Remove get_num_classes in jaccard_index

tests/test_utilities.py

@@ -16,7 +16,7 @@
 from torch import tensor
 
 from torchmetrics.utilities import rank_zero_debug, rank_zero_info, rank_zero_warn
-from torchmetrics.utilities.data import _bincount, _flatten, _flatten_dict, get_num_classes, to_categorical, to_onehot
+from torchmetrics.utilities.data import _bincount, _flatten, _flatten_dict, to_categorical, to_onehot
 from torchmetrics.utilities.distributed import class_reduce, reduce
 
 
@@ -92,18 +92,6 @@ def test_to_categorical():
     assert torch.allclose(result, expected.to(result.dtype))
 
 
-@pytest.mark.parametrize(
-    ["preds", "target", "num_classes", "expected_num_classes"],
-    [
-        (torch.rand(32, 10, 28, 28), torch.randint(10, (32, 28, 28)), 10, 10),
-        (torch.rand(32, 10, 28, 28), torch.randint(10, (32, 28, 28)), None, 10),
-        (torch.rand(32, 28, 28), torch.randint(10, (32, 28, 28)), None, 10),
-    ],
-)
-def test_get_num_classes(preds, target, num_classes, expected_num_classes):
-    assert get_num_classes(preds, target, num_classes) == expected_num_classes
-
-
 def test_flatten_list():
     """Check that _flatten utility function works as expected."""
     inp = [[1, 2, 3], [4, 5], [6]]

torchmetrics/functional/classification/jaccard.py

@@ -18,7 +18,6 @@
 from typing_extensions import Literal
 
 from torchmetrics.functional.classification.confusion_matrix import _confusion_matrix_update
-from torchmetrics.utilities.data import get_num_classes
 from torchmetrics.utilities.distributed import reduce
 
 
@@ -129,6 +128,5 @@ def jaccard_index(
         tensor(0.9660)
     """
 
-    num_classes = get_num_classes(preds=preds, target=target, num_classes=num_classes)
     confmat = _confusion_matrix_update(preds, target, num_classes, threshold)
     return _jaccard_from_confmat(confmat, num_classes, ignore_index, absent_score, reduction)

torchmetrics/utilities/data.py

@@ -16,8 +16,6 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.utilities.prints import rank_zero_warn
-
 METRIC_EPS = 1e-6
 
 
@@ -145,37 +143,6 @@ def to_categorical(x: Tensor, argmax_dim: int = 1) -> Tensor:
     return torch.argmax(x, dim=argmax_dim)
 
 
-def get_num_classes(
-    preds: Tensor,
-    target: Tensor,
-    num_classes: Optional[int] = None,
-) -> int:
-    """Calculates the number of classes for a given prediction and target tensor.
-
-    Args:
-        preds: predicted values
-        target: true labels
-        num_classes: number of classes if known
-
-    Return:
-        An integer that represents the number of classes.
-    """
-    num_target_classes = int(target.max().detach().item() + 1)
-    num_pred_classes = int(preds.max().detach().item() + 1)
-    num_all_classes = max(num_target_classes, num_pred_classes)
-
-    if num_classes is None:
-        num_classes = num_all_classes
-    elif num_classes != num_all_classes:
-        rank_zero_warn(
-            f"You have set {num_classes} number of classes which is"
-            f" different from predicted ({num_pred_classes}) and"
-            f" target ({num_target_classes}) number of classes",
-            RuntimeWarning,
-        )
-    return num_classes
-
-
 def apply_to_collection(
     data: Any,
     dtype: Union[type, tuple],