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)
########################################
parser.add_argument("--nb_gpts", type=int, default=5)
+parser.add_argument("--nb_correct_to_validate", type=int, default=4)
+
parser.add_argument("--dirty_debug", action="store_true", default=False)
######################################################################
default_args = {
"model": "37M",
"batch_size": 100,
- "nb_train_samples": 250000,
+ "nb_train_samples": 100000,
"nb_test_samples": 10000,
}
assert args.nb_test_samples % args.batch_size == 0
quizz_machine = quizz_machine.QuizzMachine(
- sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2),
+ 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,
def create_c_quizzes(
- model,
- other_models,
+ models,
quizz_machine,
nb_for_train=1000,
nb_for_test=100,
min_ave_seq_logproba=None,
):
- kept = []
+ # We will store the generated quizzes for each number of
+ # correct prediction
+ recorded = dict([(n, []) for n in range(len(models) + 1)])
+ 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:
- nb_to_generate = 4 * (nb_for_train + nb_for_test)
+ while (
+ sum([x.size(0) for x in recorded[args.nb_correct_to_validate]])
+ < nb_for_train + nb_for_test
+ ):
+ nb_to_validate = 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(
+ nb=nb_to_validate,
+ 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,
- 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)
- to_keep = new_c_quizzes[nb_correct == len(other_models) - 1]
-
if args.dirty_debug:
- to_keep = new_c_quizzes
+ nb_correct = torch.randint(
+ len(models) + 1, nb_correct.size(), device=new_c_quizzes.device
+ )
+
+ for n in range(nb_correct.max() + 1):
+ recorded[n].append(new_c_quizzes[nb_correct == n].clone())
+
+ nb_validated = sum([x.size(0) for x in recorded[args.nb_correct_to_validate]])
+ nb_generated = sum(
+ [sum([x.size(0) for x in recorded[n]]) for n in recorded.keys()]
+ )
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 {nb_validated*100/nb_generated:.02f}% kept total {nb_validated}/{nb_to_validate}"
)
- kept.append(to_keep)
+ # concatenate and shuffle
+ for n in recorded.keys():
+ if len(recorded[n]) > 0:
+ q = torch.cat(recorded[n], dim=0)
+ q = q[torch.randperm(q.size(0), device=q.device)]
+ recorded[n] = q
+ else:
+ del recorded[n]
- new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+ new_c_quizzes = recorded[args.nb_correct_to_validate][: 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.problem.save_quizzes(
- new_c_quizzes[:72],
- args.result_dir,
- f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
- log_string,
- )
+ for n in recorded.keys():
+ s = "_validated" if n == args.nb_correct_to_validate else ""
+ quizz_machine.problem.save_quizzes(
+ recorded[n][:72],
+ args.result_dir,
+ f"culture_c_quiz_{n_epoch:04d}_N{n}{s}",
+ )
return sum_logits / sum_nb_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(
- model,
- other_models,
+ models,
quizz_machine,
nb_for_train=nb_new_c_quizzes_for_train,
nb_for_test=nb_new_c_quizzes_for_test,
)
# 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:
projects / culture.git / blobdiff