| import os
|
| import ujson
|
| import random
|
|
|
| from colbert.utils.runs import Run
|
| from colbert.utils.parser import Arguments
|
| import colbert.utils.distributed as distributed
|
|
|
| from colbert.utils.utils import print_message, create_directory
|
| from colbert.indexing.encoder import CollectionEncoder
|
|
|
|
|
| def main():
|
| random.seed(12345)
|
|
|
| parser = Arguments(description='Precomputing document representations with ColBERT.')
|
|
|
| parser.add_model_parameters()
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| parser.add_model_inference_parameters()
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| parser.add_indexing_input()
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|
|
| parser.add_argument('--chunksize', dest='chunksize', default=6.0, required=False, type=float)
|
|
|
| args = parser.parse()
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|
|
| with Run.context():
|
| args.index_path = os.path.join(args.index_root, args.index_name)
|
| assert not os.path.exists(args.index_path), args.index_path
|
|
|
| distributed.barrier(args.rank)
|
|
|
| if args.rank < 1:
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| create_directory(args.index_root)
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| create_directory(args.index_path)
|
|
|
| distributed.barrier(args.rank)
|
|
|
| process_idx = max(0, args.rank)
|
| encoder = CollectionEncoder(args, process_idx=process_idx, num_processes=args.nranks)
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| encoder.encode()
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|
|
| distributed.barrier(args.rank)
|
|
|
|
|
| if args.rank < 1:
|
| metadata_path = os.path.join(args.index_path, 'metadata.json')
|
| print_message("Saving (the following) metadata to", metadata_path, "..")
|
| print(args.input_arguments)
|
|
|
| with open(metadata_path, 'w') as output_metadata:
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| ujson.dump(args.input_arguments.__dict__, output_metadata)
|
|
|
| distributed.barrier(args.rank)
|
|
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
|
|
|
|
