import ffutils
import mygpt
-import sky, quizz_machine
+import sky, wireworld, quizz_machine
# world quizzes vs. culture quizzes
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
-parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
+parser.add_argument("--log_filename", type=str, default="train.log")
parser.add_argument("--result_dir", type=str, default=None)
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)
parser.add_argument("--nb_models_for_generation", type=int, default=1)
parser.add_argument("--dirty_debug", action="store_true", default=False)
+parser.add_argument("--sky_height", type=int, default=6)
+
+parser.add_argument("--sky_width", type=int, default=8)
+
+parser.add_argument("--sky_nb_birds", type=int, default=3)
+
+parser.add_argument("--sky_nb_iterations", type=int, default=2)
+
+parser.add_argument("--sky_speed", type=int, default=3)
+
######################################################################
args = parser.parse_args()
assert args.nb_train_samples % args.batch_size == 0
assert args.nb_test_samples % args.batch_size == 0
+if args.problem == "sky":
+ problem = sky.Sky(
+ height=args.sky_height,
+ width=args.sky_width,
+ nb_birds=args.sky_nb_birds,
+ nb_iterations=args.sky_nb_iterations,
+ speed=args.sky_speed,
+ )
+elif args.problem == "wireworld":
+ problem = wireworld.Wireworld(height=8, width=10, nb_iterations=2, speed=5)
+else:
+ raise ValueError
+
quizz_machine = quizz_machine.QuizzMachine(
- problem=sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2, speed=2),
+ problem=problem,
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
batch_size=args.physical_batch_size,
n_epoch=n_epoch,
model=model,
result_dir=args.result_dir,
- logger=log_string,
deterministic_synthesis=deterministic_synthesis,
)
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,
- logger=log_string,
)
sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
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():
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}")
+ s = " ".join([f"{p[1]*100:.02f}%" for p in a])
+ log_string(f"current accuracies {s}")
# select the model with lowest accuracy
models.sort(key=lambda model: model.main_test_accuracy)