Remove unused type error suppressions

Summary:
This diff was automatically generated by the Pyre per-target upgrade tool.

It removes `# pyre-fixme` or `pyrefly: ignore` comments that are no longer needed because the underlying type errors have been resolved.
Note that it will also aim to ensure type checking runs cleanly, and will add suppressions to existing type errors.

#pyreupgrade

Differential Revision: D116557086

fbshipit-source-id: 1337613d4fb3ab79bd3a733f3a4c9a499a72f971

projects/implicitron_trainer/experiment.py

@@ -117,16 +117,12 @@ class Experiment(Configurable):
             will be saved here.
     """
 
-    # pyre-fixme[13]: Attribute `data_source` is never initialized.
     data_source: DataSourceBase
     data_source_class_type: str = "ImplicitronDataSource"
-    # pyre-fixme[13]: Attribute `model_factory` is never initialized.
     model_factory: ModelFactoryBase
     model_factory_class_type: str = "ImplicitronModelFactory"
-    # pyre-fixme[13]: Attribute `optimizer_factory` is never initialized.
     optimizer_factory: OptimizerFactoryBase
     optimizer_factory_class_type: str = "ImplicitronOptimizerFactory"
-    # pyre-fixme[13]: Attribute `training_loop` is never initialized.
     training_loop: TrainingLoopBase
     training_loop_class_type: str = "ImplicitronTrainingLoop"
 

projects/implicitron_trainer/impl/model_factory.py

@@ -59,7 +59,6 @@ class ImplicitronModelFactory(ModelFactoryBase):
 
     """
 
-    # pyre-fixme[13]: Attribute `model` is never initialized.
     model: ImplicitronModelBase
     model_class_type: str = "GenericModel"
     resume: bool = True

projects/implicitron_trainer/impl/optimizer_factory.py

@@ -169,7 +169,6 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
                 gamma=self.gamma,
             )
         elif self.lr_policy.casefold() == "Exponential".casefold():
-            # pyre-fixme[28]: Unexpected keyword argument `verbose`.
             scheduler = torch.optim.lr_scheduler.LambdaLR(
                 optimizer,
                 lambda epoch: self.gamma ** (epoch / self.exponential_lr_step_size),
@@ -190,9 +189,7 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
                     gamma = self.gamma ** (epoch_rest / self.exponential_lr_step_size)
                 return gamma
 
-            # pyre-fixme[28]: Unexpected keyword argument `verbose`.
             scheduler = torch.optim.lr_scheduler.LambdaLR(
-                # pyrefly: ignore [unexpected-keyword]
                 optimizer,
                 _get_lr,
                 # pyrefly: ignore [unexpected-keyword]

projects/implicitron_trainer/impl/training_loop.py

@@ -36,7 +36,6 @@ class TrainingLoopBase(ReplaceableBase):
         evaluator: An EvaluatorBase instance, used to evaluate training results.
     """
 
-    # pyre-fixme[13]: Attribute `evaluator` is never initialized.
     evaluator: Optional[EvaluatorBase]
     evaluator_class_type: Optional[str] = "ImplicitronEvaluator"
 
@@ -380,7 +379,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
 
             # update the stats logger
             stats.update(preds, time_start=t_start, stat_set=trainmode)
-            # pyre-ignore [16]
             assert stats.it[trainmode] == it, "inconsistent stat iteration number!"
 
             # print textual status update