Remove deprecated args and class for v1.3

docs/source/transforms.rst

@@ -996,24 +996,12 @@ Utility
     :members:
     :special-members: __call__
 
-`AsChannelFirst`
-""""""""""""""""
-.. autoclass:: AsChannelFirst
-    :members:
-    :special-members: __call__
-
 `AsChannelLast`
 """""""""""""""
 .. autoclass:: AsChannelLast
     :members:
     :special-members: __call__
 
-`AddChannel`
-""""""""""""
-.. autoclass:: AddChannel
-    :members:
-    :special-members: __call__
-
 `EnsureChannelFirst`
 """"""""""""""""""""
 .. autoclass:: EnsureChannelFirst
@@ -1032,12 +1020,6 @@ Utility
     :members:
     :special-members: __call__
 
-`SplitChannel`
-""""""""""""""
-.. autoclass:: SplitChannel
-    :members:
-    :special-members: __call__
-
 `CastToType`
 """"""""""""
 .. autoclass:: CastToType
@@ -1974,24 +1956,12 @@ Utility (Dict)
     :members:
     :special-members: __call__
 
-`AsChannelFirstd`
-"""""""""""""""""
-.. autoclass:: AsChannelFirstd
-    :members:
-    :special-members: __call__
-
 `AsChannelLastd`
 """"""""""""""""
 .. autoclass:: AsChannelLastd
     :members:
     :special-members: __call__
 
-`AddChanneld`
-"""""""""""""
-.. autoclass:: AddChanneld
-    :members:
-    :special-members: __call__
-
 `EnsureChannelFirstd`
 """""""""""""""""""""
 .. autoclass:: EnsureChannelFirstd
@@ -2010,12 +1980,6 @@ Utility (Dict)
     :members:
     :special-members: __call__
 
-`SplitChanneld`
-"""""""""""""""
-.. autoclass:: SplitChanneld
-    :members:
-    :special-members: __call__
-
 `CastToTyped`
 """""""""""""
 .. autoclass:: CastToTyped

monai/apps/auto3dseg/ensemble_builder.py

@@ -332,13 +332,6 @@ class AlgoEnsembleBuilder:
 
     """
 
-    @deprecated_arg(
-        "data_src_cfg_filename",
-        since="1.2",
-        removed="1.3",
-        new_name="data_src_cfg_name",
-        msg_suffix="please use `data_src_cfg_name` instead.",
-    )
     def __init__(self, history: Sequence[dict[str, Any]], data_src_cfg_name: str | None = None):
         self.infer_algos: list[dict[AlgoKeys, Any]] = []
         self.ensemble: AlgoEnsemble

monai/bundle/scripts.py

@@ -704,7 +704,6 @@ def get_bundle_info(
     return bundle_info[version]  # type: ignore[no-any-return]
 
 
-@deprecated_arg("runner_id", since="1.1", removed="1.3", new_name="run_id", msg_suffix="please use `run_id` instead.")
 def run(
     run_id: str | None = None,
     init_id: str | None = None,
@@ -766,7 +765,7 @@ def run(
             will patch the target config content with `tracking handlers` and the top-level items of `configs`.
             for detailed usage examples, please check the tutorial:
             https://github.com/Project-MONAI/tutorials/blob/main/experiment_management/bundle_integrate_mlflow.ipynb.
-        args_file: a JSON or YAML file to provide default values for `runner_id`, `meta_file`,
+        args_file: a JSON or YAML file to provide default values for `run_id`, `meta_file`,
             `config_file`, `logging`, and override pairs. so that the command line inputs can be simplified.
         override: id-value pairs to override or add the corresponding config content.
             e.g. ``--net#input_chns 42``, ``--net %/data/other.json#net_arg``.

monai/engines/__init__.py

@@ -12,7 +12,6 @@
 from __future__ import annotations
 
 from .evaluator import EnsembleEvaluator, Evaluator, SupervisedEvaluator
-from .multi_gpu_supervised_trainer import create_multigpu_supervised_evaluator, create_multigpu_supervised_trainer
 from .trainer import GanTrainer, SupervisedTrainer, Trainer
 from .utils import (
     IterationEvents,

monai/handlers/tensorboard_handlers.py

@@ -87,18 +87,14 @@ class TensorBoardStatsHandler(TensorBoardHandler):
 
