Fix for bug when providing superclass arguments as kwargs (#1069)

* Fix for bug when providing superclass arguments as kwargs
* Fix code formatting
* chlog

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka <[email protected]>
Co-authored-by: Jirka Borovec <[email protected]>

CHANGELOG.md

@@ -45,7 +45,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Fixed aggregation metrics when input only contains zero ([#1070](https://github.com/PyTorchLightning/metrics/pull/1070))
 
--
+
+- Fixed `TypeError` when providing superclass arguments as kwargs ([#1069](https://github.com/PyTorchLightning/metrics/pull/1069))
 
 
 ## [0.9.0] - 2022-05-30

tests/classification/test_confusion_matrix.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from functools import partial
+from typing import Any, Dict
 
 import numpy as np
 import pytest
@@ -30,6 +31,7 @@
 from tests.classification.inputs import _input_multilabel_prob as _input_mlb_prob
 from tests.helpers import seed_all
 from tests.helpers.testers import NUM_CLASSES, THRESHOLD, MetricTester
+from torchmetrics import JaccardIndex
 from torchmetrics.classification.confusion_matrix import ConfusionMatrix
 from torchmetrics.functional.classification.confusion_matrix import confusion_matrix
 
@@ -186,3 +188,18 @@ def test_warning_on_nan(tmpdir):
         match=".* nan values found in confusion matrix have been replaced with zeros.",
     ):
         confusion_matrix(preds, target, num_classes=5, normalize="true")
+
+
+@pytest.mark.parametrize(
+    "metric_args",
+    [
+        {"num_classes": 1, "normalize": "true"},
+        {"num_classes": 1, "normalize": "pred"},
+        {"num_classes": 1, "normalize": "all"},
+        {"num_classes": 1, "normalize": "none"},
+        {"num_classes": 1, "normalize": None},
+    ],
+)
+def test_provide_superclass_kwargs(metric_args: Dict[str, Any]):
+    """Test instantiating subclasses with superclass arguments as kwargs."""
+    JaccardIndex(**metric_args)

tests/classification/test_stat_scores.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from functools import partial
-from typing import Callable, Optional
+from typing import Any, Callable, Dict, Optional
 
 import numpy as np
 import pytest
@@ -30,7 +30,7 @@
 from tests.classification.inputs import _input_multilabel_prob as _input_mlb_prob
 from tests.helpers import seed_all
 from tests.helpers.testers import NUM_CLASSES, MetricTester
-from torchmetrics import StatScores
+from torchmetrics import Accuracy, Dice, FBetaScore, Precision, Recall, Specificity, StatScores
 from torchmetrics.functional import stat_scores
 from torchmetrics.utilities.checks import _input_format_classification
 
@@ -326,3 +326,20 @@ def test_top_k(k: int, preds: Tensor, target: Tensor, reduce: str, expected: Ten
 
     assert torch.equal(class_metric.compute(), expected.T)
     assert torch.equal(stat_scores(preds, target, top_k=k, reduce=reduce, num_classes=3), expected.T)
+
+
+@pytest.mark.parametrize(
+    "metric_args",
+    [
+        {"reduce": "micro"},
+        {"num_classes": 1, "reduce": "macro"},
+        {"reduce": "samples"},
+        {"mdmc_reduce": None},
+        {"mdmc_reduce": "samplewise"},
+        {"mdmc_reduce": "global"},
+    ],
+)
+@pytest.mark.parametrize("metric_cls", [Accuracy, Dice, FBetaScore, Precision, Recall, Specificity])
+def test_provide_superclass_kwargs(metric_cls: StatScores, metric_args: Dict[str, Any]):
+    """Test instantiating subclasses with superclass arguments as kwargs."""
+    metric_cls(**metric_args)

tests/text/test_mer.py

@@ -4,16 +4,15 @@
 
 from tests.text.helpers import TextTester
 from tests.text.inputs import _inputs_error_rate_batch_size_1, _inputs_error_rate_batch_size_2
+from torchmetrics.functional.text.mer import match_error_rate
+from torchmetrics.text.mer import MatchErrorRate
 from torchmetrics.utilities.imports import _JIWER_AVAILABLE
 
 if _JIWER_AVAILABLE:
     from jiwer import compute_measures
 else:
     compute_measures: Callable
 
-from torchmetrics.functional.text.mer import match_error_rate
-from torchmetrics.text.mer import MatchErrorRate
-
 
 def _compute_mer_metric_jiwer(preds: Union[str, List[str]], target: Union[str, List[str]]):
     return compute_measures(target, preds)["mer"]

tests/text/test_wer.py

@@ -4,16 +4,15 @@
 
 from tests.text.helpers import TextTester
 from tests.text.inputs import _inputs_error_rate_batch_size_1, _inputs_error_rate_batch_size_2
+from torchmetrics.functional.text.wer import word_error_rate
+from torchmetrics.text.wer import WordErrorRate
 from torchmetrics.utilities.imports import _JIWER_AVAILABLE
 
 if _JIWER_AVAILABLE:
     from jiwer import compute_measures
 else:
     compute_measures: Callable
 
