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("--generation_mode", type=str, default="groupthink")
-
parser.add_argument("--min_to_validate", type=int, default=4)
parser.add_argument("--max_to_validate", type=int, default=4)
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_test_samples % args.batch_size == 0
if args.problem == "sky":
- problem = sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2, speed=2)
+ 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=10, width=15, nb_iterations=4)
+ problem = wireworld.Wireworld(height=8, width=10, nb_iterations=2, speed=5)
else:
raise ValueError
######################################################################
+def valid_c_quizzes(recorded, criteria):
+ result = [q[criteria(c)] for q, c in recorded]
+ return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([])
+
+
+######################################################################
+
+
def create_c_quizzes(
models,
quizz_machine,
nb_for_train=1000,
nb_for_test=100,
- min_ave_seq_logproba=None,
):
- # We will store the generated quizzes for each number of
- # correct prediction
- recorded = dict([(n, []) for n in range(len(models) + 1)])
+ recorded = []
- model_indexes = []
sum_logits, sum_nb_c_quizzes = 0, 0
- def nb_generated():
- return sum([sum([x.size(0) for x in recorded[n]]) for n in recorded.keys()])
+ nb_to_create = nb_for_train + nb_for_test
- def nb_validated():
- return sum(
- [
- sum([x.size(0) for x in recorded[n]])
- for n in range(args.min_to_validate, args.max_to_validate + 1)
- ]
- )
+ # ------------------------------------------------------------
- nb_to_create = nb_for_train + nb_for_test
+ standard_validity = lambda nb_correct: torch.logical_and(
+ nb_correct >= args.min_to_validate, nb_correct <= args.max_to_validate
+ )
- while nb_validated() < nb_to_create:
- (
- new_c_quizzes,
- nb_correct,
- ave_seq_logproba,
- ) = quizz_machine.gang_create_c_quizzes(
- nb=nb_to_create,
- nb_models_for_generation=args.nb_models_for_generation,
- models=models,
- mode=args.generation_mode,
- min_ave_seq_logproba=min_ave_seq_logproba,
- n_epoch=n_epoch,
- result_dir=args.result_dir,
+ while valid_c_quizzes(recorded, standard_validity).size(0) < nb_to_create:
+ model_for_generation = models[torch.randint(len(models), (1,))]
+
+ c_quizzes, ave_seq_logproba = quizz_machine.generate_quizzes(
+ nb_to_create,
+ model_for_generation=model_for_generation,
+ reverse_cleanup=args.reverse_cleanup,
)
- sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
- sum_nb_c_quizzes += new_c_quizzes.size(0)
+ sum_logits += c_quizzes.size(0) * ave_seq_logproba
+ sum_nb_c_quizzes += c_quizzes.size(0)
+
+ nb_correct = quizz_machine.compute_correctness(
+ c_quizzes, models, both_direction=True
+ )
if args.dirty_debug:
nb_correct = torch.randint(
- len(models) + 1, nb_correct.size(), device=new_c_quizzes.device
+ len(models) + 1, nb_correct.size(), device=c_quizzes.device
)
- for n in range(nb_correct.max() + 1):
- recorded[n].append(new_c_quizzes[nb_correct == n].clone())
+ recorded.append((c_quizzes, nb_correct))
+
+ nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
+ nv = " ".join([str(x.item()) for x in nv])
+
+ nb_validated = valid_c_quizzes(recorded, standard_validity).size(0)
log_string(
- f"keep c_quizzes {nb_validated()*100/nb_generated():.02f}% kept total {nb_validated()} / {nb_to_create}"
+ f"keep c_quizzes kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
)
- # 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(
- [recorded[n] for n in range(args.min_to_validate, args.max_to_validate + 1)],
- dim=0,
- )
+ # store the new c_quizzes which have been validated
- new_c_quizzes = new_c_quizzes[
- torch.randperm(new_c_quizzes.size(0), device=new_c_quizzes.device)[
- : nb_for_train + nb_for_test
- ]
- ]
+ new_c_quizzes = valid_c_quizzes(recorded, standard_validity)
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)
- for n in recorded.keys():
+ # save a bunch of images to investigate what quizzes with a
+ # certain nb of correct predictions look like
+
+ for n in range(len(models) + 1):
s = (
"_validated"
if n >= args.min_to_validate and n <= args.max_to_validate
else ""
)
+
quizz_machine.problem.save_quizzes(
- recorded[n][:72],
+ valid_c_quizzes(recorded, criteria=lambda nb_correct: nb_correct == n)[:72],
args.result_dir,
f"culture_c_quiz_{n_epoch:04d}_N{n}{s}",
)
######################################################################
-min_ave_seq_logproba = 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])
- 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)
- model = models[0]
+ weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
log_string(
- f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
+ f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}"
)
# improve it
- one_epoch(model, quizz_machine)
-
- quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+ one_epoch(weakest_model, quizz_machine)
log_string(
f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
)
# test it
- run_tests(model, quizz_machine, deterministic_synthesis=False)
+ run_tests(weakest_model, quizz_machine, deterministic_synthesis=False)
log_string(
f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
)
+ cta = " ".join([f"{float(m.main_test_accuracy):.02f}" for m in models])
+ log_string(f"current_test_accuracies {cta}")
+
+ # replace a fraction of the w_quizzes with a fresh ones
+ quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+
if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
- ave_seq_logproba = create_c_quizzes(
+ create_c_quizzes(
models,
quizz_machine,
nb_for_train=nb_new_c_quizzes_for_train,
nb_for_test=nb_new_c_quizzes_for_test,
- min_ave_seq_logproba=min_ave_seq_logproba,
)
- # 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}"
- # )
-
# We update everyone
for model in models:
run_tests(model, quizz_machine, deterministic_synthesis=False)
projects / culture.git / blobdiff