""" This script provides the functionality to generate metadata.npz files necessary for load_balancing the DataLoader. """ import argparse import multiprocessing as mp import os import warnings import numpy as np from tqdm import tqdm from ocpmodels.datasets import SinglePointLmdbDataset, TrajectoryLmdbDataset def get_data(index): data = dataset[index] natoms = data.natoms neighbors = None if hasattr(data, "edge_index"): neighbors = data.edge_index.shape[1] return index, natoms, neighbors def main(args): path = args.data_path global dataset if os.path.isdir(path): dataset = TrajectoryLmdbDataset({"src": path}) outpath = os.path.join(path, "metadata.npz") elif os.path.isfile(path): dataset = SinglePointLmdbDataset({"src": path}) outpath = os.path.join(os.path.dirname(path), "metadata.npz") indices = range(len(dataset)) pool = mp.Pool(args.num_workers) outputs = list(tqdm(pool.imap(get_data, indices), total=len(indices))) indices = [] natoms = [] neighbors = [] for i in outputs: indices.append(i[0]) natoms.append(i[1]) neighbors.append(i[2]) _sort = np.argsort(indices) sorted_natoms = np.array(natoms, dtype=np.int32)[_sort] if None in neighbors: warnings.warn( f"edge_index information not found, {outpath} only supports atom-wise load balancing." ) np.savez(outpath, natoms=sorted_natoms) else: sorted_neighbors = np.array(neighbors, dtype=np.int32)[_sort] np.savez(outpath, natoms=sorted_natoms, neighbors=sorted_neighbors) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--data-path", required=True, type=str, help="Path to S2EF directory or IS2R* .lmdb file", ) parser.add_argument( "--num-workers", default=1, type=int, help="Num of workers to parallelize across", ) args = parser.parse_args() main(args)