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Update.
[culture.git]
/
main.py
diff --git
a/main.py
b/main.py
index
05c3557
..
c5acea7
100755
(executable)
--- a/
main.py
+++ b/
main.py
@@
-12,7
+12,8
@@
from torch import nn
from torch.nn import functional as F
import ffutils
from torch.nn import functional as F
import ffutils
-import mygpt, quizz_machine
+import mygpt
+import sky, quizz_machine
# world quizzes vs. culture quizzes
# world quizzes vs. culture quizzes
@@
-57,7
+58,7
@@
parser.add_argument("--nb_train_samples", type=int, default=None)
parser.add_argument("--nb_test_samples", type=int, default=None)
parser.add_argument("--nb_test_samples", type=int, default=None)
-parser.add_argument("--learning_rate", type=float, default=1e-
4
)
+parser.add_argument("--learning_rate", type=float, default=1e-
3
)
########################################
########################################
@@
-102,7
+103,7
@@
if args.dirty_debug:
default_args = {
"model": "37M",
"batch_size": 100,
default_args = {
"model": "37M",
"batch_size": 100,
- "nb_train_samples":
25
0000,
+ "nb_train_samples":
10
0000,
"nb_test_samples": 10000,
}
"nb_test_samples": 10000,
}
@@
-210,6
+211,7
@@
assert args.nb_train_samples % args.batch_size == 0
assert args.nb_test_samples % args.batch_size == 0
quizz_machine = quizz_machine.QuizzMachine(
assert args.nb_test_samples % args.batch_size == 0
quizz_machine = quizz_machine.QuizzMachine(
+ problem=sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2),
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
batch_size=args.physical_batch_size,
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
batch_size=args.physical_batch_size,
@@
-347,54
+349,63
@@
def run_tests(model, quizz_machine, deterministic_synthesis):
def create_c_quizzes(
def create_c_quizzes(
- model,
- other_models,
+ models,
quizz_machine,
nb_for_train=1000,
nb_for_test=100,
min_ave_seq_logproba=None,
):
kept = []
quizz_machine,
nb_for_train=1000,
nb_for_test=100,
min_ave_seq_logproba=None,
):
kept = []
-
+ model_indexes = []
sum_logits, sum_nb_c_quizzes = 0, 0
while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
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)
+ nb_to_generate = nb_for_train + nb_for_test
+
+ if len(model_indexes) == 0:
+ model_indexes = [i.item() for i in torch.randperm(len(models))]
+
+ model = models[model_indexes.pop()]
new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes(
new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes(
+ nb=nb_to_generate,
+ model_for_generation=model,
+ models_for_validation=models,
+ min_ave_seq_logproba=min_ave_seq_logproba,
n_epoch=n_epoch,
result_dir=args.result_dir,
logger=log_string,
n_epoch=n_epoch,
result_dir=args.result_dir,
logger=log_string,
- nb=nb_to_generate,
- model=model,
- other_models=other_models,
- min_ave_seq_logproba=min_ave_seq_logproba,
)
sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
sum_nb_c_quizzes += new_c_quizzes.size(0)
)
sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
sum_nb_c_quizzes += new_c_quizzes.size(0)
- to_keep = new_c_quizzes[nb_correct == len(
other_
models) - 1]
+ to_keep = new_c_quizzes[nb_correct == len(models) - 1]
if args.dirty_debug:
if args.dirty_debug:
- to_keep = new_c_quizzes
+ to_keep = new_c_quizzes[
+ torch.randint(3, (new_c_quizzes.size(0),), device=new_c_quizzes.device)
+ == 0
+ ]
+
+ kept.append(to_keep)
log_string(
log_string(
- f"keep
{to_keep.size(0)}/{new_c_quizzes.size(0)} c_quizzes ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%)
"
+ f"keep
c_quizzes {to_keep.size(0)}/{new_c_quizzes.size(0)} ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%) total {sum([ x.size(0) for x in kept])}/{nb_to_generate}
"
)
)
- kept.append(to_keep)
-
- new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+ new_c_quizzes = torch.cat(kept, dim=0)
+ new_c_quizzes = new_c_quizzes[
+ torch.randperm(new_c_quizzes.size(0))[: nb_for_train + nb_for_test]
+ ]
quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
- quizz_machine.save_quizzes(
+ quizz_machine.
problem.
save_quizzes(
new_c_quizzes[:72],
args.result_dir,
f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
new_c_quizzes[:72],
args.result_dir,
f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
- log_string,
)
return sum_logits / sum_nb_c_quizzes
)
return sum_logits / sum_nb_c_quizzes
@@
-461,12
+472,8
@@
for n_epoch in range(args.nb_epochs):
)
if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
)
if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
- other_models = models.copy()
- other_models.remove(model)
-
ave_seq_logproba = create_c_quizzes(
ave_seq_logproba = create_c_quizzes(
- model,
- other_models,
+ models,
quizz_machine,
nb_for_train=nb_new_c_quizzes_for_train,
nb_for_test=nb_new_c_quizzes_for_test,
quizz_machine,
nb_for_train=nb_new_c_quizzes_for_train,
nb_for_test=nb_new_c_quizzes_for_test,
@@
-474,12
+481,12
@@
for n_epoch in range(args.nb_epochs):
)
# We keep the first average logits as a reference
)
# We keep the first average logits as a reference
- if min_ave_seq_logproba is None:
-
min_ave_seq_logproba = ave_seq_logproba
- else:
-
log_string(
-
f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}"
-
)
+
#
if min_ave_seq_logproba is None:
+
#
min_ave_seq_logproba = ave_seq_logproba
+
#
else:
+
#
log_string(
+
#
f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}"
+
#
)
# We update everyone
for model in models:
# We update everyone
for model in models: