+ 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}")