Error ‘object not callable’

[Abhishek-Sood]

Hi so i was using gnn explainer in this GNN based model using OPENPOM model and pytorch’s GNNExplainer algorithm. But i was encountering error while generating explanation using the below code. Any help would be beneficial.

!wget https://raw.githubusercontent.com/ARY2260/openpom/main/openpom/data/curated_datasets/curated_GS_LF_merged_4983.csv

input_file = 'curated_GS_LF_merged_4983.csv' # or new downloaded file path
featurizer = GraphFeaturizer()
smiles_field = 'nonStereoSMILES' # column that contains SMILES
loader = dc.data.CSVLoader(tasks=TASKS,
feature_field=smiles_field,
featurizer=featurizer)
dataset = loader.create_dataset(inputs=[input_file])
n_tasks = len(dataset.tasks)
len(dataset)

randomstratifiedsplitter = dc.splits.RandomStratifiedSplitter()
train_dataset, test_dataset, valid_dataset = randomstratifiedsplitter.train_valid_test_split(dataset, frac_train = 0.8, frac_valid = 0.1, frac_test = 0.1, seed = 1)

print("train_dataset: ", len(train_dataset))
print("valid_dataset: ", len(valid_dataset))
print("test_dataset: ", len(test_dataset))
train_ratios = get_class_imbalance_ratio(train_dataset)
assert len(train_ratios) == n_tasks
learning_rate = dc.models.optimizers.ExponentialDecay(initial_rate=0.001, decay_rate=0.5, decay_steps=32*20, staircase=True)
model = MPNNPOMModel(n_tasks = n_tasks,
                            batch_size=128,
                            learning_rate=learning_rate,
                            class_imbalance_ratio = train_ratios,
                            loss_aggr_type = 'sum',
                            node_out_feats = 100,
                            edge_hidden_feats = 75,
                            edge_out_feats = 100,
                            num_step_message_passing = 5,
                            mpnn_residual = True,
                            message_aggregator_type = 'sum',
                            mode = 'classification',
                            number_atom_features = GraphConvConstants.ATOM_FDIM,
                            number_bond_features = GraphConvConstants.BOND_FDIM,
                            n_classes = 1,
                            readout_type = 'set2set',
                            num_step_set2set = 3,
                            num_layer_set2set = 2,
                            ffn_hidden_list= [392, 392],
                            ffn_embeddings = 256,
                            ffn_activation = 'relu',
                            ffn_dropout_p = 0.12,
                            ffn_dropout_at_input_no_act = False,
                            weight_decay = 1e-5,
                            self_loop = False,
                            optimizer_name = 'adam',
                            log_frequency = 32,
                            model_dir = './examples/experiments',
                            device_name='cuda')

explainer = Explainer(
    model=model,
    algorithm=GNNExplainer(epochs=200),
    explanation_type='model',
    node_mask_type='attributes',
    edge_mask_type='object',
    model_config=dict(
        mode='multiclass_classification',
        task_level='graph',
        return_type='log_probs',
    ),
)

batch = torch.zeros(data.x.size(0), dtype=torch.long)

with torch.no_grad():
    output = model(data.x, data.edge_index, batch)
print(f"Model output shape: {output.shape}")
print("Forward pass successful!")
print("Running explainer...")
exp = explainer(
    x=data.x,
    edge_index=data.edge_index,
    target=label_id,
    batch=batch
)
print(f"Explanation for '{TASKS[label_id]}' odor:")
print(f"Node importance: max={exp.node_mask.max().item():.3f}, min={exp.node_mask.min().item():.3f}")
print(f"Edge importance: max={exp.edge_mask.max().item():.3f}, min={exp.edge_mask.min().item():.3f}")
   
    # Optionally, if you need to visualize the results
print("Explanation generated successfully!") 

Outcome : 'MPNNPOMModel' object is not callable

while the explainer uses mode='multiclass_classification' and is set as multiclass, and the problem that OPENPOM solves is multi label, i was handling it already from extracting 571 smile strings that have only single label as their discriptor, so it sorts of represent multi class so considering that the dataset is already converted to PyG format , please anyone help how to resolve this error.

And one of the faults that i found is that ‘model’ isn’t a proper pytorch “model” derived from Module and doesn’t have
a proper forward() method. So for that i did defined a custom wrapper with a forward defined but nothing to avail. Please any help would be beneficial Thanks.