docs: rm type hints in ignite.contrib.handlers (2)

ignite/contrib/handlers/base_logger.py

@@ -107,7 +107,7 @@ class BaseWeightsScalarHandler(BaseHandler):
     Helper handler to log model's weights as scalars.
     """
 
-    def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None:
+    def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None):
         if not isinstance(model, torch.nn.Module):
             raise TypeError(f"Argument model should be of type torch.nn.Module, but given {type(model)}")
 
@@ -152,14 +152,14 @@ def attach(
         """Attach the logger to the engine and execute `log_handler` function at `event_name` events.
 
         Args:
-            engine (Engine): engine object.
-            log_handler (callable): a logging handler to execute
+            engine: engine object.
+            log_handler: a logging handler to execute
             event_name: event to attach the logging handler to. Valid events are from
-                :class:`~ignite.engine.events.Events` or class:`~ignite.engine.events.EventsList` or any `event_name`
+                :class:`~ignite.engine.events.Events` or :class:`~ignite.engine.events.EventsList` or any `event_name`
                 added by :meth:`~ignite.engine.engine.Engine.register_events`.
 
         Returns:
-            :class:`~ignite.engine.RemovableEventHandle`, which can be used to remove the handler.
+            :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.
         """
         if isinstance(event_name, EventsList):
             for name in event_name:
@@ -180,15 +180,15 @@ def attach_output_handler(self, engine: Engine, event_name: Any, *args: Any, **k
         """Shortcut method to attach `OutputHandler` to the logger.
 
         Args:
-            engine (Engine): engine object.
+            engine: engine object.
             event_name: event to attach the logging handler to. Valid events are from
                 :class:`~ignite.engine.events.Events` or any `event_name` added by
                 :meth:`~ignite.engine.engine.Engine.register_events`.
-            *args: args to initialize `OutputHandler`
-            **kwargs: kwargs to initialize `OutputHandler`
+            args: args to initialize `OutputHandler`
+            kwargs: kwargs to initialize `OutputHandler`
 
         Returns:
-            :class:`~ignite.engine.RemovableEventHandle`, which can be used to remove the handler.
+            :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.
         """
         return self.attach(engine, self._create_output_handler(*args, **kwargs), event_name=event_name)
 
@@ -198,15 +198,15 @@ def attach_opt_params_handler(
         """Shortcut method to attach `OptimizerParamsHandler` to the logger.
 
         Args:
-            engine (Engine): engine object.
+            engine: engine object.
             event_name: event to attach the logging handler to. Valid events are from
                 :class:`~ignite.engine.events.Events` or any `event_name` added by
                 :meth:`~ignite.engine.engine.Engine.register_events`.
-            *args: args to initialize `OptimizerParamsHandler`
-            **kwargs: kwargs to initialize `OptimizerParamsHandler`
+            args: args to initialize `OptimizerParamsHandler`
+            kwargs: kwargs to initialize `OptimizerParamsHandler`
 
         Returns:
-            :class:`~ignite.engine.RemovableEventHandle`, which can be used to remove the handler.
+            :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.
 
         .. versionchanged:: 0.4.3
             Added missing return statement.

ignite/contrib/handlers/clearml_logger.py

@@ -49,11 +49,11 @@ class ClearMLLogger(BaseLogger):
         clearml-init
 
     Args:
-        project_name (str): The name of the project in which the experiment will be created. If the project
+        project_name: The name of the project in which the experiment will be created. If the project
             does not exist, it is created. If ``project_name`` is ``None``, the repository name is used. (Optional)
-        task_name (str): The name of Task (experiment). If ``task_name`` is ``None``, the Python experiment
+        task_name: The name of Task (experiment). If ``task_name`` is ``None``, the Python experiment
             script's file name is used. (Optional)
-        task_type (str): Optional. The task type. Valid values are:
+        task_type: Optional. The task type. Valid values are:
             - ``TaskTypes.training`` (Default)
             - ``TaskTypes.train``
             - ``TaskTypes.testing``
@@ -119,7 +119,7 @@ class ClearMLLogger(BaseLogger):
 
