chore(evo1): delete added test + reduce diff

src/lerobot/envs/configs.py

@@ -24,11 +24,7 @@ import gymnasium as gym
 from gymnasium.envs.registration import registry as gym_registry
 
 from lerobot.configs import FeatureType, PolicyFeature
-from lerobot.processor import (
-    IsaaclabArenaProcessorStep,
-    LiberoProcessorStep,
-    PolicyProcessorPipeline,
-)
+from lerobot.processor import IsaaclabArenaProcessorStep, LiberoProcessorStep, PolicyProcessorPipeline
 from lerobot.robots import RobotConfig
 from lerobot.teleoperators.config import TeleoperatorConfig
 from lerobot.utils.constants import (

src/lerobot/scripts/lerobot_eval.py

@@ -283,7 +283,7 @@ def rollout(
             action = action_transition[ACTION]
 
             # Convert to CPU / numpy.
-            action_numpy = _action_to_env_numpy(action)
+            action_numpy: np.ndarray = action.detach().to(device="cpu", dtype=torch.float32).numpy()
             assert action_numpy.ndim == 2, "Action dimensions should be (batch, action_dim)"
 
             # Apply the next action.
@@ -384,11 +384,6 @@ def rollout(
     return ret
 
 
-def _action_to_env_numpy(action: Tensor) -> np.ndarray:
-    """Convert policy actions to a NumPy array accepted by Gym environments."""
-    return action.detach().to(device="cpu", dtype=torch.float32).numpy()
-
-
 def eval_policy(
     env: gym.vector.VectorEnv,
     policy: PreTrainedPolicy,

tests/scripts/test_lerobot_eval.py

@@ -1,14 +0,0 @@
-import numpy as np
-import torch
-
-from lerobot.scripts.lerobot_eval import _action_to_env_numpy
-
-
-def test_action_to_env_numpy_casts_bfloat16_to_float32():
-    action = torch.tensor([[0.5, -1.0]], dtype=torch.bfloat16)
-
-    action_numpy = _action_to_env_numpy(action)
-
-    assert action_numpy.shape == (1, 2)
-    assert action_numpy.dtype == np.float32
-    np.testing.assert_allclose(action_numpy, np.array([[0.5, -1.0]], dtype=np.float32))