Source code for torch_geometric.nn.conv.mf_conv

from typing import Tuple, Union

import torch
from torch import Tensor
from torch.nn import ModuleList

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptPairTensor, Size, SparseTensor
from torch_geometric.utils import degree, spmm

[docs]class MFConv(MessagePassing): r"""The graph neural network operator from the `"Convolutional Networks on Graphs for Learning Molecular Fingerprints" <>`_ paper. .. math:: \mathbf{x}^{\prime}_i = \mathbf{W}^{(\deg(i))}_1 \mathbf{x}_i + \mathbf{W}^{(\deg(i))}_2 \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j which trains a distinct weight matrix for each possible vertex degree. 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. max_degree (int, optional): The maximum node degree to consider when updating weights (default: :obj:`10`) 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: - **inputs:** 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}|)` - **outputs:** 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, max_degree: int = 10, bias=True, **kwargs): kwargs.setdefault('aggr', 'add') super().__init__(**kwargs) self.in_channels = in_channels self.out_channels = out_channels self.max_degree = max_degree if isinstance(in_channels, int): in_channels = (in_channels, in_channels) self.lins_l = ModuleList([ Linear(in_channels[0], out_channels, bias=bias) for _ in range(max_degree + 1) ]) self.lins_r = ModuleList([ Linear(in_channels[1], out_channels, bias=False) for _ in range(max_degree + 1) ]) self.reset_parameters()
[docs] def reset_parameters(self): super().reset_parameters() for lin in self.lins_l: lin.reset_parameters() for lin in self.lins_r: lin.reset_parameters()
[docs] def forward( self, x: Union[Tensor, OptPairTensor], edge_index: Adj, size: Size = None, ) -> Tensor: if isinstance(x, Tensor): x = (x, x) x_r = x[1] deg = x[0] # Dummy. if isinstance(edge_index, SparseTensor): deg = elif isinstance(edge_index, Tensor): i = 1 if self.flow == 'source_to_target' else 0 N = x[0].size(self.node_dim) N = size[1] if size is not None else N N = x_r.size(self.node_dim) if x_r is not None else N deg = degree(edge_index[i], N, dtype=torch.long) deg.clamp_(max=self.max_degree) # propagate_type: (x: OptPairTensor) h = self.propagate(edge_index, x=x, size=size) out = h.new_empty(list(h.size())[:-1] + [self.out_channels]) for i, (lin_l, lin_r) in enumerate(zip(self.lins_l, self.lins_r)): idx = (deg == i).nonzero().view(-1) r = lin_l(h.index_select(self.node_dim, idx)) if x_r is not None: r = r + lin_r(x_r.index_select(self.node_dim, idx)) out.index_copy_(self.node_dim, idx, r) return out
def message(self, x_j: Tensor) -> Tensor: return x_j def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: if isinstance(adj_t, SparseTensor): adj_t = adj_t.set_value(None, layout=None) return spmm(adj_t, x[0], reduce=self.aggr)