af61e2fbb45c53df3ff2a91f81a2026a8f66d90d,python/dgl/nn/mxnet/conv/sageconv.py,SAGEConv,__init__,#SAGEConv#Any#Any#Any#Any#Any#Any#Any#,50

Before Change

                 activation=None):
        super(SAGEConv, self).__init__()

        if isinstance(in_feats, tuple):
            self._in_src_feats = in_feats[0]
            self._in_dst_feats = in_feats[1]
        elif isinstance(in_feats, Integral):
            self._in_src_feats = self._in_dst_feats = in_feats
        else:
            raise TypeError("in_feats must be either int or pair of ints")

        self._out_feats = out_feats
        self._aggre_type = aggregator_type
        with self.name_scope():
            self.norm = norm
            self.feat_drop = nn.Dropout(feat_drop)
            self.activation = activation
            if aggregator_type == "pool":
                self.fc_pool = nn.Dense(self._in_src_feats, use_bias=bias,
                                        weight_initializer=mx.init.Xavier(magnitude=math.sqrt(2.0)),
                                        in_units=self._in_src_feats)
            if aggregator_type == "lstm":
                raise NotImplementedError
            if aggregator_type != "gcn":
                self.fc_self = nn.Dense(out_feats, use_bias=bias,
                                        weight_initializer=mx.init.Xavier(magnitude=math.sqrt(2.0)),
                                        in_units=self._in_dst_feats)
            self.fc_neigh = nn.Dense(out_feats, use_bias=bias,
                                     weight_initializer=mx.init.Xavier(magnitude=math.sqrt(2.0)),
                                     in_units=self._in_src_feats)

After Change

                 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
        with self.name_scope():
            self.norm = norm

Instances