Remove all deprecated args, kwargs for v0.5.0 (#1396)

ignite/contrib/engines/common.py

@@ -43,7 +43,6 @@ def setup_common_training_handlers(
     with_pbars: bool = True,
     with_pbar_on_iters: bool = True,
     log_every_iters: int = 100,
-    device: Optional[Union[str, torch.device]] = None,
     stop_on_nan: bool = True,
     clear_cuda_cache: bool = True,
     save_handler: Optional[Union[Callable, BaseSaveHandler]] = None,
@@ -86,10 +85,7 @@ def setup_common_training_handlers(
             class to use to store ``to_save``. See :class:`~ignite.handlers.checkpoint.Checkpoint` for more details.
             Argument is mutually exclusive with ``output_path``.
         **kwargs: optional keyword args to be passed to construct :class:`~ignite.handlers.checkpoint.Checkpoint`.
-        device (str of torch.device, optional): deprecated argument, it will be removed in v0.5.0.
     """
-    if device is not None:
-        warnings.warn("Argument device is unused and deprecated. It will be removed in v0.5.0")
 
     _kwargs = dict(
         to_save=to_save,
@@ -257,8 +253,8 @@ def empty_cuda_cache(_):
 
 def setup_any_logging(logger, logger_module, trainer, optimizers, evaluators, log_every_iters):
     raise DeprecationWarning(
-        "ignite.contrib.engines.common.setup_any_logging is deprecated since 0.4.0. "
-        "Please use ignite.contrib.engines.common._setup_logging instead."
+        "ignite.contrib.engines.common.setup_any_logging is deprecated since 0.4.0. and will be remove in 0.6.0. "
+        "Please use instead: setup_tb_logging, setup_visdom_logging or setup_mlflow_logging etc."
     )
 
 

ignite/engine/engine.py

@@ -473,7 +473,7 @@ def state_dict_user_keys(self) -> List:
         return self._state_dict_user_keys
 
     def state_dict(self) -> OrderedDict:
-        """Returns a dictionary containing engine's state: "seed", "epoch_length", "max_epochs" and "iteration" and
+        """Returns a dictionary containing engine's state: "epoch_length", "max_epochs" and "iteration" and
         other state values defined by `engine.state_dict_user_keys`
 
         .. code-block:: python
@@ -506,8 +506,8 @@ def save_engine(_):
     def load_state_dict(self, state_dict: Mapping) -> None:
         """Setups engine from `state_dict`.
 
-        State dictionary should contain keys: `iteration` or `epoch` and `max_epochs`, `epoch_length` and
-        `seed`. If `engine.state_dict_user_keys` contains keys, they should be also present in the state dictionary.
+        State dictionary should contain keys: `iteration` or `epoch`, `max_epochs` and `epoch_length`.
+        If `engine.state_dict_user_keys` contains keys, they should be also present in the state dictionary.
         Iteration and epoch values are 0-based: the first iteration or epoch is zero.
 
         This method does not remove any custom attributs added by user.
@@ -595,18 +595,12 @@ def switch_dataloader():
         self.state.dataloader = data
         self._dataloader_iter = iter(self.state.dataloader)
 
-    def run(
-        self,
-        data: Iterable,
-        max_epochs: Optional[int] = None,
-        epoch_length: Optional[int] = None,
-        seed: Optional[int] = None,
-    ) -> State:
+    def run(self, data: Iterable, max_epochs: Optional[int] = None, epoch_length: Optional[int] = None,) -> State:
         """Runs the `process_function` over the passed data.
 
         Engine has a state and the following logic is applied in this function:
 
-        - At the first call, new state is defined by `max_epochs`, `epoch_length`, `seed` if provided. A timer for
+        - At the first call, new state is defined by `max_epochs`, `epoch_length` if provided. A timer for
             total and per-epoch time is initialized when Events.STARTED is handled.
         - If state is already defined such that there are iterations to run until `max_epochs` and no input arguments
             provided, state is kept and used in the function.
@@ -623,8 +617,6 @@ def run(
                 `len(data)`. If `data` is an iterator and `epoch_length` is not set, then it will be automatically
                 determined as the iteration on which data iterator raises `StopIteration`.
                 This argument should not change if run is resuming from a state.
-            seed (int, optional): Deprecated argument. Please, use `torch.manual_seed` or
-                :meth:`~ignite.utils.manual_seed`.
 
