sageinitconv.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.inits import glorot, zeros
from torch_geometric.utils import add_remaining_self_loops

from graphgym.register import register_layer


class SAGEConvLayer(MessagePassing):
    r"""The GraphSAGE operator from the `"Inductive Representation Learning on
    Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper

    .. math::
        \mathbf{\hat{x}}_i &= \mathbf{\Theta} \cdot
        \mathrm{mean}_{j \in \mathcal{N(i) \cup \{ i \}}}(\mathbf{x}_j)

        \mathbf{x}^{\prime}_i &= \frac{\mathbf{\hat{x}}_i}
        {\| \mathbf{\hat{x}}_i \|_2}.

    Args:
        in_channels (int): Size of each input sample.
        out_channels (int): Size of each output sample.
        normalize (bool, optional): If set to :obj:`True`, output features
            will be :math:`\ell_2`-normalized. (default: :obj:`False`)
        concat (bool, optional): If set to :obj:`True`, will concatenate
            current node features with aggregated ones. (default: :obj:`False`)
        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`.
    """

    def __init__(self, in_channels, out_channels, normalize=False,
                 concat=False, bias=True, **kwargs):
        super(SAGEConvLayer, self).__init__(aggr='mean', **kwargs)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.normalize = normalize
        self.concat = concat

        in_channels = 2 * in_channels if concat else in_channels
        self.weight = Parameter(torch.Tensor(in_channels, out_channels))

        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)

        self.reset_parameters()

    def reset_parameters(self):
        # original initialization
        # uniform(self.weight.size(0), self.weight)
        # uniform(self.weight.size(0), self.bias)
        # change to new initialization
        glorot(self.weight)
        zeros(self.bias)

    def forward(self, x, edge_index, edge_weight=None, size=None,
                res_n_id=None):
        """
        Args:
            res_n_id (Tensor, optional): Residual node indices coming from
                :obj:`DataFlow` generated by :obj:`NeighborSampler` are used to
                select central node features in :obj:`x`.
                Required if operating in a bipartite graph and :obj:`concat` is
                :obj:`True`. (default: :obj:`None`)
        """
        if not self.concat and torch.is_tensor(x):
            edge_index, edge_weight = add_remaining_self_loops(
                edge_index, edge_weight, 1, x.size(self.node_dim))

        return self.propagate(edge_index, size=size, x=x,
                              edge_weight=edge_weight, res_n_id=res_n_id)

    def message(self, x_j, edge_weight):
        return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j

    def update(self, aggr_out, x, res_n_id):
        if self.concat and torch.is_tensor(x):
            aggr_out = torch.cat([x, aggr_out], dim=-1)
        elif self.concat and (isinstance(x, tuple) or isinstance(x, list)):
            assert res_n_id is not None
            aggr_out = torch.cat([x[0][res_n_id], aggr_out], dim=-1)

        aggr_out = torch.matmul(aggr_out, self.weight)

        if self.bias is not None:
            aggr_out = aggr_out + self.bias

        if self.normalize:
            aggr_out = F.normalize(aggr_out, p=2, dim=-1)

        return aggr_out

    def __repr__(self):
        return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
                                   self.out_channels)


class SAGEinitConv(nn.Module):
    def __init__(self, dim_in, dim_out, bias=False, **kwargs):
        super(SAGEinitConv, self).__init__()
        self.model = SAGEConvLayer(dim_in, dim_out, bias=bias, concat=True)

    def forward(self, batch):
        batch.node_feature = self.model(batch.node_feature, batch.edge_index)
        return batch


register_layer('sageinitconv', SAGEinitConv)