-from torchmetrics.functional.text.wer import word_error_rate
-from torchmetrics.text.wer import WordErrorRate
-
 
 def _compute_wer_metric_jiwer(preds: Union[str, List[str]], target: Union[str, List[str]]):
     return compute_measures(target, preds)["wer"]

torchmetrics/classification/accuracy.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 from torch import Tensor, tensor
 
@@ -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
 
@@ -170,15 +170,19 @@ def __init__(
         top_k: Optional[int] = None,
         multiclass: Optional[bool] = None,
         subset_accuracy: bool = False,
-        **kwargs: Dict[str, Any],
+        **kwargs: Any,
     ) -> None:
         allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        _reduce_options = (AverageMethod.WEIGHTED, AverageMethod.NONE, None)
+        if "reduce" not in kwargs:
+            kwargs["reduce"] = AverageMethod.MACRO if average in _reduce_options else average
+        if "mdmc_reduce" not in kwargs:
+            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

@@ -11,12 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 from torch import Tensor
 
 from torchmetrics.classification.stat_scores import StatScores
 from torchmetrics.functional.classification.dice import _dice_compute
+from torchmetrics.utilities.enums import AverageMethod
 
 
 class Dice(StatScores):
@@ -128,15 +129,19 @@ def __init__(
         ignore_index: Optional[int] = None,
         top_k: Optional[int] = None,
         multiclass: Optional[bool] = None,
-        **kwargs: Dict[str, Any],
+        **kwargs: Any,
     ) -> None:
         allowed_average = ("micro", "macro", "weighted", "samples", "none", None)
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        _reduce_options = (AverageMethod.WEIGHTED, AverageMethod.NONE, None)
+        if "reduce" not in kwargs:
+            kwargs["reduce"] = AverageMethod.MACRO if average in _reduce_options else average
+        if "mdmc_reduce" not in kwargs:
+            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

@@ -130,16 +130,20 @@ def __init__(
         ignore_index: Optional[int] = None,
         top_k: Optional[int] = None,
         multiclass: Optional[bool] = None,
-        **kwargs: Dict[str, Any],
+        **kwargs: Any,
     ) -> None:
         self.beta = beta
         allowed_average = list(AverageMethod)
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        _reduce_options = (AverageMethod.WEIGHTED, AverageMethod.NONE, None)
+        if "reduce" not in kwargs:
+            kwargs["reduce"] = AverageMethod.MACRO if average in _reduce_options else average
+        if "mdmc_reduce" not in kwargs:
+            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

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 import torch
 from torch import Tensor
@@ -88,11 +88,12 @@ def __init__(
         absent_score: float = 0.0,
         threshold: float = 0.5,
         multilabel: bool = False,
-        **kwargs: Dict[str, Any],
+        **kwargs: Any,
     ) -> None:
+        kwargs["normalize"] = kwargs.get("normalize")
+
         super().__init__(
             num_classes=num_classes,
-            normalize=None,
             threshold=threshold,
             multilabel=multilabel,
             **kwargs,

torchmetrics/classification/precision_recall.py

@@ -11,12 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 from torch import Tensor
 
 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):
@@ -121,15 +122,19 @@ def __init__(
         ignore_index: Optional[int] = None,
         top_k: Optional[int] = None,
         multiclass: Optional[bool] = None,
-        **kwargs: Dict[str, Any],
+        **kwargs: Any,
     ) -> None:
         allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        _reduce_options = (AverageMethod.WEIGHTED, AverageMethod.NONE, None)
+        if "reduce" not in kwargs:
+            kwargs["reduce"] = AverageMethod.MACRO if average in _reduce_options else average
+        if "mdmc_reduce" not in kwargs:
+            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,
@@ -256,15 +261,19 @@ def __init__(
         ignore_index: Optional[int] = None,
         top_k: Optional[int] = None,
         multiclass: Optional[bool] = None,
-        **kwargs: Dict[str, Any],
+        **kwargs: Any,
     ) -> None:
         allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        _reduce_options = (AverageMethod.WEIGHTED, AverageMethod.NONE, None)
+        if "reduce" not in kwargs:
+            kwargs["reduce"] = AverageMethod.MACRO if average in _reduce_options else average
+        if "mdmc_reduce" not in kwargs:
+            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

@@ -11,13 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 import torch
 from torch import Tensor
 
 from torchmetrics.classification.stat_scores import StatScores
 from torchmetrics.functional.classification.specificity import _specificity_compute
+from torchmetrics.utilities.enums import AverageMethod
 
 
 class Specificity(StatScores):
@@ -123,15 +124,19 @@ def __init__(
         ignore_index: Optional[int] = None,
         top_k: Optional[int] = None,
         multiclass: Optional[bool] = None,
-        **kwargs: Dict[str, Any],
+        **kwargs: Any,
     ) -> None:
         allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
         if average not in allowed_average:
             raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
 
+        _reduce_options = (AverageMethod.WEIGHTED, AverageMethod.NONE, None)
+        if "reduce" not in kwargs:
+            kwargs["reduce"] = AverageMethod.MACRO if average in _reduce_options else average
+        if "mdmc_reduce" not in kwargs:
+            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(