Converting numpy adjacency matrix and node features into torch_geometric data object?

[monk1337]

I have adj matrix and features per node in NumPy format:

    num_of_nodes = 14
    num_of_feats  = 14
    output_dim      = 32
    
    
    adj              = np.random.randint(0,2,[num_of_nodes,num_of_nodes])
    node_feats       = np.random.uniform(-1,1,[num_of_nodes, num_of_feats])

I want to convert it into Torch geometric dataset format. What I have tried:

I am converting the data first into networkx format and then pyg format

from torch_geometric.utils.convert import from_networkx
import numpy as np
import networkx as nx



num_of_nodes = 14
num_of_feats = 14
output_dim   = 32


def numpy_to_graph(A, node_features=None):
    
    G = nx.from_numpy_array(A)
    if node_features != None:
        for n in G.nodes():
            for k,v in node_features.items():
                G.nodes[n][k] = v[n]
    return G


def get_data():
    
    adj         = np.random.randint(0,2,[num_of_nodes,num_of_nodes])
    feats       = np.random.uniform(-1,1,[num_of_nodes, num_of_feats])
    G           = numpy_to_graph(adj, node_features={'x': feats})
    pyg_graph   = from_networkx(G)

    return pyg_graph

is it correct? and is there any better way to do this?

[rusty1s]

There is no real reason to go through networkx first:

data = Data()
data.edge_index = torch.from_numpy(adj).nonzero().t()
data.x = torch.from_numpy(node_feats)