from typing import Optional
import torch
from torch import Tensor
from torch_scatter import scatter
from torch_geometric.typing import OptTensor
[docs]def to_dense_adj(
edge_index: Tensor,
batch: OptTensor = None,
edge_attr: OptTensor = None,
max_num_nodes: Optional[int] = None,
) -> Tensor:
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`)
max_num_nodes (int, optional): The size of the output node dimension.
(default: :obj:`None`)
:rtype: :class:`Tensor`
Examples:
>>> edge_index = torch.tensor([[0, 0, 1, 2, 3],
... [0, 1, 0, 3, 0]])
>>> batch = torch.tensor([0, 0, 1, 1])
>>> to_dense_adj(edge_index, batch)
tensor([[[1., 1.],
[1., 0.]],
[[0., 1.],
[1., 0.]]])
>>> to_dense_adj(edge_index, batch, max_num_nodes=4)
tensor([[[1., 1., 0., 0.],
[1., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 1., 0., 0.],
[1., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]])
>>> edge_attr = torch.Tensor([1, 2, 3, 4, 5])
>>> to_dense_adj(edge_index, batch, edge_attr)
tensor([[[1., 2.],
[3., 0.]],
[[0., 4.],
[5., 0.]]])
"""
if batch is None:
num_nodes = int(edge_index.max()) + 1 if edge_index.numel() > 0 else 0
batch = edge_index.new_zeros(num_nodes)
batch_size = int(batch.max()) + 1 if batch.numel() > 0 else 1
one = batch.new_ones(batch.size(0))
num_nodes = scatter(one, batch, dim=0, dim_size=batch_size, reduce='add')
cum_nodes = torch.cat([batch.new_zeros(1), num_nodes.cumsum(dim=0)])
idx0 = batch[edge_index[0]]
idx1 = edge_index[0] - cum_nodes[batch][edge_index[0]]
idx2 = edge_index[1] - cum_nodes[batch][edge_index[1]]
if max_num_nodes is None:
max_num_nodes = num_nodes.max().item()
elif ((idx1.numel() > 0 and idx1.max() >= max_num_nodes)
or (idx2.numel() > 0 and idx2.max() >= max_num_nodes)):
mask = (idx1 < max_num_nodes) & (idx2 < max_num_nodes)
idx0 = idx0[mask]
idx1 = idx1[mask]
idx2 = idx2[mask]
edge_attr = None if edge_attr is None else edge_attr[mask]
if edge_attr is None:
edge_attr = torch.ones(idx0.numel(), device=edge_index.device)
size = [batch_size, max_num_nodes, max_num_nodes]
size += list(edge_attr.size())[1:]
adj = torch.zeros(size, dtype=edge_attr.dtype, device=edge_index.device)
flattened_size = batch_size * max_num_nodes * max_num_nodes
adj = adj.view([flattened_size] + list(adj.size())[3:])
idx = idx0 * max_num_nodes * max_num_nodes + idx1 * max_num_nodes + idx2
scatter(edge_attr, idx, dim=0, out=adj, reduce='add')
adj = adj.view(size)
return adj