Added docstrings to aggregate, fix test_policies.py

tests/datasets/test_datasets.py

@@ -511,23 +511,19 @@ def test_backward_compatibility(repo_id):
             )
 
     # test2 first frames of first episode
-    i = dataset.meta.episodes["dataset_from_index"][0].item()
+    i = dataset.meta.episodes[0]["dataset_from_index"]
     load_and_compare(i)
     load_and_compare(i + 1)
 
     # test 2 frames at the middle of first episode
     i = int(
-        (
-            dataset.meta.episodes["dataset_to_index"][0].item()
-            - dataset.meta.episodes["dataset_from_index"][0].item()
-        )
-        / 2
+        (dataset.meta.episodes[0]["dataset_to_index"] - dataset.meta.episodes[0]["dataset_from_index"]) / 2
     )
     load_and_compare(i)
     load_and_compare(i + 1)
 
     # test 2 last frames of first episode
-    i = dataset.meta.episodes["dataset_to_index"][0].item()
+    i = dataset.meta.episodes[0]["dataset_to_index"]
     load_and_compare(i - 2)
     load_and_compare(i - 1)
 

tests/policies/test_policies.py

@@ -20,6 +20,7 @@ from pathlib import Path
 import einops
 import pytest
 import torch
+from packaging import version
 from safetensors.torch import load_file
 
 from lerobot import available_policies
@@ -68,11 +69,7 @@ def dummy_dataset_metadata(lerobot_dataset_metadata_factory, info_factory, tmp_p
         },
     }
     info = info_factory(
-        total_episodes=1,
-        total_frames=1,
-        total_tasks=1,
-        camera_features=camera_features,
-        motor_features=motor_features,
+        total_episodes=1, total_frames=1, camera_features=camera_features, motor_features=motor_features
     )
     ds_meta = lerobot_dataset_metadata_factory(root=tmp_path / "init", info=info)
     return ds_meta
@@ -141,14 +138,13 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
     Note: We test various combinations of policy and dataset. The combinations are by no means exhaustive,
           and for now we add tests as we see fit.
     """
-    policy_kwargs["device"] = DEVICE
-
     train_cfg = TrainPipelineConfig(
         # TODO(rcadene, aliberts): remove dataset download
         dataset=DatasetConfig(repo_id=ds_repo_id, episodes=[0]),
-        policy=make_policy_config(policy_name, **policy_kwargs),
+        policy=make_policy_config(policy_name, push_to_hub=False, **policy_kwargs),
         env=make_env_config(env_name, **env_kwargs),
     )
+    train_cfg.validate()
 
     # Check that we can make the policy object.
     dataset = make_dataset(train_cfg)
@@ -217,7 +213,7 @@ def test_act_backbone_lr():
     cfg = TrainPipelineConfig(
         # TODO(rcadene, aliberts): remove dataset download
         dataset=DatasetConfig(repo_id="lerobot/aloha_sim_insertion_scripted", episodes=[0]),
-        policy=make_policy_config("act", optimizer_lr=0.01, optimizer_lr_backbone=0.001),
+        policy=make_policy_config("act", optimizer_lr=0.01, optimizer_lr_backbone=0.001, push_to_hub=False),
     )
     cfg.validate()  # Needed for auto-setting some parameters
 
@@ -413,7 +409,17 @@ def test_backward_compatibility(ds_repo_id: str, policy_name: str, policy_kwargs
         4. Check that this test now passes.
         5. Remember to restore `tests/scripts/save_policy_to_safetensors.py` to its original state.
         6. Remember to stage and commit the resulting changes to `tests/artifacts`.
+
+    NOTE: If the test does not pass, and you don't change the policy, it is likely that the test artifact
+    is out of date. For example, some PyTorch versions have different randomness, see this PR:
+    https://github.com/huggingface/lerobot/pull/1127.
+
     """
+
+    # NOTE: ACT policy has different randomness, after PyTorch 2.7.0
+    if policy_name == "act" and version.parse(torch.__version__) < version.parse("2.7.0"):
+        pytest.skip(f"Skipping act policy test with PyTorch {torch.__version__}. Requires PyTorch >= 2.7.0")
+
     ds_name = ds_repo_id.split("/")[-1]
     artifact_dir = Path("tests/artifacts/policies") / f"{ds_name}_{policy_name}_{file_name_extra}"
     saved_output_dict = load_file(artifact_dir / "output_dict.safetensors")