Merge branch 'master' into benchmark-pr

src/imitation/algorithms/bc.py

@@ -9,6 +9,7 @@
 from typing import (
     Any,
     Callable,
+    Dict,
     Iterable,
     Iterator,
     Mapping,
@@ -22,7 +23,7 @@
 import numpy as np
 import torch as th
 import tqdm
-from stable_baselines3.common import policies, utils, vec_env
+from stable_baselines3.common import policies, torch_layers, utils, vec_env
 
 from imitation.algorithms import base as algo_base
 from imitation.data import rollout, types
@@ -99,7 +100,12 @@ class BehaviorCloningLossCalculator:
     def __call__(
         self,
         policy: policies.ActorCriticPolicy,
-        obs: Union[th.Tensor, np.ndarray],
+        obs: Union[
+            types.AnyTensor,
+            types.DictObs,
+            Dict[str, np.ndarray],
+            Dict[str, th.Tensor],
+        ],
         acts: Union[th.Tensor, np.ndarray],
     ) -> BCTrainingMetrics:
         """Calculate the supervised learning loss used to train the behavioral clone.
@@ -113,9 +119,18 @@ def __call__(
             A BCTrainingMetrics object with the loss and all the components it
             consists of.
         """
-        obs = util.safe_to_tensor(obs)
+        tensor_obs = types.map_maybe_dict(
+            util.safe_to_tensor,
+            types.maybe_unwrap_dictobs(obs),
+        )
         acts = util.safe_to_tensor(acts)
-        _, log_prob, entropy = policy.evaluate_actions(obs, acts)
+
+        # policy.evaluate_actions's type signatures are incorrect.
+        # See https://github.com/DLR-RM/stable-baselines3/issues/1679
+        (_, log_prob, entropy) = policy.evaluate_actions(
+            tensor_obs,  # type: ignore[arg-type]
+            acts,
+        )
         prob_true_act = th.exp(log_prob).mean()
         log_prob = log_prob.mean()
         entropy = entropy.mean() if entropy is not None else None
@@ -324,12 +339,18 @@ def __init__(
         self.rng = rng
 
         if policy is None:
+            extractor = (
+                torch_layers.CombinedExtractor
+                if isinstance(observation_space, gym.spaces.Dict)
+                else torch_layers.FlattenExtractor
+            )
             policy = policy_base.FeedForward32Policy(
                 observation_space=observation_space,
                 action_space=action_space,
                 # Set lr_schedule to max value to force error if policy.optimizer
                 # is used by mistake (should use self.optimizer instead).
                 lr_schedule=lambda _: th.finfo(th.float32).max,
+                features_extractor_class=extractor,
             )
         self._policy = policy.to(utils.get_device(device))
         # TODO(adam): make policy mandatory and delete observation/action space params?
@@ -464,9 +485,14 @@ def process_batch():
             minibatch_size,
             num_samples_so_far,
         ), batch in batches_with_stats:
-            obs = th.as_tensor(batch["obs"], device=self.policy.device).detach()
-            acts = th.as_tensor(batch["acts"], device=self.policy.device).detach()
-            training_metrics = self.loss_calculator(self.policy, obs, acts)
+            obs_tensor: Union[th.Tensor, Dict[str, th.Tensor]]
+            # unwraps the observation if it's a dictobs and converts arrays to tensors
+            obs_tensor = types.map_maybe_dict(
+                lambda x: util.safe_to_tensor(x, device=self.policy.device),
+                types.maybe_unwrap_dictobs(batch["obs"]),
+            )
+            acts = util.safe_to_tensor(batch["acts"], device=self.policy.device)
+            training_metrics = self.loss_calculator(self.policy, obs_tensor, acts)
 
