Delete testing.py

testing.py

@@ -1,80 +0,0 @@
-from SRL_model import SRL_BERT_model
-from collections import Counter
-import torch
-
-def bio_to_spans(tags):
-    spans = []
-    i = 0
-    while i < len(tags):
-        t = tags[i]
-        if t == "O" or t.endswith("-V"):
-            i += 1; continue
-        if t.startswith("B-"):
-            role = t[2:]; j = i + 1
-            while j < len(tags) and tags[j] == f"I-{role}":
-                j += 1
-            spans.append((role, i, j-1))
-            i = j
-        else:
-            i += 1
-    return spans
-
-@torch.no_grad()
-def eval_span_f1(model, dataloader, id2label, device="cuda"):
-    model.eval()
-    tp = fp = fn = 0
-    for batch in dataloader:
-        gold = batch["labels"]               # [B, Lw]
-        mask = (gold != -100)
-
-        batch = {k:(v.to(device) if torch.is_tensor(v) else v) for k,v in batch.items()}
-        logits, _ = model(**batch)
-        pred = logits.argmax(-1).cpu()       # [B, Lw]
-        print(pred)
-        for g_seq, p_seq, m in zip(gold, pred, mask):
-            gl = [id2label[int(i)] for i in g_seq[m].tolist()]
-            pl = [id2label[int(i)] for i in p_seq[m].tolist()]
-            G = Counter(bio_to_spans(gl))
-            P = Counter(bio_to_spans(pl))
-            # micro counts
-            common = G & P
-            tp += sum(common.values())
-            fp += sum(P.values()) - sum(common.values())
-            fn += sum(G.values()) - sum(common.values())
-
-    prec = tp / (tp + fp + 1e-12)
-    rec  = tp / (tp + fn + 1e-12)
-    f1   = 2 * prec * rec / (prec + rec + 1e-12)
-    return prec, rec, f1
-
-
-if __name__ =="__main__": 
-    
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    ckpt_path = "/blue/bonniejdorr/youms/SRL-Aware_Model/model/best_srl_Sep_29.ckpt"   # <-- change if needed
-    ckpt = torch.load(ckpt_path, map_location=device)
-    hp = ckpt["hparams"]
-
-    model = SRL_BERT_model.PredicateAwareSRL(**hp).to(device)
-    model.load_state_dict(ckpt["state_dict"])
-    model.eval()
-
-    label2id = ckpt["label2id"]
-    id2label = {v: k for k, v in label2id.items()}
-
-    h = ckpt.get("hparams", {
-        "bert_name": "bert-base-cased",
-        "num_labels": len(label2id),
-        "use_indicator": True,
-        "use_distance": True,
-        "indicator_dim": 10,
-        "lstm_hidden": 768,
-        "mlp_hidden": 300,
-        "pos_dim": 50,
-        "max_distance": 128,
-        "dropout": 0.1,
-    })
-
-    #test_loader from SRL_BERT_model
-    prec, rec, span_f1 = eval_span_f1(model, test_loader, id2label, device=device)
-    print(f"[TEST-SPAN] P={prec:.3f} R={rec:.3f} F1={span_f1:.3f}")