Fix code formatting and remove debug prints

Signed-off-by: Matthias Hadlich <[email protected]>

monai/handlers/ignite_metric.py

@@ -13,14 +13,14 @@
 
 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, LossMetric
-from monai.utils import deprecated, min_version, optional_import
+from monai.utils import MetricReduction, deprecated, min_version, optional_import
 
 idist, _ = optional_import("ignite", IgniteInfo.OPT_IMPORT_VERSION, min_version, "distributed")
 
@@ -59,7 +59,8 @@ class IgniteMetricHandler(Metric):
             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.
-        kwargs: keyword argument that will be passed into the LossMetric
+        reduction: Argument for the LossMetric, look there for details
+        get_not_nans: Argument for the LossMetric, look there for details
 
     """
 
@@ -69,10 +70,11 @@ def __init__(
         loss_fn: _Loss | None = None,
         output_transform: Callable = lambda x: x,
         save_details: bool = True,
-        **kwargs: dict,
+        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 = []
@@ -84,7 +86,9 @@ def __init__(
         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)
+            self.metric_fn = LossMetric(loss_fn=self.loss_fn, reduction=reduction, get_not_nans=get_not_nans)
+        # else:
+        #     self.metric_fn = cast(CumulativeIterationMetric, metric_fn)
 
         super().__init__(output_transform)
 
@@ -161,8 +165,14 @@ def __init__(
         loss_fn: _Loss | None = None,
         output_transform: Callable = lambda x: x,
         save_details: bool = True,
-        **kwargs: dict,
+        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, **kwargs
+            metric_fn=metric_fn,
+            loss_fn=loss_fn,
+            output_transform=output_transform,
+            save_details=save_details,
+            reduction=reduction,
+            get_not_nans=get_not_nans,
         )

tests/test_handler_ignite_metric.py

@@ -123,9 +123,6 @@ def _val_func(engine, batch):
         engine.fire_event(Events.ITERATION_COMPLETED)
 
         engine.fire_event(Events.EPOCH_COMPLETED)
-        print(f"{engine.state.metrics['ignite_dice_loss']}")
-        print(f"{engine.state.metric_details['ignite_dice_loss'].shape}")
-        print(f"{engine.state.metric_details['ignite_dice_loss']}")
         assert_allclose(engine.state.metrics["ignite_dice_loss"], expected_avg, atol=1e-4, rtol=1e-4, type_test=False)
 
     @SkipIfNoModule("ignite")
@@ -150,9 +147,6 @@ def _val_func(engine, batch):
         engine.fire_event(Events.ITERATION_COMPLETED)
 
         engine.fire_event(Events.EPOCH_COMPLETED)
-        print(f"{engine.state.metrics['ignite_dice_loss']}")
-        print(f"{engine.state.metric_details['ignite_dice_loss'].shape}")
-        print(f"{engine.state.metric_details['ignite_dice_loss']}")
         assert_allclose(engine.state.metrics["ignite_dice_loss"], expected_avg, atol=1e-4, rtol=1e-4, type_test=False)
 
     @SkipIfNoModule("ignite")