class World(Task):
def save_image(self, input, result_dir, filename, logger):
- img = world.sample2img(self.train_input.to("cpu"), self.height, self.width)
+ img = world.sample2img(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=8, padding=2)
logger(f"wrote {image_name}")
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}")
+ main_test_accuracy = test_nb_correct / test_nb_total
+ logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
##############################
device=self.device,
)
- self.save_image(result, result_dir, f"world_result_{n_epoch:04d}.png", logger)
+ self.save_image(
+ result[:96], result_dir, f"world_result_{n_epoch:04d}.png", logger
+ )
+
+ return main_test_accuracy
- def store_new_problems(self, new_problems):
- nb_current = self.train_input.size(0)
- nb_new = new_problems.size(0)
+ def store_new_quizzes(self, new_quizzes, for_train=True):
+ input = self.train_input if for_train else self.test_input
+
+ nb_current = input.size(0)
+ nb_new = new_quizzes.size(0)
if nb_new >= nb_current:
- self.train_input[...] = new_problems[:nb_current]
+ input[...] = new_quizzes[:nb_current]
else:
nb_kept = nb_current - nb_new
- self.train_input[:nb_kept] = self.train_input[-nb_kept:].clone()
- self.train_input[nb_kept:] = new_problems
+ input[:nb_kept] = input[-nb_kept:].clone()
+ input[nb_kept:] = new_quizzes
- def create_new_problems(self, n_epoch, result_dir, logger, nb, model, nb_runs):
- new_problems = torch.empty(
+ def create_new_quizzes(
+ self, n_epoch, result_dir, logger, nb, models, other_models, nb_runs
+ ):
+ new_quizzes = torch.empty(
nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64
)
- ar_mask = torch.full(new_problems.size(), 1, device=self.device)
+ ar_mask = torch.full(new_quizzes.size(), 1, device=self.device)
masked_inplace_autoregression(
model,
self.batch_size,
- new_problems,
+ new_quizzes,
ar_mask,
deterministic_synthesis=False,
- progress_bar_desc="new problems",
+ progress_bar_desc="new quizzes",
device=self.device,
)
- nb_correct = torch.empty(nb, device=self.device, dtype=torch.int64)
+ input = (
+ new_quizzes[:, None, :]
+ .expand(-1, nb_runs, -1)
+ .clone()
+ .reshape(-1, new_quizzes.size(-1))
+ )
+ result = input.clone()
- for n in tqdm.tqdm(
- range(new_problems.size(0)), dynamic_ncols=True, desc="checking problems"
- ):
- result = new_problems[n][None, :].expand(nb_runs, -1).clone()
- ar_mask = (
- (torch.arange(result.size(1), device=self.device) > result.size(1) // 2)
- .long()[None, :]
- .expand_as(result)
- )
+ ar_mask = (
+ (torch.arange(result.size(1), device=self.device) > result.size(1) // 2)
+ .long()[None, :]
+ .expand_as(result)
+ )
+
+ dispatch = torch.randint(len(other_models), (result.size(0),))
+ for n, m in enumerate(other_models):
masked_inplace_autoregression(
- model,
+ m,
self.batch_size,
- result,
- ar_mask,
+ result[dispatch == n],
+ ar_mask[dispatch == n],
deterministic_synthesis=False,
progress_bar_desc=None,
device=self.device,
)
- nb_correct[n] = (
- (new_problems[n][None, :] == result).long().min(dim=1).values.sum()
- )
+ nb_correct = (
+ (input == result).long().min(dim=-1).values.reshape(-1, nb_runs).sum(dim=-1)
+ )
- return new_problems, nb_correct
+ return new_quizzes, nb_correct