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():
######################################################################
+
import world
nb_train_samples,
nb_test_samples,
batch_size,
+ vqae_nb_epochs,
device=torch.device("cpu"),
):
self.batch_size = batch_size
self.device = device
(
- self.train_input,
+ train_frames,
self.train_actions,
- self.test_input,
+ test_frames,
self.test_actions,
self.frame2seq,
self.seq2frame,
nb_test_samples,
mode="first_last",
nb_steps=30,
- nb_epochs=2,
+ 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):
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
- pass
+ 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}")
######################################################################