ModuleNotFoundError: No module named 'torch_scatter.utils.gen'

[twythebest]

how to solve that ModuleNotFoundError: No module named 'torch_scatter.utils.gen'

[rusty1s]

torch_scatter.utils.gen does not exist. Which codebase tries to import it?

[twythebest]

like this file:

import torch from torch.nn import Parameter as Param from torch_geometric.nn.conv import MessagePassing import torch_scatter.utils.gen as gen import init import math

class BRGCNRelation(MessagePassing):

def __init__(self, in_channels, out_channels, num_relations, num_bases, num_nodes, num_edges,
             root_weight=True, bias=False, **kwargs):
    super(BRGCNRelation, self).__init__(aggr='add', **kwargs)

    self.in_channels = in_channels
    self.out_channels = out_channels
    self.num_relations = num_relations
    self.num_bases = num_bases
    self.num_nodes = num_nodes

    self.basis = Param(torch.Tensor(num_bases, in_channels, out_channels))
    self.att = Param(torch.Tensor(num_relations, num_bases))
    self.basis_0 = Param(torch.Tensor(num_relations, in_channels, out_channels))
    self.relation_weight = Param(torch.Tensor(num_relations, 1))

    if root_weight:
        self.root = Param(torch.Tensor(in_channels, out_channels))
    else:
        self.register_parameter('root', None)

    if bias:
        self.bias = Param(torch.Tensor(out_channels))
    else:
        self.register_parameter('bias', None)

    self.reset_parameters()

    def maybe_dim_size_custom(index, dim_size=None):
        if dim_size is not None:
            return dim_size
        return self.num_nodes if index.numel() > 0 else 0

    gen.maybe_dim_size = maybe_dim_size_custom

def reset_parameters(self):
    if self.num_bases == 0:
        size = self.in_channels
    else:
        size = self.num_bases * self.in_channels

    init.glorot(self.basis)
    init.glorot(self.att)
    init.glorot(self.root)
    init.glorot(self.bias)
    init.glorot(self.basis_0)
    init.glorot(self.relation_weight)

def forward(self, x, edge_index, edge_type, edge_norm=None, size=None):

    final_node_embeddings = torch.zeros(self.num_nodes, self.out_channels)
    k = math.floor(self.num_relations * 0.40)
    for relation in range(0, k):
        idx = (edge_type == relation).nonzero()
        idx = idx.view(-1)
        self.idx = idx
        edge_index_subset = edge_index[:, idx]
        edge_type_subset = edge_type[idx].long()

        aggr_for_edge_type = self.propagate(edge_index_subset, size=size, x=x, edge_type=edge_type_subset,
                                            edge_norm=edge_norm)

        rel_out = aggr_for_edge_type * self.relation_weight[relation]

        final_node_embeddings += rel_out

    return final_node_embeddings

def message(self, x_j, edge_index_j, edge_type, edge_norm):
    if self.num_bases > 0:
        w = torch.matmul(self.att, self.basis.view(self.num_bases, -1))
    else:
        w = self.basis_0

    if x_j is None:
        w = w.view(-1, self.out_channels)
        index = edge_type * self.in_channels + edge_index_j
        out = torch.index_select(w, 0, index)
    else:
        w = w.view(self.num_relations, self.in_channels, self.out_channels)
        w = torch.index_select(w, 0, edge_type)
        out_pre = x_j.unsqueeze(1)
        out_before = torch.bmm(x_j.unsqueeze(1), w)
        out = torch.bmm(x_j.unsqueeze(1), w).squeeze(-2)

    return out if edge_norm is None else out * edge_norm.view(-1, 1)

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

    if self.bias is not None:
        out = out + self.bias
    else:
        out = aggr_out
    return out

def __repr__(self):
    return '{}({}, {}, num_relations={})'.format(
        self.__class__.__name__, self.in_channels, self.out_channels,
        self.num_relations)

[rusty1s]

You should be able to simply remove the import and the gen.maybe_dim_size assignment.