IRENE/utils/build_graphs.py

import sys
sys.path.append('/projects/bortoletto/icml2023_matteo/utils')
from dataset import TransitionDataset, TestTransitionDatasetSequence
import multiprocessing as mp 
import argparse
import pickle as pkl 
import os 

# Instantiate the parser
parser = argparse.ArgumentParser()
parser.add_argument('--cpus', type=int,
                    help='Number of processes')
parser.add_argument('--mode', type=str,
                    help='Train (train) or validation (val)')
args = parser.parse_args()

NUM_PROCESSES = args.cpus 
MODE = args.mode

def generate_files(idx):
    print('Generating idx', idx)
    if os.path.exists(PATH+str(idx)+'.pkl'):
        print('Index', idx, 'skipped.')
        return 
    if MODE == 'train' or MODE == 'val':
        states, actions, lens, n_nodes = dataset.__getitem__(idx)
        with open(PATH+str(idx)+'.pkl', 'wb') as f:
            pkl.dump([states, actions, lens, n_nodes], f)
    elif MODE == 'test':
        dem_expected_states, dem_expected_actions, dem_expected_lens, dem_expected_nodes, \
        dem_unexpected_states, dem_unexpected_actions, dem_unexpected_lens, dem_unexpected_nodes, \
        query_expected_frames, target_expected_actions, \
        query_unexpected_frames, target_unexpected_actions = dataset.__getitem__(idx)
        with open(PATH+str(idx)+'.pkl', 'wb') as f:
            pkl.dump([
                dem_expected_states, dem_expected_actions, dem_expected_lens, dem_expected_nodes, \
                dem_unexpected_states, dem_unexpected_actions, dem_unexpected_lens, dem_unexpected_nodes, \
                query_expected_frames, target_expected_actions, \
                query_unexpected_frames, target_unexpected_actions], f
            )
    else:
        raise ValueError('MODE can be only train, val or test.')
    print(PATH+str(idx)+'.pkl saved.')

if __name__ == "__main__":
    if MODE == 'train':
        print('TRAIN MODE')
        PATH = '/datasets/external/bib_train/graphs/all_tasks/train_dgl_hetero_nobound_4feats/'
        if not os.path.exists(PATH):
            os.makedirs(PATH) 
            print(PATH, 'directory created.')
        dataset = TransitionDataset(
            path='/datasets/external/bib_train/', 
            types=['instrumental_action', 'multi_agent', 'preference', 'single_object'], 
            mode="train", 
            max_len=30, 
            num_test=1, 
            num_trials=9,
            action_range=10, 
            process_data=0
        )
        pool = mp.Pool(processes=NUM_PROCESSES)
        print('Starting graph generation with', NUM_PROCESSES, 'processes...')
        pool.map(generate_files, [i for i in range(dataset.__len__())])
        pool.close()
    elif MODE == 'val':
        print('VALIDATION MODE')
        types = ['multi_agent', 'instrumental_action', 'preference', 'single_object']
        for t in range(len(types)):
            PATH = '/datasets/external/bib_train/graphs/all_tasks/val_dgl_hetero_nobound_4feats/'+types[t]+'/'
            if not os.path.exists(PATH):
                os.makedirs(PATH) 
                print(PATH, 'directory created.')
            dataset = TransitionDataset(
                path='/datasets/external/bib_train/', 
                types=[types[t]], 
                mode="val", 
                max_len=30, 
                num_test=1, 
                num_trials=9,
                action_range=10, 
                process_data=0
            )
            pool = mp.Pool(processes=NUM_PROCESSES)
            print('Starting', types[t], 'graph generation with', NUM_PROCESSES, 'processes...')
            pool.map(generate_files, [i for i in range(dataset.__len__())])
            pool.close()
    elif MODE == 'test':
        print('TEST MODE')
        types = [
            'preference', 'multi_agent', 'inaccessible_goal',
            'efficiency_irrational', 'efficiency_time','efficiency_path',
            'instrumental_no_barrier', 'instrumental_blocking_barrier', 'instrumental_inconsequential_barrier'
        ]
        for t in range(len(types)):
            PATH = '/datasets/external/bib_evaluation_1_1/graphs/all_tasks_dgl_hetero_nobound_4feats/'+types[t]+'/'
            if not os.path.exists(PATH):
                os.makedirs(PATH) 
                print(PATH, 'directory created.')
            dataset = TestTransitionDatasetSequence(
                path='/datasets/external/bib_evaluation_1_1/', 
                task_type=types[t], 
                mode="test", 
                num_test=1, 
                num_trials=9,
                action_range=10, 
                process_data=0,
                max_len=30
            )
            pool = mp.Pool(processes=NUM_PROCESSES)
            print('Starting', types[t], 'graph generation with', NUM_PROCESSES, 'processes...')
            pool.map(generate_files, [i for i in range(dataset.__len__())])
            pool.close()
    else:
        raise ValueError