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import argparse
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import os
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import time
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from dgl.data import QM9Dataset
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from dgl.dataloading import GraphDataLoader
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from rdkit import Chem
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from rdkit import RDLogger;
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from torch.utils.data import Dataset
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import torch.nn.functional as F
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from tqdm import tqdm
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import ast
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from QM9_dataset_class import PreprocessedQM9Dataset
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RDLogger.DisableLog('rdApp.*')
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import torch
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import torch.nn as nn
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import torch.optim as optim
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QM9_label_keys = ['mu','alpha','homo','lumo','gap','r2','zpve','U0','U','H','G','Cv']
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def prepare_main(label_keys=None, cutoff=5.0,save_path="dataset"):
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assert save_path !="","save_path shouldn't be empty"
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if label_keys is None:
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raise ValueError('label_keys cannot be None')
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for label_key in label_keys:
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if label_key not in QM9_label_keys:
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raise ValueError('label_key must be in QM9_label_keys,refer:https://docs.dgl.ai/en/0.8.x/generated/dgl.data.QM9Dataset.html')
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dataset = QM9Dataset(label_keys=label_keys, cutoff=5.0)
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dataset_processed = PreprocessedQM9Dataset(dataset)
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print("Store processed QM9 dataset:",save_path)
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dataset_processed.save_dataset("dataset")
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return dataset_processed
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def main():
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parser = argparse.ArgumentParser(description="Prepare QM9 dataset")
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parser.add_argument('--label_keys', nargs='+', help="label keys in QM9 dataset,like 'mu' 'gap'....")
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parser.add_argument('--cutoff', type=float, default=5.0, help="cutoff for atom number")
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parser.add_argument('--save_path', type=str, default="dataset", help="processed_dataset save path")
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args = parser.parse_args()
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prepare_main(label_keys=args.label_keys, cutoff=args.cutoff)
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if __name__ == '__main__':
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main() |