Improves Type Annotations (#252)

lerobot/common/logger.py

@@ -241,5 +241,6 @@ class Logger:
 
     def log_video(self, video_path: str, step: int, mode: str = "train"):
         assert mode in {"train", "eval"}
+        assert self._wandb is not None
         wandb_video = self._wandb.Video(video_path, fps=self._cfg.fps, format="mp4")
         self._wandb.log({f"{mode}/video": wandb_video}, step=step)

lerobot/common/policies/policy_protocol.py

@@ -57,7 +57,7 @@ class Policy(Protocol):
         other items should be logging-friendly, native Python types.
         """
 
-    def select_action(self, batch: dict[str, Tensor]):
+    def select_action(self, batch: dict[str, Tensor]) -> Tensor:
         """Return one action to run in the environment (potentially in batch mode).
 
         When the model uses a history of observations, or outputs a sequence of actions, this method deals

lerobot/common/policies/tdmpc/modeling_tdmpc.py

@@ -134,7 +134,7 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin):
         self._prev_mean: torch.Tensor | None = None
 
     @torch.no_grad()
-    def select_action(self, batch: dict[str, Tensor]):
+    def select_action(self, batch: dict[str, Tensor]) -> Tensor:
         """Select a single action given environment observations."""
         batch = self.normalize_inputs(batch)
         batch["observation.image"] = batch[self.input_image_key]