From: François Fleuret Date: Wed, 26 Jul 2023 20:11:28 +0000 (-1000) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=960c93d7c0aea41d180814c46d3a05686a426764;p=picoclvr.git Update. --- diff --git a/main.py b/main.py index ed4adf5..9a3d346 100755 --- a/main.py +++ b/main.py @@ -33,7 +33,7 @@ parser.add_argument( "--task", type=str, default="sandbox", - help="sandbox, picoclvr, mnist, maze, snake, stack, expr, rpl, world", + help="byheart, learnop, guessop, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl", ) parser.add_argument("--log_filename", type=str, default="train.log", help=" ") @@ -99,17 +99,6 @@ parser.add_argument("--rpl_nb_runs", type=int, default=8) parser.add_argument("--rpl_no_prog", action="store_true", default=False) -############################## -# sandbox options - -parser.add_argument("--sandbox_level", type=int, default=0) - -parser.add_argument("--sandbox_levels_nb_items", type=int, default=25) - -parser.add_argument("--sandbox_levels_len_source", type=int, default=6) - -parser.add_argument("--sandbox_levels_len_result", type=int, default=8) - ############################## # picoclvr options @@ -341,32 +330,52 @@ picoclvr_pruner_eval = ( ###################################################################### -if args.task == "sandbox": - if args.sandbox_level == 0: - problem = problems.ProblemLevel0( - nb_sentences=args.sandbox_levels_nb_items, - len_prompt=args.sandbox_levels_len_source, - len_result=args.sandbox_levels_len_result, - ) - elif args.sandbox_level == 1: - problem = problems.ProblemLevel1( - nb_operators=args.sandbox_levels_nb_items, - len_source=args.sandbox_levels_len_source, - len_result=args.sandbox_levels_len_result, - ) - elif args.sandbox_level == 2: - problem = problems.ProblemLevel2( - len_source=args.sandbox_levels_len_source, - len_result=args.sandbox_levels_len_result, - ) - else: - raise ValueError(f"Unknown sandbox level {args.sandbox_level}") +if args.task == "byheart": + task = tasks.SandBox( + problem=problems.ProblemByHeart(), + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, + logger=log_string, + device=device, + ) + + +elif args.task == "learnop": + task = tasks.SandBox( + problem=problems.ProblemLearnOperator(), + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, + logger=log_string, + device=device, + ) + + +elif args.task == "guessop": + task = tasks.SandBox( + problem=problems.ProblemGuessOperator(), + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, + logger=log_string, + device=device, + ) + + +elif args.task == "twotargets": + task = tasks.SandBox( + problem=problems.ProblemTwoTargets(), + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, + logger=log_string, + device=device, + ) +elif args.task == "addition": task = tasks.SandBox( - # problem, - # problems.ProblemAddition(zero_padded=False, inverted_result=False), - # problems.ProblemLenId(len_max=args.sandbox_levels_len_source), - problems.ProblemTwoTargets(len_total=16, len_targets=4), + problem=problems.ProblemAddition(), nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, batch_size=args.batch_size, diff --git a/problems.py b/problems.py index 2e0ca36..5686404 100755 --- a/problems.py +++ b/problems.py @@ -68,38 +68,8 @@ class ProblemTwoTargets(Problem): #################### -class ProblemLenId(Problem): - def __init__(self, len_max=10): - self.len_max = len_max - - def generate_sequences(self, nb): - k = torch.arange(self.len_max * 3 + 3)[None, :] - l = torch.randint(self.len_max, (2, nb))[:, :, None] + 1 - i = torch.randint(10, (2, nb))[:, :, None] - a = l[0] - b = l[0] + 1 + l[1] - c = l[0] + 1 + l[1] + 1 + l[0] - sequences = ( - (k < a) * i[0] - + (k == a) * 10 - + (k > a) * (k < b) * i[1] - + (k == b) * 11 - + (k > b) * (k < c) * i[1] - + (k >= c) * 12 - ) - ar_mask = (sequences == 11).long() - ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) - return sequences, ar_mask - - def seq2str(self, seq): - return "".join("0123456789|>_"[x.item()] for x in seq) - - -#################### - - -class ProblemLevel0(Problem): - def __init__(self, nb_sentences=100, len_prompt=5, len_result=5): +class ProblemByHeart(Problem): + def __init__(self, nb_sentences=100, len_prompt=8, len_result=8): self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result)) self.seq[:, len_prompt] = 10 @@ -116,7 +86,7 @@ class ProblemLevel0(Problem): #################### -class ProblemLevel1(Problem): +class ProblemLearnOperator(Problem): def __init__(self, nb_operators=100, len_source=5, len_result=8): self.len_source = len_source self.len_result = len_result @@ -134,7 +104,6 @@ class ProblemLevel1(Problem): // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1) ) % 10 marker1 = torch.full((nb, 1), 10) - # source = torch.randint(10, (nb, self.len_source)) source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source] marker2 = torch.full((nb, 1), 11) result = operators.bmm(source[:, :, None]).squeeze(-1) @@ -150,7 +119,7 @@ class ProblemLevel1(Problem): #################### -class ProblemLevel2(Problem): +class ProblemGuessOperator(Problem): def __init__(self, len_source=5, len_result=8): self.len_source = len_source self.len_result = len_result diff --git a/tasks.py b/tasks.py index cc3aea0..5019aed 100755 --- a/tasks.py +++ b/tasks.py @@ -181,6 +181,8 @@ class SandBox(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + if save_attention_image is None: logger("no save_attention_image (is pycairo installed?)") else: @@ -371,6 +373,10 @@ class PicoCLVR(Task): f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%" ) + logger( + f"main_test_accuracy {n_epoch} {1-nb_missing_properties/nb_requested_properties}" + ) + ###################################################################### def produce_results( @@ -641,6 +647,8 @@ class Maze(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + if count is not None: proportion_optimal = count.diagonal().sum().float() / count.sum() logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%") @@ -780,6 +788,8 @@ class Snake(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + ###################################################################### @@ -889,6 +899,8 @@ class Stack(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + ############################################################## # Log a few generated sequences input = self.test_input[:10, : 12 * (1 + self.nb_digits)] @@ -1161,6 +1173,8 @@ class RPL(Task): f"accuracy_prog_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {1-test_nb_errors/test_nb_total}") + test_nb_total, test_nb_errors = compute_nb_errors_output( self.test_input[:1000].to(self.device), nb_to_log=10 ) @@ -1359,6 +1373,8 @@ class Expr(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + nb_total = test_nb_delta.sum() + test_nb_missed for d in range(test_nb_delta.size(0)): logger(