X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=c1f4dc7f0540e2dcbbdf8c71b9a3c1ca29db457b;hb=f91736e6e56152746b3c44342748b70ad1c89888;hp=7cb8d4f96ab03542205db29e333c8b22e176a1ae;hpb=74311726e42dccb8bc096e86a7e9000576099bab;p=picoclvr.git diff --git a/main.py b/main.py index 7cb8d4f..c1f4dc7 100755 --- a/main.py +++ b/main.py @@ -31,11 +31,13 @@ parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) -parser.add_argument("--task", type=str, default="picoclvr") +parser.add_argument( + "--task", type=str, default="picoclvr", help="picoclvr, mnist, maze, snake, stack" +) -parser.add_argument("--log_filename", type=str, default="train.log") +parser.add_argument("--log_filename", type=str, default="train.log", help=" ") -parser.add_argument("--result_dir", type=str, default="results_default") +parser.add_argument("--result_dir", type=str, default=None) parser.add_argument("--seed", type=int, default=0) @@ -43,9 +45,9 @@ parser.add_argument("--nb_epochs", type=int, default=None) parser.add_argument("--batch_size", type=int, default=None) -parser.add_argument("--nb_train_samples", type=int, default=250000) +parser.add_argument("--nb_train_samples", type=int, default=None) -parser.add_argument("--nb_test_samples", type=int, default=10000) +parser.add_argument("--nb_test_samples", type=int, default=None) parser.add_argument("--optim", type=str, default="adam") @@ -104,28 +106,25 @@ parser.add_argument("--snake_nb_colors", type=int, default=5) parser.add_argument("--snake_length", type=int, default=200) +############################## +# Snake options + +parser.add_argument("--stack_nb_steps", type=int, default=100) + +parser.add_argument("--stack_nb_stacks", type=int, default=1) + +parser.add_argument("--stack_nb_digits", type=int, default=3) + +parser.add_argument("--stack_fraction_values_for_train", type=float, default=None) + ###################################################################### args = parser.parse_args() assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"} -try: - os.mkdir(args.result_dir) -except FileExistsError: - if not args.overwrite_results: - print(f"result directory {args.result_dir} already exists") - exit(1) - -log_file = open(os.path.join(args.result_dir, args.log_filename), "a") - -if args.seed >= 0: - # torch.backends.cudnn.deterministic = True - # torch.backends.cudnn.benchmark = False - # torch.use_deterministic_algorithms(True) - torch.manual_seed(args.seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(args.seed) +if args.result_dir is None: + args.result_dir = f"results_{args.task}" ###################################################################### @@ -133,18 +132,32 @@ default_args = { "picoclvr": { "nb_epochs": 25, "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, }, "mnist": { "nb_epochs": 25, "batch_size": 10, + "nb_train_samples": 250000, + "nb_test_samples": 10000, }, "maze": { "nb_epochs": 25, "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, }, "snake": { "nb_epochs": 5, "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, + }, + "stack": { + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 100000, + "nb_test_samples": 1000, }, } @@ -155,6 +168,25 @@ if args.task in default_args: ###################################################################### +try: + os.mkdir(args.result_dir) +except FileExistsError: + if not args.overwrite_results: + print(f"result directory {args.result_dir} already exists") + exit(1) + +log_file = open(os.path.join(args.result_dir, args.log_filename), "a") + +if args.seed >= 0: + # torch.backends.cudnn.deterministic = True + # torch.backends.cudnn.benchmark = False + # torch.use_deterministic_algorithms(True) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + +###################################################################### + def log_string(s): t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime()) @@ -173,15 +205,29 @@ for n in vars(args): ###################################################################### +# ra_mask is boolean, with 1s on the values to generate + + def