'GlobalStorage' object has no attribute ...

[thistlillo]

Hello, I followed the examples given here for creating mini-batches of bipartite graphs.

But I get this error:
'GlobalStorage' object has no attribute 'left_x'

Is there anyone who can explain what I am doing wrong?
Using the packages:

torch==2.5.0
torch-cluster==1.6.3+pt25cu124
torch-geometric==2.6.1
torch-scatter==2.1.2+pt25cu124
torch-sparse==0.6.18+pt25cu124
torch-spline-conv==1.2.2+pt25cu124
torchaudio==2.5.0
torchvision==0.20.0

on Linux.

This is a MWE for reproducing the error:

import random
import torch
from torch_geometric.data import Data, Batch

class BipartiteData(Data):
    def __init__(self, left_x, right_x, edge_index, **kwargs):
        super().__init__()
        self.left_x = left_x      
        self.right_x = right_x    
        self.edge_index = edge_index
        self.num_nodes = self.left_x.size(0) + self.right_x.size(0)
        for key, val in kwargs.items():
            setattr(self, key, val)
    
    def __inc__(self, key, value, *args, **kwargs):
        if key == 'edge_index':
            return torch.tensor([[self.left_x.size(0)], [self.right_x.size(0)]])
        return super().__inc__(key, value, *args, **kwargs)
    #<

    # @property
    # def num_nodes(self):
    #     return self.left_x.size(0) + self.right_x.size(0)
    # #<
#<

def generate_bipartite_example(n_left, n_right):
    edges = []
    edge_features = []
    for l in range(n_left):
        num_connections = random.randint(1, n_right)
        connected_right = random.sample(range(n_right), num_connections)
        for r in connected_right:
            edges.append([l, r])  # constraint → variable
            edge_features.append(1)
    edge_index = torch.tensor(edges, dtype=torch.long).t().contiguous()  # [2, num_edges]
    edge_features = torch.tensor(edge_features, dtype=torch.float).view(-1, 1)  # [num_edges, 1]

    # Feature tensors
    left_features = torch.randn(n_left, 6)
    right_features = torch.randn(n_right, 3)

    return BipartiteData(
        left_x=left_features,
        right_x=right_features,
        edge_x=edge_features,
        edge_index=edge_index,
        n_left=n_left,
        n_right=n_right
    )
#<


def generate_graphs(n_graphs, max_left, max_right):
    graphs = [
        generate_bipartite_example(
            n_left=random.randint(3, max_left),
            n_right=random.randint(3, max_right)
        ) for _ in range(n_graphs)
    ]
    return graphs
#<

graphs = generate_graphs(8, 10, 20)
print(type(graphs))
batch = Batch.from_data_list(graphs)
print(batch)

I get the same error also with this code:

...
bs = 2
loader = DataLoader(graphs, batch_size=bs)
batch = next(iter(loader))
print(batch)

in place of batch = Batch.from_data_list

[thistlillo]

The problem was this line:

self.num_nodes = self.left_x.size(0) + self.right_x.size(0)

Removing that, I get the warning:

UserWarning: Unable to accurately infer 'num_nodes' from the attribute set ...

So, now I have another question: how can I remove that warning? Using the commented @property method does not help.

If I write in the constructor:

self.num_nodes = self.left_x.size(0) + self.right_x.size(0)

then I get another error:

'NoneType' object has no attribute 'size'