401e194d78492271e2eb66ad8e16930302dcbe53,torch_geometric/nn/conv/gmm_conv.py,GMMConv,forward,#GMMConv#Any#Any#Any#,81

Before Change

        out = self.propagate(edge_index, x=x, pseudo=pseudo)

        out = self.lin(out)
        if self.root is not None:
            out = out + torch.matmul(x, self.root)
        return out

After Change

        x = x.unsqueeze(-1) if x.dim() == 1 else x
        pseudo = pseudo.unsqueeze(-1) if pseudo.dim() == 1 else pseudo

        out = self.lin(x).view(-1, self.kernel_size, self.out_channels)
        out = self.propagate(edge_index, x=out, pseudo=pseudo)

        if self.root is not None:
            out = out + torch.matmul(x, self.root)

        if self.bias is not None:
            out = out + self.bias

        return out

    def message(self, x_j, pseudo):
        (E, D), K = pseudo.size(), self.mu.size(0)

Instances