Refactor eval.py (#127)

tests/test_envs.py

@@ -37,7 +37,7 @@ def test_factory(env_name):
         overrides=[f"env={env_name}", f"device={DEVICE}"],
     )
 
-    env = make_env(cfg, num_parallel_envs=1)
+    env = make_env(cfg, n_envs=1)
     obs, _ = env.reset()
     obs = preprocess_observation(obs)
 

tests/test_policies.py

@@ -8,7 +8,7 @@ from lerobot import available_policies
 from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.datasets.utils import cycle
 from lerobot.common.envs.factory import make_env
-from lerobot.common.envs.utils import postprocess_action, preprocess_observation
+from lerobot.common.envs.utils import preprocess_observation
 from lerobot.common.policies.factory import get_policy_and_config_classes, make_policy
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.policy_protocol import Policy
@@ -80,7 +80,7 @@ def test_policy(env_name, policy_name, extra_overrides):
     assert isinstance(policy, PyTorchModelHubMixin)
 
     # Check that we run select_actions and get the appropriate output.
-    env = make_env(cfg, num_parallel_envs=2)
+    env = make_env(cfg, n_envs=2)
 
     dataloader = torch.utils.data.DataLoader(
         dataset,
@@ -112,10 +112,7 @@ def test_policy(env_name, policy_name, extra_overrides):
 
     # get the next action for the environment
     with torch.inference_mode():
-        action = policy.select_action(observation)
-
-    # convert action to cpu numpy array
-    action = postprocess_action(action)
+        action = policy.select_action(observation).cpu().numpy()
 
     # Test step through policy
     env.step(action)