e60669c5aa467d5c11a508c01b37c4ed8e352fa6,torch_geometric/nn/conv/supergat_conv.py,SuperGATConv,forward,#SuperGATConv#Any#Any#Any#Any#,159

Before Change

            if neg_edge_index is None:
                neg_edge_index = self.negative_sampling(edge_index, N, batch)

            if self.edge_sample_ratio < 1.0:
                pos_edge_index = self.sample_pos_edges(edge_index)
            else:
                pos_edge_index = edge_index

            att_with_neg_edges = self._get_att_logits_with_neg_edges(
                x=x,
                pos_edge_index=pos_edge_index,
                neg_edge_index=neg_edge_index,
            )  // [E + neg_E, heads]

            // X, Y for the self-supervised task
            self.att_with_neg_edges = att_with_neg_edges
            num_neg_edges = att_with_neg_edges.size(0)
            att_label = torch.zeros(num_neg_edges).float().to(x.device)
            att_label[:pos_edge_index.size(1)] = 1.
            self.att_label = att_label

        if self.concat is True:

After Change

            if neg_edge_index is None:
                neg_edge_index = self.negative_sampling(edge_index, N, batch)

            neg_att = self.get_attention(
                edge_index_i=neg_edge_index[1],
                x_i=x[neg_edge_index[1]],
                x_j=x[neg_edge_index[0]],
                num_nodes=x.size(0),
                return_logits=True,
            )

            self.att_x = torch.cat([pos_att, neg_att], dim=0)
            self.att_y = self.att_x.new_zeros(self.att_x.size(0))
            self.att_y[:pos_edge_index.size(1)] = 1.

Instances