4e43734dd0b7f1c026069af64151a8f52f41060d,torch_geometric/nn/conv/gat_conv.py,GATConv,forward,#GATConv#Any#Any#,86

Before Change

        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))

        x = torch.mm(x, self.weight).view(-1, self.heads, self.out_channels)
        return self.propagate(edge_index, x=x, num_nodes=x.size(0))

    def message(self, edge_index_i, x_i, x_j, num_nodes):
        // Compute attention coefficients.
        alpha = (torch.cat([x_i, x_j], dim=-1) * self.att).sum(dim=-1)

After Change

        \right)\right)}
        {\sum_{k \in \mathcal{N}(i) \cup \{ i \}}
        \exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top}
        [\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_k]
        \right)\right)}.

    Args:
        in_channels (int): Size of each input sample.
        out_channels (int): Size of each output sample.
        heads (int, optional): Number of multi-head-attentions.
            (default: :obj:`1`)
        concat (bool, optional): If set to :obj:`False`, the multi-head
            attentions are averaged instead of concatenated.

Instances