Fix for bug when providing superclass arguments as kwargs

tests/classification/test_accuracy.py

@@ -448,3 +448,9 @@ def test_negmetric_noneavg(noneavg=_negmetric_noneavg):
     assert torch.allclose(noneavg["res1"], result1, equal_nan=True)
     result2 = acc(noneavg["pred2"], noneavg["target2"])
     assert torch.allclose(noneavg["res2"], result2, equal_nan=True)
+
+
+def test_provide_superclass_kwargs():
+    """Test instantiating class providing superclass arguments."""
+    Accuracy(reduce="micro")
+    Accuracy(mdmc_reduce="global")

tests/classification/test_dice.py

@@ -163,3 +163,9 @@ def test_dice_fn(self, preds, target, ignore_index):
             sk_metric=partial(_sk_dice, ignore_index=ignore_index),
             metric_args={"ignore_index": ignore_index},
         )
+
+
+def test_provide_superclass_kwargs():
+    """Test instantiating class providing superclass arguments."""
+    Dice(reduce="micro")
+    Dice(mdmc_reduce="global")

tests/classification/test_f_beta.py

@@ -460,3 +460,11 @@ def test_same_input(metric_class, metric_functional, sk_fn, average, ignore_inde
 
     assert torch.allclose(class_res, torch.tensor(sk_res).float())
     assert torch.allclose(func_res, torch.tensor(sk_res).float())
+
+
+def test_provide_superclass_kwargs():
+    """Test instantiating class providing superclass arguments."""
+    FBetaScore(reduce="micro")
+    FBetaScore(mdmc_reduce="global")
+    F1Score(reduce="micro")
+    F1Score(mdmc_reduce="global")

tests/classification/test_jaccard.py

@@ -237,3 +237,8 @@ def test_jaccard_ignore_index(pred, target, ignore_index, num_classes, average,
         # reduction=reduction,
     )
     assert torch.allclose(jaccard_val, tensor(expected).to(jaccard_val))
+
+
+def test_provide_superclass_kwargs():
+    """Test instantiating class providing superclass arguments."""
+    jaccard = JaccardIndex(num_classes=1, normalize="true")

tests/classification/test_precision_recall.py

@@ -468,3 +468,11 @@ def test_noneavg(metric_cls, noneavg=_negmetric_noneavg):
     assert torch.allclose(noneavg["res1"], result1, equal_nan=True)
     result2 = prec(noneavg["pred2"], noneavg["target2"])
     assert torch.allclose(noneavg["res2"], result2, equal_nan=True)
+
+
+def test_provide_superclass_kwargs():
+    """Test instantiating class providing superclass arguments."""
+    Precision(reduce="micro")
+    Precision(mdmc_reduce="global")
+    Recall(reduce="micro")
+    Recall(mdmc_reduce="global")

tests/classification/test_specificity.py

@@ -410,3 +410,9 @@ def test_class_not_present(metric_class, metric_fn, ignore_index, expected):
     cl_metric(preds, target)
     result_cl = cl_metric.compute()
     assert torch.allclose(expected, result_cl, equal_nan=True)
+
+
+def test_provide_superclass_kwargs():
+    """Test instantiating class providing superclass arguments."""
+    Specificity(reduce="micro")
+    Specificity(mdmc_reduce="global")

torchmetrics/classification/accuracy.py

@@ -23,7 +23,7 @@
     _subset_accuracy_compute,
     _subset_accuracy_update,
 )
-from torchmetrics.utilities.enums import DataType
+from torchmetrics.utilities.enums import AverageMethod, DataType
 
 from torchmetrics.classification.stat_scores import StatScores  # isort:skip
 
@@ -176,9 +176,12 @@ def __init__(
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        if "reduce" not in kwargs or kwargs["reduce"] is None:
+            kwargs["reduce"] = "macro" if average in [AverageMethod.WEIGHTED, AverageMethod.NONE, "none"] else average
+        if "mdmc_reduce" not in kwargs or kwargs["mdmc_reduce"] is None:
+            kwargs["mdmc_reduce"] = mdmc_average
+
         super().__init__(
-            reduce="macro" if average in ["weighted", "none", None] else average,
-            mdmc_reduce=mdmc_average,
             threshold=threshold,
             top_k=top_k,
             num_classes=num_classes,

torchmetrics/classification/dice.py

@@ -17,6 +17,7 @@
 
 from torchmetrics.classification.stat_scores import StatScores
 from torchmetrics.functional.classification.dice import _dice_compute
+from torchmetrics.utilities.enums import AverageMethod
 
 
 class Dice(StatScores):
@@ -134,9 +135,12 @@ def __init__(
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        if "reduce" not in kwargs or kwargs["reduce"] is None:
+            kwargs["reduce"] = "macro" if average in [AverageMethod.WEIGHTED, AverageMethod.NONE, "none"] else average
+        if "mdmc_reduce" not in kwargs or kwargs["mdmc_reduce"] is None:
+            kwargs["mdmc_reduce"] = mdmc_average
+
         super().__init__(
-            reduce="macro" if average in ("weighted", "none", None) else average,
-            mdmc_reduce=mdmc_average,
             threshold=threshold,
             top_k=top_k,
             num_classes=num_classes,

