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

"""Torch Module for GraphSAGE layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
from torch import nn
from torch.nn import functional as F

from .... import function as fn
from ....utils import expand_as_pair, check_eq_shape


[docs]class SAGEConv(nn.Module): r"""GraphSAGE layer from paper `Inductive Representation Learning on Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__. .. math:: h_{\mathcal{N}(i)}^{(l+1)} & = \mathrm{aggregate} \left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right) h_{i}^{(l+1)} & = \sigma \left(W \cdot \mathrm{concat} (h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1} + b) \right) h_{i}^{(l+1)} & = \mathrm{norm}(h_{i}^{l}) Parameters ---------- in_feats : int, or pair of ints Input feature size. If the layer is to be applied on a unidirectional bipartite graph, ``in_feats`` specifies the input feature size on both the source and destination nodes. If a scalar is given, the source and destination node feature size would take the same value. If aggregator type is ``gcn``, the feature size of source and destination nodes are required to be the same. out_feats : int Output feature size. feat_drop : float Dropout rate on features, default: ``0``. aggregator_type : str Aggregator type to use (``mean``, ``gcn``, ``pool``, ``lstm``). bias : bool If True, adds a learnable bias to the output. Default: ``True``. norm : callable activation function/layer or None, optional If not None, applies normalization to the updated node features. activation : callable activation function/layer or None, optional If not None, applies an activation function to the updated node features. Default: ``None``. """ def __init__(self, in_feats, out_feats, aggregator_type, feat_drop=0., bias=True, norm=None, activation=None): super(SAGEConv, self).__init__() self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats) self._out_feats = out_feats self._aggre_type = aggregator_type self.norm = norm self.feat_drop = nn.Dropout(feat_drop) self.activation = activation # aggregator type: mean/pool/lstm/gcn if aggregator_type == 'pool': self.fc_pool = nn.Linear(self._in_src_feats, self._in_src_feats) if aggregator_type == 'lstm': self.lstm = nn.LSTM(self._in_src_feats, self._in_src_feats, batch_first=True) if aggregator_type != 'gcn': self.fc_self = nn.Linear(self._in_dst_feats, out_feats, bias=bias) self.fc_neigh = nn.Linear(self._in_src_feats, out_feats, bias=bias) self.reset_parameters() def reset_parameters(self): """Reinitialize learnable parameters.""" gain = nn.init.calculate_gain('relu') if self._aggre_type == 'pool': nn.init.xavier_uniform_(self.fc_pool.weight, gain=gain) if self._aggre_type == 'lstm': self.lstm.reset_parameters() if self._aggre_type != 'gcn': nn.init.xavier_uniform_(self.fc_self.weight, gain=gain) nn.init.xavier_uniform_(self.fc_neigh.weight, gain=gain) def _lstm_reducer(self, nodes): """LSTM reducer NOTE(zihao): lstm reducer with default schedule (degree bucketing) is slow, we could accelerate this with degree padding in the future. """ m = nodes.mailbox['m'] # (B, L, D) batch_size = m.shape[0] h = (m.new_zeros((1, batch_size, self._in_src_feats)), m.new_zeros((1, batch_size, self._in_src_feats))) _, (rst, _) = self.lstm(m, h) return {'neigh': rst.squeeze(0)}
[docs] def forward(self, graph, feat): r"""Compute GraphSAGE layer. Parameters ---------- graph : DGLGraph The graph. feat : torch.Tensor or pair of torch.Tensor If a torch.Tensor is given, the input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes. If a pair of torch.Tensor is given, the pair must contain two tensors of shape :math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`. Returns ------- torch.Tensor The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is size of output feature. """ graph = graph.local_var() if isinstance(feat, tuple): feat_src = self.feat_drop(feat[0]) feat_dst = self.feat_drop(feat[1]) else: feat_src = feat_dst = self.feat_drop(feat) h_self = feat_dst if self._aggre_type == 'mean': graph.srcdata['h'] = feat_src graph.update_all(fn.copy_src('h', 'm'), fn.mean('m', 'neigh')) h_neigh = graph.dstdata['neigh'] elif self._aggre_type == 'gcn': check_eq_shape(feat) graph.srcdata['h'] = feat_src graph.dstdata['h'] = feat_dst # same as above if homogeneous graph.update_all(fn.copy_src('h', 'm'), fn.sum('m', 'neigh')) # divide in_degrees degs = graph.in_degrees().to(feat_dst) h_neigh = (graph.dstdata['neigh'] + graph.dstdata['h']) / (degs.unsqueeze(-1) + 1) elif self._aggre_type == 'pool': graph.srcdata['h'] = F.relu(self.fc_pool(feat_src)) graph.update_all(fn.copy_src('h', 'm'), fn.max('m', 'neigh')) h_neigh = graph.dstdata['neigh'] elif self._aggre_type == 'lstm': graph.srcdata['h'] = feat_src graph.update_all(fn.copy_src('h', 'm'), self._lstm_reducer) h_neigh = graph.dstdata['neigh'] else: raise KeyError('Aggregator type {} not recognized.'.format(self._aggre_type)) # GraphSAGE GCN does not require fc_self. if self._aggre_type == 'gcn': rst = self.fc_neigh(h_neigh) else: rst = self.fc_self(h_self) + self.fc_neigh(h_neigh) # activation if self.activation is not None: rst = self.activation(rst) # normalization if self.norm is not None: rst = self.norm(rst) return rst