add deprecation warnings to modules

src/torchmetrics/classification/accuracy.py

@@ -28,6 +28,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.enums import AverageMethod, DataType
+from torchmetrics.utilities.prints import rank_zero_warn
 
 from torchmetrics.classification.stat_scores import (  # isort:skip
     StatScores,
@@ -325,6 +326,13 @@ def compute(self) -> Tensor:
 
 class Accuracy(StatScores):
     r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
     Computes Accuracy_:
 
     .. math::
@@ -488,6 +496,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(

src/torchmetrics/classification/auroc.py

@@ -324,7 +324,16 @@ def compute(self) -> Tensor:
 
 
 class AUROC(Metric):
-    r"""Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_).
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+
+    Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_).
     Works for both binary, multilabel and multiclass problems. In the case of
     multiclass, the values will be calculated based on a one-vs-the-rest approach.
 
@@ -428,6 +437,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(

src/torchmetrics/classification/average_precision.py

@@ -323,7 +323,15 @@ def compute(self) -> Tensor:
 
 
 class AveragePrecision(Metric):
-    """Computes the average precision score, which summarises the precision recall curve into one number. Works for
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+    Computes the average precision score, which summarises the precision recall curve into one number. Works for
     both binary and multiclass problems. In the case of multiclass, the values will be calculated based on a one-
     vs-the-rest approach.
 
@@ -406,6 +414,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(

src/torchmetrics/classification/calibration_error.py

@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.prints import rank_zero_warn
 
 
 class BinaryCalibrationError(Metric):
@@ -222,7 +223,15 @@ def compute(self) -> Tensor:
 
 
 class CalibrationError(Metric):
-    r"""`Computes the Top-label Calibration Error`_
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+    `Computes the Top-label Calibration Error`_
     Three different norms are implemented, each corresponding to variations on the calibration error metric.
 
     L1 norm (Expected Calibration Error)
@@ -282,6 +291,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(

src/torchmetrics/classification/cohen_kappa.py

@@ -26,6 +26,7 @@
     _multiclass_cohen_kappa_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.prints import rank_zero_warn
 
 
 class BinaryCohenKappa(BinaryConfusionMatrix):
@@ -182,7 +183,15 @@ def compute(self) -> Tensor:
 
 
 class CohenKappa(Metric):
-    r"""Calculates `Cohen's kappa score`_ that measures inter-annotator agreement. It is defined as
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+    Calculates `Cohen's kappa score`_ that measures inter-annotator agreement. It is defined as
 
     .. math::
         \kappa = (p_o - p_e) / (1 - p_e)
@@ -253,6 +262,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(

src/torchmetrics/classification/confusion_matrix.py

@@ -37,6 +37,7 @@
     _multilabel_confusion_matrix_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.prints import rank_zero_warn
 
 
 class BinaryConfusionMatrix(Metric):
@@ -318,7 +319,15 @@ def compute(self) -> Tensor:
 
 
 class ConfusionMatrix(Metric):
-    r"""Computes the `confusion matrix`_.
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+    Computes the `confusion matrix`_.
 
     Works with binary, multiclass, and multilabel data. Accepts probabilities or logits from a model output
     or integer class values in prediction. Works with multi-dimensional preds and target, but it should be noted that
@@ -412,6 +421,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(

src/torchmetrics/classification/f_beta.py

@@ -32,6 +32,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.enums import AverageMethod
+from torchmetrics.utilities.prints import rank_zero_warn
 
 
 class BinaryFBetaScore(BinaryStatScores):
@@ -716,7 +717,15 @@ def __init__(
 
 
 class FBetaScore(StatScores):
-    r"""Computes `F-score`_, specifically:
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+    Computes `F-score`_, specifically:
 
     .. math::
         F_\beta = (1 + \beta^2) * \frac{\text{precision} * \text{recall}}
@@ -848,6 +857,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(
@@ -891,7 +909,15 @@ def compute(self) -> Tensor:
 
 
 class F1Score(FBetaScore):
-    """Computes F1 metric.
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+    Computes F1 metric.
 
     F1 metrics correspond to a harmonic mean of the precision and recall scores.
     Works with binary, multiclass, and multilabel data. Accepts logits or probabilities from a model
@@ -1013,6 +1039,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(

src/torchmetrics/classification/hamming.py

@@ -24,6 +24,7 @@
     _hamming_distance_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.prints import rank_zero_warn
 
 
 class BinaryHammingDistance(BinaryStatScores):
@@ -318,7 +319,15 @@ def compute(self) -> Tensor:
 
 
 class HammingDistance(Metric):
-    r"""Computes the average `Hamming distance`_ (also known as Hamming loss) between targets and predictions:
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+    Computes the average `Hamming distance`_ (also known as Hamming loss) between targets and predictions:
 
     .. math::
         \text{Hamming distance} = \frac{1}{N \cdot L}\sum_i^N \sum_l^L 1(y_{il} \neq \hat{y_{il}})
@@ -389,6 +398,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(

src/torchmetrics/classification/hinge.py

@@ -32,6 +32,7 @@
     _multiclass_hinge_loss_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.prints import rank_zero_warn
 
 
 class BinaryHingeLoss(Metric):
@@ -205,7 +206,15 @@ def compute(self) -> Tensor:
 
 
 class HingeLoss(Metric):
-    r"""Computes the mean `Hinge loss`_, typically used for Support Vector Machines (SVMs).
+    r"""
+    .. note::
+        From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
+        metric. Moving forward we recommend using these versions. This base metric will still work as it did
+        prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required
+        and the general order of arguments may change, such that this metric will just function as an single
+        entrypoint to calling the three specialized versions.
+
+    Computes the mean `Hinge loss`_, typically used for Support Vector Machines (SVMs).
 
     In the binary case it is defined as:
 
@@ -299,6 +308,15 @@ def __new__(
             raise ValueError(
                 f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
             )
+        else:
+            rank_zero_warn(
+                "From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification"
+                " metric. Moving forward we recommend using these versions. This base metric will still work as it did"
+                " prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required"
+                " and the general order of arguments may change, such that this metric will just function as an single"
+                " entrypoint to calling the three specialized versions.",
+                DeprecationWarning,
+            )
         return super().__new__(cls)
 
     def __init__(