Source code for torch_geometric.nn.models.label_prop

from typing import Callable, Optional
from torch_geometric.typing import Adj, OptTensor

import torch
from torch import Tensor
import torch.nn.functional as F
from torch_sparse import SparseTensor, matmul
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm

[docs]class LabelPropagation(MessagePassing): r"""The label propagation operator from the `"Learning from Labeled and Unlabeled Datawith Label Propagation" <>`_ paper .. math:: \mathbf{Y}^{\prime} = \alpha \cdot \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2} \mathbf{Y} + (1 - \alpha) \mathbf{Y}, where unlabeled data is inferred by labeled data via propagation. Args: num_layers (int): The number of propagations. alpha (float): The :math:`\alpha` coefficient. """ def __init__(self, num_layers: int, alpha: float): super(LabelPropagation, self).__init__(aggr='add') self.num_layers = num_layers self.alpha = alpha
[docs] @torch.no_grad() def forward( self, y: Tensor, edge_index: Adj, mask: Optional[Tensor] = None, edge_weight: OptTensor = None, post_step: Callable = lambda y: y.clamp_(0., 1.) ) -> Tensor: """""" if y.dtype == torch.long: y = F.one_hot(y.view(-1)).to(torch.float) out = y if mask is not None: out = torch.zeros_like(y) out[mask] = y[mask] if isinstance(edge_index, SparseTensor) and not edge_index.has_value(): edge_index = gcn_norm(edge_index, add_self_loops=False) elif isinstance(edge_index, Tensor) and edge_weight is None: edge_index, edge_weight = gcn_norm(edge_index, num_nodes=y.size(0), add_self_loops=False) res = (1 - self.alpha) * out for _ in range(self.num_layers): # propagate_type: (y: Tensor, edge_weight: OptTensor) out = self.propagate(edge_index, x=out, edge_weight=edge_weight, size=None) out.mul_(self.alpha).add_(res) out = post_step(out) return out
def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j def message_and_aggregate(self, adj_t: SparseTensor, x: Tensor) -> Tensor: return matmul(adj_t, x, reduce=self.aggr) def __repr__(self): return '{}(num_layers={}, alpha={})'.format(self.__class__.__name__, self.num_layers, self.alpha)