         Returns:
             State: output state.
@@ -653,12 +645,6 @@ def switch_batch(engine):
                 trainer.run(train_loader, max_epochs=2)
 
         """
-        if seed is not None:
-            warnings.warn(
-                "Argument seed is deprecated. It will be removed in 0.5.0. "
-                "Please, use torch.manual_seed or ignite.utils.manual_seed"
-            )
-
         if not isinstance(data, Iterable):
             raise TypeError("Argument data should be iterable")
 

ignite/engine/events.py

@@ -135,17 +135,6 @@ def __or__(self, other: Any) -> "EventsList":
         return EventsList() | self | other
 
 
-class CallableEvents(CallableEventWithFilter):
-    # For backward compatibility
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super(CallableEvents, self).__init__(*args, **kwargs)
-        warnings.warn(
-            "Class ignite.engine.events.CallableEvents is deprecated. It will be removed in 0.5.0. "
-            "Please, use ignite.engine.EventEnum instead",
-            DeprecationWarning,
-        )
-
-
 class EventEnum(CallableEventWithFilter, Enum):  # type: ignore[misc]
     """Base class for all :class:`~ignite.engine.events.Events`. User defined custom events should also inherit
     this class. For example, Custom events based on the loss calculation and backward pass can be created as follows:

ignite/handlers/checkpoint.py

@@ -93,7 +93,6 @@ class Checkpoint(Serializable):
             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 :meth:`~ignite.handlers.global_step_from_engine`.
-        archived (bool, optional): Deprecated argument as models saved by ``torch.save`` are already compressed.
         filename_pattern (str, optional): If ``filename_pattern`` is provided, this pattern will be used to render
             checkpoint filenames. If the pattern is not defined, the default pattern would be used. See Note for
             details.
@@ -246,7 +245,6 @@ def __init__(
         score_name: Optional[str] = None,
         n_saved: Optional[int] = 1,
         global_step_transform: Callable = None,
-        archived: bool = False,
         filename_pattern: Optional[str] = None,
         include_self: bool = False,
     ):
@@ -279,8 +277,6 @@ def __init__(
             raise TypeError(
                 "global_step_transform should be a function, got {} instead.".format(type(global_step_transform))
             )
-        if archived:
-            warnings.warn("Argument archived is deprecated and will be removed in 0.5.0")
 
         self.to_save = to_save
         self.filename_prefix = filename_prefix
@@ -626,11 +622,6 @@ class ModelCheckpoint(Checkpoint):
 
         Behaviour of this class has been changed since v0.3.0.
 
-        Argument ``save_as_state_dict`` is deprecated and should not be used. It is considered as True.
-
-        Argument ``save_interval`` is deprecated and should not be used. Please, use events filtering instead, e.g.
-        :attr:`~ignite.engine.events.Events.ITERATION_STARTED(every=1000)`
-
         There is no more internal counter that has been used to indicate the number of save actions. User could
         see its value `step_number` in the filename, e.g. `{filename_prefix}_{name}_{step_number}.pt`. Actually,
         `step_number` is replaced by current engine's epoch if `score_function` is specified and current iteration
@@ -659,7 +650,6 @@ class ModelCheckpoint(Checkpoint):
             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 :meth:`~ignite.handlers.global_step_from_engine`.
-        archived (bool, optional): Deprecated argument as models saved by `torch.save` are already compressed.
         include_self (bool): Whether to include the `state_dict` of this object in the checkpoint. If `True`, then
             there must not be another object in ``to_save`` with key ``checkpointer``.
         **kwargs: Accepted keyword arguments for `torch.save` or `xm.save` in `DiskSaver`.
@@ -684,37 +674,17 @@ def __init__(
         self,
         dirname: str,
         filename_prefix: str,
-        save_interval: Optional[Callable] = None,
         score_function: Optional[Callable] = None,
         score_name: Optional[str] = None,
         n_saved: Union[int, None] = 1,
         atomic: bool = True,
         require_empty: bool = True,
         create_dir: bool = True,
-        save_as_state_dict: bool = True,
         global_step_transform: Optional[Callable] = None,
-        archived: bool = False,
         include_self: bool = False,
         **kwargs
     ):
 
