X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=80ffdbbbcc7d57ad10860907b615ef7ae6a0bebd;hb=5eca344648e789099dbad211f41ca690d50e7475;hp=50ded2c016aebf2c3e982d418a0343aa98d4ff72;hpb=336130cc923761658029a0af9d5862d59405d47a;p=culture.git diff --git a/tasks.py b/tasks.py index 50ded2c..80ffdbb 100755 --- a/tasks.py +++ b/tasks.py @@ -22,7 +22,7 @@ def masked_inplace_autoregression( batch_size, input, ar_mask, - seq_logproba, + summed_logits, temperature, deterministic_synthesis, forbidden_tokens=None, @@ -32,11 +32,7 @@ def masked_inplace_autoregression( ): assert input.size() == ar_mask.size() - batches = zip( - input.split(batch_size), - ar_mask.split(batch_size), - seq_logproba.split(batch_size), - ) + batches = zip(input.split(batch_size), ar_mask.split(batch_size)) if progress_bar_desc is not None: batches = tqdm.tqdm( @@ -50,11 +46,11 @@ def masked_inplace_autoregression( t = model.training model.eval() - for input, ar_mask, seq_logproba in batches: + for input, ar_mask in batches: model.masked_inplace_autoregression( input=input, ar_mask=ar_mask, - seq_logproba=seq_logproba, + summed_logits=summed_logits, temperature=temperature, deterministic_synthesis=deterministic_synthesis, forbidden_tokens=forbidden_tokens, @@ -85,16 +81,13 @@ class Task: import world -class QuizzMachine(Task): +class World(Task): def save_image(self, input, result_dir, filename, logger): img = world.seq2img(input.to("cpu"), self.height, self.width) image_name = os.path.join(result_dir, filename) torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=6, padding=4) logger(f"wrote {image_name}") - def save_quizzes(self, input, result_dir, filename_prefix, logger): - self.save_image(input, result_dir, filename_prefix + ".png", logger) - def make_ar_mask(self, input): b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2 return b.long()[None, :].expand_as(input) @@ -115,52 +108,49 @@ class QuizzMachine(Task): self.height = 6 self.width = 8 - self.train_w_quizzes = world.generate_seq( + self.train_input = world.generate_seq( nb_train_samples, height=self.height, width=self.width ).to(device) - self.test_w_quizzes = world.generate_seq( + self.test_input = world.generate_seq( nb_test_samples, height=self.height, width=self.width ).to(device) - self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1 + self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 - self.train_c_quizzes = [] - self.test_c_quizzes = [] + self.train_quizzes = [] + self.test_quizzes = [] if result_dir is not None: - self.save_quizzes( - self.train_w_quizzes[:72], result_dir, f"culture_w_quizzes", logger + self.save_image( + self.train_input[:72], result_dir, f"world_train.png", logger ) def batches(self, split="train", desc=None): assert split in {"train", "test"} if split == "train": - w_quizzes = self.train_w_quizzes - c_quizzes = self.train_c_quizzes + input = self.train_input + quizzes = self.train_quizzes else: - w_quizzes = self.test_w_quizzes - c_quizzes = self.test_c_quizzes + input = self.test_input + quizzes = self.test_quizzes - if len(c_quizzes) > 0: - c_quizzes = torch.cat(c_quizzes, dim=0) - if c_quizzes.size(0) > w_quizzes.size(0) // 2: - i = torch.randperm(w_quizzes.size(0))[: w_quizzes.size(0) // 2] - c_quizzes = c_quizzes[i] + if len(quizzes) > 0: + quizzes = torch.cat(quizzes, dim=0) + if quizzes.size(0) > input.size(0) // 2: + i = torch.randperm(input.size(0))[: input.size(0) // 2] + quizzes = quizzes[i] - i = torch.randperm(w_quizzes.size(0))[ - : w_quizzes.size(0) - c_quizzes.size(0) - ] - w_quizzes = w_quizzes[i] + i = torch.randperm(input.size(0))[: