Deprecate num_outputs in R2 because it is no longer needed

CHANGELOG.md

@@ -32,6 +32,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - update `InfoLM` class to dynamically set `higher_is_better` ([#2674](https://github.com/Lightning-AI/torchmetrics/pull/2674))
 
 
+### Deprecated
+
+- Deprecated `num_outputs` in `R2Score` ([#2705](https://github.com/Lightning-AI/torchmetrics/pull/2705))
+
+
 ### Removed
 
 -

src/torchmetrics/regression/r2.py

@@ -13,11 +13,11 @@
 # limitations under the License.
 from typing import Any, Optional, Sequence, Union
 
-import torch
 from torch import Tensor, tensor
 
 from torchmetrics.functional.regression.r2 import _r2_score_compute, _r2_score_update
 from torchmetrics.metric import Metric
+from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
 
@@ -65,23 +65,32 @@ class R2Score(Metric):
             * ``'variance_weighted'`` scores are weighted by their individual variances
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
+    .. warning::
+        Argument ``num_outputs`` in ``R2Score`` has been deprecated because it is no longer necessary and will be
+        removed in v1.6.0 of TorchMetrics. The number of outputs is now automatically inferred from the shape
+        of the input tensors.
+
     Raises:
         ValueError:
             If ``adjusted`` parameter is not an integer larger or equal to 0.
         ValueError:
             If ``multioutput`` is not one of ``"raw_values"``, ``"uniform_average"`` or ``"variance_weighted"``.
 
-    Example:
+    Example (single output):
+        >>> from torch import tensor
         >>> from torchmetrics.regression import R2Score
-        >>> target = torch.tensor([3, -0.5, 2, 7])
-        >>> preds = torch.tensor([2.5, 0.0, 2, 8])
+        >>> target = tensor([3, -0.5, 2, 7])
+        >>> preds = tensor([2.5, 0.0, 2, 8])
         >>> r2score = R2Score()
         >>> r2score(preds, target)
         tensor(0.9486)
 
-        >>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
-        >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
-        >>> r2score = R2Score(num_outputs=2, multioutput='raw_values')
+    Example (multioutput):
+        >>> from torch import tensor
+        >>> from torchmetrics.regression import R2Score
+        >>> target = tensor([[0.5, 1], [-1, 1], [7, -6]])
+        >>> preds = tensor([[0, 2], [-1, 2], [8, -5]])
+        >>> r2score = R2Score(multioutput='raw_values')
         >>> r2score(preds, target)
         tensor([0.9654, 0.9082])
 
@@ -100,14 +109,20 @@ class R2Score(Metric):
 
     def __init__(
         self,
-        num_outputs: int = 1,
+        num_outputs: Optional[int] = None,
         adjusted: int = 0,
         multioutput: str = "uniform_average",
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
 
-        self.num_outputs = num_outputs
+        if num_outputs is not None:
+            rank_zero_warn(
+                "Argument `num_outputs` in `R2Score` has been deprecated because it is no longer necessary and will be"
+                "removed in v1.6.0 of TorchMetrics. The number of outputs is now automatically inferred from the shape"
+                "of the input tensors.",
+                DeprecationWarning,
+            )
 
         if adjusted < 0 or not isinstance(adjusted, int):
             raise ValueError("`adjusted` parameter should be an integer larger or equal to 0.")
@@ -120,19 +135,19 @@ def __init__(
             )
         self.multioutput = multioutput
 
-        self.add_state("sum_squared_error", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
-        self.add_state("sum_error", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
-        self.add_state("residual", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
+        self.add_state("sum_squared_error", default=tensor(0.0), dist_reduce_fx="sum")
+        self.add_state("sum_error", default=tensor(0.0), dist_reduce_fx="sum")
+        self.add_state("residual", default=tensor(0.0), dist_reduce_fx="sum")
         self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:
         """Update state with predictions and targets."""
         sum_squared_error, sum_error, residual, total = _r2_score_update(preds, target)
 
-        self.sum_squared_error += sum_squared_error
-        self.sum_error += sum_error
-        self.residual += residual
-        self.total += total
+        self.sum_squared_error = self.sum_squared_error + sum_squared_error
+        self.sum_error = self.sum_error + sum_error
+        self.residual = self.residual + residual
+        self.total = self.total + total
 
     def compute(self) -> Tensor:
         """Compute r2 score over the metric states."""

tests/unittests/test_deprecated.py

@@ -1,7 +1,7 @@
 import pytest
 import torch
 from torchmetrics.functional.regression import kl_divergence
-from torchmetrics.regression import KLDivergence
+from torchmetrics.regression import KLDivergence, R2Score
 
 
 def test_deprecated_kl_divergence_input_order():
@@ -14,3 +14,9 @@ def test_deprecated_kl_divergence_input_order():
 
     with pytest.deprecated_call(match="The input order and naming in metric `KLDivergence` is set to be deprecated.*"):
         KLDivergence()
+
+
+def test_deprecated_r2_score_num_outputs():
+    """Ensure that the deprecated num_outputs argument in R2Score raises a warning."""
+    with pytest.deprecated_call(match="Argument `num_outputs` in `R2Score` has been deprecated"):
+        R2Score(num_outputs=2)