INNER CODE UNIT · Python

train_val_pipeline

graphdeeplearning/benchmarking-gnns · main_GraphTheoryProp_multitask.py:88

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
    
    root_log_dir, root_ckpt_dir, write_file_name, write_config_file = dirs
    device = net_params['device']
    
    # Write the network and optimization hyper-parameters in folder config/
    with open(write_config_file + '.txt', 'w') as f:
        f.write("""Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n\nTotal Parameters: {}\n\n"""                .format(DATASET_NAME, MODEL_NAME, params, net_params, net_params['total_param']))

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