-        if not save_as_state_dict:
-            raise ValueError(
-                "Argument save_as_state_dict is deprecated and should be True."
-                "This argument will be removed in 0.5.0."
-            )
-        if save_interval is not None:
-            msg = (
-                "Argument save_interval is deprecated and should be None. This argument will be removed in 0.5.0."
-                "Please, use events filtering instead, e.g. Events.ITERATION_STARTED(every=1000)"
-            )
-            if save_interval == 1:
-                # Do not break for old version who used `save_interval=1`
-                warnings.warn(msg)
-            else:
-                # No choice
-                raise ValueError(msg)
-
         disk_saver = DiskSaver(dirname, atomic=atomic, create_dir=create_dir, require_empty=require_empty, **kwargs)
 
         super(ModelCheckpoint, self).__init__(
@@ -725,7 +695,6 @@ def __init__(
             score_name=score_name,
             n_saved=n_saved,
             global_step_transform=global_step_transform,
-            archived=archived,
             include_self=include_self,
         )
 

tests/ignite/contrib/engines/test_common.py

@@ -140,9 +140,6 @@ def test_asserts_setup_common_training_handlers():
         train_sampler = MagicMock(spec=DistributedSampler)
         setup_common_training_handlers(trainer, train_sampler=train_sampler)
 
-    with pytest.warns(UserWarning, match=r"Argument device is unused and deprecated"):
-        setup_common_training_handlers(trainer, device="cpu")
-
 
 def test_no_warning_with_train_sampler(recwarn):
     from torch.utils.data import RandomSampler

tests/ignite/engine/test_custom_events.py

@@ -6,19 +6,7 @@
 
 import ignite.distributed as idist
 from ignite.engine import Engine, Events
-from ignite.engine.events import CallableEvents, CallableEventWithFilter, EventEnum, EventsList
-
-
-def test_deprecated_callable_events_class():
-    engine = Engine(lambda engine, batch: 0)
-
-    with pytest.warns(DeprecationWarning, match=r"Class ignite\.engine\.events\.CallableEvents is deprecated"):
-
-        class CustomEvents(CallableEvents, Enum):
-            TEST_EVENT = "test_event"
-
-        with pytest.raises(TypeError, match=r"Value at \d of event_names should be a str or EventEnum"):
-            engine.register_events(*CustomEvents)
+from ignite.engine.events import CallableEventWithFilter, EventEnum, EventsList
 
 
 def test_custom_events():

tests/ignite/engine/test_engine.py

@@ -352,9 +352,6 @@ def test_run_asserts():
     with pytest.raises(ValueError, match=r"Input data has zero size. Please provide non-empty data"):
         engine.run([])
 
-    with pytest.warns(UserWarning, match="Argument seed is deprecated"):
-        engine.run([0, 1, 2, 3, 4], seed=1234)
-
 
 def test_state_get_event_attrib_value():
     state = State()

tests/ignite/handlers/test_checkpoint.py

@@ -62,9 +62,6 @@ def test_checkpoint_wrong_input():
     with pytest.raises(TypeError, match=r"global_step_transform should be a function."):
         Checkpoint(to_save, lambda x: x, score_function=lambda e: 123, score_name="acc", global_step_transform=123)
 
-    with pytest.warns(UserWarning, match=r"Argument archived is deprecated"):
-        Checkpoint(to_save, lambda x: x, score_function=lambda e: 123, score_name="acc", archived=True)
-
     with pytest.raises(ValueError, match=r"Cannot have key 'checkpointer' if `include_self` is True"):
         Checkpoint({"checkpointer": model}, lambda x: x, include_self=True)
 
@@ -494,24 +491,15 @@ def test_model_checkpoint_args_validation(dirname):
     with pytest.raises(ValueError, match=r"with extension '.pt' are already present "):
         ModelCheckpoint(nonempty, _PREFIX)
 
-    with pytest.raises(ValueError, match=r"Argument save_interval is deprecated and should be None"):
-        ModelCheckpoint(existing, _PREFIX, save_interval=42)
-
     with pytest.raises(ValueError, match=r"Directory path '\S+' is not found"):
         ModelCheckpoint(os.path.join(dirname, "non_existing_dir"), _PREFIX, create_dir=False)
 
-    with pytest.raises(ValueError, match=r"Argument save_as_state_dict is deprecated and should be True"):
-        ModelCheckpoint(existing, _PREFIX, create_dir=False, save_as_state_dict=False)
-
     with pytest.raises(ValueError, match=r"If `score_name` is provided, then `score_function` "):
         ModelCheckpoint(existing, _PREFIX, create_dir=False, score_name="test")
 
     with pytest.raises(TypeError, match=r"global_step_transform should be a function"):
         ModelCheckpoint(existing, _PREFIX, create_dir=False, global_step_transform=1234)
 
-    with pytest.warns(UserWarning, match=r"Argument archived is deprecated"):
-        ModelCheckpoint(existing, _PREFIX, create_dir=False, archived=True)
-
     h = ModelCheckpoint(dirname, _PREFIX, create_dir=False)
     assert h.last_checkpoint is None
     with pytest.raises(RuntimeError, match=r"No objects to checkpoint found."):