fix: HuggingFace save_to_disk takes PathLike type which is defined …

…as str, bytes or os.PathLike. imitation.util.parse_path always returned pathlib.Path which is not one of these types. This commit converts pathlib.Path to str before calling the HF fn.

src/imitation/data/serialize.py

@@ -19,7 +19,7 @@ def save(path: AnyPath, trajectories: Sequence[Trajectory]) -> None:
         path: Trajectories are saved to this path.
         trajectories: The trajectories to save.
     """
-    p = util.parse_path(path)
+    p = str(util.parse_path(path))
     huggingface_utils.trajectories_to_dataset(trajectories).save_to_disk(p)
     logging.info(f"Dumped demonstrations to {p}.")
 

tests/data/conftest.py

@@ -0,0 +1,62 @@
+import gymnasium as gym
+import numpy as np
+import pytest
+
+from imitation.data import types
+
+SPACES = [
+    gym.spaces.Discrete(3),
+    gym.spaces.MultiDiscrete([3, 4]),
+    gym.spaces.Box(-1, 1, shape=(1,)),
+    gym.spaces.Box(-1, 1, shape=(2,)),
+    gym.spaces.Box(-np.inf, np.inf, shape=(2,)),
+]
+DICT_SPACE = gym.spaces.Dict(
+    {"a": gym.spaces.Discrete(3), "b": gym.spaces.Box(-1, 1, shape=(2,))},
+)
+LENGTHS = [0, 1, 2, 10]
+
+
+@pytest.fixture(params=SPACES)
+def act_space(request):
+    return request.param
+
+
+@pytest.fixture(params=SPACES + [DICT_SPACE])
+def obs_space(request):
+    return request.param
+
+
+@pytest.fixture(params=LENGTHS)
+def length(request):
+    return request.param
+
+
+@pytest.fixture
+def trajectory(
+    obs_space: gym.Space,
+    act_space: gym.Space,
+    length: int,
+) -> types.Trajectory:
+    """Fixture to generate trajectory of length `length` iid sampled from spaces."""
+    if length == 0:
+        pytest.skip()
+
+    raw_obs = [obs_space.sample() for _ in range(length + 1)]
+    if isinstance(obs_space, gym.spaces.Dict):
+        obs: types.Observation = types.DictObs.from_obs_list(raw_obs)
+    else:
+        obs = np.array(raw_obs)
+    acts = np.array([act_space.sample() for _ in range(length)])
+    infos = np.array([{f"key{i}": i} for i in range(length)])
+    return types.Trajectory(obs=obs, acts=acts, infos=infos, terminal=True)
+
+
+@pytest.fixture
+def trajectory_rew(trajectory: types.Trajectory) -> types.TrajectoryWithRew:
+    """Like `trajectory` but with reward randomly sampled from a Gaussian."""
+    rews = np.random.randn(len(trajectory))
+    return types.TrajectoryWithRew(
+        **types.dataclass_quick_asdict(trajectory),
+        rews=rews,
+    )

