X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=main.py;h=ebecad8a471353400fc7e6e472bdb95f594b48ab;hb=15192743a5dee8d88650319d64610f1603d21472;hp=09ae82345ec0258063f8a107a633b41b0c7779c8;hpb=4ec52fe66419a6e1d2b231108ccbb45902395fcc;p=culture.git diff --git a/main.py b/main.py index 09ae823..ebecad8 100755 --- a/main.py +++ b/main.py @@ -14,6 +14,14 @@ from torch.nn import functional as F 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(): @@ -73,7 +81,7 @@ parser.add_argument("--deterministic_synthesis", action="store_true", default=Fa 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) ###################################################################### @@ -84,6 +92,13 @@ if args.result_dir is None: ###################################################################### +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, @@ -182,8 +197,8 @@ for n in vars(args): ###################################################################### -if args.check: - args.nb_train_samples = 500 +if args.dirty_debug: + args.nb_train_samples = 2500 args.nb_test_samples = 100 if args.physical_batch_size is None: @@ -329,43 +344,59 @@ def run_tests(model, task, deterministic_synthesis): ###################################################################### -def create_quizzes( +def create_c_quizzes( model, other_models, task, nb_for_train=1000, nb_for_test=100, + desired_average_logits=None, ): kept = [] + sum_logits, sum_nb_c_quizzes = 0, 0 + while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test: - new_quizzes, nb_correct = task.create_new_quizzes( + nb_to_generate = 4 * (nb_for_train + nb_for_test) + + new_c_quizzes, nb_correct, average_logits = task.create_c_quizzes( n_epoch=n_epoch, result_dir=args.result_dir, logger=log_string, - nb=4 * (nb_for_train + nb_for_test), + nb=nb_to_generate, model=model, other_models=other_models, + desired_average_logits=desired_average_logits, ) - print(nb_correct) + 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 + + 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}%)" + ) - to_keep = new_quizzes[nb_correct == len(other_models) - 1] - log_string(f"keep {to_keep.size(0)} quizzes") 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[:96], + 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 / sum_nb_c_quizzes + ###################################################################### @@ -394,17 +425,12 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") ###################################################################### -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): - a = [(model.id, model.main_test_accuracy) for model in models] + 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}") @@ -419,25 +445,40 @@ for n_epoch in range(args.nb_epochs): # improve it one_epoch(model, task) + 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) - if model.main_test_accuracy >= accuracy_to_make_quizzes: + log_string( + 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_c_quizzes: other_models = models.copy() other_models.remove(model) - 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, ) + # We keep the first average logits as a reference + if desired_average_logits is None: + desired_average_logits = average_logits + else: + log_string( + f"desired_average_logits {desired_average_logits} average_logits {average_logits}" + ) + # We update everyone for model in models: run_tests(model, task, deterministic_synthesis=False)