     """
 
-    @deprecated_arg("epoch_interval", since="1.1", removed="1.3")
-    @deprecated_arg("iteration_interval", since="1.1", removed="1.3")
     def __init__(
         self,
         summary_writer: SummaryWriter | SummaryWriterX | None = None,
         log_dir: str = "./runs",
         iteration_log: bool | Callable[[Engine, int], bool] = True,
         epoch_log: bool | Callable[[Engine, int], bool] = True,
         epoch_event_writer: Callable[[Engine, Any], Any] | None = None,
-        epoch_interval: int = 1,
         iteration_event_writer: Callable[[Engine, Any], Any] | None = None,
-        iteration_interval: int = 1,
         output_transform: Callable = lambda x: x[0],
         global_epoch_transform: Callable = lambda x: x,
         state_attributes: Sequence[str] | None = None,
@@ -118,12 +114,8 @@ def __init__(
                 See ``iteration_log`` argument for more details.
             epoch_event_writer: customized callable TensorBoard writer for epoch level.
                 Must accept parameter "engine" and "summary_writer", use default event writer if None.
-            epoch_interval: the epoch interval at which the epoch_event_writer is called. Defaults to 1.
-                ``epoch_interval`` must be 1 if ``epoch_log`` is callable.
             iteration_event_writer: customized callable TensorBoard writer for iteration level.
                 Must accept parameter "engine" and "summary_writer", use default event writer if None.
-            iteration_interval: the iteration interval at which the iteration_event_writer is called. Defaults to 1.
-                ``iteration_interval`` must be 1 if ``iteration_log`` is callable.
             output_transform: a callable that is used to transform the
                 ``ignite.engine.state.output`` into a scalar to plot, or a dictionary of {key: scalar}.
                 In the latter case, the output string will be formatted as key: value.
@@ -140,19 +132,12 @@ def __init__(
                 when epoch completed.
             tag_name: when iteration output is a scalar, tag_name is used to plot, defaults to ``'Loss'``.
         """
-        if callable(iteration_log) and iteration_interval > 1:
-            raise ValueError("If iteration_log is callable, then iteration_interval should be 1")
-
-        if callable(epoch_log) and epoch_interval > 1:
-            raise ValueError("If epoch_log is callable, then epoch_interval should be 1")
 
         super().__init__(summary_writer=summary_writer, log_dir=log_dir)
         self.iteration_log = iteration_log
         self.epoch_log = epoch_log
         self.epoch_event_writer = epoch_event_writer
-        self.epoch_interval = epoch_interval
         self.iteration_event_writer = iteration_event_writer
-        self.iteration_interval = iteration_interval
         self.output_transform = output_transform
         self.global_epoch_transform = global_epoch_transform
         self.state_attributes = state_attributes
@@ -170,15 +155,15 @@ def attach(self, engine: Engine) -> None:
             event = Events.ITERATION_COMPLETED
             if callable(self.iteration_log):  # substitute event with new one using filter callable
                 event = event(event_filter=self.iteration_log)
-            elif self.iteration_interval > 1:
-                event = event(every=self.iteration_interval)
+            elif self.iteration_log > 1:
+                event = event(every=self.iteration_log)
             engine.add_event_handler(event, self.iteration_completed)
         if self.epoch_log and not engine.has_event_handler(self.epoch_completed, Events.EPOCH_COMPLETED):
             event = Events.EPOCH_COMPLETED
             if callable(self.epoch_log):  # substitute event with new one using filter callable
                 event = event(event_filter=self.epoch_log)
             elif self.epoch_log > 1:
-                event = event(every=self.epoch_interval)
+                event = event(every=self.epoch_log)
             engine.add_event_handler(event, self.epoch_completed)
 
     def epoch_completed(self, engine: Engine) -> None: