X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=00e19ac78f1695265286ce4e1160423468f753bd;hb=ce969e8372fb161d86be29042a20b044ee6efe2a;hp=9a3d34633bd88cff4ac1e05ee77989e4d725e7b1;hpb=960c93d7c0aea41d180814c46d3a05686a426764;p=picoclvr.git diff --git a/main.py b/main.py index 9a3d346..00e19ac 100755 --- a/main.py +++ b/main.py @@ -32,8 +32,8 @@ parser = argparse.ArgumentParser( parser.add_argument( "--task", type=str, - default="sandbox", - help="byheart, learnop, guessop, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl", + default="twotargets", + help="byheart, learnop, guessop, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid", ) parser.add_argument("--log_filename", type=str, default="train.log", help=" ") @@ -62,7 +62,7 @@ parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: ######################################## -parser.add_argument("--model", type=str, default="37M") +parser.add_argument("--model", type=str, default=None) parser.add_argument("--dim_model", type=int, default=None) @@ -89,13 +89,13 @@ parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth") ############################## # rpl options -parser.add_argument("--rpl_nb_starting_values", type=int, default=5) +parser.add_argument("--rpl_nb_starting_values", type=int, default=3) parser.add_argument("--rpl_max_input", type=int, default=9) -parser.add_argument("--rpl_prog_len", type=int, default=10) +parser.add_argument("--rpl_prog_len", type=int, default=8) -parser.add_argument("--rpl_nb_runs", type=int, default=8) +parser.add_argument("--rpl_nb_runs", type=int, default=5) parser.add_argument("--rpl_no_prog", action="store_true", default=False) @@ -113,18 +113,18 @@ parser.add_argument("--picocvlr_prune_properties", type=str, default="none") ############################## # Maze options -parser.add_argument("--maze_height", type=int, default=23) +parser.add_argument("--maze_height", type=int, default=13) -parser.add_argument("--maze_width", type=int, default=39) +parser.add_argument("--maze_width", type=int, default=21) -parser.add_argument("--maze_nb_walls", type=int, default=45) +parser.add_argument("--maze_nb_walls", type=int, default=15) ############################## # Snake options -parser.add_argument("--snake_height", type=int, default=6) +parser.add_argument("--snake_height", type=int, default=9) -parser.add_argument("--snake_width", type=int, default=8) +parser.add_argument("--snake_width", type=int, default=12) parser.add_argument("--snake_nb_colors", type=int, default=5) @@ -171,60 +171,104 @@ if args.result_dir is None: ###################################################################### default_task_args = { - "sandbox": { + "byheart": { + "model": "37M", + "nb_epochs": 2, + "batch_size": 25, + "nb_train_samples": 50000, + "nb_test_samples": 10000, + }, + "learnop": { + "model": "37M", + "nb_epochs": 15, + "batch_size": 25, + "nb_train_samples": 50000, + "nb_test_samples": 10000, + }, + "guessop": { + "model": "352M", + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 1000000, + "nb_test_samples": 10000, + }, + "twotargets": { + "model": "37M", + "nb_epochs": 10, + "batch_size": 25, + "nb_train_samples": 50000, + "nb_test_samples": 10000, + }, + "addition": { + "model": "352M", "nb_epochs": 50, "batch_size": 25, - "nb_train_samples": 100000, + "nb_train_samples": 250000, "nb_test_samples": 10000, }, "picoclvr": { + "model": "37M", "nb_epochs": 25, "batch_size": 25, "nb_train_samples": 250000, "nb_test_samples": 10000, }, "mnist": { + "model": "37M", "nb_epochs": 25, "batch_size": 10, - "nb_train_samples": 250000, + "nb_train_samples": 60000, "nb_test_samples": 10000, }, "maze": { + "model": "37M", "nb_epochs": 25, "batch_size": 5, - "nb_train_samples": 250000, + "nb_train_samples": 100000, "nb_test_samples": 10000, }, "snake": { + "model": "37M", "nb_epochs": 5, "batch_size": 25, "nb_train_samples": 250000, "nb_test_samples": 10000, }, "stack": { - "nb_epochs": 5, + "model": "37M", + "nb_epochs": 15, "batch_size": 25, "nb_train_samples": 100000, "nb_test_samples": 1000, }, "expr": { - "nb_epochs": 40, + "model": "352M", + "nb_epochs": 25, "batch_size": 25, - "nb_train_samples": 1000000, + "nb_train_samples": 2500000, "nb_test_samples": 10000, }, "rpl": { - "nb_epochs": 40, - "batch_size": 25, - "nb_train_samples": 100000, + "model": "122M", + "nb_epochs": 50, + "batch_size": 5, + "nb_train_samples": 1000000, "nb_test_samples": 10000, }, "world": { + "model": "37M", "nb_epochs": 10, "batch_size": 25, "nb_train_samples": 25000, "nb_test_samples": 1000, }, + "grid": { + "model": "37M", + "nb_epochs": 25, + "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, + }, } if args.task in default_task_args: @@ -339,7 +383,7 @@ if args.task == "byheart": logger=log_string, device=device, ) - + args.max_percents_of_test_in_train = -1 elif args.task == "learnop": task = tasks.SandBox( @@ -468,6 +512,17 @@ elif args.task == "rpl": device=device, ) +elif args.task == "grid": + task = tasks.Grid( + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, + height=args.picoclvr_height, + width=args.picoclvr_width, + logger=log_string, + device=device, + ) + elif args.task == "world": task = tasks.World( nb_train_samples=args.nb_train_samples, @@ -563,33 +618,33 @@ train_set_perplexity = math.exp(entropy) ###################################################################### # A bit of paranoia never hurts +if args.max_percents_of_test_in_train >= 0: + + def subsets_as_tuples(batches, cs): + s = set() + for batch in batches: + for x in batch: + s.add(tuple([v.item() for v in x])) + if len(s) == cs: + yield s + s = set() + yield s + + nb_test, nb_in_train = 0, 0 + for test_subset in subsets_as_tuples(task.batches(split="test"), 25000): + in_train = set() + for train_subset in subsets_as_tuples(task.batches(split="train"), 25000): + in_train.update(test_subset.intersection(train_subset)) + nb_in_train += len(in_train) + nb_test += len(test_subset) + + log_string( + f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set" + ) -def subsets_as_tuples(batches, cs): - s = set() - for batch in batches: - for x in batch: - s.add(tuple([v.item() for v in x])) - if len(s) == cs: - yield s - s = set() - yield s - - -nb_test, nb_in_train = 0, 0 -for test_subset in subsets_as_tuples(task.batches(split="test"), 25000): - in_train = set() - for train_subset in subsets_as_tuples(task.batches(split="train"), 25000): - in_train.update(test_subset.intersection(train_subset)) - nb_in_train += len(in_train) - nb_test += len(test_subset) - -log_string( - f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set" -) - -assert ( - nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100 -), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set" + assert ( + nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100 + ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set" ##############################