[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

lerobot/scripts/eval_on_robot.py

@@ -46,7 +46,11 @@ import torch
 from tqdm import trange
 
 from lerobot.common.policies.policy_protocol import Policy
-from lerobot.common.robot_devices.control_utils import busy_wait, is_headless, reset_follower_position
+from lerobot.common.robot_devices.control_utils import (
+    busy_wait,
+    is_headless,
+    reset_follower_position,
+)
 from lerobot.common.robot_devices.robots.factory import Robot, make_robot
 from lerobot.common.utils.utils import (
     init_hydra_config,
@@ -60,13 +64,19 @@ def get_classifier(pretrained_path, config_path):
         return
 
     from lerobot.common.policies.factory import _policy_cfg_from_hydra_cfg
-    from lerobot.common.policies.hilserl.classifier.configuration_classifier import ClassifierConfig
-    from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier
+    from lerobot.common.policies.hilserl.classifier.configuration_classifier import (
+        ClassifierConfig,
+    )
+    from lerobot.common.policies.hilserl.classifier.modeling_classifier import (
+        Classifier,
+    )
 
     cfg = init_hydra_config(config_path)
 
     classifier_config = _policy_cfg_from_hydra_cfg(ClassifierConfig, cfg)
-    classifier_config.num_cameras = len(cfg.training.image_keys)  # TODO automate these paths
+    classifier_config.num_cameras = len(
+        cfg.training.image_keys
+    )  # TODO automate these paths
     model = Classifier(classifier_config)
     model.load_state_dict(Classifier.from_pretrained(pretrained_path).state_dict())
     model = model.to("mps")
@@ -151,11 +161,17 @@ def rollout(
         images = []
         for key in image_keys:
             if display_cameras:
-                cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR))
+                cv2.imshow(
+                    key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR)
+                )
                 cv2.waitKey(1)
             images.append(observation[key].to("mps"))
 
-        reward = reward_classifier.predict_reward(images) if reward_classifier is not None else 0.0
+        reward = (
+            reward_classifier.predict_reward(images)
+            if reward_classifier is not None
+            else 0.0
+        )
         all_rewards.append(reward)
 
         # print("REWARD : ", reward)
@@ -219,11 +235,19 @@ def eval_policy(
 
     start_eval = time.perf_counter()
     progbar = trange(n_episodes, desc="Evaluating policy on real robot")
-    reward_classifier = get_classifier(reward_classifier_pretrained_path, reward_classifier_config_file)
+    reward_classifier = get_classifier(
+        reward_classifier_pretrained_path, reward_classifier_config_file
+    )
 
     for _ in progbar:
         rollout_data = rollout(
-            robot, policy, reward_classifier, fps, control_time_s, use_amp, display_cameras
+            robot,
+            policy,
+            reward_classifier,
+            fps,
+            control_time_s,
+            use_amp,
+            display_cameras,
         )
 
         rollouts.append(rollout_data)
@@ -289,7 +313,9 @@ def init_keyboard_listener():
                 print("Right arrow key pressed. Exiting loop...")
                 events["exit_early"] = True
             elif key == keyboard.Key.left:
-                print("Left arrow key pressed. Exiting loop and rerecord the last episode...")
+                print(
+                    "Left arrow key pressed. Exiting loop and rerecord the last episode..."
+                )
                 events["rerecord_episode"] = True
                 events["exit_early"] = True
             elif key == keyboard.Key.space:
@@ -301,7 +327,10 @@ def init_keyboard_listener():
                         "Place the leader in similar pose to the follower and press space again."
                     )
                     events["pause_policy"] = True
-                    log_say("Human intervention stage. Get ready to take over.", play_sounds=True)
+                    log_say(
+                        "Human intervention stage. Get ready to take over.",
+                        play_sounds=True,
+                    )
                 else:
                     events["human_intervention_step"] = True
                     print("Space key pressed. Human intervention starting.")
@@ -351,7 +380,9 @@ if __name__ == "__main__":
             "debugging). This argument is mutually exclusive with `--pretrained-policy-name-or-path` (`-p`)."
         ),
     )
-    parser.add_argument("--revision", help="Optionally provide the Hugging Face Hub revision ID.")
+    parser.add_argument(
+        "--revision", help="Optionally provide the Hugging Face Hub revision ID."
+    )
     parser.add_argument(
         "--out-dir",
         help=(
@@ -360,7 +391,8 @@ if __name__ == "__main__":
         ),
     )
     parser.add_argument(
-        "--display-cameras", help=("Whether to display the camera feed while the rollout is happening")
+        "--display-cameras",
+        help=("Whether to display the camera feed while the rollout is happening"),
     )
     parser.add_argument(
         "--reward-classifier-pretrained-path",