     """
 
-    def __init__(self, *_: Any, **kwargs: Any) -> None:
+    def __init__(self, *_: Any, **kwargs: Any):
         try:
             from clearml import Task
             from clearml.binding.frameworks.tensorflow_bind import WeightsGradientHistHelper
@@ -270,14 +270,14 @@ def global_step_transform(*args, **kwargs):
             )
 
     Args:
-        tag (str): common title for all produced plots. For example, "training"
-        metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available
+        tag: common title for all produced plots. For example, "training"
+        metric_names: list of metric names to plot or a string "all" to plot all available
             metrics.
-        output_transform (callable, optional): output transform function to prepare `engine.state.output` as a number.
+        output_transform: output transform function to prepare `engine.state.output` as a number.
             For example, `output_transform = lambda output: output`
             This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot
             with corresponding keys.
-        global_step_transform (callable, optional): global step transform function to output a desired global step.
+        global_step_transform: global step transform function to output a desired global step.
             Input of the function is `(engine, event_name)`. Output of function should be an integer.
             Default is None, global_step based on attached engine. If provided,
             uses function output as global_step. To setup global step from another engine, please use
@@ -299,7 +299,7 @@ def __init__(
         metric_names: Optional[List[str]] = None,
         output_transform: Optional[Callable] = None,
         global_step_transform: Optional[Callable] = None,
-    ) -> None:
+    ):
         super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform)
 
     def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None:
@@ -359,13 +359,13 @@ class OptimizerParamsHandler(BaseOptimizerParamsHandler):
             )
 
     Args:
-        optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups``
+        optimizer: torch optimizer or any object with attribute ``param_groups``
             as a sequence.
-        param_name (str): parameter name
-        tag (str, optional): common title for all produced plots. For example, "generator"
+        param_name: parameter name
+        tag: common title for all produced plots. For example, "generator"
     """
 
-    def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None) -> None:
+    def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None):
         super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag)
 
     def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None:
@@ -410,13 +410,13 @@ class WeightsScalarHandler(BaseWeightsScalarHandler):
             )
 
     Args:
-        model (torch.nn.Module): model to log weights
-        reduction (callable): function to reduce parameters into scalar
-        tag (str, optional): common title for all produced plots. For example, "generator"
+        model: model to log weights
+        reduction: function to reduce parameters into scalar
+        tag: common title for all produced plots. For example, "generator"
 
     """
 
-    def __init__(self, model: Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None:
+    def __init__(self, model: Module, reduction: Callable = torch.norm, tag: Optional[str] = None):
         super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag)
 
     def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None:
@@ -463,12 +463,12 @@ class WeightsHistHandler(BaseWeightsHistHandler):
             )
 
     Args:
-        model (torch.nn.Module): model to log weights
-        tag (str, optional): common title for all produced plots. For example, 'generator'
+        model: model to log weights
+        tag: common title for all produced plots. For example, 'generator'
 
     """
 
-    def __init__(self, model: Module, tag: Optional[str] = None) -> None:
+    def __init__(self, model: Module, tag: Optional[str] = None):
         super(WeightsHistHandler, self).__init__(model, tag=tag)
 
     def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None:
@@ -517,13 +517,13 @@ class GradsScalarHandler(BaseWeightsScalarHandler):
             )
 
     Args:
-        model (torch.nn.Module): model to log weights
-        reduction (callable): function to reduce parameters into scalar
-        tag (str, optional): common title for all produced plots. For example, "generator"
+        model: model to log weights
+        reduction: function to reduce parameters into scalar
+        tag: common title for all produced plots. For example, "generator"
 
     """
 
-    def __init__(self, model: Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None:
+    def __init__(self, model: Module, reduction: Callable = torch.norm, tag: Optional[str] = None):
         super(GradsScalarHandler, self).__init__(model, reduction, tag=tag)
 
     def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None:
@@ -569,12 +569,12 @@ class GradsHistHandler(BaseWeightsHistHandler):
             )
 