masked_inplace_autoregression( - model, batch_size, input, ar_mask, forbidden_tokens=None, device=torch.device("cpu") + model, + batch_size, + input, + ar_mask, + forbidden_tokens=None, + progress_bar_desc="autoregression", + device=torch.device("cpu"), ): - for input, ar_mask in tqdm.tqdm( - zip(input.split(batch_size), ar_mask.split(batch_size)), - dynamic_ncols=True, - desc="autoregression", - total=input.size(0) // batch_size, - ): + # p = logits.softmax(1) + # entropy[:,s]= p.xlogy(p).sum(1) / math.log(2) + batches = zip(input.split(batch_size), ar_mask.split(batch_size)) + if progress_bar_desc is not None: + batches = tqdm.tqdm( + batches, + dynamic_ncols=True, + desc=progress_bar_desc, + total=input.size(0) // batch_size, + ) + for input, ar_mask in batches: i = (ar_mask.sum(0) > 0).nonzero() if i.min() > 0: model( @@ -317,6 +363,7 @@ class TaskPicoCLVR(Task): input, ar_masks, forbidden_tokens, + progress_bar_desc=None, device=self.device, ) model.train(t) @@ -496,7 +543,7 @@ class TaskPicoCLVR(Task): image_name = os.path.join(args.result_dir, f"picoclvr_result_{n_epoch:04d}.png") torchvision.utils.save_image( - img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0 + img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0 ) log_string(f"wrote {image_name}") @@ -606,39 +653,83 @@ class TaskMaze(Task): def compute_error(self, model, split="train", nb_to_use=-1): nb_total, nb_correct = 0, 0 - for input in task.batches(split, nb_to_use): + count = torch.zeros( + self.width * self.height, + self.width * self.height, + device=self.device, + dtype=torch.int64, + ) + for input in tqdm.tqdm( + task.batches(split, nb_to_use), + dynamic_ncols=True, + desc=f"test-mazes", + ): result = input.clone() ar_mask = result.new_zeros(result.size()) ar_mask[:, self.height * self.width :] = 1 result *= 1 - ar_mask masked_inplace_autoregression( - model, self.batch_size, result, ar_mask, device=self.device + model, + self.batch_size, + result, + ar_mask, + progress_bar_desc=None, + device=self.device, ) mazes, paths = self.seq2map(result) - nb_correct += maze.path_correctness(mazes, paths).long().sum() + path_correctness = maze.path_correctness(mazes, paths) + nb_correct += path_correctness.long().sum() nb_total += mazes.size(0) - return nb_total, nb_correct + optimal_path_lengths = ( + (input[:, self.height * self.width :] == maze.v_path).long().sum(1) + ) + predicted_path_lengths = ( + (result[:, self.height * self.width :] == maze.v_path).long().sum(1) + ) + optimal_path_lengths = optimal_path_lengths[path_correctness] + predicted_path_lengths = predicted_path_lengths[path_correctness] + count[optimal_path_lengths, predicted_path_lengths] += 1 + + if count.max() == 0: + count = None + else: + count = count[ + : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1 + ] + + return nb_total, nb_correct, count def produce_results(self, n_epoch, model): with torch.autograd.no_grad(): t = model.training model.eval() - train_nb_total, train_nb_correct = self.compute_error( + train_nb_total, train_nb_correct, count = self.compute_error( model, "train", nb_to_use=1000 ) log_string( - f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%" + f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%" ) - test_nb_total, test_nb_correct = self.compute_error( + test_nb_total, test_nb_correct, count = self.compute_error( model, "test", nb_to_use=1000 ) log_string( - f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + 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}%" ) + if count is not None: + proportion_optimal = count.diagonal().sum().float() / count.sum() + log_string(f"proportion_optimal_test {proportion_optimal*100:.02f}%") + with open( + os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.txt"), "w" + ) as f: + for i in range(count.size(0)): + for j in range(count.size(1)): + eol = " " if j < count.size(1) - 1 else "\n" + f.write(f"{count[i,j]}{eol}") + input = self.test_input[:48] result = input.clone() ar_mask = result.new_zeros(result.size()) @@ -658,6 +749,7 @@ class TaskMaze(Task): target_paths=paths, predicted_paths=predicted_paths, path_correct=maze.path_correctness(mazes, predicted_paths), + path_optimal=maze.path_optimality(paths, predicted_paths), ) log_string(f"wrote {filename}") @@ -770,8 +862,132 @@ class