INNER CODE UNIT · Python

train_val_pipeline

graphdeeplearning/benchmarking-gnns · main_CYCLES_graph_classification.py:92

def train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs):
    t0 = time.time()
    per_epoch_time = []
        
    DATASET_NAME = dataset.name
    
    if net_params['pos_enc']:
        print("[!] Adding graph positional encoding.")
        dataset._add_positional_encodings(net_params['pos_enc_dim'])
    
    trainset, valset, testset = dataset.train, dataset.val, dataset.test
    
    if net_params['num_train_data'] is not None:
        # net_params['num_train_data'] is the number of train samples to use
        from data.cycles import DGLFormDataset
        trainset = DGLFormDataset(trainset[:net_params['num_train_data']][0], trainset[:net_params['num_train_data']][1])
    
    root_log_dir, root_ckpt_dir, write_file_name, write_config_file = dirs

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