             # Renormalise the loss to be averaged over the whole
             # batch size instead of the minibatch size.

src/imitation/algorithms/density.py

@@ -134,9 +134,9 @@ def __init__(
 
     def _get_demo_from_batch(
         self,
-        obs_b: np.ndarray,
+        obs_b: types.Observation,
         act_b: np.ndarray,
-        next_obs_b: Optional[np.ndarray],
+        next_obs_b: Optional[types.Observation],
     ) -> Dict[Optional[int], List[np.ndarray]]:
         if next_obs_b is None and self.density_type == DensityType.STATE_STATE_DENSITY:
             raise ValueError(
@@ -145,11 +145,18 @@ def _get_demo_from_batch(
             )
 
         assert act_b.shape[1:] == self.venv.action_space.shape
-        assert obs_b.shape[1:] == self.venv.observation_space.shape
+        ob_space = self.venv.observation_space
+        if isinstance(obs_b, types.DictObs):
+            exp_shape = {
+                k: v.shape for k, v in ob_space.items()  # type: ignore[attr-defined]
+            }
+            obs_shape = {k: v.shape[1:] for k, v in obs_b.items()}
+            assert exp_shape == obs_shape, f"Expected {exp_shape}, got {obs_shape}"
+        else:
+            assert obs_b.shape[1:] == ob_space.shape
         assert len(act_b) == len(obs_b)
         if next_obs_b is not None:
-            assert next_obs_b.shape[1:] == self.venv.observation_space.shape
-            assert len(next_obs_b) == len(obs_b)
+            assert next_obs_b.shape == obs_b.shape
 
         if next_obs_b is not None:
             next_obs_b_iterator: Iterable = next_obs_b
@@ -200,14 +207,17 @@ def set_demonstrations(self, demonstrations: base.AnyTransitions) -> None:
                 # analogous to cast above.
                 demonstrations = cast(Iterable[types.TransitionMapping], demonstrations)
 
+                def to_np_maybe_dictobs(x):
+                    if isinstance(x, types.DictObs):
+                        return x
+                    else:
+                        return util.safe_to_numpy(x, warn=True)
+
                 for batch in demonstrations:
-                    transitions.update(
-                        self._get_demo_from_batch(
-                            util.safe_to_numpy(batch["obs"], warn=True),
-                            util.safe_to_numpy(batch["acts"], warn=True),
-                            util.safe_to_numpy(batch.get("next_obs"), warn=True),
-                        ),
-                    )
+                    obs = to_np_maybe_dictobs(batch["obs"])
+                    acts = util.safe_to_numpy(batch["acts"], warn=True)
+                    next_obs = to_np_maybe_dictobs(batch.get("next_obs"))
+                    transitions.update(self._get_demo_from_batch(obs, acts, next_obs))
             else:
                 raise TypeError(
                     f"Unsupported demonstration type {type(demonstrations)}",
@@ -253,65 +263,40 @@ def _fit_density(self, transitions: np.ndarray) -> neighbors.KernelDensity:
 
     def _preprocess_transition(
         self,
-        obs: np.ndarray,
+        obs: types.Observation,
         act: np.ndarray,
-        next_obs: Optional[np.ndarray],
+        next_obs: Optional[types.Observation],
     ) -> np.ndarray:
         """Compute flattened transition on subset specified by `self.density_type`."""
+        flattened_obs = space_utils.flatten(
+            self.venv.observation_space,
+            types.maybe_unwrap_dictobs(obs),
+        )
+        flattened_obs = _check_data_is_np_array(flattened_obs, "observation")
         if self.density_type == DensityType.STATE_DENSITY:
-            flat_observations = space_utils.flatten(self.venv.observation_space, obs)
-            if not isinstance(flat_observations, np.ndarray):
-                raise ValueError(
-                    "The density estimator only supports spaces that "
-                    "flatten to a numpy array but the observation space "
-                    f"flattens to {type(flat_observations)}",
-                )
-
-            return flat_observations
+            return flattened_obs
         elif self.density_type == DensityType.STATE_ACTION_DENSITY:
-            flat_observation = space_utils.flatten(self.venv.observation_space, obs)
-            flat_action = space_utils.flatten(self.venv.action_space, act)
-
-            if not isinstance(flat_observation, np.ndarray):
-                raise ValueError(
-                    "The density estimator only supports spaces that "
-                    "flatten to a numpy array but the observation space "
-                    f"flattens to {type(flat_observation)}",
-                )
-            if not isinstance(flat_action, np.ndarray):
-                raise ValueError(
-                    "The density estimator only supports spaces that "
-                    "flatten to a numpy array but the action space "
-                    f"flattens to {type(flat_action)}",
-                )
-
-            return np.concatenate([flat_observation, flat_action])
+            flattened_action = space_utils.flatten(self.venv.action_space, act)
+            flattened_action = _check_data_is_np_array(flattened_action, "action")
+            return np.concatenate([flattened_obs, flattened_action])
         elif self.density_type == DensityType.STATE_STATE_DENSITY:
             assert next_obs is not None
-            flat_observation = space_utils.flatten(self.venv.observation_space, obs)
-            flat_next_observation = space_utils.flatten(
+            flat_next_obs = space_utils.flatten(
                 self.venv.observation_space,
-                next_obs,
+                types.maybe_unwrap_dictobs(next_obs),
             )
+            flat_next_obs = _check_data_is_np_array(flat_next_obs, "observation")
+            assert type(flattened_obs) is type(flat_next_obs)
 
