Add new metric usages and update RunningAverage accordingly

docs/source/metrics.rst

@@ -290,7 +290,10 @@ Complete list of usages
 
     - :class:`~ignite.metrics.metric.MetricUsage`
     - :class:`~ignite.metrics.metric.EpochWise`
+    - :class:`~ignite.metrics.metric.RunningEpochWise`
     - :class:`~ignite.metrics.metric.BatchWise`
+    - :class:`~ignite.metrics.metric.RunningBatchWise`
+    - :class:`~ignite.metrics.metric.SingleEpochRunningBatchWise`
     - :class:`~ignite.metrics.metric.BatchFiltered`
 
 Metrics and distributed computations
@@ -359,10 +362,22 @@ EpochWise
 ~~~~~~~~~
 .. autoclass:: ignite.metrics.metric.EpochWise
 
+RunningEpochWise
+~~~~~~~~~~~~~~~~
+.. autoclass:: ignite.metrics.metric.RunningEpochWise
+
 BatchWise
 ~~~~~~~~~
 .. autoclass:: ignite.metrics.metric.BatchWise
 
+RunningBatchWise
+~~~~~~~~~~~~~~~~
+.. autoclass:: ignite.metrics.metric.RunningBatchWise
+
+SingleEpochRunningBatchWise
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: ignite.metrics.metric.SingleEpochRunningBatchWise
+
 BatchFiltered
 ~~~~~~~~~~~~~
 .. autoclass:: ignite.metrics.metric.BatchFiltered

ignite/contrib/engines/common.py

@@ -33,6 +33,7 @@
 from ignite.handlers.checkpoint import BaseSaveHandler
 from ignite.handlers.param_scheduler import ParamScheduler
 from ignite.metrics import RunningAverage
+from ignite.metrics.metric import RunningBatchWise
 from ignite.utils import deprecated
 
 
@@ -209,8 +210,8 @@ def output_transform(x: Any, index: int, name: str) -> Any:
                 )
 
         for i, n in enumerate(output_names):
-            RunningAverage(output_transform=partial(output_transform, index=i, name=n), epoch_bound=False).attach(
-                trainer, n
+            RunningAverage(output_transform=partial(output_transform, index=i, name=n)).attach(
+                trainer, n, RunningBatchWise()
             )
 
     if with_pbars:

ignite/metrics/metric.py

@@ -12,7 +12,16 @@
 if TYPE_CHECKING:
     from ignite.metrics.metrics_lambda import MetricsLambda
 
-__all__ = ["Metric", "MetricUsage", "EpochWise", "BatchWise", "BatchFiltered"]
+__all__ = [
+    "Metric",
+    "MetricUsage",
+    "EpochWise",
+    "BatchWise",
+    "BatchFiltered",
+    "RunningEpochWise",
+    "RunningBatchWise",
+    "SingleEpochRunningBatchWise",
+]
 
 
 class MetricUsage:
@@ -31,6 +40,8 @@ class MetricUsage:
             :meth:`~ignite.metrics.metric.Metric.iteration_completed`.
     """
 
+    usage_name: str
+
     def __init__(self, started: Events, completed: Events, iteration_completed: CallableEventWithFilter) -> None:
         self.__started = started
         self.__completed = completed
@@ -74,6 +85,33 @@ def __init__(self) -> None:
         )
 
 
+class RunningEpochWise(EpochWise):
+    """
+    Running epoch-wise usage of Metrics. It's the running version of the :class:`~.metrics.metric.EpochWise` metric
+    usage. A metric with such a usage most likely accompanies an :class:`~.metrics.metric.EpochWise` one to compute
+    a running measure of it e.g. running average.
+
+    Metric's methods are triggered on the following engine events:
+
+    - :meth:`~ignite.metrics.metric.Metric.started` on every ``STARTED``
+      (See :class:`~ignite.engine.events.Events`).
+    - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``EPOCH_COMPLETED``.
+    - :meth:`~ignite.metrics.metric.Metric.completed` on every ``EPOCH_COMPLETED``.
+
+    Attributes:
+        usage_name: usage name string
+    """
+
+    usage_name: str = "running_epoch_wise"
+
+    def __init__(self) -> None:
+        super(EpochWise, self).__init__(
+            started=Events.STARTED,
+            completed=Events.EPOCH_COMPLETED,
+            iteration_completed=Events.EPOCH_COMPLETED,
+        )
+
+
 class BatchWise(MetricUsage):
     """
     Batch-wise usage of Metrics.
