Formatting

examples/rtc/eval_with_real_robot.py

@@ -461,7 +461,9 @@ def demo_cli(cfg: RTCDemoConfig):
         peft_pretrained_path = cfg.policy.pretrained_path
         peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
 
-        policy = policy_class.from_pretrained(pretrained_name_or_path=peft_config.base_model_name_or_path, config=config)
+        policy = policy_class.from_pretrained(
+            pretrained_name_or_path=peft_config.base_model_name_or_path, config=config
+        )
         policy = PeftModel.from_pretrained(policy, peft_pretrained_path, config=peft_config)
     else:
         policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)

src/lerobot/policies/factory.py

@@ -493,7 +493,6 @@ def make_policy(
         # Make a fresh policy.
         policy = policy_cls(**kwargs)
 
-
     policy.to(cfg.device)
     assert isinstance(policy, torch.nn.Module)
 

src/lerobot/scripts/lerobot_eval.py

@@ -289,7 +289,6 @@ def eval_policy(
         except ImportError:
             raise exc
 
-
     start = time.time()
     policy.eval()
 

tests/test_cli.py

@@ -121,13 +121,7 @@ def test_peft_training_params_are_fewer(policy_type, tmp_path):
     model_id = resolve_model_id_for_peft_training(policy_type)
 
     def dummy_update_policy(
-        train_metrics,
-        policy,
-        batch,
-        optimizer,
-        grad_clip_norm: float,
-        accelerator,
-        **kwargs
+        train_metrics, policy, batch, optimizer, grad_clip_norm: float, accelerator, **kwargs
     ):
         params_total = sum(p.numel() for p in policy.parameters())
         params_trainable = sum(p.numel() for p in policy.parameters() if p.requires_grad)