| import os | |
| import time | |
| import faiss | |
| import random | |
| import torch | |
| from colbert.utils.runs import Run | |
| from multiprocessing import Pool | |
| from colbert.modeling.inference import ModelInference | |
| from colbert.evaluation.ranking_logger import RankingLogger | |
| from colbert.utils.utils import print_message, batch | |
| from colbert.ranking.rankers import Ranker | |
| def rerank(args): | |
| inference = ModelInference(args.colbert, amp=args.amp) | |
| ranker = Ranker(args, inference, faiss_depth=None) | |
| ranking_logger = RankingLogger(Run.path, qrels=None) | |
| milliseconds = 0 | |
| with ranking_logger.context('ranking.tsv', also_save_annotations=False) as rlogger: | |
| queries = args.queries | |
| qids_in_order = list(queries.keys()) | |
| for qoffset, qbatch in batch(qids_in_order, 100, provide_offset=True): | |
| qbatch_text = [queries[qid] for qid in qbatch] | |
| qbatch_pids = [args.topK_pids[qid] for qid in qbatch] | |
| rankings = [] | |
| for query_idx, (q, pids) in enumerate(zip(qbatch_text, qbatch_pids)): | |
| torch.cuda.synchronize('cuda:0') | |
| s = time.time() | |
| Q = ranker.encode([q]) | |
| pids, scores = ranker.rank(Q, pids=pids) | |
| torch.cuda.synchronize() | |
| milliseconds += (time.time() - s) * 1000.0 | |
| if len(pids): | |
| print(qoffset+query_idx, q, len(scores), len(pids), scores[0], pids[0], | |
| milliseconds / (qoffset+query_idx+1), 'ms') | |
| rankings.append(zip(pids, scores)) | |
| for query_idx, (qid, ranking) in enumerate(zip(qbatch, rankings)): | |
| query_idx = qoffset + query_idx | |
| if query_idx % 100 == 0: | |
| print_message(f"#> Logging query #{query_idx} (qid {qid}) now...") | |
| ranking = [(score, pid, None) for pid, score in ranking] | |
| rlogger.log(qid, ranking, is_ranked=True) | |
| print('\n\n') | |
| print(ranking_logger.filename) | |
| print("#> Done.") | |
| print('\n\n') | |