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

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

from .... import function as fn
from ..utils import Identity
from ....utils import expand_as_pair


[docs]class NNConv(nn.Module): r"""Graph Convolution layer introduced in `Neural Message Passing for Quantum Chemistry <https://arxiv.org/pdf/1704.01212.pdf>`__. .. math:: h_{i}^{l+1} = h_{i}^{l} + \mathrm{aggregate}\left(\left\{ f_\Theta (e_{ij}) \cdot h_j^{l}, j\in \mathcal{N}(i) \right\}\right) Parameters ---------- in_feats : int 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. out_feats : int Output feature size. edge_func : callable activation function/layer Maps each edge feature to a vector of shape ``(in_feats * out_feats)`` as weight to compute messages. Also is the :math:`f_\Theta` in the formula. aggregator_type : str Aggregator type to use (``sum``, ``mean`` or ``max``). residual : bool, optional If True, use residual connection. Default: ``False``. bias : bool, optional If True, adds a learnable bias to the output. Default: ``True``. """ def __init__(self, in_feats, out_feats, edge_func, aggregator_type, residual=False, bias=True): super(NNConv, self).__init__() self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats) self._out_feats = out_feats self.edge_nn = edge_func if aggregator_type == 'sum': self.reducer = fn.sum elif aggregator_type == 'mean': self.reducer = fn.mean elif aggregator_type == 'max': self.reducer = fn.max else: raise KeyError('Aggregator type {} not recognized: '.format(aggregator_type)) self._aggre_type = aggregator_type if residual: if self._in_dst_feats != out_feats: self.res_fc = nn.Linear(self._in_dst_feats, out_feats, bias=False) else: self.res_fc = Identity() else: self.register_buffer('res_fc', None) if bias: self.bias = nn.Parameter(th.Tensor(out_feats)) else: self.register_buffer('bias', None) self.reset_parameters() def reset_parameters(self): """Reinitialize learnable parameters.""" gain = init.calculate_gain('relu') if self.bias is not None: nn.init.zeros_(self.bias) if isinstance(self.res_fc, nn.Linear): nn.init.xavier_normal_(self.res_fc.weight, gain=gain)
[docs] def forward(self, graph, feat, efeat): r"""Compute MPNN Graph Convolution layer. Parameters ---------- graph : DGLGraph The graph. feat : torch.Tensor or pair of torch.Tensor The input feature of shape :math:`(N, D_{in})` where :math:`N` is the number of nodes of the graph and :math:`D_{in}` is the input feature size. efeat : torch.Tensor The edge feature of shape :math:`(N, *)`, should fit the input shape requirement of ``edge_nn``. Returns ------- torch.Tensor The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is the output feature size. """ with graph.local_scope(): feat_src, feat_dst = expand_as_pair(feat) # (n, d_in, 1) graph.srcdata['h'] = feat_src.unsqueeze(-1) # (n, d_in, d_out) graph.edata['w'] = self.edge_nn(efeat).view(-1, self._in_src_feats, self._out_feats) # (n, d_in, d_out) graph.update_all(fn.u_mul_e('h', 'w', 'm'), self.reducer('m', 'neigh')) rst = graph.dstdata['neigh'].sum(dim=1) # (n, d_out) # residual connection if self.res_fc is not None: rst = rst + self.res_fc(feat_dst) # bias if self.bias is not None: rst = rst + self.bias return rst