-            if not isinstance(flat_observation, np.ndarray):
-                raise ValueError(
-                    "The density estimator only supports spaces that "
-                    "flatten to a numpy array but the observation space "
-                    f"flattens to {type(flat_observation)}",
-                )
-
-            assert type(flat_observation) is type(flat_next_observation)
-
-            return np.concatenate([flat_observation, flat_next_observation])
+            return np.concatenate([flattened_obs, flat_next_obs])
         else:
             raise ValueError(f"Unknown density type {self.density_type}")
 
     def __call__(
         self,
-        state: np.ndarray,
+        state: types.Observation,
         action: np.ndarray,
-        next_state: np.ndarray,
+        next_state: types.Observation,
         done: np.ndarray,
         steps: Optional[np.ndarray] = None,
     ) -> np.ndarray:
@@ -347,6 +332,8 @@ def __call__(
 
         rew_list = []
         assert len(state) == len(action) and len(state) == len(next_state)
+        state = types.maybe_wrap_in_dictobs(state)
+        next_state = types.maybe_wrap_in_dictobs(next_state)
         for idx, (obs, act, next_obs) in enumerate(zip(state, action, next_state)):
             flat_trans = self._preprocess_transition(obs, act, next_obs)
             assert self._scaler is not None
@@ -424,3 +411,13 @@ def policy(self) -> base_class.BasePolicy:
         assert self.rl_algo is not None
         assert self.rl_algo.policy is not None
         return self.rl_algo.policy
+
+
+def _check_data_is_np_array(data: space_utils.FlatType, name: str) -> np.ndarray:
+    """Raises error if the flattened data is not a numpy array."""
+    assert isinstance(data, np.ndarray), (
+        "The density estimator only supports spaces that "
+        f"flatten to a numpy array but the {name} space "
+        f"flattens to {type(data)}",
+    )
+    return data

src/imitation/algorithms/mce_irl.py

@@ -7,7 +7,18 @@
 """
 import collections
 import warnings
-from typing import Any, Iterable, List, Mapping, NoReturn, Optional, Tuple, Type, Union
+from typing import (
+    Any,
+    Dict,
+    Iterable,
+    List,
+    Mapping,
+    NoReturn,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+)
 
 import gymnasium as gym
 import numpy as np
@@ -347,7 +358,7 @@ def _set_demo_from_trajectories(self, trajs: Iterable[types.Trajectory]) -> None
         num_demos = 0
         for traj in trajs:
             cum_discount = 1.0
-            for obs in traj.obs:
+            for obs in types.assert_not_dictobs(traj.obs):
                 self.demo_state_om[obs] += cum_discount
                 cum_discount *= self.discount
             num_demos += 1
@@ -411,23 +422,32 @@ def set_demonstrations(self, demonstrations: MCEDemonstrations) -> None:
 
