conv.py

import torch
from torch.nn import Parameter
from torch_scatter import scatter_add
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import add_remaining_self_loops, remove_self_loops

from torch_geometric.nn.inits import glorot, zeros


class IConv(MessagePassing):
    """
    This `IConv` is modified from the `torch.geometric.nn.GCNConv`.
    The `bias` matrix is removed, and `weight` matrix is replaced by an untrainable Identity matrix directly.
    """
    def __init__(self, in_channels, out_channels, improved=False, cached=False,
                 normalize=True, **kwargs):
        super(IConv, self).__init__(aggr='add', **kwargs)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.improved = improved
        self.cached = cached
        self.normalize = normalize

        self.weight = Parameter(torch.eye(in_channels), requires_grad=False)

        self.register_parameter('bias', None)

        self.reset_parameters()

    def reset_parameters(self):
        self.cached_result = None
        self.cached_num_edges = None

    @staticmethod
    def norm(edge_index, num_nodes, edge_weight=None, improved=False,
             dtype=None):
        if edge_weight is None:
            edge_weight = torch.ones((edge_index.size(1), ), dtype=dtype,
                                     device=edge_index.device)

        fill_value = 1 if not improved else 2
        edge_index, edge_weight = add_remaining_self_loops(
            edge_index, edge_weight, fill_value, num_nodes)

        row, col = edge_index
        deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
        deg_inv_sqrt = deg.pow(-0.5)
        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0

        return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]

    def forward(self, x, edge_index, edge_weight=None):
        x = torch.matmul(x, self.weight)

        if self.cached and self.cached_result is not None:
            if edge_index.size(1) != self.cached_num_edges:
                raise RuntimeError(
                    'Cached {} number of edges, but found {}. Please '
                    'disable the caching behavior of this layer by removing '
                    'the `cached=True` argument in its constructor.'.format(
                        self.cached_num_edges, edge_index.size(1)))

        if not self.cached or self.cached_result is None:
            self.cached_num_edges = edge_index.size(1)
            if self.normalize:
                edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
                                             edge_weight, self.improved,
                                             x.dtype)
            else:
                norm = edge_weight
            self.cached_result = edge_index, norm

        edge_index, norm = self.cached_result

        return self.propagate(edge_index, x=x, norm=norm)

    def message(self, x_j, norm):
        return norm.view(-1, 1) * x_j if norm is not None else x_j

    def update(self, aggr_out):
        if self.bias is not None:
            aggr_out = aggr_out + self.bias
        return aggr_out

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