tests/data/test_serialize.py

@@ -0,0 +1,63 @@
+"""Tests for `imitation.data.serialize`."""
+
+import pathlib
+
+import gymnasium as gym
+import numpy as np
+import pytest
+
+from imitation.data import serialize, types
+from imitation.data.types import DictObs
+
+
+@pytest.fixture
+def data_path(tmp_path):
+    return tmp_path / "data"
+
+
+@pytest.mark.parametrize("path_type", [str, pathlib.Path])
+def test_save_trajectory(data_path, trajectory, path_type):
+    if isinstance(trajectory.obs, DictObs):
+        pytest.skip("serialize.save does not yet support DictObs")
+
+    serialize.save(path_type(data_path), [trajectory])
+    assert data_path.exists()
+
+
+@pytest.mark.parametrize("path_type", [str, pathlib.Path])
+def test_save_trajectory_rew(data_path, trajectory_rew, path_type):
+    if isinstance(trajectory_rew.obs, DictObs):
+        pytest.skip("serialize.save does not yet support DictObs")
+    serialize.save(path_type(data_path), [trajectory_rew])
+    assert data_path.exists()
+
+
+def test_save_load_trajectory(data_path, trajectory):
+    if isinstance(trajectory.obs, DictObs):
+        pytest.skip("serialize.save does not yet support DictObs")
+    serialize.save(data_path, [trajectory])
+
+    reconstructed = list(serialize.load(data_path))
+    reconstructedi = reconstructed[0]
+
+    assert len(reconstructed) == 1
+    assert np.allclose(reconstructedi.obs, trajectory.obs)
+    assert np.allclose(reconstructedi.acts, trajectory.acts)
+    assert np.allclose(reconstructedi.terminal, trajectory.terminal)
+    assert not hasattr(reconstructedi, "rews")
+
+
+@pytest.mark.parametrize("load_fn", [serialize.load, serialize.load_with_rewards])
+def test_save_load_trajectory_rew(data_path, trajectory_rew, load_fn):
+    if isinstance(trajectory_rew.obs, DictObs):
+        pytest.skip("serialize.save does not yet support DictObs")
+    serialize.save(data_path, [trajectory_rew])
+
+    reconstructed = list(load_fn(data_path))
+    reconstructedi = reconstructed[0]
+
+    assert len(reconstructed) == 1
+    assert np.allclose(reconstructedi.obs, trajectory_rew.obs)
+    assert np.allclose(reconstructedi.acts, trajectory_rew.acts)
+    assert np.allclose(reconstructedi.terminal, trajectory_rew.terminal)
+    assert np.allclose(reconstructedi.rews, trajectory_rew.rews)

tests/data/test_types.py

@@ -15,58 +15,13 @@
 from imitation.data import serialize, types
 from imitation.util import util
 
-SPACES = [
-    gym.spaces.Discrete(3),
-    gym.spaces.MultiDiscrete([3, 4]),
-    gym.spaces.Box(-1, 1, shape=(1,)),
-    gym.spaces.Box(-1, 1, shape=(2,)),
-    gym.spaces.Box(-np.inf, np.inf, shape=(2,)),
-]
-DICT_SPACE = gym.spaces.Dict(
-    {"a": gym.spaces.Discrete(3), "b": gym.spaces.Box(-1, 1, shape=(2,))},
-)
-
-OBS_SPACES = SPACES + [DICT_SPACE]
-ACT_SPACES = SPACES
-LENGTHS = [0, 1, 2, 10]
-
 
 def _check_1d_shape(fn: Callable[[np.ndarray], Any], length: int, expected_msg: str):
     for shape in [(), (length, 1), (length, 2), (length - 1,), (length + 1,)]:
         with pytest.raises(ValueError, match=expected_msg):
             fn(np.zeros(shape))
 
 
-@pytest.fixture
-def trajectory(
-    obs_space: gym.Space,
-    act_space: gym.Space,
-    length: int,
-) -> types.Trajectory:
-    """Fixture to generate trajectory of length `length` iid sampled from spaces."""
-    if length == 0:
-        pytest.skip()
-
-    raw_obs = [obs_space.sample() for _ in range(length + 1)]
-    if isinstance(obs_space, gym.spaces.Dict):
-        obs: types.Observation = types.DictObs.from_obs_list(raw_obs)
-    else:
-        obs = np.array(raw_obs)
-    acts = np.array([act_space.sample() for _ in range(length)])
-    infos = np.array([{f"key{i}": i} for i in range(length)])
-    return types.Trajectory(obs=obs, acts=acts, infos=infos, terminal=True)
-
-
-@pytest.fixture
-def trajectory_rew(trajectory: types.Trajectory) -> types.TrajectoryWithRew:
-    """Like `trajectory` but with reward randomly sampled from a Gaussian."""
-    rews = np.random.randn(len(trajectory))
-    return types.TrajectoryWithRew(
-        **types.dataclass_quick_asdict(trajectory),
-        rews=rews,
-    )
-
-
 @pytest.fixture
 def transitions_min(
     obs_space: gym.Space,
@@ -134,9 +89,6 @@ def pushd(dir_path):
         os.chdir(orig_dir)
 
 
-@pytest.mark.parametrize("obs_space", OBS_SPACES)
-@pytest.mark.parametrize("act_space", ACT_SPACES)
-@pytest.mark.parametrize("length", LENGTHS)
 class TestData:
     """Tests of imitation.util.data.