parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
+parser.add_argument("--reverse_cleanup", action="store_true", default=False)
+
parser.add_argument("--problem", type=str, default="sky")
parser.add_argument("--nb_gpts", type=int, default=5)
nb_to_create = nb_for_train + nb_for_test
+ warnings.warn(
+ f"{args.nb_gpts=} {args.nb_models_for_generation=} {args.min_to_validate=} {args.max_to_validate=}"
+ )
+
while nb_validated() < nb_to_create:
(
new_c_quizzes,
nb_models_for_generation=args.nb_models_for_generation,
models=models,
mode=args.generation_mode,
+ reverse_cleanup=args.reverse_cleanup,
min_ave_seq_logproba=min_ave_seq_logproba,
n_epoch=n_epoch,
result_dir=args.result_dir,
for n in range(nb_correct.max() + 1):
recorded[n].append(new_c_quizzes[nb_correct == n].clone())
- log_string(
- f"keep c_quizzes {nb_validated()*100/nb_generated():.02f}% kept total {nb_validated()} / {nb_to_create}"
- )
+ nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
+ nv = " ".join([str(x.item()) for x in nv])
+
+ log_string(f"keep c_quizzes kept {nv} total {nb_validated()} / {nb_to_create}")
# concatenate and shuffle
for n in recorded.keys():