@@ -99,6 +137,59 @@ def __init__(self) -> None:
         )
 
 
+class RunningBatchWise(BatchWise):
+    """
+    Running batch-wise usage of Metrics. It's the running version of the :class:`~.metrics.metric.EpochWise` metric
+    usage. A metric with such a usage could for example accompany a :class:`~.metrics.metric.BatchWise` one to compute
+    a running measure of it e.g. running average.
+
+    Metric's methods are triggered on the following engine events:
+
+    - :meth:`~ignite.metrics.metric.Metric.started` on every ``STARTED``
+      (See :class:`~ignite.engine.events.Events`).
+    - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``ITERATION_COMPLETED``.
+    - :meth:`~ignite.metrics.metric.Metric.completed` on every ``ITERATION_COMPLETED``.
+
+    Attributes:
+        usage_name: usage name string
+    """
+
+    usage_name: str = "running_batch_wise"
+
+    def __init__(self) -> None:
+        super(BatchWise, self).__init__(
+            started=Events.STARTED,
+            completed=Events.ITERATION_COMPLETED,
+            iteration_completed=Events.ITERATION_COMPLETED,
+        )
+
+
+class SingleEpochRunningBatchWise(BatchWise):
+    """
+    Running batch-wise usage of Metrics in a single epoch. It's like :class:`~.metrics.metric.RunningBatchWise` metric
+    usage with the difference that is used during a single epoch.
+
+    Metric's methods are triggered on the following engine events:
+
+    - :meth:`~ignite.metrics.metric.Metric.started` on every ``EPOCH_STARTED``
+      (See :class:`~ignite.engine.events.Events`).
+    - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``ITERATION_COMPLETED``.
+    - :meth:`~ignite.metrics.metric.Metric.completed` on every ``ITERATION_COMPLETED``.
+
+    Attributes:
+        usage_name: usage name string
+    """
+
+    usage_name: str = "single_epoch_running_batch_wise"
+
+    def __init__(self) -> None:
+        super(BatchWise, self).__init__(
+            started=Events.EPOCH_STARTED,
+            completed=Events.ITERATION_COMPLETED,
+            iteration_completed=Events.ITERATION_COMPLETED,
+        )
+
+
 class BatchFiltered(MetricUsage):
     """
     Batch filtered usage of Metrics. This usage is similar to epoch-wise but update event is filtered.
@@ -344,12 +435,16 @@ def completed(self, engine: Engine, name: str) -> None:
 
     def _check_usage(self, usage: Union[str, MetricUsage]) -> MetricUsage:
         if isinstance(usage, str):
-            if usage == EpochWise.usage_name:
-                usage = EpochWise()
-            elif usage == BatchWise.usage_name:
-                usage = BatchWise()
-            else:
-                raise ValueError(f"usage should be 'EpochWise.usage_name' or 'BatchWise.usage_name', get {usage}")
+            usages = [EpochWise, RunningEpochWise, BatchWise, RunningBatchWise, SingleEpochRunningBatchWise]
+            for usage_cls in usages:
+                if usage == usage_cls.usage_name:
+                    usage = usage_cls()
+                    break
+            if not isinstance(usage, MetricUsage):
+                raise ValueError(
+                    "usage should be '(Running)EpochWise.usage_name' or "
+                    f"'((SingleEpoch)Running)BatchWise.usage_name', got {usage}"
+                )
         if not isinstance(usage, MetricUsage):
             raise TypeError(f"Unhandled usage type {type(usage)}")
         return usage

ignite/metrics/running_average.py

@@ -1,10 +1,11 @@
-from typing import Callable, cast, Optional, Sequence, Union
+import warnings
+from typing import Any, Callable, cast, Optional, Union
 
 import torch
 
 import ignite.distributed as idist
 from ignite.engine import Engine, Events
-from ignite.metrics.metric import EpochWise, Metric, MetricUsage, reinit__is_reduced, sync_all_reduce
+from ignite.metrics.metric import Metric, MetricUsage, reinit__is_reduced, RunningBatchWise, SingleEpochRunningBatchWise
 
 __all__ = ["RunningAverage"]
 
@@ -18,8 +19,10 @@ class RunningAverage(Metric):
         alpha: running average decay factor, default 0.98
         output_transform: a function to use to transform the output if `src` is None and
             corresponds the output of process function. Otherwise it should be None.
