X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=0a7be99f3cbf6dca2131fe512c2b9a3304e0b2d8;hb=240870f5535bac35a08c552108d032854a8e2c38;hp=6e5545c6621806499211a61dbff1ed7e25126ab1;hpb=9ff76e2a501ec296fbb8f6c2d12ad4ceac148b5f;p=culture.git diff --git a/main.py b/main.py index 6e5545c..0a7be99 100755 --- a/main.py +++ b/main.py @@ -79,14 +79,12 @@ parser.add_argument("--dropout", type=float, default=0.1) 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) @@ -381,94 +379,83 @@ def run_tests(model, quizz_machine, deterministic_synthesis): ###################################################################### +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}", ) @@ -503,57 +490,43 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") ###################################################################### -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)