Bugfix: Double2Float default behaviour (pytorch#242)

ai4co · Jul 2, 2022 · 225f92f · 225f92f
1 parent 5ed32a9
commit 225f92f
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Showing 3 changed files with 36 additions and 11 deletions.
diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import warnings
 from typing import Optional, Union, Tuple
 
 import numpy as np
@@ -38,6 +39,11 @@ def __init__(self, keys=None):
         self.keys = keys
 
     def __call__(self, info_dict: dict, tensordict: _TensorDict) -> _TensorDict:
+        if not isinstance(info_dict, dict) and len(self.keys):
+            warnings.warn(
+                f"Found an info_dict of type {type(info_dict)} "
+                f"but expected type or subtype `dict`."
+            )
         for key in self.keys:
             if key in info_dict:
                 tensordict[key] = info_dict[key]
@@ -67,6 +73,11 @@ class GymLikeEnv(_EnvWrapper):
     It is also expected that env.reset() returns an observation similar to the one observed after a step is completed.
     """
 
+    @classmethod
+    def __new__(cls, *args, **kwargs):
+        cls._info_dict_reader = None
+        return super().__new__(cls, *args, **kwargs)
+
     def _step(self, tensordict: _TensorDict) -> _TensorDict:
         action = tensordict.get("action")
         action_np = self.action_spec.to_numpy(action, safe=False)
@@ -98,7 +109,8 @@ def _step(self, tensordict: _TensorDict) -> _TensorDict:
         )
         tensordict_out.set("reward", reward)
         tensordict_out.set("done", done)
-        self.info_dict_reader(info, tensordict_out)
+        if self.info_dict_reader is not None:
+            self.info_dict_reader(*info, tensordict_out)
 
         return tensordict_out
 
@@ -156,17 +168,15 @@ def set_info_dict_reader(self, info_dict_reader: callable) -> GymLikeEnv:
         self.info_dict_reader = info_dict_reader
         return self
 
+    def __repr__(self) -> str:
+        return (
+            f"{self.__class__.__name__}(env={self._env}, batch_size={self.batch_size})"
+        )
+
     @property
     def info_dict_reader(self):
-        if "_info_dict_reader" not in self.__dir__():
-            self._info_dict_reader = default_info_dict_reader()
         return self._info_dict_reader
 
     @info_dict_reader.setter
     def info_dict_reader(self, value: callable):
         self._info_dict_reader = value
-
-    def __repr__(self) -> str:
-        return (
-            f"{self.__class__.__name__}(env={self._env}, batch_size={self.batch_size})"
-        )
diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py
@@ -19,7 +19,7 @@
     UnboundedContinuousTensorSpec,
 )
 from ...data.utils import numpy_to_torch_dtype_dict
-from ..gym_like import GymLikeEnv
+from ..gym_like import GymLikeEnv, default_info_dict_reader
 from ..utils import classproperty
 
 try:
@@ -226,6 +226,16 @@ def rebuild_with_kwargs(self, **new_kwargs):
         self._env = self._build_env(**self._constructor_kwargs)
         self._make_specs(self._env)
 
+    @property
+    def info_dict_reader(self):
+        if self._info_dict_reader is None:
+            self._info_dict_reader = default_info_dict_reader()
+        return self._info_dict_reader
+
+    @info_dict_reader.setter
+    def info_dict_reader(self, value: callable):
+        self._info_dict_reader = value
+
 
 class GymEnv(GymWrapper):
     """

diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py
@@ -1245,8 +1245,6 @@ def __init__(
         keys_in: Optional[Sequence[str]] = None,
         keys_inv_in: Optional[Sequence[str]] = None,
     ):
-        if keys_inv_in is None:
-            keys_inv_in = ["action"]
         super().__init__(keys_in=keys_in, keys_inv_in=keys_inv_in)
 
     def _apply_transform(self, obs: torch.Tensor) -> torch.Tensor:
@@ -1286,6 +1284,13 @@ def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec
         self._transform_spec(observation_spec)
         return observation_spec
 
+    def __repr__(self) -> str:
+        s = (
+            f"{self.__class__.__name__}(keys_in={self.keys_in}, keys_out={self.keys_out},"
+            f"keys_inv_in={self.keys_inv_in}, keys_inv_out={self.keys_inv_out})"
+        )
+        return s
+
 
 class CatTensors(Transform):
     """