fix(make_policy): rename mapping edge cases in training (#2332)

* fix bug

* update fixes

* add hf license

* more fixes

* add transformers

* iterate on review

* more fixes

* more fixes

* add a False test

* reduce img size

* reduce img size

* skip the test

* add

* add style

src/lerobot/policies/factory.py

@@ -38,6 +38,7 @@ from lerobot.policies.sac.configuration_sac import SACConfig
 from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
 from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
 from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
+from lerobot.policies.utils import validate_visual_features_consistency
 from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.processor import PolicyAction, PolicyProcessorPipeline
 from lerobot.processor.converters import (
@@ -420,20 +421,7 @@ def make_policy(
     # policy = torch.compile(policy, mode="reduce-overhead")
 
     if not rename_map:
-        expected_features = set(cfg.input_features.keys()) | set(cfg.output_features.keys())
-        provided_features = set(features.keys())
-        if expected_features and provided_features != expected_features:
-            missing = expected_features - provided_features
-            extra = provided_features - expected_features
-            # TODO (jadechoghari): provide a dynamic rename map suggestion to the user.
-            raise ValueError(
-                f"Feature mismatch between dataset/environment and policy config.\n"
-                f"- Missing features: {sorted(missing) if missing else 'None'}\n"
-                f"- Extra features: {sorted(extra) if extra else 'None'}\n\n"
-                f"Please ensure your dataset and policy use consistent feature names.\n"
-                f"If your dataset uses different observation keys (e.g., cameras named differently), "
-                f"use the `--rename_map` argument, for example:\n"
-                f'  --rename_map=\'{{"observation.images.left": "observation.images.camera1", '
-                f'"observation.images.top": "observation.images.camera2"}}\''
-            )
+        validate_visual_features_consistency(cfg, features)
+        # TODO: (jadechoghari) - add a check_state(cfg, features) and check_action(cfg, features)
+
     return policy

src/lerobot/policies/utils.py

@@ -22,6 +22,8 @@ import numpy as np
 import torch
 from torch import nn
 
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.datasets.utils import build_dataset_frame
 from lerobot.processor import PolicyAction, RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
@@ -198,3 +200,42 @@ def make_robot_action(action_tensor: PolicyAction, ds_features: dict[str, dict])
         f"{name}": float(action_tensor[i]) for i, name in enumerate(action_names)
     }
     return act_processed_policy
+
+
+def raise_feature_mismatch_error(
+    provided_features: set[str],
+    expected_features: set[str],
+) -> None:
+    """
+    Raises a standardized ValueError for feature mismatches between dataset/environment and policy config.
+    """
+    missing = expected_features - provided_features
+    extra = provided_features - expected_features
+    # TODO (jadechoghari): provide a dynamic rename map suggestion to the user.
+    raise ValueError(
+        f"Feature mismatch between dataset/environment and policy config.\n"
+        f"- Missing features: {sorted(missing) if missing else 'None'}\n"
+        f"- Extra features: {sorted(extra) if extra else 'None'}\n\n"
+        f"Please ensure your dataset and policy use consistent feature names.\n"
+        f"If your dataset uses different observation keys (e.g., cameras named differently), "
+        f"use the `--rename_map` argument, for example:\n"
+        f'  --rename_map=\'{{"observation.images.left": "observation.images.camera1", '
+        f'"observation.images.top": "observation.images.camera2"}}\''
+    )
+
+
+def validate_visual_features_consistency(
+    cfg: PreTrainedConfig,
+    features: dict[str, PolicyFeature],
+) -> None:
+    """
+    Validates visual feature consistency between a policy config and provided dataset/environment features.
+
+    Args:
+        cfg (PreTrainedConfig): The model or policy configuration containing input_features and type.
+        features (Dict[str, PolicyFeature]): A mapping of feature names to PolicyFeature objects.
+    """
+    expected_visuals = {k for k, v in cfg.input_features.items() if v.type == FeatureType.VISUAL}
+    provided_visuals = {k for k, v in features.items() if v.type == FeatureType.VISUAL}
+    if not provided_visuals.issubset(expected_visuals):
+        raise_feature_mismatch_error(provided_visuals, expected_visuals)