Source code for torch_geometric.nn.conv.le_conv

from typing import Tuple, Union

from torch import Tensor

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptTensor, PairTensor

[docs]class LEConv(MessagePassing): r"""The local extremum graph neural network operator from the `"ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations" <>`_ paper. :class:`LEConv` finds the importance of nodes with respect to their neighbors using the difference operator: .. math:: \mathbf{x}^{\prime}_i = \mathbf{x}_i \cdot \mathbf{\Theta}_1 + \sum_{j \in \mathcal{N}(i)} e_{j,i} \cdot (\mathbf{\Theta}_2 \mathbf{x}_i - \mathbf{\Theta}_3 \mathbf{x}_j) where :math:`e_{j,i}` denotes the edge weight from source node :obj:`j` to target node :obj:`i` (default: :obj:`1`) Args: in_channels (int or tuple): Size of each input sample, or :obj:`-1` to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities. out_channels (int): Size of each output sample. bias (bool, optional): If set to :obj:`False`, the layer will not learn an additive bias. (default: :obj:`True`). **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. Shapes: - **input:** node features :math:`(|\mathcal{V}|, F_{in})` or :math:`((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))` if bipartite, edge indices :math:`(2, |\mathcal{E}|)`, edge features :math:`(|\mathcal{E}|, D)` *(optional)* - **output:** node features :math:`(|\mathcal{V}|, F_{out})` or :math:`(|\mathcal{V}_t|, F_{out})` if bipartite """ def __init__(self, in_channels: Union[int, Tuple[int, int]], out_channels: int, bias: bool = True, **kwargs): kwargs.setdefault('aggr', 'add') super().__init__(**kwargs) self.in_channels = in_channels self.out_channels = out_channels if isinstance(in_channels, int): in_channels = (in_channels, in_channels) self.lin1 = Linear(in_channels[0], out_channels, bias=bias) self.lin2 = Linear(in_channels[1], out_channels, bias=False) self.lin3 = Linear(in_channels[1], out_channels, bias=bias) self.reset_parameters()
[docs] def reset_parameters(self): super().reset_parameters() self.lin1.reset_parameters() self.lin2.reset_parameters() self.lin3.reset_parameters()
[docs] def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj, edge_weight: OptTensor = None) -> Tensor: if isinstance(x, Tensor): x = (x, x) a = self.lin1(x[0]) b = self.lin2(x[1]) # propagate_type: (a: Tensor, b: Tensor, edge_weight: OptTensor) out = self.propagate(edge_index, a=a, b=b, edge_weight=edge_weight) return out + self.lin3(x[1])
def message(self, a_j: Tensor, b_i: Tensor, edge_weight: OptTensor) -> Tensor: out = a_j - b_i return out if edge_weight is None else out * edge_weight.view(-1, 1) def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels})')