TaskSnake(Task): ) log_string( - f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + 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}%" + ) + + model.train(t) + + +###################################################################### + + +import stack + + +class TaskStack(Task): + def __init__( + self, + nb_train_samples, + nb_test_samples, + batch_size, + nb_steps, + nb_stacks, + nb_digits, + fraction_values_for_train=None, + device=torch.device("cpu"), + ): + self.batch_size = batch_size + self.nb_steps = nb_steps + self.nb_stacks = nb_stacks + self.nb_digits = nb_digits + self.device = device + + if fraction_values_for_train is None: + values_for_train = None + values_for_test = None + else: + all = torch.randperm(10**nb_digits) + nb_for_train = int(all.size(0) * fraction_values_for_train) + values_for_train = all[:nb_for_train] + values_for_test = all[nb_for_train:] + + self.train_input, self.train_stack_counts = stack.generate_sequences( + nb_train_samples, + nb_steps, + nb_stacks, + nb_digits, + values_for_train, + self.device, + ) + + self.test_input, self.test_stack_counts = stack.generate_sequences( + nb_test_samples, + nb_steps, + nb_stacks, + nb_digits, + values_for_test, + self.device, + ) + + i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks) + counts = self.test_stack_counts.flatten()[i.flatten()] + counts = F.one_hot(counts).sum(0) + log_string(f"pop_stack_counts {counts}") + + self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 + + def batches(self, split="train", nb_to_use=-1, desc=None): + assert split in {"train", "test"} + input = self.train_input if split == "train" else self.test_input + if nb_to_use > 0: + input = input[:nb_to_use] + if desc is None: + desc = f"epoch-{split}" + for batch in tqdm.tqdm( + input.split(self.batch_size), dynamic_ncols=True, desc=desc + ): + yield batch + + def vocabulary_size(self): + return self.nb_codes + + def produce_results(self, n_epoch, model): + with torch.autograd.no_grad(): + t = model.training + model.eval() + + def compute_nb_correct(input): + result = input.clone() + stack.remove_popped_values(result, self.nb_stacks, self.nb_digits) + ar_mask = (result != input).long() + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device + ) + + errors = ((result != input).long() * ar_mask).reshape( + -1, 1 + self.nb_digits + ) + ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits) + + nb_total = ar_mask.max(1).values.sum() + nb_correct = nb_total - errors.max(1).values.sum() + + return nb_total, nb_correct + + test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000]) + + log_string( + 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}%" + ) + + ############################################################## + # Log a few generated sequences + input = self.test_input[:10, : 12 * (1 + self.nb_digits)] + result = input.clone() + stack.remove_popped_values(result, self.nb_stacks, self.nb_digits) + ar_mask = (result != input).long() + for n in range(result.size(0)): + log_string( + f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}" + ) + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device ) + for n in range(result.size(0)): + log_string( + f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}" + ) + ############################################################## model.train(t) @@ -840,6 +1056,18 @@ elif args.task == "snake": device=device, ) +elif args.task == "stack": + task = TaskStack( + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, + nb_steps=args.stack_nb_steps, + nb_stacks=args.stack_nb_stacks, + nb_digits=args.stack_nb_digits, + fraction_values_for_train=args.stack_fraction_values_for_train, + device=device, + ) + else: raise ValueError(f"Unknown task {args.task}") @@ -975,9 +1203,6 @@ for n_epoch in range(nb_epochs_finished, nb_epochs): for input in task.batches(split="test"): input = input.to(device) - # input, loss_masks, true_images = task.excise_last_image(input) - # input, loss_masks = task.add_true_image(input, true_images, loss_masks) - output = model(mygpt.BracketedSequence(input)).x loss = F.cross_entropy(output.transpose(1, 2), input) acc_test_loss += loss.item() * input.size(0)