9f4bcf25821b115fbd25e31165b91bdd6f22dcbe,torch_geometric/nn/conv/gravnet_conv.py,GravNetConv,aggregate,#GravNetConv#Any#Any#Any#Any#,69

Before Change

    def aggregate(self, inputs, index, ptr=None, dim_size=None):
        
        //this is probably not needed
        if ptr is not None:
            raise ValueError("`GravNetConv` does not support `ptr` in aggregate.")
        return torch.cat([scatter(inputs, index, dim=self.node_dim, dim_size=dim_size,reduce="mean"),
                       scatter(inputs, index, dim=self.node_dim, dim_size=dim_size,reduce="max")], dim=1)
     
    def message(self, x_i, x_j, edge_weights):

After Change

        return x_j * edge_weight.unsqueeze(1)

    def aggregate(self, inputs, index, ptr=None, dim_size=None):
        if ptr is not None:
            for _ in range(self.node_dim):
                ptr = ptr.unsqueeze(0)
            aggr_mean = segment_csr(inputs, ptr, reduce="mean")
            aggr_max = segment_csr(inputs, ptr, reduce="max")
        else:
            aggr_mean = scatter(inputs, index, dim=self.node_dim,
                                dim_size=dim_size, reduce="mean")
            aggr_max = scatter(inputs, index, dim=self.node_dim,
                               dim_size=dim_size, reduce="max")

        return torch.cat([aggr_mean, aggr_max], dim=-1)

    def __repr__(self):
        return "{}({}, {}, k={})".format(self.__class__.__name__,

Instances