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import os
import random
from colbert.utils.parser import Arguments
from colbert.utils.runs import Run
from colbert.evaluation.loaders import load_colbert, load_qrels, load_queries, load_topK_pids
from colbert.ranking.reranking import rerank
from colbert.ranking.batch_reranking import batch_rerank
def main():
random.seed(12345)
parser = Arguments(description='Re-ranking over a ColBERT index')
parser.add_model_parameters()
parser.add_model_inference_parameters()
parser.add_reranking_input()
parser.add_index_use_input()
parser.add_argument('--step', dest='step', default=1, type=int)
parser.add_argument('--part-range', dest='part_range', default=None, type=str)
parser.add_argument('--log-scores', dest='log_scores', default=False, action='store_true')
parser.add_argument('--batch', dest='batch', default=False, action='store_true')
parser.add_argument('--depth', dest='depth', default=1000, type=int)
args = parser.parse()
if args.part_range:
part_offset, part_endpos = map(int, args.part_range.split('..'))
args.part_range = range(part_offset, part_endpos)
with Run.context():
args.colbert, args.checkpoint = load_colbert(args)
args.queries = load_queries(args.queries)
args.qrels = load_qrels(args.qrels)
args.topK_pids, args.qrels = load_topK_pids(args.topK, qrels=args.qrels)
args.index_path = os.path.join(args.index_root, args.index_name)
if args.batch:
batch_rerank(args)
else:
rerank(args)
if __name__ == "__main__":
main()
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