Source code for torch_geometric.nn.conv.tag_conv

from torch_geometric.typing import Adj, OptTensor

import torch
from torch import Tensor
from torch.nn import Linear
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 TAGConv(MessagePassing): r"""The topology adaptive graph convolutional networks operator from the `"Topology Adaptive Graph Convolutional Networks" <>`_ paper .. math:: \mathbf{X}^{\prime} = \sum_{k=0}^K \left( \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2} \right)^k \mathbf{X} \mathbf{\Theta}_{k}, where :math:`\mathbf{A}` denotes the adjacency matrix and :math:`D_{ii} = \sum_{j=0} A_{ij}` its diagonal degree matrix. The adjacency matrix can include other values than :obj:`1` representing edge weights via the optional :obj:`edge_weight` tensor. Args: in_channels (int): Size of each input sample. out_channels (int): Size of each output sample. K (int, optional): Number of hops :math:`K`. (default: :obj:`3`) bias (bool, optional): If set to :obj:`False`, the layer will not learn an additive bias. (default: :obj:`True`) normalize (bool, optional): Whether to apply symmetric normalization. (default: :obj:`True`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. """ def __init__(self, in_channels: int, out_channels: int, K: int = 3, bias: bool = True, normalize: bool = True, **kwargs): kwargs.setdefault('aggr', 'add') super(TAGConv, self).__init__(**kwargs) self.in_channels = in_channels self.out_channels = out_channels self.K = K self.normalize = normalize self.lin = Linear(in_channels * (self.K + 1), out_channels, bias=bias) self.reset_parameters()
[docs] def reset_parameters(self): self.lin.reset_parameters()
[docs] def forward(self, x: Tensor, edge_index: Adj, edge_weight: OptTensor = None) -> Tensor: """""" if self.normalize: if isinstance(edge_index, Tensor): edge_index, edge_weight = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(self.node_dim), improved=False, add_self_loops=False, dtype=x.dtype) elif isinstance(edge_index, SparseTensor): edge_index = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(self.node_dim), add_self_loops=False, dtype=x.dtype) xs = [x] for k in range(self.K): # propagate_type: (x: Tensor, edge_weight: OptTensor) out = self.propagate(edge_index, x=xs[-1], edge_weight=edge_weight, size=None) xs.append(out) return self.lin(, dim=-1))
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 '{}({}, {}, K={})'.format(self.__class__.__name__, self.in_channels, self.out_channels, self.K)