torchmetrics/classification/f_beta.py

@@ -137,9 +137,12 @@ def __init__(
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        if "reduce" not in kwargs or kwargs["reduce"] is None:
+            kwargs["reduce"] = "macro" if average in [AverageMethod.WEIGHTED, AverageMethod.NONE, "none"] else average
+        if "mdmc_reduce" not in kwargs or kwargs["mdmc_reduce"] is None:
+            kwargs["mdmc_reduce"] = mdmc_average
+
         super().__init__(
-            reduce="macro" if average in [AverageMethod.WEIGHTED, AverageMethod.NONE] else average,
-            mdmc_reduce=mdmc_average,
             threshold=threshold,
             top_k=top_k,
             num_classes=num_classes,

torchmetrics/classification/jaccard.py

@@ -90,9 +90,11 @@ def __init__(
         multilabel: bool = False,
         **kwargs: Dict[str, Any],
     ) -> None:
+        if "normalize" not in kwargs:
+            kwargs["normalize"] = None
+
         super().__init__(
             num_classes=num_classes,
-            normalize=None,
             threshold=threshold,
             multilabel=multilabel,
             **kwargs,

torchmetrics/classification/precision_recall.py

@@ -17,6 +17,7 @@
 
 from torchmetrics.classification.stat_scores import StatScores
 from torchmetrics.functional.classification.precision_recall import _precision_compute, _recall_compute
+from torchmetrics.utilities.enums import AverageMethod
 
 
 class Precision(StatScores):
@@ -127,9 +128,12 @@ def __init__(
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        if "reduce" not in kwargs or kwargs["reduce"] is None:
+            kwargs["reduce"] = "macro" if average in [AverageMethod.WEIGHTED, AverageMethod.NONE, "none"] else average
+        if "mdmc_reduce" not in kwargs or kwargs["mdmc_reduce"] is None:
+            kwargs["mdmc_reduce"] = mdmc_average
+
         super().__init__(
-            reduce="macro" if average in ["weighted", "none", None] else average,
-            mdmc_reduce=mdmc_average,
             threshold=threshold,
             top_k=top_k,
             num_classes=num_classes,
@@ -262,9 +266,12 @@ def __init__(
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        if "reduce" not in kwargs or kwargs["reduce"] is None:
+            kwargs["reduce"] = "macro" if average in [AverageMethod.WEIGHTED, AverageMethod.NONE, "none"] else average
+        if "mdmc_reduce" not in kwargs or kwargs["mdmc_reduce"] is None:
+            kwargs["mdmc_reduce"] = mdmc_average
+
         super().__init__(
-            reduce="macro" if average in ["weighted", "none", None] else average,
-            mdmc_reduce=mdmc_average,
             threshold=threshold,
             top_k=top_k,
             num_classes=num_classes,

torchmetrics/classification/specificity.py

@@ -18,6 +18,7 @@
 
 from torchmetrics.classification.stat_scores import StatScores
 from torchmetrics.functional.classification.specificity import _specificity_compute
+from torchmetrics.utilities.enums import AverageMethod
 
 
 class Specificity(StatScores):
@@ -129,9 +130,12 @@ def __init__(
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        if "reduce" not in kwargs or kwargs["reduce"] is None:
+            kwargs["reduce"] = "macro" if average in [AverageMethod.WEIGHTED, AverageMethod.NONE, "none"] else average
+        if "mdmc_reduce" not in kwargs or kwargs["mdmc_reduce"] is None:
+            kwargs["mdmc_reduce"] = mdmc_average
+
         super().__init__(
-            reduce="macro" if average in ["weighted", "none", None] else average,
-            mdmc_reduce=mdmc_average,
             threshold=threshold,
             top_k=top_k,
             num_classes=num_classes,

torchmetrics/functional/audio/pesq.py

@@ -12,17 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import numpy as np
+import torch
+from torch import Tensor
 
+from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.imports import _PESQ_AVAILABLE
 
 if _PESQ_AVAILABLE:
     import pesq as pesq_backend
 else:
     pesq_backend = None
-import torch
-from torch import Tensor
 
-from torchmetrics.utilities.checks import _check_same_shape
 
 __doctest_requires__ = {("perceptual_evaluation_speech_quality",): ["pesq"]}
 

torchmetrics/functional/audio/stoi.py

@@ -13,17 +13,16 @@
 # limitations under the License.
 import numpy as np
 import torch
+from torch import Tensor
 
+from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.imports import _PYSTOI_AVAILABLE
 
 if _PYSTOI_AVAILABLE:
     from pystoi import stoi as stoi_backend
 else:
     stoi_backend = None
     __doctest_skip__ = ["short_time_objective_intelligibility"]
-from torch import Tensor
-
-from torchmetrics.utilities.checks import _check_same_shape
 
 
 def short_time_objective_intelligibility(