-        epoch_bound: whether the running average should be reset after each epoch (defaults
-            to True).
+        epoch_bound: whether the running average should be reset after each epoch. It is depracated in favor of
+            ``usage`` argument in :meth:`attach` method. Setting ``epoch_bound`` to ``False`` is equivalent to
+            ``usage=SingleEpochRunningBatchWise()`` and setting it to ``True`` is equivalent to
+            ``usage=RunningBatchWise()`` in the :meth:`attach` method. Default None.
         device: specifies which device updates are accumulated on. Should be
             None when ``src`` is an instance of :class:`~ignite.metrics.metric.Metric`, as the running average will
             use the ``src``'s device. Otherwise, defaults to CPU. Only applicable when the computed value
@@ -90,7 +93,7 @@ def __init__(
         src: Optional[Metric] = None,
         alpha: float = 0.98,
         output_transform: Optional[Callable] = None,
-        epoch_bound: bool = True,
+        epoch_bound: Optional[bool] = None,
         device: Optional[Union[str, torch.device]] = None,
     ):
         if not (isinstance(src, Metric) or src is None):
@@ -101,70 +104,119 @@ def __init__(
         if isinstance(src, Metric):
             if output_transform is not None:
                 raise ValueError("Argument output_transform should be None if src is a Metric.")
+
+            def output_transform(x: Any) -> Any:
+                return x
+
             if device is not None:
                 raise ValueError("Argument device should be None if src is a Metric.")
-            self.src = src
-            self._get_src_value = self._get_metric_value
-            setattr(self, "iteration_completed", self._metric_iteration_completed)
+            self.src: Union[Metric, None] = src
             device = src._device
         else:
             if output_transform is None:
                 raise ValueError(
                     "Argument output_transform should not be None if src corresponds "
                     "to the output of process function."
                 )
-            self._get_src_value = self._get_output_value
-            setattr(self, "update", self._output_update)
+            self.src = None
             if device is None:
                 device = torch.device("cpu")
 
-        self.alpha = alpha
         self.epoch_bound = epoch_bound
-        super(RunningAverage, self).__init__(output_transform=output_transform, device=device)  # type: ignore[arg-type]
+        self.alpha = alpha
+        super(RunningAverage, self).__init__(output_transform=output_transform, device=device)
 
     @reinit__is_reduced
     def reset(self) -> None:
         self._value: Optional[Union[float, torch.Tensor]] = None
+        if isinstance(self.src, Metric):
+            self.src.reset()
 
     @reinit__is_reduced
-    def update(self, output: Sequence) -> None:
-        # Implement abstract method
-        pass
-
-    def compute(self) -> Union[torch.Tensor, float]:
-        if self._value is None:
-            self._value = self._get_src_value()
+    def update(self, output: Union[torch.Tensor, float]) -> None:
+        if self.src is None:
+            output = output.detach().to(self._device) if isinstance(output, torch.Tensor) else output
+            value = idist.all_reduce(output) / idist.get_world_size()
         else:
-            self._value = self._value * self.alpha + (1.0 - self.alpha) * self._get_src_value()
+            value = self.src.compute()
+            self.src.reset()
 
-        return self._value
-
-    def attach(self, engine: Engine, name: str, _usage: Union[str, MetricUsage] = EpochWise()) -> None:
-        if self.epoch_bound:
-            # restart average every epoch
-            engine.add_event_handler(Events.EPOCH_STARTED, self.started)
+        if self._value is None:
+            self._value = value
         else:
-            engine.add_event_handler(Events.STARTED, self.started)
-        # compute metric
-        engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)
-        # apply running average
-        engine.add_event_handler(Events.ITERATION_COMPLETED, self.completed, name)
-
-    def _get_metric_value(self) -> Union[torch.Tensor, float]:
-        return self.src.compute()
-
-    @sync_all_reduce("src")
-    def _get_output_value(self) -> Union[torch.Tensor, float]:
-        # we need to compute average instead of sum produced by @sync_all_reduce("src")
-        output = cast(Union[torch.Tensor, float], self.src) / idist.get_world_size()
-        return output
+            self._value = self._value * self.alpha + (1.0 - self.alpha) * value
 
-    def _metric_iteration_completed(self, engine: Engine) -> None:
-        self.src.started(engine)
-        self.src.iteration_completed(engine)
-
-    @reinit__is_reduced
-    def _output_update(self, output: Union[torch.Tensor, float]) -> None:
-        if isinstance(output, torch.Tensor):
-            output = output.detach().to(self._device, copy=True)
-        self.src = output  # type: ignore[assignment]
+    def compute(self) -> Union[torch.Tensor, float]:
+        return cast(Union[torch.Tensor, float], self._value)
+
+    def attach(self, engine: Engine, name: str, usage: Union[str, MetricUsage] = RunningBatchWise()) -> None:
+        r"""
+        Attach the metric to the ``engine`` using the events determined by the ``usage``.
+
+        Args:
+            engine: the engine to get attached to.
+            name: by which, the metric is inserted into ``engine.state.metrics`` dictionary.
+            usage: the usage determining on which events the metric is reset, updated and computed. It should be an
+                instance of the :class:`~ignite.metrics.metric.MetricUsage`\ s in the following table.
+
+                ======================================================= ===========================================
+                ``usage`` **class**                                     **Description**
+                ======================================================= ===========================================
+                :class:`~.metrics.metric.RunningBatchWise`              Running average of the ``src`` metric or
+                                                                        ``engine.state.output`` is computed across
+                                                                        batches. In the former case, on each batch,
+                                                                        ``src`` is reset, updated and computed then
+                                                                        its value is retrieved. Default.
+                :class:`~.metrics.metric.SingleEpochRunningBatchWise`   Same as above but the running average is
+                                                                        computed across batches in an epoch so it
+                                                                        is reset at the end of the epoch.
+                :class:`~.metrics.metric.RunningEpochWise`              Running average of the ``src`` metric or
+                                                                        ``engine.state.output`` is computed across
+                                                                        epochs. In the former case, ``src`` works
+                                                                        as if it was attached in a
+                                                                        :class:`~ignite.metrics.metric.EpochWise`
+                                                                        manner and its computed value is retrieved
+                                                                        at the end of the epoch. The latter case
+                                                                        doesn't make much sense for this usage as
+                                                                        the ``engine.state.output`` of the last
+                                                                        batch is retrieved then.
+                ======================================================= ===========================================
+
+        ``RunningAverage`` retrieves ``engine.state.output`` at ``usage.ITERATION_COMPLETED`` if the ``src`` is not
+        given and it's computed and updated using ``src``, by manually calling its ``compute`` method, or
+        ``engine.state.output`` at ``usage.COMPLETED`` event.
+        Also if ``src`` is given, it is updated at ``usage.ITERATION_COMPLETED``, but its reset event is determined by
+        ``usage`` type. If ``isinstance(usage, BatchWise)`` holds true, ``src`` is reset on ``BatchWise().STARTED``,
+        otherwise on ``EpochWise().STARTED`` if ``isinstance(usage, EpochWise)``.
+
+        .. versionchanged:: 0.5.1
+            Added `usage` argument
+        """
+        usage = self._check_usage(usage)
+
+        if self.epoch_bound is not None:
+            warnings.warn(
+                "`epoch_bound` is deprecated and will be removed in the future. Consider using `usage` argument"
+                "instead. `epoch_bound=True` is equivalent with `usage=SingleEpochRunningBatchWise()` and "
+                "`epoch_bound=False` is equivalent with `usage=RunningBatchWise()`."
+            )
+            usage = SingleEpochRunningBatchWise() if self.epoch_bound else RunningBatchWise()
+
+        if isinstance(self.src, Metric) and not engine.has_event_handler(
+            self.src.iteration_completed, Events.ITERATION_COMPLETED
+        ):
+            engine.add_event_handler(Events.ITERATION_COMPLETED, self.src.iteration_completed)
+
+        super().attach(engine, name, usage)
+
+    def detach(self, engine: Engine, usage: Union[str, MetricUsage] = RunningBatchWise()) -> None:
+        usage = self._check_usage(usage)
+        if self.epoch_bound is not None:
+            usage = SingleEpochRunningBatchWise() if self.epoch_bound else RunningBatchWise()
+
+        if isinstance(self.src, Metric) and engine.has_event_handler(
+            self.src.iteration_completed, Events.ITERATION_COMPLETED
+        ):
+            engine.remove_event_handler(self.src.iteration_completed, Events.ITERATION_COMPLETED)
+
+        super().detach(engine, usage)