import ffutils
import mygpt, tasks
+# world quizzes vs. culture quizzes
+
+######################################################################
+
+accuracy_to_make_c_quizzes = 0.975
+nb_new_c_quizzes_for_train = 1000
+nb_new_c_quizzes_for_test = 100
+
######################################################################
if torch.cuda.is_available():
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.dirty_debug:
+ accuracy_to_make_c_quizzes = 0.0
+ nb_new_c_quizzes_for_train = 100
+ nb_new_c_quizzes_for_test = 10
+
+######################################################################
+
default_args = {
"model": "37M",
"batch_size": 100,
######################################################################
-if args.check:
+if args.dirty_debug:
args.nb_train_samples = 2500
args.nb_test_samples = 100
######################################################################
-def create_quizzes(
+def create_c_quizzes(
model,
other_models,
task,
desired_average_logits=None,
):
kept = []
- nb_generated_tokens, sum_logits = 0, 0
+
+ sum_logits, sum_nb_c_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(
+
+ new_c_quizzes, nb_correct, average_logits = task.create_c_quizzes(
n_epoch=n_epoch,
result_dir=args.result_dir,
logger=log_string,
desired_average_logits=desired_average_logits,
)
- nb_generated_tokens += new_quizzes.numel()
- sum_logits += average_logits * new_quizzes.numel()
+ sum_logits += new_c_quizzes.size(0) * average_logits
+ sum_nb_c_quizzes += new_c_quizzes.size(0)
+
+ to_keep = new_c_quizzes[nb_correct == len(other_models) - 1]
+
+ if args.dirty_debug:
+ to_keep = new_c_quizzes
- to_keep = new_quizzes[nb_correct == len(other_models) - 1]
log_string(
- f"keep {to_keep.size(0)}/{new_quizzes.size(0)} quizzes ({to_keep.size(0)*100/new_quizzes.size(0):.02f}%)"
+ f"keep {to_keep.size(0)}/{new_c_quizzes.size(0)} c_quizzes ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%)"
)
+
kept.append(to_keep)
- new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+ new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
- task.store_new_quizzes(new_quizzes[:nb_for_train], for_train=True)
- task.store_new_quizzes(new_quizzes[nb_for_train:], for_train=False)
+ task.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
+ task.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
- task.save_image(
- new_quizzes[:72],
+ task.save_quizzes(
+ new_c_quizzes[:72],
args.result_dir,
- f"world_quiz_{n_epoch:04d}_{model.id:02d}.png",
+ f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
log_string,
)
- return sum_logits / nb_generated_tokens
+ return sum_logits / sum_nb_c_quizzes
######################################################################
######################################################################
-accuracy_to_make_quizzes = 0.975
-nb_new_quizzes_for_train = 1000
-nb_new_quizzes_for_test = 100
-
-if args.check:
- accuracy_to_make_quizzes = 0.0
- nb_new_quizzes_for_train = 10
- nb_new_quizzes_for_test = 10
-
desired_average_logits = None
for n_epoch in range(args.nb_epochs):
+ 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])
log_string(f"current accuracies {a}")
# improve it
one_epoch(model, task)
- task.renew_samples(args.nb_train_samples // args.nb_gpts)
+ task.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
log_string(
- f"train_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
+ f"train_set_composition w_quizzes {task.nb_batch_w_quizzes} c_quizzes {task.nb_batch_c_quizzes}"
)
# test it
run_tests(model, task, deterministic_synthesis=False)
log_string(
- f"test_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
+ f"test_set_composition w_quizzes {task.nb_batch_w_quizzes} c_quizzes {task.nb_batch_c_quizzes}"
)
- if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_quizzes:
+ if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
other_models = models.copy()
other_models.remove(model)
- average_logits = create_quizzes(
+ average_logits = create_c_quizzes(
model,
other_models,
task,
- nb_for_train=nb_new_quizzes_for_train,
- nb_for_test=nb_new_quizzes_for_test,
+ nb_for_train=nb_new_c_quizzes_for_train,
+ nb_for_test=nb_new_c_quizzes_for_test,
desired_average_logits=desired_average_logits,
)