Remove/Resolve import duplicates + add from_dict classmethod to MemoryNetworkInput

reagent/core/types.py

@@ -891,6 +891,23 @@ class MemoryNetworkInput(BaseInput):
     valid_step: Optional[torch.Tensor] = None
     extras: ExtraData = field(default_factory=ExtraData)
 
+    @classmethod
+    def from_dict(cls, d):
+        return cls(
+            state=FeatureData(
+                float_features=d["state"],
+            ),
+            next_state=FeatureData(
+                float_features=d["next_state"],
+            ),
+            action=d["action"],
+            reward=d["reward"],
+            time_diff=d["time_diff"],
+            not_terminal=d["not_terminal"],
+            step=d["step"],
+            extras=ExtraData.from_dict(d),
+        )
+
     def __len__(self):
         if len(self.state.float_features.size()) == 2:
             return self.state.float_features.size()[0]

reagent/gym/preprocessors/trainer_preprocessor.py

@@ -28,7 +28,6 @@
 ONLINE_MAKER_MAP = {}
 REPLAY_BUFFER_MAKER_MAP = {}
 
-
 def make_trainer_preprocessor(
     trainer: Trainer,
     device: torch.device,
@@ -344,16 +343,16 @@ def __call__(self, batch):
             stacked_not_terminal[-1] = scalar_fields["not_terminal"]
             scalar_fields["not_terminal"] = stacked_not_terminal
 
-        return rlt.MemoryNetworkInput(
-            state=rlt.FeatureData(float_features=vector_fields["state"]),
-            next_state=rlt.FeatureData(float_features=vector_fields["next_state"]),
-            action=vector_fields["action"],
-            reward=scalar_fields["reward"],
-            not_terminal=scalar_fields["not_terminal"],
-            step=None,
-            time_diff=None,
-        )
-
+        dict_batch = {
+            "state": vector_fields["state"],
+            "next_state": vector_fields["next_state"],
+            "action": vector_fields["action"],
+            "reward": scalar_fields["reward"],
+            "not_terminal": scalar_fields["not_terminal"],
+            "step": None,
+            "time_diff": None,
+        }
+        return rlt.MemoryNetworkInput.from_dict(dict_batch)
 
 def get_possible_actions_for_gym(batch_size: int, num_actions: int) -> rlt.FeatureData:
     """

reagent/workflow/model_managers/model_manager.py

@@ -9,8 +9,7 @@
 from reagent.core.dataclasses import dataclass
 from reagent.core.parameters import NormalizationData
 from reagent.core.registry_meta import RegistryMeta
-from reagent.core.tensorboardX import summary_writer_context
-from reagent.training import ReAgentLightningModule, Trainer
+from reagent.training import Trainer
 from reagent.workflow.data import ReAgentDataModule
 from reagent.workflow.types import (
     Dataset,