Source code for torch_geometric.utils.dropout

from typing import Optional, Tuple

import torch
from torch import Tensor

from torch_geometric.typing import OptTensor

def filter_adj(row: Tensor, col: Tensor, edge_attr: OptTensor,
               mask: Tensor) -> Tuple[Tensor, Tensor, OptTensor]:
    return row[mask], col[mask], None if edge_attr is None else edge_attr[mask]

[docs]def dropout_adj( edge_index: Tensor, edge_attr: OptTensor = None, p: float = 0.5, force_undirected: bool = False, num_nodes: Optional[int] = None, training: bool = True, ) -> Tuple[Tensor, OptTensor]: 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(f'Dropout probability has to be between 0 and 1 ' f'(got {p}') if not training or p == 0.0: return edge_index, edge_attr row, col = edge_index mask = torch.rand(row.size(0), device=edge_index.device) >= p if force_undirected: mask[row > col] = False 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) else: edge_index = torch.stack([row, col], dim=0) return edge_index, edge_attr