input.size(0) - quizzes.size(0)] + input = input[i] - self.nb_batch_w_quizzes = w_quizzes.size(0) - self.nb_batch_c_quizzes = c_quizzes.size(0) + self.nb_batch_samples_world = input.size(0) + self.nb_batch_samples_quizzes = quizzes.size(0) - input = torch.cat([w_quizzes, c_quizzes], dim=0) + input = torch.cat([input, quizzes], dim=0) else: - input = w_quizzes - self.nb_batch_w_quizzes = w_quizzes.size(0) - self.nb_batch_c_quizzes = 0 + self.nb_batch_samples_world = input.size(0) + self.nb_batch_samples_quizzes = 0 # Shuffle input = input[torch.randperm(input.size(0))] @@ -182,14 +172,13 @@ class QuizzMachine(Task): input = input[:nmax] ar_mask = self.make_ar_mask(input) result = input.clone() * (1 - ar_mask) - seq_logproba = torch.empty(input.size(0), device=self.device) masked_inplace_autoregression( model=model, batch_size=self.batch_size, input=result, ar_mask=ar_mask, - seq_logproba=seq_logproba, + summed_logits=None, temperature=1.0, deterministic_synthesis=deterministic_synthesis, progress_bar_desc=None, @@ -203,13 +192,13 @@ class QuizzMachine(Task): return nb_total, nb_correct - train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes) + train_nb_total, train_nb_correct = compute_accuracy(self.train_input) logger( 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 = compute_accuracy(self.test_w_quizzes, logger) + test_nb_total, test_nb_correct = compute_accuracy(self.test_input, logger) logger( 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}%" @@ -220,47 +209,46 @@ class QuizzMachine(Task): ############################## - input = self.test_w_quizzes[:96] + input = self.test_input[:96] ar_mask = self.make_ar_mask(input) result = input.clone() * (1 - ar_mask) - seq_logproba = torch.empty(input.size(0), device=self.device) masked_inplace_autoregression( model=model, batch_size=self.batch_size, input=result, ar_mask=ar_mask, - seq_logproba=seq_logproba, + summed_logits=None, temperature=1.0, deterministic_synthesis=deterministic_synthesis, progress_bar_desc=None, device=self.device, ) - self.save_quizzes( + self.save_image( result[:72], result_dir, - f"culture_prediction_{n_epoch:04d}_{model.id:02d}", + f"world_prediction_{n_epoch:04d}_{model.id:02d}.png", logger, ) return main_test_accuracy - def renew_w_quizzes(self, nb, for_train=True): - input = self.train_w_quizzes if for_train else self.test_w_quizzes + def renew_samples(self, nb, for_train=True): + input = self.train_input if for_train else self.test_input nb = min(nb, input.size(0)) input[:-nb] = input[nb:].clone() input[-nb:] = world.generate_seq(nb, height=self.height, width=self.width).to( self.device ) - def store_c_quizzes(self, new_c_quizzes, for_train=True): + def store_new_quizzes(self, new_quizzes, for_train=True): if for_train: - self.train_c_quizzes.append(new_c_quizzes) + self.train_quizzes.append(new_quizzes) else: - self.test_c_quizzes.append(new_c_quizzes) + self.test_quizzes.append(new_quizzes) - def create_c_quizzes( + def create_new_quizzes( self, n_epoch, result_dir, @@ -268,71 +256,70 @@ class QuizzMachine(Task): nb, model, other_models, - min_ave_seq_logproba, + desired_average_logits=None, ): ############################################################### # Generate quizzes with model - c_quizzes = torch.empty( + quizzes = torch.empty( nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64 ) - ar_mask = torch.full(c_quizzes.size(), 1, device=self.device) - seq_logproba = torch.empty(ar_mask.size(0), device=self.device) + ar_mask = torch.full(quizzes.size(), 1, device=self.device) + summed_logits = torch.empty(nb, device=self.device) temperature = 1 d_temperature = 1 while True: - seq_logproba[...] = 0 + summed_logits[...] = 0 masked_inplace_autoregression( model=model, batch_size=self.batch_size, - input=c_quizzes, + input=quizzes, ar_mask=ar_mask, - seq_logproba=seq_logproba, + summed_logits=summed_logits, temperature=temperature, deterministic_synthesis=False, - progress_bar_desc="sampling c_quizzes", + progress_bar_desc="creating quizzes", device=self.device, ) - ave_seq_logproba = seq_logproba.mean() + average_logits = summed_logits.mean() - logger(f"{ave_seq_logproba=} {min_ave_seq_logproba=}") + logger(f"{average_logits=} {desired_average_logits=}") - if min_ave_seq_logproba is None: + if desired_average_logits is None: break # Oh man that's ugly - if ave_seq_logproba < min_ave_seq_logproba * 1.1: + if average_logits < desired_average_logits * 1.1: if d_temperature > 0: - d_temperature *= -1 / 3 + d_temperature *= -0.5 temperature += d_temperature - elif ave_seq_logproba > min_ave_seq_logproba: + elif average_logits > desired_average_logits: if d_temperature < 0: - d_temperature *= -1 / 3 + d_temperature *= -0.5 temperature += d_temperature else: break - logger(f"chaging temperature to {temperature}") + logger(f"changing temperature to {temperature}") ############################################################### # Create the reverse quizzes l = self.height * self.width - direction = c_quizzes[:, l : l + 1] + direction = quizzes[:, l : l + 1] direction = world.token_forward * ( direction == world.token_backward ) + world.token_backward * (direction == world.token_forward) - reverse_c_quizzes = torch.cat( - [c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1 + reverse_quizzes = torch.cat( + [quizzes[:, l + 1 :], direction, quizzes[:, :l]], dim=1 ) - ar_mask = self.make_ar_mask(c_quizzes) - seq_logproba = torch.empty(ar_mask.size(0), device=self.device) + ar_mask = self.make_ar_mask(quizzes) ############################################################### # Check how many of the other models can solve them in both @@ -341,42 +328,47 @@ class QuizzMachine(Task): nb_correct = [] for m in other_models: - result = c_quizzes.clone() + result = quizzes.clone() masked_inplace_autoregression( model=m, batch_size=self.batch_size, input=result, ar_mask=ar_mask, - seq_logproba=seq_logproba, + summed_logits=None, temperature=1.0, deterministic_synthesis=True, - progress_bar_desc="solving c_quizzes", + progress_bar_desc="solving quizzes", device=self.device, ) - correct = (c_quizzes == result).long().min(dim=-1).values + correct = (quizzes == result).long().min(dim=-1).values - reverse_result = reverse_c_quizzes.clone() + reverse_result = reverse_quizzes.clone() masked_inplace_autoregression( model=m, batch_size=self.batch_size, input=reverse_result, ar_mask=ar_mask, - seq_logproba=seq_logproba, + summed_logits=None, temperature=1.0, deterministic_synthesis=True, - progress_bar_desc="solving reversed c_quizzes", + progress_bar_desc="solving reversed quizzes", device=self.device, ) reverse_correct = ( - (reverse_c_quizzes == reverse_result).long().min(dim=-1).values + (reverse_quizzes == reverse_result).long().min(dim=-1).values ) nb_correct.append((correct * reverse_correct)[None, :]) - nb_correct = torch.cat(nb_correct, dim=0).sum(dim=0) + nb_correct = torch.cat(nb_correct, dim=0) + + # filename = os.path.join(result_dir, "correct_{n_epoch:04d}.dat") + # with open(filename, "w") as f: + # for k in nb_correct: + # f.write(f"{k}\n") - return c_quizzes, nb_correct, seq_logproba.mean() + return quizzes, nb_correct.sum(dim=0), summed_logits.mean()