parser.add_argument("--nb_gpts", type=int, default=5)
-parser.add_argument("--check", action="store_true", default=False)
+parser.add_argument("--dirty_debug", action="store_true", default=False)
######################################################################
######################################################################
-if args.check:
+if args.dirty_debug:
args.nb_train_samples = 2500
args.nb_test_samples = 100
desired_average_logits=None,
):
kept = []
- nb_generated_tokens, sum_logits = 0, 0
+
+ sum_logits, sum_nb_quizzes = 0, 0
while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
nb_to_generate = 4 * (nb_for_train + nb_for_test)
+
new_quizzes, nb_correct, average_logits = task.create_new_quizzes(
n_epoch=n_epoch,
result_dir=args.result_dir,
desired_average_logits=desired_average_logits,
)
- nb_generated_tokens += new_quizzes.numel()
- sum_logits += average_logits * new_quizzes.numel()
+ sum_logits += new_quizzes.size(0) * average_logits
+ sum_nb_quizzes += new_quizzes.size(0)
to_keep = new_quizzes[nb_correct == len(other_models) - 1]
+
+ if args.dirty_debug:
+ to_keep = new_quizzes
+
log_string(
f"keep {to_keep.size(0)}/{new_quizzes.size(0)} quizzes ({to_keep.size(0)*100/new_quizzes.size(0):.02f}%)"
)
+
kept.append(to_keep)
new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
log_string,
)
- return sum_logits / nb_generated_tokens
+ return sum_logits / sum_nb_quizzes
######################################################################
nb_new_quizzes_for_train = 1000
nb_new_quizzes_for_test = 100
-if args.check:
+if args.dirty_debug:
accuracy_to_make_quizzes = 0.0
- nb_new_quizzes_for_train = 10
+ nb_new_quizzes_for_train = 100
nb_new_quizzes_for_test = 10
desired_average_logits = None
for n_epoch in range(args.nb_epochs):
- log_string(f"--- epoch {n_epoch+1} ----------------------------------------")
+ log_string(f"--- epoch {n_epoch} ----------------------------------------")
a = [(model.id, float(model.main_test_accuracy)) for model in models]
a.sort(key=lambda p: p[0])