X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=b88847ef75e58bd8aca48df3a706319c768f1087;hb=504f61114d90b57e1d0faf55a298756da2c8fbfa;hp=232c7240ee5d492969239cac426fbbdb07cfaef7;hpb=979cff406de06137b7b5fb1876b906b2eb45153e;p=culture.git diff --git a/main.py b/main.py index 232c724..b88847e 100755 --- a/main.py +++ b/main.py @@ -13,13 +13,12 @@ from torch.nn import functional as F import ffutils import mygpt -import sky, quizz_machine +import sky, wireworld, quizz_machine # 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 @@ -38,7 +37,7 @@ parser = argparse.ArgumentParser( 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) @@ -80,9 +79,19 @@ parser.add_argument("--dropout", type=float, default=0.1) parser.add_argument("--deterministic_synthesis", 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_correct_to_validate", type=int, default=4) +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("--accuracy_to_make_c_quizzes", type=float, default=0.975) parser.add_argument("--dirty_debug", action="store_true", default=False) @@ -96,7 +105,7 @@ if args.result_dir is None: ###################################################################### if args.dirty_debug: - accuracy_to_make_c_quizzes = 0.0 + args.accuracy_to_make_c_quizzes = 0.0 nb_new_c_quizzes_for_train = 100 nb_new_c_quizzes_for_test = 10 @@ -212,8 +221,15 @@ else: assert args.nb_train_samples % args.batch_size == 0 assert args.nb_test_samples % args.batch_size == 0 -quizz_machine = quizz_machine.QuizzMachine( +if args.problem=="sky": problem=sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2, speed=2), +elif args.problem="wireworld": + problem=wireworld.Wireworld(height=10, width=15, nb_iterations=4) +else: + raise ValueError + +quizz_machine = quizz_machine.QuizzMachine( + problem=problem, nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, batch_size=args.physical_batch_size, @@ -336,7 +352,6 @@ def run_tests(model, quizz_machine, deterministic_synthesis): n_epoch=n_epoch, model=model, result_dir=args.result_dir, - logger=log_string, deterministic_synthesis=deterministic_synthesis, ) @@ -371,26 +386,25 @@ def create_c_quizzes( return sum( [ sum([x.size(0) for x in recorded[n]]) - for n in range(args.nb_correct_to_validate, len(models)) + for n in range(args.min_to_validate, args.max_to_validate + 1) ] ) - while nb_validated() < 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, + nb_to_create = nb_for_train + nb_for_test + + 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, - logger=log_string, ) sum_logits += new_c_quizzes.size(0) * ave_seq_logproba @@ -405,7 +419,7 @@ def create_c_quizzes( 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_validate}" + f"keep c_quizzes {nb_validated()*100/nb_generated():.02f}% kept total {nb_validated()} / {nb_to_create}" ) # concatenate and shuffle @@ -418,7 +432,8 @@ def create_c_quizzes( del recorded[n] new_c_quizzes = torch.cat( - [recorded[n] for n in range(args.nb_correct_to_validate, len(models))], dim=0 + [recorded[n] for n in range(args.min_to_validate, args.max_to_validate + 1)], + dim=0, ) new_c_quizzes = new_c_quizzes[ @@ -431,7 +446,11 @@ def create_c_quizzes( quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False) for n in recorded.keys(): - s = "_validated" if n >= args.nb_correct_to_validate and n < len(models) else "" + s = ( + "_validated" + if n >= args.min_to_validate and n <= args.max_to_validate + else "" + ) quizz_machine.problem.save_quizzes( recorded[n][:72], args.result_dir, @@ -475,7 +494,8 @@ for n_epoch in range(args.nb_epochs): 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) @@ -501,7 +521,7 @@ for n_epoch in range(args.nb_epochs): f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}" ) - 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]) >= args.accuracy_to_make_c_quizzes: ave_seq_logproba = create_c_quizzes( models, quizz_machine,