     Args:
-        model (torch.nn.Module): model to log weights
-        tag (str, optional): common title for all produced plots. For example, 'generator'
+        model: model to log weights
+        tag: common title for all produced plots. For example, 'generator'
 
     """
 
-    def __init__(self, model: Module, tag: Optional[str] = None) -> None:
+    def __init__(self, model: Module, tag: Optional[str] = None):
         super(GradsHistHandler, self).__init__(model, tag=tag)
 
     def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None:
@@ -602,12 +602,12 @@ class ClearMLSaver(DiskSaver):
     Handler that saves input checkpoint as ClearML artifacts
 
     Args:
-        logger (ClearMLLogger, optional): An instance of :class:`~ignite.contrib.handlers.clearml_logger.ClearMLLogger`,
+        logger: An instance of :class:`~ignite.contrib.handlers.clearml_logger.ClearMLLogger`,
             ensuring a valid ClearML ``Task`` has been initialized. If not provided, and a ClearML Task
             has not been manually initialized, a runtime error will be raised.
-        output_uri (str, optional): The default location for output models and other artifacts uploaded by ClearML. For
+        output_uri: The default location for output models and other artifacts uploaded by ClearML. For
             more information, see ``clearml.Task.init``.
-        dirname (str, optional): Directory path where the checkpoint will be saved. If not provided, a temporary
+        dirname: Directory path where the checkpoint will be saved. If not provided, a temporary
             directory will be created.
 
     Examples:
@@ -645,7 +645,7 @@ def __init__(
         dirname: Optional[str] = None,
         *args: Any,
         **kwargs: Any,
-    ) -> None:
+    ):
 
         self._setup_check_clearml(logger, output_uri)
 
@@ -793,7 +793,7 @@ def get_local_copy(self, filename: str) -> Optional[str]:
             In distributed configuration this method should be called on rank 0 process.
 
         Args:
-            filename (str): artifact name.
+            filename: artifact name.
 
         Returns:
              a local path to a downloaded copy of the artifact

ignite/contrib/handlers/lr_finder.py

@@ -196,11 +196,11 @@ def plot(self, skip_start: int = 10, skip_end: int = 5, log_lr: bool = True) ->
             pip install matplotlib
 
         Args:
-            skip_start (int, optional): number of batches to trim from the start.
+            skip_start: number of batches to trim from the start.
                 Default: 10.
-            skip_end (int, optional): number of batches to trim from the start.
+            skip_end: number of batches to trim from the start.
                 Default: 5.
-            log_lr (bool, optional): True to plot the learning rate in a logarithmic
+            log_lr: True to plot the learning rate in a logarithmic
                 scale; otherwise, plotted in a linear scale. Default: True.
         """
         try:
@@ -273,27 +273,27 @@ def attach(
                 trainer_with_lr_finder.run(dataloader)`
 
         Args:
-            trainer (Engine): lr_finder is attached to this trainer. Please, keep in mind that all attached handlers
+            trainer: lr_finder is attached to this trainer. Please, keep in mind that all attached handlers
                 will be executed.
-            to_save (Mapping): dictionary with optimizer and other objects that needs to be restored after running
+            to_save: dictionary with optimizer and other objects that needs to be restored after running
                 the LR finder. For example, `to_save={'optimizer': optimizer, 'model': model}`. All objects should
                 implement `state_dict` and `load_state_dict` methods.
-            output_transform (callable, optional): function that transforms the trainer's `state.output` after each
+            output_transform: function that transforms the trainer's `state.output` after each
                 iteration. It must return the loss of that iteration.
-            num_iter (int, optional): number of iterations for lr schedule between base lr and end_lr. Default, it will
+            num_iter: number of iterations for lr schedule between base lr and end_lr. Default, it will
                 run for `trainer.state.epoch_length * trainer.state.max_epochs`.
-            end_lr (float, optional): upper bound for lr search. Default, 10.0.
-            step_mode (str, optional): "exp" or "linear", which way should the lr be increased from optimizer's initial
+            end_lr: upper bound for lr search. Default, 10.0.
+            step_mode: "exp" or "linear", which way should the lr be increased from optimizer's initial
                 lr to `end_lr`. Default, "exp".
-            smooth_f (float, optional): loss smoothing factor in range `[0, 1)`. Default, 0.05
-            diverge_th (float, optional): Used for stopping the search when `current loss > diverge_th * best_loss`.
+            smooth_f: loss smoothing factor in range `[0, 1)`. Default, 0.05
+            diverge_th: Used for stopping the search when `current loss > diverge_th * best_loss`.
                 Default, 5.0.
 
