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

"""MXNet Module for Graph Isomorphism Network layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
import mxnet as mx
from mxnet.gluon import nn

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


[docs]class GINConv(nn.Block): r"""Graph Isomorphism layer from `How Powerful are Graph Neural Networks? <https://arxiv.org/pdf/1810.00826.pdf>`__ .. 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. Default: ``False``. Example ------- >>> import dgl >>> import numpy as np >>> import mxnet as mx >>> from mxnet import gluon >>> from dgl.nn import GINConv >>> >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> feat = mx.nd.ones((6, 10)) >>> lin = gluon.nn.Dense(10) >>> lin.initialize(ctx=mx.cpu(0)) >>> conv = GINConv(lin, 'max') >>> conv.initialize(ctx=mx.cpu(0)) >>> res = conv(g, feat) >>> res [[ 0.44832918 -0.05283341 0.20823681 0.16020004 0.37311912 -0.03372726 -0.05716725 -0.20730163 0.14121324 0.46083626] [ 0.44832918 -0.05283341 0.20823681 0.16020004 0.37311912 -0.03372726 -0.05716725 -0.20730163 0.14121324 0.46083626] [ 0.44832918 -0.05283341 0.20823681 0.16020004 0.37311912 -0.03372726 -0.05716725 -0.20730163 0.14121324 0.46083626] [ 0.44832918 -0.05283341 0.20823681 0.16020004 0.37311912 -0.03372726 -0.05716725 -0.20730163 0.14121324 0.46083626] [ 0.44832918 -0.05283341 0.20823681 0.16020004 0.37311912 -0.03372726 -0.05716725 -0.20730163 0.14121324 0.46083626] [ 0.22416459 -0.0264167 0.10411841 0.08010002 0.18655956 -0.01686363 -0.02858362 -0.10365082 0.07060662 0.23041813]] <NDArray 6x10 @cpu(0)> """ def __init__( self, apply_func, aggregator_type, init_eps=0, learn_eps=False ): super(GINConv, self).__init__() 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) ) with self.name_scope(): self.apply_func = apply_func self.eps = self.params.get( "eps", shape=(1,), grad_req="write" if learn_eps else "null", init=mx.init.Constant(init_eps), )
[docs] def forward(self, graph, feat): r""" Description ----------- Compute Graph Isomorphism Network layer. Parameters ---------- graph : DGLGraph The graph. feat : mxnet.NDArray or a pair of mxnet.NDArray If a mxnet.NDArray 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 mxnet.NDArray 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 ------- mxnet.NDArray 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. """ with graph.local_scope(): feat_src, feat_dst = expand_as_pair(feat, graph) graph.srcdata["h"] = feat_src graph.update_all(fn.copy_u("h", "m"), self._reducer("m", "neigh")) rst = ( 1 + self.eps.data(feat_dst.context) ) * feat_dst + graph.dstdata["neigh"] if self.apply_func is not None: rst = self.apply_func(rst) return rst