Source code for torch_geometric.utils.dropout

import torch
from torch_sparse import coalesce

from .num_nodes import maybe_num_nodes

def filter_adj(row, col, edge_attr, mask):
    return row[mask], col[mask], None if edge_attr is None else edge_attr[mask]

[docs]def dropout_adj(edge_index, edge_attr=None, p=0.5, force_undirected=False, num_nodes=None, training=True): r"""Randomly drops edges from the adjacency matrix :obj:`(edge_index, edge_attr)` with probability :obj:`p` using samples from a Bernoulli distribution. Args: edge_index (LongTensor): The edge indices. edge_attr (Tensor, optional): Edge weights or multi-dimensional edge features. (default: :obj:`None`) p (float, optional): Dropout probability. (default: :obj:`0.5`) force_undirected (bool, optional): If set to :obj:`True`, will either drop or keep both edges of an undirected edge. (default: :obj:`False`) num_nodes (int, optional): The number of nodes, *i.e.* :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`) training (bool, optional): If set to :obj:`False`, this operation is a no-op. (default: :obj:`True`) """ if p < 0. or p > 1.: raise ValueError('Dropout probability has to be between 0 and 1, ' 'but got {}'.format(p)) if not training: return edge_index, edge_attr N = maybe_num_nodes(edge_index, num_nodes) row, col = edge_index if force_undirected: row, col, edge_attr = filter_adj(row, col, edge_attr, row < col) mask = edge_index.new_full((row.size(0), ), 1 - p, dtype=torch.float) mask = torch.bernoulli(mask).to(torch.bool) row, col, edge_attr = filter_adj(row, col, edge_attr, mask) if force_undirected: edge_index = torch.stack( [[row, col], dim=0),[col, row], dim=0)], dim=0) if edge_attr is not None: edge_attr =[edge_attr, edge_attr], dim=0) edge_index, edge_attr = coalesce(edge_index, edge_attr, N, N) else: edge_index = torch.stack([row, col], dim=0) return edge_index, edge_attr