Source code for graphwar.nn.layers.dagnn_conv

import torch
from torch import nn
from torch import Tensor
from torch_sparse import SparseTensor, matmul

from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptTensor

from graphwar import is_edge_index
from graphwar.functional import spmm
from graphwar.nn.layers.gcn_conv import dense_gcn_norm


[docs]class DAGNNConv(nn.Module): r"""The DAGNN operator from the `"Towards Deeper Graph Neural Networks" <https://arxiv.org/abs/2007.09296>`_ paper (KDD'20) Parameters ---------- in_channels : int dimensions of input samples out_channels : int, optional dimensions of output samples, by default 1 K : int, optional the number of propagation steps, by default 1 add_self_loops : bool, optional whether to add self-loops to the input graph, by default True bias : bool, optional whether to use bias in the layers, by default True Note ---- * :obj:`out_channels` must be 1 for any cases 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]` See also -------- :class:`graphwar.nn.models.DAGNN` """ def __init__(self, in_channels: int, out_channels: int = 1, K: int = 1, add_self_loops: bool = True, bias: bool = True): super().__init__() assert out_channels == 1 self.in_channels = in_channels self.out_channels = out_channels self.K = K self.add_self_loops = add_self_loops self.lin = Linear(in_channels, out_channels, bias=bias, weight_initializer='glorot') self.reset_parameters()
[docs] def reset_parameters(self): self.lin.reset_parameters()
[docs] def forward(self, x: Tensor, edge_index: Adj, edge_weight: OptTensor = None) -> Tensor: is_edge_like = is_edge_index(edge_index) if is_edge_like: edge_index, edge_weight = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(0), False, self.add_self_loops, dtype=x.dtype) elif isinstance(edge_index, SparseTensor): edge_index = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(0), False, self.add_self_loops, dtype=x.dtype) else: # N by N dense adjacency matrix adj = dense_gcn_norm( edge_index, add_self_loops=self.add_self_loops) xs = [x] for _ in range(self.K): if is_edge_like: x = spmm(x, edge_index, edge_weight) else: x = adj @ x xs.append(x) H = torch.stack(xs, dim=1) S = self.lin(H).sigmoid() S = S.permute(0, 2, 1) out = torch.matmul(S, H).squeeze() return out
def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels}, K={self.K})')