b48c943b9f6248dbdd42d0fce44658b937098372,torch_geometric/nn/functional/spline_gcn.py,,spline_gcn,#Any#Any#Any#Any#Any#Any#Any#,9

Before Change

    // Convolution via sparse row sum. Converts [|E| x M_out] feature matrix to
    // [n x M_out] feature matrix.
    size = torch.Size([adj.size(0), adj.size(1), output.size(1)])
    adj = torch.sparse.FloatTensor(indices, output, size)
    output = sum(adj, dim=1)

    // TODO: root node and weight mean
    // root_weight = weight[torch.arange(kernel_size[-1])]

After Change

    // Weighten root node features by multiplying with the meaned weights at the
    // origin.
    index = torch.arange(0, kernel_size[-1]).long()
    root_weight = weight[index].mean(0)
    output += torch.mm(features, root_weight)

    if bias is not None:
        output += bias

Instances