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

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

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

[docs]class GINConv(nn.Module): r"""Graph Isomorphism Network layer from paper `How Powerful are Graph Neural Networks? <>`__. .. math:: h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} + \mathrm{aggregate}\left(\left\{h_j^{l}, j\in\mathcal{N}(i) \right\}\right)\right) Parameters ---------- apply_func : callable activation function/layer or None If not None, apply this function to the updated node feature, the :math:`f_\Theta` in the formula. aggregator_type : str Aggregator type to use (``sum``, ``max`` or ``mean``). init_eps : float, optional Initial :math:`\epsilon` value, default: ``0``. learn_eps : bool, optional If True, :math:`\epsilon` will be a learnable parameter. """ def __init__(self, apply_func, aggregator_type, init_eps=0, learn_eps=False): super(GINConv, self).__init__() self.apply_func = apply_func if aggregator_type == 'sum': self._reducer = fn.sum elif aggregator_type == 'max': self._reducer = fn.max elif aggregator_type == 'mean': self._reducer = fn.mean else: raise KeyError('Aggregator type {} not recognized.'.format(aggregator_type)) # to specify whether eps is trainable or not. if learn_eps: self.eps = th.nn.Parameter(th.FloatTensor([init_eps])) else: self.register_buffer('eps', th.FloatTensor([init_eps]))
[docs] def forward(self, graph, feat): r"""Compute Graph Isomorphism Network 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})` and :math:`(N_{out}, D_{in})`. If ``apply_func`` is not None, :math:`D_{in}` should fit the input dimensionality requirement of ``apply_func``. Returns ------- torch.Tensor The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is the output dimensionality of ``apply_func``. If ``apply_func`` is None, :math:`D_{out}` should be the same as input dimensionality. """ graph = graph.local_var() feat_src, feat_dst = expand_as_pair(feat) graph.srcdata['h'] = feat_src graph.update_all(fn.copy_u('h', 'm'), self._reducer('m', 'neigh')) rst = (1 + self.eps) * feat_dst + graph.dstdata['neigh'] if self.apply_func is not None: rst = self.apply_func(rst) return rst