         if isinstance(demonstrations, types.Transitions):
             self._set_demo_from_obs(
-                demonstrations.obs,
+                types.assert_not_dictobs(demonstrations.obs),
                 demonstrations.dones,
-                demonstrations.next_obs,
+                types.assert_not_dictobs(demonstrations.next_obs),
             )
         elif isinstance(demonstrations, types.TransitionsMinimal):
-            self._set_demo_from_obs(demonstrations.obs, None, None)
+            self._set_demo_from_obs(
+                types.assert_not_dictobs(demonstrations.obs),
+                None,
+                None,
+            )
         elif isinstance(demonstrations, Iterable):
             # Demonstrations are a Torch DataLoader or other Mapping iterable
             # Collect them together into one big NumPy array. This is inefficient,
             # we could compute the running statistics instead, but in practice do
             # not expect large dataset sizes together with MCE IRL.
-            collated_list = collections.defaultdict(list)
+            collated_list: Dict[
+                str,
+                List[types.AnyTensor],
+            ] = collections.defaultdict(list)
             for batch in demonstrations:
                 assert isinstance(batch, Mapping)
                 for k in ("obs", "dones", "next_obs"):
-                    if k in batch:
-                        collated_list[k].append(batch[k])
+                    x = batch.get(k)
+                    if x is not None:
+                        assert isinstance(x, (np.ndarray, th.Tensor))
+                        collated_list[k].append(x)
             collated = {k: np.concatenate(v) for k, v in collated_list.items()}
 
             assert "obs" in collated

src/imitation/algorithms/preference_comparisons.py

@@ -465,9 +465,9 @@ def rewards(self, transitions: Transitions) -> th.Tensor:
             Shape - (num_transitions, ) for Single reward network and
             (num_transitions, num_networks) for ensemble of networks.
         """
-        state = transitions.obs
+        state = types.assert_not_dictobs(transitions.obs)
         action = transitions.acts
-        next_state = transitions.next_obs
+        next_state = types.assert_not_dictobs(transitions.next_obs)
         done = transitions.dones
         if self.ensemble_model is not None:
             rews_np = self.ensemble_model.predict_processed_all(

src/imitation/data/buffer.py

@@ -1,6 +1,5 @@
 """Buffers to store NumPy arrays and transitions in."""
 
-import dataclasses
 from typing import Any, Mapping, Optional, Tuple
 
 import numpy as np
@@ -368,15 +367,16 @@ def from_data(
         Returns:
             A new ReplayBuffer.
         """
-        obs_shape = transitions.obs.shape[1:]
+        obs = types.assert_not_dictobs(transitions.obs)
+        obs_shape = obs.shape[1:]
         act_shape = transitions.acts.shape[1:]
         if capacity is None:
-            capacity = transitions.obs.shape[0]
+            capacity = obs.shape[0]
         instance = cls(
             capacity=capacity,
             obs_shape=obs_shape,
             act_shape=act_shape,
-            obs_dtype=transitions.obs.dtype,
+            obs_dtype=obs.dtype,
             act_dtype=transitions.acts.dtype,
         )
         instance.store(transitions, truncate_ok=truncate_ok)
@@ -406,7 +406,7 @@ def store(self, transitions: types.Transitions, truncate_ok: bool = True) -> Non
         Raises:
             ValueError: The arguments didn't have the same length.
         """  # noqa: DAR402
-        trans_dict = dataclasses.asdict(transitions)
+        trans_dict = types.dataclass_quick_asdict(transitions)
         # Remove unnecessary fields
         trans_dict = {k: trans_dict[k] for k in self._buffer.sample_shapes.keys()}
         self._buffer.store(trans_dict, truncate_ok=truncate_ok)

src/imitation/data/huggingface_utils.py

@@ -124,6 +124,8 @@ def trajectories_to_dict(
         ],
         terminal=[traj.terminal for traj in trajectories],
     )
+    if any(isinstance(traj.obs, types.DictObs) for traj in trajectories):
+        raise ValueError("DictObs are not currently supported")
 
     # Encode infos as jsonpickled strings
     trajectory_dict["infos"] = [