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alea/train.py

@@ -65,19 +65,24 @@ def load_into(model, ckpt_path):
     return ck.get("args", {})
 
 
-def question_losses(outputs, targets):
+def question_losses(outputs, targets, eps=0.0):
     losses = []
     for qres in outputs:
         for r in qres:
             q = targets[r["qid"]]
             if r["qtype"] == "choice":
                 qt = torch.tensor(q, device=r["logits"].device)
+                if eps > 0 and float(qt.max()) > 0.99:
+                    n = qt.numel()
+                    qt = (1 - eps) * qt + eps / n
                 losses.append(choice_loss(r["logits"][None], qt[None]))
             elif r["qtype"] == "score":
                 qt = torch.tensor(q, device=r["logits"].device)
                 losses.append(crps_loss(r["logits"][None], qt[None]))
             elif r["qtype"] == "noul":
                 qt = torch.tensor(q, device=r["p_yes"].device)
+                if eps > 0:
+                    qt = (1 - eps) * qt + eps * 0.5
                 losses.append(bernoulli_brier(
                     torch.logit(r["p_yes"].clamp(1e-6, 1 - 1e-6))[None],
                     qt[None]))
@@ -136,6 +141,7 @@ def main():
     ap.add_argument("--max-items", type=int, default=0)
     ap.add_argument("--save-every", type=int, default=1000)
     ap.add_argument("--seed", type=int, default=0)
+    ap.add_argument("--label-smooth", type=float, default=0.0)
     args = ap.parse_args()
 
     torch.manual_seed(args.seed)
@@ -233,7 +239,8 @@ def main():
             out = model(ids, mask, pos, packeds)
             losses = []
             for qi, qres in enumerate(out):
-                losses += question_losses([qres], targs[qi])
+                losses += question_losses([qres], targs[qi],
+                                          getattr(args, "label_smooth", 0.0))
             loss = torch.stack(losses).mean() / args.accum
             loss.backward()
             running += loss.item() * args.accum