Add loss_fn to IgniteMetric and rename to IgniteMetricHandler

docs/source/handlers.rst

@@ -29,9 +29,9 @@ CSV saver
     :members:
 
 
-Ignite Metric
--------------
-.. autoclass:: IgniteMetric
+Ignite Metric Handler
+---------------------
+.. autoclass:: IgniteMetricHandler
     :members:
 
 

monai/handlers/__init__.py

@@ -20,7 +20,7 @@
 from .earlystop_handler import EarlyStopHandler
 from .garbage_collector import GarbageCollector
 from .hausdorff_distance import HausdorffDistance
-from .ignite_metric import IgniteMetric
+from .ignite_metric import IgniteMetric, IgniteMetricHandler
 from .logfile_handler import LogfileHandler
 from .lr_schedule_handler import LrScheduleHandler
 from .mean_dice import MeanDice

monai/handlers/confusion_matrix.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import ConfusionMatrixMetric
 from monai.utils.enums import MetricReduction
 
 
-class ConfusionMatrix(IgniteMetric):
+class ConfusionMatrix(IgniteMetricHandler):
     """
     Compute confusion matrix related metrics from full size Tensor and collects average over batch, class-channels, iterations.
     """

monai/handlers/hausdorff_distance.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import HausdorffDistanceMetric
 from monai.utils import MetricReduction
 
 
-class HausdorffDistance(IgniteMetric):
+class HausdorffDistance(IgniteMetricHandler):
     """
     Computes Hausdorff distance from full size Tensor and collects average over batch, class-channels, iterations.
     """

monai/handlers/ignite_metric.py

@@ -13,20 +13,26 @@
 
 import warnings
 from collections.abc import Callable, Sequence
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, cast
 
 import torch
+from torch.nn.modules.loss import _Loss
 
 from monai.config import IgniteInfo
-from monai.metrics import CumulativeIterationMetric
-from monai.utils import min_version, optional_import
+from monai.metrics import CumulativeIterationMetric, LossMetric
+from monai.utils import MetricReduction, deprecated, min_version, optional_import
 
 idist, _ = optional_import("ignite", IgniteInfo.OPT_IMPORT_VERSION, min_version, "distributed")
 
 if TYPE_CHECKING:
-    from ignite.engine import Engine
-    from ignite.metrics import Metric
-    from ignite.metrics.metric import reinit__is_reduced
+    try:
+        _, has_ignite = optional_import("ignite")
+        from ignite.engine import Engine
+        from ignite.metrics import Metric
+        from ignite.metrics.metric import reinit__is_reduced
+    except ImportError:
+        has_ignite = False
+
 else:
     Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine")
     Metric, _ = optional_import("ignite.metrics", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Metric", as_type="base")
@@ -35,7 +41,7 @@
     )
 
 
-class IgniteMetric(Metric):
+class IgniteMetricHandler(Metric):
     """
     Base Metric class based on ignite event handler mechanism.
     The input `prediction` or `label` data can be a PyTorch Tensor or numpy array with batch dim and channel dim,
@@ -44,6 +50,7 @@
     Args:
         metric_fn: callable function or class to compute raw metric results after every iteration.
             expect to return a Tensor with shape (batch, channel, ...) or tuple (Tensor, not_nans).
+        loss_fn: A torch _Loss function which is used to generate the LossMetric
         output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
             construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
             lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
@@ -52,18 +59,35 @@
             https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
         save_details: whether to save metric computation details per image, for example: mean_dice of every image.
             default to True, will save to `engine.state.metric_details` dict with the metric name as key.
+        reduction: Argument for the LossMetric, look there for details
+        get_not_nans: Argument for the LossMetric, look there for details
 
     """
 
     def __init__(
-        self, metric_fn: CumulativeIterationMetric, output_transform: Callable = lambda x: x, save_details: bool = True
+        self,
+        metric_fn: CumulativeIterationMetric | None = None,
+        loss_fn: _Loss | None = None,
+        output_transform: Callable = lambda x: x,
+        save_details: bool = True,
+        reduction: MetricReduction | str = MetricReduction.MEAN,
+        get_not_nans: bool = False,
     ) -> None:
         self._is_reduced: bool = False
-        self.metric_fn = metric_fn
+        self.metric_fn: CumulativeIterationMetric = cast(CumulativeIterationMetric, metric_fn)
+        self.loss_fn = loss_fn
         self.save_details = save_details
         self._scores: list = []
         self._engine: Engine | None = None
         self._name: str | None = None
+
+        if self.metric_fn is None and self.loss_fn is None:
+            raise ValueError("Either metric_fn or loss_fn have to be passed.")
+        if self.metric_fn is not None and self.loss_fn is not None:
+            raise ValueError("Either metric_fn or loss_fn have to be passed, but not both.")
+        if self.loss_fn:
+            self.metric_fn = LossMetric(loss_fn=self.loss_fn, reduction=reduction, get_not_nans=get_not_nans)
+
         super().__init__(output_transform)
 
     @reinit__is_reduced
@@ -129,3 +153,24 @@
         self._name = name
         if self.save_details and not hasattr(engine.state, "metric_details"):
             engine.state.metric_details = {}  # type: ignore
+
+
+@deprecated(since="1.3", removed="1.4", msg_suffix="Use IgniteMetricHandler instead of IgniteMetric.")
+class IgniteMetric(IgniteMetricHandler):
+    def __init__(
+        self,
+        metric_fn: CumulativeIterationMetric | None = None,
+        loss_fn: _Loss | None = None,
+        output_transform: Callable = lambda x: x,
+        save_details: bool = True,
+        reduction: MetricReduction | str = MetricReduction.MEAN,
+        get_not_nans: bool = False,
+    ) -> None:
+        super().__init__(
+            metric_fn=metric_fn,
+            loss_fn=loss_fn,
+            output_transform=output_transform,
+            save_details=save_details,
+            reduction=reduction,
+            get_not_nans=get_not_nans,
+        )

monai/handlers/mean_dice.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import DiceMetric
 from monai.utils import MetricReduction
 
 
-class MeanDice(IgniteMetric):
+class MeanDice(IgniteMetricHandler):
     """
     Computes Dice score metric from full size Tensor and collects average over batch, class-channels, iterations.
     """

monai/handlers/mean_iou.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import MeanIoU
 from monai.utils import MetricReduction
 
 
-class MeanIoUHandler(IgniteMetric):
+class MeanIoUHandler(IgniteMetricHandler):
     """
     Computes IoU score metric from full size Tensor and collects average over batch, class-channels, iterations.
     """

monai/handlers/metrics_reloaded_handler.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import MetricsReloadedBinary, MetricsReloadedCategorical
 from monai.utils.enums import MetricReduction
 
 
-class MetricsReloadedBinaryHandler(IgniteMetric):
+class MetricsReloadedBinaryHandler(IgniteMetricHandler):
     """
     Handler of MetricsReloadedBinary, which wraps the binary pairwise metrics of MetricsReloaded.
     """
@@ -65,7 +65,7 @@ def __init__(
         super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)
 
 
-class MetricsReloadedCategoricalHandler(IgniteMetric):
+class MetricsReloadedCategoricalHandler(IgniteMetricHandler):
     """
     Handler of MetricsReloadedCategorical, which wraps the categorical pairwise metrics of MetricsReloaded.
     """

monai/handlers/panoptic_quality.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import PanopticQualityMetric
 from monai.utils import MetricReduction
 
 
-class PanopticQuality(IgniteMetric):
+class PanopticQuality(IgniteMetricHandler):
     """
     Computes Panoptic quality from full size Tensor and collects average over batch, class-channels, iterations.
     """

monai/handlers/regression_metrics.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import MAEMetric, MSEMetric, PSNRMetric, RMSEMetric
 from monai.utils import MetricReduction
 
 
-class MeanSquaredError(IgniteMetric):
+class MeanSquaredError(IgniteMetricHandler):
     """
     Computes Mean Squared Error from full size Tensor and collects average over batch, iterations.
     """
@@ -51,7 +51,7 @@ def __init__(
         super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)
 
 
-class MeanAbsoluteError(IgniteMetric):
+class MeanAbsoluteError(IgniteMetricHandler):
     """
     Computes Mean Absolute Error from full size Tensor and collects average over batch, iterations.
     """
@@ -84,7 +84,7 @@ def __init__(
         super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)
 
 
-class RootMeanSquaredError(IgniteMetric):
+class RootMeanSquaredError(IgniteMetricHandler):
     """
     Computes Root Mean Squared Error from full size Tensor and collects average over batch, iterations.
     """
@@ -117,7 +117,7 @@ def __init__(
         super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)
 
 
-class PeakSignalToNoiseRatio(IgniteMetric):
+class PeakSignalToNoiseRatio(IgniteMetricHandler):
     """
     Computes Peak Signal to Noise Ratio from full size Tensor and collects average over batch, iterations.
     """

monai/handlers/roc_auc.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import ROCAUCMetric
 from monai.utils import Average
 
 
-class ROCAUC(IgniteMetric):
+class ROCAUC(IgniteMetricHandler):
     """
     Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC).
     accumulating predictions and the ground-truth during an epoch and applying `compute_roc_auc`.

monai/handlers/surface_distance.py

@@ -13,12 +13,12 @@
 
 from collections.abc import Callable
 
-from monai.handlers.ignite_metric import IgniteMetric
+from monai.handlers.ignite_metric import IgniteMetricHandler
 from monai.metrics import SurfaceDistanceMetric
 from monai.utils import MetricReduction
 
 
-class SurfaceDistance(IgniteMetric):
+class SurfaceDistance(IgniteMetricHandler):
     """
     Computes surface distance from full size Tensor and collects average over batch, class-channels, iterations.
     """

tests/min_tests.py

@@ -83,6 +83,7 @@ def run_testsuit():
         "test_handler_early_stop",
         "test_handler_garbage_collector",
         "test_handler_hausdorff_distance",
+        "test_handler_ignite_metric",
         "test_handler_lr_scheduler",
         "test_handler_mean_dice",
         "test_handler_panoptic_quality",