typing

torchmetrics/classification/__init__.py

@@ -24,7 +24,7 @@
 from torchmetrics.classification.hamming_distance import HammingDistance # noqa: F401
 from torchmetrics.classification.hinge import Hinge # noqa: F401
 from torchmetrics.classification.iou import IoU # noqa: F401
-from torchmetrics.classification.kldivergence import KLDivergence # noqa: F401
+from torchmetrics.classification.kl_divergence import KLDivergence # noqa: F401
 from torchmetrics.classification.matthews_corrcoef import MatthewsCorrcoef # noqa: F401
 from torchmetrics.classification.precision_recall import Precision, Recall # noqa: F401
 from torchmetrics.classification.precision_recall_curve import PrecisionRecallCurve # noqa: F401

torchmetrics/classification/kldivergence.py → torchmetrics/classification/kl_divergence.py

@@ -11,12 +11,12 @@
 # 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, Callable, List, Optional, Union
+from typing import Any, Callable, Optional
 
 import torch
 from torch import Tensor
 
-from torchmetrics.functional.classification.kldivergence import _kld_compute, _kld_update
+from torchmetrics.functional.classification.kl_divergence import _kld_compute, _kld_update
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
 
@@ -60,6 +60,8 @@ class KLDivergence(Metric):
  >>> kldivergence(p, q)
  tensor(0.0853)
  """
+ # canot be used because if scripting
+ # measures: Union[List[Tensor], Tensor]
  total: Tensor
 
  def __init__(

torchmetrics/classification/stat_scores.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, Callable, Optional, Tuple, Union, List
+from typing import Any, Callable, Optional, Tuple
 
 import torch
 from torch import Tensor
@@ -130,6 +130,12 @@ class StatScores(Metric):
 
  """
 
+ # canot be used because if scripting
+ # tp: Union[Tensor, List[Tensor]]
+ # fp: Union[Tensor, List[Tensor]]
+ # tn: Union[Tensor, List[Tensor]]
+ # fn: Union[Tensor, List[Tensor]]
+
  def __init__(
  self,
  threshold: float = 0.5,

torchmetrics/functional/__init__.py

@@ -25,7 +25,7 @@
 from torchmetrics.functional.classification.hamming_distance import hamming_distance # noqa: F401
 from torchmetrics.functional.classification.hinge import hinge # noqa: F401
 from torchmetrics.functional.classification.iou import iou # noqa: F401
-from torchmetrics.functional.classification.kldivergence import kldivergence # noqa: F401
+from torchmetrics.functional.classification.kl_divergence import kldivergence # noqa: F401
 from torchmetrics.functional.classification.matthews_corrcoef import matthews_corrcoef # noqa: F401
 from torchmetrics.functional.classification.precision_recall import precision, precision_recall, recall # noqa: F401
 from torchmetrics.functional.classification.precision_recall_curve import precision_recall_curve # noqa: F401

torchmetrics/functional/classification/__init__.py

@@ -22,7 +22,7 @@
 from torchmetrics.functional.classification.hamming_distance import hamming_distance # noqa: F401
 from torchmetrics.functional.classification.hinge import hinge # noqa: F401
 from torchmetrics.functional.classification.iou import iou # noqa: F401
-from torchmetrics.functional.classification.kldivergence import kldivergence # noqa: F401
+from torchmetrics.functional.classification.kl_divergence import kldivergence # noqa: F401
 from torchmetrics.functional.classification.matthews_corrcoef import matthews_corrcoef # noqa: F401
 from torchmetrics.functional.classification.precision_recall import precision, precision_recall, recall # noqa: F401
 from torchmetrics.functional.classification.precision_recall_curve import precision_recall_curve # noqa: F401