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import os
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import copy
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import sys
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import importlib
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import argparse
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import pandas as pd
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from easydict import EasyDict as edict
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if __name__ == '__main__':
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dataset_utils = importlib.import_module(f'datasets.{sys.argv[1]}')
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parser = argparse.ArgumentParser()
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parser.add_argument('--output_dir', type=str, required=True,
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help='Directory to save the metadata')
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parser.add_argument('--filter_low_aesthetic_score', type=float, default=None,
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help='Filter objects with aesthetic score lower than this value')
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parser.add_argument('--instances', type=str, default=None,
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help='Instances to process')
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dataset_utils.add_args(parser)
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parser.add_argument('--rank', type=int, default=0)
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parser.add_argument('--world_size', type=int, default=1)
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opt = parser.parse_args(sys.argv[2:])
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opt = edict(vars(opt))
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os.makedirs(opt.output_dir, exist_ok=True)
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if not os.path.exists(os.path.join(opt.output_dir, 'metadata.csv')):
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raise ValueError('metadata.csv not found')
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metadata = pd.read_csv(os.path.join(opt.output_dir, 'metadata.csv'))
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if opt.instances is None:
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if opt.filter_low_aesthetic_score is not None:
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metadata = metadata[metadata['aesthetic_score'] >= opt.filter_low_aesthetic_score]
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if 'local_path' in metadata.columns:
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metadata = metadata[metadata['local_path'].isna()]
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else:
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if os.path.exists(opt.instances):
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with open(opt.instances, 'r') as f:
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instances = f.read().splitlines()
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else:
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instances = opt.instances.split(',')
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metadata = metadata[metadata['sha256'].isin(instances)]
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start = len(metadata) * opt.rank // opt.world_size
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end = len(metadata) * (opt.rank + 1) // opt.world_size
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metadata = metadata[start:end]
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print(f'Processing {len(metadata)} objects...')
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downloaded = dataset_utils.download(metadata, **opt)
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downloaded.to_csv(os.path.join(opt.output_dir, f'downloaded_{opt.rank}.csv'), index=False)
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