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""" Description ----------- 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) where :math:`e_{ij}` is the edge feature, :math:`f_\Theta` is a function with learnable parameters. Parameters ---------- in_feats : int Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`. NNConv can be applied on homogeneous graph and unidirectional `bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__. 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; i.e., the number of dimensions of :math:`h_i^{(l+1)}`. 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``. Examples -------- >>> import dgl >>> import numpy as np >>> import torch as th >>> from dgl.nn import NNConv >>> # Case 1: Homogeneous graph >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> g = dgl.add_self_loop(g) >>> feat = th.ones(6, 10) >>> lin = th.nn.Linear(5, 20) >>> def edge_func(efeat): ... return lin(efeat) >>> efeat = th.ones(6+6, 5) >>> conv = NNConv(10, 2, edge_func, 'mean') >>> res = conv(g, feat, efeat) >>> res tensor([[-1.5243, -0.2719], [-1.5243, -0.2719], [-1.5243, -0.2719], [-1.5243, -0.2719], [-1.5243, -0.2719], [-1.5243, -0.2719]], grad_fn=<AddBackward0>) >>> # Case 2: Unidirectional bipartite graph >>> u = [0, 1, 0, 0, 1] >>> v = [0, 1, 2, 3, 2] >>> g = dgl.bipartite((u, v)) >>> u_feat = th.tensor(np.random.rand(2, 10).astype(np.float32)) >>> v_feat = th.tensor(np.random.rand(4, 10).astype(np.float32)) >>> conv = NNConv(10, 2, edge_func, 'mean') >>> efeat = th.ones(5, 5) >>> res = conv(g, (u_feat, v_feat), efeat) >>> res tensor([[-0.6568, 0.5042], [ 0.9089, -0.5352], [ 0.1261, -0.0155], [-0.6568, 0.5042]], grad_fn=<AddBackward0>) """ def __init__(self, in_feats, out_feats, edge_func, aggregator_type='mean', 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_func = 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): r""" Description ----------- Reinitialize learnable parameters. Note ---- The model parameters are initialized using Glorot uniform initialization and the bias is initialized to be zero. """ 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:`(E, *)`, which should fit the input shape requirement of ``edge_func``. :math:`E` is the number of edges of the graph. 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, graph) # (n, d_in, 1) graph.srcdata['h'] = feat_src.unsqueeze(-1) # (n, d_in, d_out) graph.edata['w'] = self.edge_func(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