progress_bar_desc="autoregression",
device=torch.device("cpu"),
):
+ assert input.size() == ar_mask.size()
+
batches = zip(input.split(batch_size), ar_mask.split(batch_size))
if progress_bar_desc is not None:
batches,
dynamic_ncols=True,
desc=progress_bar_desc,
- total=input.size(0) // batch_size,
+ #total=input.size(0) // batch_size,
)
with torch.autograd.no_grad():
)
result *= 1 - ar_mask
- # snake.solver(result,ar_mask)
-
masked_inplace_autoregression(
model,
self.batch_size,
nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
- # nb_total = result.size(0)
- # nb_correct = ((result - input).abs().sum(1) == 0).sum()
-
return nb_total, nb_correct
- # train_nb_total, train_nb_correct = compute_nb_correct(
- # self.train_input, self.train_prior_visits
- # )
-
- # logger(
- # f"accuracy_train 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_nb_correct(
self.test_input[:1000], self.test_prior_visits[:1000]
)
######################################################################
+
+import world
+
+
+class World(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ vqae_nb_epochs,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.device = device
+
+ (
+ train_frames,
+ self.train_actions,
+ test_frames,
+ self.test_actions,
+ self.frame2seq,
+ self.seq2frame,
+ ) = world.create_data_and_processors(
+ nb_train_samples,
+ nb_test_samples,
+ mode="first_last",
+ nb_steps=30,
+ nb_epochs=vqae_nb_epochs,
+ device=device,
+ )
+
+ self.train_input = self.frame2seq(train_frames)
+ self.train_input = self.train_input.reshape(self.train_input.size(0) // 2, -1)
+ self.test_input = self.frame2seq(test_frames)
+ self.test_input = self.test_input.reshape(self.test_input.size(0) // 2, -1)
+
+ 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, result_dir, logger, deterministic_synthesis
+ ):
+ l = self.train_input.size(1)
+ k = torch.arange(l, device=self.device)[None, :]
+ result = self.test_input[:64].clone()
+
+ ar_mask = (k >= l // 2).long().expand_as(result)
+ result *= 1 - ar_mask
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ result = result.reshape(result.size(0) * 2, -1)
+
+ frames = self.seq2frame(result)
+ image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
+ torchvision.utils.save_image(
+ frames.float() / (world.Box.nb_rgb_levels - 1),
+ image_name,
+ nrow=8,
+ padding=1,
+ pad_value=0.0,
+ )
+ logger(f"wrote {image_name}")
+
+
+######################################################################