Source code for torch_geometric.nn.conv.tag_conv

import torch
from torch import Tensor

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.inits import zeros
from torch_geometric.typing import Adj, OptTensor, SparseTensor
from torch_geometric.utils import spmm

[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{W}_{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, or :obj:`-1` to derive the size from the first input(s) to the forward method. 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`. Shapes: - **input:** node_features :math:`(|\mathcal{V}|, F_{in})`, edge_index :math:`(2, |\mathcal{E}|)`, edge_weights :math:`(|\mathcal{E}|)` *(optional)* - **output:** node features :math:`(|\mathcal{V}|, F_{out})` """ def __init__(self, in_channels: int, out_channels: int, K: int = 3, bias: bool = True, normalize: bool = True, **kwargs): kwargs.setdefault('aggr', 'add') super().__init__(**kwargs) self.in_channels = in_channels self.out_channels = out_channels self.K = K self.normalize = normalize self.lins = torch.nn.ModuleList([ Linear(in_channels, out_channels, bias=False) for _ in range(K + 1) ]) if bias: self.bias = torch.nn.Parameter(torch.empty(out_channels)) else: self.register_parameter('bias', None) self.reset_parameters()
[docs] def reset_parameters(self): super().reset_parameters() for lin in self.lins: lin.reset_parameters() zeros(self.bias)
[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, flow=self.flow, 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, flow=self.flow, dtype=x.dtype) out = self.lins[0](x) for lin in self.lins[1:]: # propagate_type: (x: Tensor, edge_weight: OptTensor) x = self.propagate(edge_index, x=x, edge_weight=edge_weight) out = out + lin.forward(x) if self.bias is not None: out = out + self.bias 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: Adj, x: Tensor) -> Tensor: return spmm(adj_t, x, reduce=self.aggr) def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels}, K={self.K})')