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G2Retrieval_bce.py

@@ -51,7 +51,7 @@ else:
 
 
 @torch.no_grad()
-def getTopK(corpusEmbeds, qEmbeds, qid, k=10):
+def getTopK(corpusEmbeds, qEmbeds, qid, k=200):
     scores = qEmbeds @ corpusEmbeds
     top_k_indices = torch.argsort(scores, descending=True)[:k]
     scores = scores.cpu()
@@ -62,24 +62,29 @@ def getTopK(corpusEmbeds, qEmbeds, qid, k=10):
         retn.append((qid, corpus['cid'][x], float(scores[x])))
     return retn
 
-
-with torch.no_grad():
-    results = []
-    for i in tqdm(range(len(queries)), desc="Converting"):
-        results.extend(getTopK(corpusEmbeds, queriesEmbeds[i], queries['qid'][i]))
-
-results = pd.DataFrame(results, columns=['qid', 'cid', 'score'])
-results['score'] = results['score'].astype(float)
-tmp = ndcg_in_all(qrels, results)
-ndcgs = torch.tensor([x for x in tmp.values()], device=device)
-
-mean = torch.mean(ndcgs)
-std = torch.std(ndcgs)
-
-print(f'NDCG@10: {mean*100:.2f}±{std*100:.2f}')
-
-# 手动释放CUDA缓存内存
-del queriesEmbeds
-del corpusEmbeds
-del model
-torch.cuda.empty_cache()
+def print_ndcgs(k):
+    with torch.no_grad():
+        results = []
+        for i in tqdm(range(len(queries)), desc="Converting"):
+            results.extend(getTopK(corpusEmbeds, queriesEmbeds[i], queries['qid'][i], k=k))
+
+    results = pd.DataFrame(results, columns=['qid', 'cid', 'score'])
+    results['score'] = results['score'].astype(float)
+    tmp = ndcg_in_all(qrels, results)
+    ndcgs = torch.tensor([x for x in tmp.values()], device=device)
+
+    mean = torch.mean(ndcgs)
+    std = torch.std(ndcgs)
+
+    print(f'NDCG@{k}: {mean*100:.2f}±{std*100:.2f}')
+
+print_ndcgs(3)
+print_ndcgs(10)
+print_ndcgs(50)
+print_ndcgs(100)
+print_ndcgs(200)
+# # 手动释放CUDA缓存内存
+# del queriesEmbeds
+# del corpusEmbeds
+# del model
+# torch.cuda.empty_cache()

test_pytrec_eval.py

@@ -39,7 +39,7 @@ def ndcg_in_all(qrels, results):
     retn = {}
     _qrels = {qid: group for qid, group in qrels.groupby('qid')}
     _results = {qid: group for qid, group in results.groupby('qid')}
-    for qid in tqdm(_qrels, desc="计算 ndcg 中..."):
+    for qid in tqdm(_results, desc="计算 ndcg 中..."):
         retn[qid] = ndcg(_qrels[qid], _results[qid])
     return retn