr (Tensor, optional): Edge weights or multi-dimensional edge features. (default: :obj:`None`) :rtype: :class:`Tensor` """ if batch is None: batch = edge_index.new_zeros(edge_index.max().item() + 1) batch_size = batch[-1].item() + 1 one = batch.new_ones(batch.size(0)) num_nodes = scatter_add(one, batch, dim=0, dim_size=batch_size) cum_nodes =[batch.new_zeros(1), num_nodes.cumsum(dim=0)]) max_num_nodes = num_nodes.max().item() size = [batch_size, max_num_nodes, max_num_nodes] size = size if edge_attr is None else size + list(edge_attr.size())[1:] dtype = torch.float if edge_attr is None else edge_attr.dtype adj = torch.zeros(size, dtype=dtype, device=edge_index.device) edge_index_0 = batch[edge_index[0]].view(1, -1) edge_index_1 = edge_index[0] - cum_nodes[batch][edge_index[0]] edge_index_2 = edge_index[1] - cum_nodes[batch][edge_index[1]] if edge_attr is None: adj[edge_index_0, edge_index_1, edge_index_2] = 1 else: adj[edge_index_0, edge_index_1, edge_index_2] = edge_attr return adj