Source code for dgl.nn.pytorch.conv.densegraphconv

"""Torch Module for DenseGraphConv"""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch as th
from torch import nn
from torch.nn import init

[docs]class DenseGraphConv(nn.Module): """Graph Convolutional Network layer where the graph structure is given by an adjacency matrix. We recommend user to use this module when applying graph convolution on dense graphs. Parameters ---------- in_feats : int Input feature size. out_feats : int Output feature size. norm : str, optional How to apply the normalizer. If is `'right'`, divide the aggregated messages by each node's in-degrees, which is equivalent to averaging the received messages. If is `'none'`, no normalization is applied. Default is `'both'`, where the :math:`c_{ij}` in the paper is applied. bias : bool If True, adds a learnable bias to the output. Default: ``True``. activation : callable activation function/layer or None, optional If not None, applies an activation function to the updated node features. Default: ``None``. See also -------- GraphConv """ def __init__(self, in_feats, out_feats, norm='both', bias=True, activation=None): super(DenseGraphConv, self).__init__() self._in_feats = in_feats self._out_feats = out_feats self._norm = norm self.weight = nn.Parameter(th.Tensor(in_feats, out_feats)) if bias: self.bias = nn.Parameter(th.Tensor(out_feats)) else: self.register_buffer('bias', None) self.reset_parameters() self._activation = activation def reset_parameters(self): """Reinitialize learnable parameters.""" init.xavier_uniform_(self.weight) if self.bias is not None: init.zeros_(self.bias)
[docs] def forward(self, adj, feat): r"""Compute (Dense) Graph Convolution layer. Parameters ---------- adj : torch.Tensor The adjacency matrix of the graph to apply Graph Convolution on, when applied to a unidirectional bipartite graph, ``adj`` should be of shape should be of shape :math:`(N_{out}, N_{in})`; when applied to a homo graph, ``adj`` should be of shape :math:`(N, N)`. In both cases, a row represents a destination node while a column represents a source node. feat : torch.Tensor The input feature. Returns ------- torch.Tensor The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is size of output feature. """ adj = adj.float().to(feat.device) src_degrees = adj.sum(dim=0).clamp(min=1) dst_degrees = adj.sum(dim=1).clamp(min=1) feat_src = feat if self._norm == 'both': norm_src = th.pow(src_degrees, -0.5) shp = norm_src.shape + (1,) * (feat.dim() - 1) norm_src = th.reshape(norm_src, shp).to(feat.device) feat_src = feat_src * norm_src if self._in_feats > self._out_feats: # mult W first to reduce the feature size for aggregation. feat_src = th.matmul(feat_src, self.weight) rst = adj @ feat_src else: # aggregate first then mult W rst = adj @ feat_src rst = th.matmul(rst, self.weight) if self._norm != 'none': if self._norm == 'both': norm_dst = th.pow(dst_degrees, -0.5) else: # right norm_dst = 1.0 / dst_degrees shp = norm_dst.shape + (1,) * (feat.dim() - 1) norm_dst = th.reshape(norm_dst, shp).to(feat.device) rst = rst * norm_dst if self.bias is not None: rst = rst + self.bias if self._activation is not None: rst = self._activation(rst) return rst