- # concatenate and shuffle
- for n in recorded.keys():
- if len(recorded[n]) > 0:
- q = torch.cat(recorded[n], dim=0)
- q = q[torch.randperm(q.size(0), device=q.device)]
- recorded[n] = q
- else:
- del recorded[n]
-
- new_c_quizzes = torch.cat(
- [recorded[n] for n in range(args.min_to_validate, args.max_to_validate + 1)],
- dim=0,
- )
+ if args.dirty_debug:
+ nb_correct = torch.randint(
+ len(models) + 1, nb_correct.size(), device=c_quizzes.device
+ )
+
+ recorded.append((c_quizzes, nb_correct))
+
+ nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
+ nv = " ".join([str(x.item()) for x in nv])
+
+ nb_validated = valid_c_quizzes(recorded, standard_validity).size(0)
+
+ log_string(
+ f"keep c_quizzes model {model_for_generation.id} kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
+ )
+
+ # store the new c_quizzes which have been validated