Source code for graphwar.nn.layers.gcn_conv

import torch
from torch import nn
from torch import Tensor
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
from torch_sparse import SparseTensor

from graphwar import is_edge_index
from graphwar.functional import spmm


def dense_gcn_norm(adj: Tensor, improved: bool = False,
                   add_self_loops: bool = True, rate: float = -0.5):
    fill_value = 2. if improved else 1.
    if add_self_loops:
        adj = adj + torch.diag(adj.new_full((adj.size(0),), fill_value))
    deg = adj.sum(dim=1)
    deg_inv_sqrt = deg.pow_(rate)
    deg_inv_sqrt.masked_fill_(deg_inv_sqrt == float('inf'), 0.)
    norm_src = deg_inv_sqrt.view(1, -1)
    norm_dst = deg_inv_sqrt.view(-1, 1)
    adj = norm_src * adj * norm_dst
    return adj


[docs]class GCNConv(nn.Module): r"""The graph convolutional operator from the `"Semi-supervised Classification with Graph Convolutional Networks" <https://arxiv.org/abs/1609.02907>`_ paper (ICLR'17) Parameters ---------- in_channels : int dimensions of int samples out_channels : int dimensions of output samples improved : bool, optional whether the layer computes :math:`\mathbf{\hat{A}}` as :math:`\mathbf{A} + 2\mathbf{I}`, by default False cached : bool, optional (*UNUSED*) whether the layer will cache the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2}` on first execution, and will use the cached version for further executions, by default False add_self_loops : bool, optional whether to add self-loops to the input graph, by default True normalize : bool, optional whether to compute symmetric normalization coefficients on the fly, by default True bias : bool, optional whether to use bias in the layers, by default True Note ---- Different from that in :class:`torch_geometric`, for the inputs :obj:`x`, :obj:`edge_index`, and :obj:`edge_weight`, our implementation supports: * :obj:`edge_index` is :class:`torch.FloatTensor`: dense adjacency matrix with shape :obj:`[N, N]` * :obj:`edge_index` is :class:`torch.LongTensor`: edge indices with shape :obj:`[2, M]` * :obj:`edge_index` is :class:`torch_sparse.SparseTensor`: sparse matrix with sparse shape :obj:`[N, N]` In addition, the argument :obj:`cached` is unused. We add this argument to be compatible with :class:`torch_geometric`. See also -------- :class:`graphwar.nn.models.GCN` """ def __init__(self, in_channels: int, out_channels: int, improved: bool = False, cached: bool = False, add_self_loops: bool = True, normalize: bool = True, bias: bool = True): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.improved = improved self.cached = cached # NOTE: unused now self.add_self_loops = add_self_loops self.normalize = normalize self.lin = Linear(in_channels, out_channels, bias=False, weight_initializer='glorot') if bias: self.bias = nn.Parameter(torch.Tensor(out_channels)) else: self.register_parameter('bias', None) self.reset_parameters()
[docs] def reset_parameters(self): self.lin.reset_parameters() zeros(self.bias)
[docs] def forward(self, x: Tensor, edge_index: Adj, edge_weight: OptTensor = None) -> Tensor: x = self.lin(x) is_edge_like = is_edge_index(edge_index) if self.normalize: if is_edge_like: edge_index, edge_weight = gcn_norm(edge_index, edge_weight, x.size(0), self.improved, self.add_self_loops, dtype=x.dtype) elif isinstance(edge_index, SparseTensor): edge_index = gcn_norm(edge_index, x.size(0), improved=self.improved, add_self_loops=self.add_self_loops, dtype=x.dtype) else: # N by N dense adjacency matrix edge_index = dense_gcn_norm(edge_index, improved=self.improved, add_self_loops=self.add_self_loops) if is_edge_like: out = spmm(x, edge_index, edge_weight) else: out = edge_index @ x if self.bias is not None: out += self.bias return out
def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels})')