Swtich from Hydra call to hydra instantiate.

Author: ernestum

src/imitation_cli/airl.py

@@ -7,7 +7,7 @@
 import hydra
 import torch as th
 from hydra.core.config_store import ConfigStore
-from hydra.utils import call
+from hydra.utils import instantiate
 from omegaconf import MISSING
 
 from imitation.policies import serialize
@@ -65,7 +65,7 @@ def run_airl(cfg: RunConfig) -> Dict[str, Any]:
     from imitation.data import rollout
     from imitation.data.types import TrajectoryWithRew
 
-    trainer: airl.AIRL = call(cfg.airl)
+    trainer: airl.AIRL = instantiate(cfg.airl)
 
     checkpoints_path = pathlib.Path("checkpoints")
 

src/imitation_cli/utils/environment.py

@@ -9,7 +9,7 @@
     from stable_baselines3.common.vec_env import VecEnv
 
 from hydra.core.config_store import ConfigStore
-from hydra.utils import call
+from hydra.utils import instantiate
 from omegaconf import MISSING
 
 from imitation_cli.utils import randomness
@@ -40,7 +40,7 @@ def make(log_dir: Optional[str] = None, **kwargs) -> VecEnv:
 def make_rollout_venv(environment_config: Config) -> VecEnv:
     from imitation.data import wrappers
 
-    return call(
+    return instantiate(
         environment_config,
         log_dir=None,
         post_wrappers=[lambda env, i: wrappers.RolloutInfoWrapper(env)],

src/imitation_cli/utils/policy.py

@@ -11,7 +11,7 @@
     from stable_baselines3.common.policies import BasePolicy
 
 from hydra.core.config_store import ConfigStore
-from hydra.utils import call
+from hydra.utils import instantiate
 from omegaconf import MISSING
 
 from imitation_cli.utils import activation_function_class as act_fun_class_cfg
@@ -91,9 +91,9 @@ def make_args(
         del kwargs["_target_"]
         del kwargs["environment"]
 
-        kwargs["activation_fn"] = call(activation_fn)
-        kwargs["features_extractor_class"] = call(features_extractor_class)
-        kwargs["optimizer_class"] = call(optimizer_class)
+        kwargs["activation_fn"] = instantiate(activation_fn)
+        kwargs["features_extractor_class"] = instantiate(features_extractor_class)
+        kwargs["optimizer_class"] = instantiate(optimizer_class)
 
         return dict(
             **kwargs,
@@ -182,7 +182,7 @@ def make(
         model = serialize.load_stable_baselines_model(
             Loaded.type_to_class(policy_type),
             filename,
-            call(environment),
+            instantiate(environment),
         )
         return model.policy
 

src/imitation_cli/utils/reward_network.py

@@ -10,7 +10,7 @@
     from imitation.rewards.reward_nets import RewardNet
 
 from hydra.core.config_store import ConfigStore
-from hydra.utils import call
+from hydra.utils import instantiate
 from omegaconf import MISSING
 
 import imitation_cli.utils.environment as environment_cfg
@@ -99,11 +99,11 @@ def make(
     ) -> RewardNet:
         from imitation.rewards import reward_nets
 
-        venv = call(environment)
+        venv = instantiate(environment)
         reward_net = reward_nets.RewardEnsemble(
             venv.observation_space,
             venv.action_space,
-            [call(ensemble_member_config) for _ in range(ensemble_size)],
+            [instantiate(ensemble_member_config) for _ in range(ensemble_size)],
         )
         if add_std_alpha is not None:
             return reward_nets.AddSTDRewardWrapper(

src/imitation_cli/utils/rl_algorithm.py

@@ -10,7 +10,7 @@
     import stable_baselines3 as sb3
     from stable_baselines3.common.vec_env import VecEnv
 
-from hydra.utils import call, to_absolute_path
+from hydra.utils import instantiate, to_absolute_path
 from omegaconf import MISSING
 
 from imitation_cli.utils import environment as environment_cfg
@@ -73,9 +73,9 @@ def make(
         return sb3.PPO(
             policy=sb3.common.policies.ActorCriticPolicy,
             policy_kwargs=policy_kwargs,
-            env=call(environment),
-            learning_rate=call(learning_rate),
-            clip_range=call(clip_range),
+            env=instantiate(environment),
+            learning_rate=instantiate(learning_rate),
+            clip_range=instantiate(clip_range),
             **kwargs,
         )
 

src/imitation_cli/utils/trajectories.py

@@ -12,7 +12,7 @@
     import numpy as np
 
 from hydra.core.config_store import ConfigStore
-from hydra.utils import call
+from hydra.utils import instantiate
 from omegaconf import MISSING
 
 from imitation_cli.utils import environment as environment_cfg
@@ -60,9 +60,9 @@ def make(
     ) -> Sequence[Trajectory]:
         from imitation.data import rollout
 
-        expert = call(expert_policy)
-        env = call(expert_policy.environment)
-        rng = call(rng)
+        expert = instantiate(expert_policy)
+        env = instantiate(expert_policy.environment)
+        rng = instantiate(rng)
         return rollout.generate_trajectories(
             expert,
             env,