+        Returns:
+            trainer_with_lr_finder (trainer used for finding the lr)
+
         Note:
             lr_finder cannot be attached to more than one trainer at a time.
-
-        Returns:
-            trainer_with_lr_finder: trainer used for finding the lr
         """
         if not isinstance(to_save, Mapping):
             raise TypeError(f"Argument to_save should be a mapping, but given {type(to_save)}")
@@ -363,16 +363,16 @@ class _ExponentialLR(_LRScheduler):
     iterations.
 
     Args:
-        optimizer (torch.optim.Optimizer): wrapped optimizer.
-        end_lr (float, optional): the initial learning rate which is the lower
+        optimizer: wrapped optimizer.
+        end_lr: the initial learning rate which is the lower
             boundary of the test. Default: 10.
-        num_iter (int, optional): the number of iterations over which the test
+        num_iter: the number of iterations over which the test
             occurs. Default: 100.
-        last_epoch (int): the index of last epoch. Default: -1.
+        last_epoch: the index of last epoch. Default: -1.
 
     """
 
-    def __init__(self, optimizer: Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1) -> None:
+    def __init__(self, optimizer: Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1):
         self.end_lr = end_lr
         self.num_iter = num_iter
         super(_ExponentialLR, self).__init__(optimizer, last_epoch)

ignite/contrib/handlers/mlflow_logger.py

@@ -24,7 +24,7 @@ class MLflowLogger(BaseLogger):
         pip install mlflow
 
     Args:
-        tracking_uri (str): MLflow tracking uri. See MLflow docs for more details
+        tracking_uri: MLflow tracking uri. See MLflow docs for more details
 
     Examples:
 
@@ -86,7 +86,7 @@ class MLflowLogger(BaseLogger):
             )
     """
 
-    def __init__(self, tracking_uri: Optional[str] = None) -> None:
+    def __init__(self, tracking_uri: Optional[str] = None):
         try:
             import mlflow
         except ImportError:
@@ -182,14 +182,14 @@ def global_step_transform(*args, **kwargs):
             )
 
     Args:
-        tag (str): common title for all produced plots. For example, 'training'
-        metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available
+        tag: common title for all produced plots. For example, 'training'
+        metric_names: list of metric names to plot or a string "all" to plot all available
             metrics.
-        output_transform (callable, optional): output transform function to prepare `engine.state.output` as a number.
+        output_transform: output transform function to prepare `engine.state.output` as a number.
             For example, `output_transform = lambda output: output`
             This function can also return a dictionary, e.g `{'loss': loss1, 'another_loss': loss2}` to label the plot
             with corresponding keys.
-        global_step_transform (callable, optional): global step transform function to output a desired global step.
+        global_step_transform: global step transform function to output a desired global step.
             Input of the function is `(engine, event_name)`. Output of function should be an integer.
             Default is None, global_step based on attached engine. If provided,
             uses function output as global_step. To setup global step from another engine, please use
@@ -284,13 +284,13 @@ class OptimizerParamsHandler(BaseOptimizerParamsHandler):
             )
 
     Args:
-        optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups``
+        optimizer: torch optimizer or any object with attribute ``param_groups``
             as a sequence.
-        param_name (str): parameter name
-        tag (str, optional): common title for all produced plots. For example, 'generator'
+        param_name: parameter name
+        tag: common title for all produced plots. For example, 'generator'
     """
 
-    def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None) -> None:
+    def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None):
         super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag)
 
     def __call__(self, engine: Engine, logger: MLflowLogger, event_name: Union[str, Events]) -> None: