Rename IgniteMetric to IgniteMetricHandler

Signed-off-by: Matthias Hadlich <matthiashadlich@posteo.de>

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

@@ -26,7 +26,7 @@
 
 if TYPE_CHECKING:
     from ignite.engine import Engine
-    from ignite.metrics import LossMetric, Metric
+    from ignite.metrics import Metric
     from ignite.metrics.metric import reinit__is_reduced
 else:
     Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine")
@@ -35,8 +35,10 @@
         "ignite.metrics.metric", IgniteInfo.OPT_IMPORT_VERSION, min_version, "reinit__is_reduced", as_type="decorator"
     )
 
+warnings.warn("IgniteMetric has been renamed to IgniteMetricHandler")
 
-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,
@@ -74,9 +76,9 @@ def __init__(
         self._engine: Engine | None = None
         self._name: str | None = None
 
-        if self.metric_fn is None and  self.loss_fn is 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:
+        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, **kwargs)
@@ -146,3 +148,6 @@ def attach(self, engine: Engine, name: str) -> None:  # type: ignore[override]
         self._name = name
         if self.save_details and not hasattr(engine.state, "metric_details"):
             engine.state.metric_details = {}  # type: ignore
+
+
+IgniteMetric = IgniteMetricHandler

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/test_handler_ignite_metric_handler.py

@@ -17,7 +17,7 @@
 from ignite.engine import Engine, Events
 from parameterized import parameterized
 
-from monai.handlers import IgniteMetric, from_engine
+from monai.handlers import IgniteMetricHandler, from_engine
 from monai.losses import DiceLoss
 from monai.metrics import LossMetric
 from tests.utils import assert_allclose
@@ -98,7 +98,7 @@ class TestHandlerIgniteMetricHandler(unittest.TestCase):
     def test_metric_fn(self, loss_params, metric_params, handler_params, expected_avg):
         loss_fn = DiceLoss(**loss_params)
         metric_fn = LossMetric(loss_fn=loss_fn, **metric_params)
-        ignite_metric = IgniteMetric(metric_fn=metric_fn, **handler_params)
+        ignite_metric = IgniteMetricHandler(metric_fn=metric_fn, **handler_params)
 
         def _val_func(engine, batch):
             pass
@@ -124,7 +124,7 @@ def _val_func(engine, batch):
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
     def test_loss_fn(self, loss_params, metric_params, handler_params, expected_avg):
         loss_fn = DiceLoss(**loss_params)
-        ignite_metric = IgniteMetric(loss_fn=loss_fn, **handler_params, **metric_params)
+        ignite_metric = IgniteMetricHandler(loss_fn=loss_fn, **handler_params, **metric_params)
 
         def _val_func(engine, batch):
             pass
@@ -150,7 +150,7 @@ def _val_func(engine, batch):
     @parameterized.expand(TEST_CASES)
     def test_dice_loss(self, input_param, input_data, expected_val):
         loss_fn = DiceLoss(**input_param)
-        ignite_metric = IgniteMetric(loss_fn=loss_fn, output_transform=from_engine(["pred", "label"]))
+        ignite_metric = IgniteMetricHandler(loss_fn=loss_fn, output_transform=from_engine(["pred", "label"]))
 
         def _val_func(engine, batch):
             pass