rename reward classifier

lerobot/common/policies/factory.py

@@ -24,10 +24,10 @@ from lerobot.common.envs.configs import EnvConfig
 from lerobot.common.envs.utils import env_to_policy_features
 from lerobot.common.policies.act.configuration_act import ACTConfig
 from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.common.policies.hilserl.classifier.configuration_classifier import ClassifierConfig
 from lerobot.common.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.common.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
 from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.policies.reward_model.configuration_classifier import RewardClassifierConfig
 from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.configs.policies import PreTrainedConfig
@@ -64,8 +64,8 @@ def get_policy_class(name: str) -> PreTrainedPolicy:
         from lerobot.common.policies.sac.modeling_sac import SACPolicy
 
         return SACPolicy
-    elif name == "hilserl_classifier":
-        from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier
+    elif name == "reward_classifier":
+        from lerobot.common.policies.reward_model.modeling_classifier import Classifier
 
         return Classifier
     else:
@@ -85,8 +85,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return PI0Config(**kwargs)
     elif policy_type == "pi0fast":
         return PI0FASTConfig(**kwargs)
-    elif policy_type == "hilserl_classifier":
-        return ClassifierConfig(**kwargs)
+    elif policy_type == "reward_classifier":
+        return RewardClassifierConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{policy_type}' is not available.")
 

lerobot/common/policies/normalize.py

@@ -80,7 +80,7 @@ def create_stats_buffers(
 
         # TODO(aliberts, rcadene): harmonize this to only use one framework (np or torch)
         if stats and key in stats:
-            # NOTE:(maractingi, azouitine): Change the order of these conditions becuase in online environments we don't have dataset stats
+            # NOTE:(maractingi, azouitine): Change the order of these conditions because in online environments we don't have dataset stats
             # Therefore, we don't access to full stats of the data, some elements either have min-max or mean-std only
             if norm_mode is NormalizationMode.MEAN_STD:
                 if "mean" not in stats[key] or "std" not in stats[key]:

lerobot/common/policies/reward_model/configuration_classifier.py

@@ -7,12 +7,12 @@ from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import NormalizationMode
 
 
-@PreTrainedConfig.register_subclass(name="hilserl_classifier")
+@PreTrainedConfig.register_subclass(name="reward_classifier")
 @dataclass
-class ClassifierConfig(PreTrainedConfig):
-    """Configuration for the Classifier model."""
+class RewardClassifierConfig(PreTrainedConfig):
+    """Configuration for the Reward Classifier model."""
 
-    name: str = "hilserl_classifier"
+    name: str = "reward_classifier"
     num_classes: int = 2
     hidden_dim: int = 256
     dropout_rate: float = 0.1

lerobot/common/policies/reward_model/modeling_classifier.py

@@ -5,11 +5,9 @@ import torch
 from torch import Tensor, nn
 
 from lerobot.common.constants import OBS_IMAGE
-from lerobot.common.policies.hilserl.classifier.configuration_classifier import (
-    ClassifierConfig,
-)
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.policies.reward_model.configuration_classifier import RewardClassifierConfig
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)
@@ -39,12 +37,12 @@ class ClassifierOutput:
 class Classifier(PreTrainedPolicy):
     """Image classifier built on top of a pre-trained encoder."""
 
-    name = "hilserl_classifier"
-    config_class = ClassifierConfig
+    name = "reward_classifier"
+    config_class = RewardClassifierConfig
 
     def __init__(
         self,
-        config: ClassifierConfig,
+        config: RewardClassifierConfig,
         dataset_stats: Dict[str, Dict[str, Tensor]] | None = None,
     ):
         from transformers import AutoModel