Remove deprecated code (#2800)

CHANGELOG.md

@@ -17,14 +17,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
--
+- Changed naming and input order arguments in `KLDivergence` ([#2800](https://github.com/Lightning-AI/torchmetrics/pull/2800))
 
 
 ### Removed
 
 - Changed minimum supported Pytorch version to 2.0 ([#2671](https://github.com/Lightning-AI/torchmetrics/pull/2671))
 
 
+- Removed `num_outputs` in `R2Score` ([#2800](https://github.com/Lightning-AI/torchmetrics/pull/2800))
+
+
 ### Fixed
 
 - Changing `_modules` dict type in Pytorch 2.5 preventing to fail collections metrics ([#2793](https://github.com/Lightning-AI/torchmetrics/pull/2793))

src/torchmetrics/functional/regression/kl_divergence.py

@@ -20,7 +20,6 @@
 
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.compute import _safe_xlogy
-from torchmetrics.utilities.prints import rank_zero_warn
 
 
 def _kld_update(p: Tensor, q: Tensor, log_prob: bool) -> Tuple[Tensor, int]:
@@ -92,14 +91,6 @@ def kl_divergence(
     over data and :math:`Q` is often a prior or approximation of :math:`P`. It should be noted that the KL divergence
     is a non-symmetrical metric i.e. :math:`D_{KL}(P||Q) \neq D_{KL}(Q||P)`.
 
-    .. warning::
-        The input order and naming in metric ``kl_divergence`` is set to be deprecated in v1.4 and changed in v1.5.
-        Input argument ``p`` will be renamed to ``target`` and will be moved to be the second argument of the metric.
-        Input argument ``q`` will be renamed to ``preds`` and will be moved to the first argument of the metric.
-        Thus, ``kl_divergence(p, q)`` will equal ``kl_divergence(target=q, preds=p)`` in the future to be consistent
-        with the rest of ``torchmetrics``. From v1.4 the two new arguments will be added as keyword arguments and
-        from v1.5 the two old arguments will be removed.
-
     Args:
         p: data distribution with shape ``[N, d]``
         q: prior or approximate distribution with shape ``[N, d]``
@@ -120,14 +111,5 @@ def kl_divergence(
         tensor(0.0853)
 
     """
-    rank_zero_warn(
-        "The input order and naming in metric `kl_divergence` is set to be deprecated in v1.4 and changed in v1.5."
-        "Input argument `p` will be renamed to `target` and will be moved to be the second argument of the metric."
-        "Input argument `q` will be renamed to `preds` and will be moved to the first argument of the metric."
-        "Thus, `kl_divergence(p, q)` will equal `kl_divergence(target=q, preds=p)` in the future to be consistent with"
-        " the rest of torchmetrics. From v1.4 the two new arguments will be added as keyword arguments and from v1.5"
-        " the two old arguments will be removed.",
-        DeprecationWarning,
-    )
     measures, total = _kld_update(p, q, log_prob)
     return _kld_compute(measures, total, reduction)

src/torchmetrics/regression/kl_divergence.py

@@ -22,7 +22,6 @@
 from torchmetrics.utilities.data import dim_zero_cat
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
-from torchmetrics.utilities.prints import rank_zero_warn
 
 if not _MATPLOTLIB_AVAILABLE:
     __doctest_skip__ = ["KLDivergence.plot"]
@@ -47,14 +46,6 @@ class KLDivergence(Metric):
 
     - ``kl_divergence`` (:class:`~torch.Tensor`): A tensor with the KL divergence
 
-    .. warning::
-        The input order and naming in metric ``KLDivergence`` is set to be deprecated in v1.4 and changed in v1.5.
-        Input argument ``p`` will be renamed to ``target`` and will be moved to be the second argument of the metric.
-        Input argument ``q`` will be renamed to ``preds`` and will be moved to the first argument of the metric.
-        Thus, ``KLDivergence(p, q)`` will equal ``KLDivergence(target=q, preds=p)`` in the future to be consistent
-        with the rest of ``torchmetrics``. From v1.4 the two new arguments will be added as keyword arguments and
-        from v1.5 the two old arguments will be removed.
-
     Args:
         log_prob: bool indicating if input is log-probabilities or probabilities. If given as probabilities,
             will normalize to make sure the distributes sum to 1.
@@ -102,15 +93,6 @@ def __init__(
         reduction: Literal["mean", "sum", "none", None] = "mean",
         **kwargs: Any,
     ) -> None:
-        rank_zero_warn(
-            "The input order and naming in metric `KLDivergence` is set to be deprecated in v1.4 and changed in v1.5."
-            "Input argument `p` will be renamed to `target` and will be moved to be the second argument of the metric."
-            "Input argument `q` will be renamed to `preds` and will be moved to the first argument of the metric."
-            "Thus, `KLDivergence(p, q)` will equal `KLDivergence(target=q, preds=p)` in the future to be consistent"
-            " with the rest of torchmetrics. From v1.4 the two new arguments will be added as keyword arguments and"
-            " from v1.5 the two old arguments will be removed.",
-            DeprecationWarning,
-        )
         super().__init__(**kwargs)
         if not isinstance(log_prob, bool):
             raise TypeError(f"Expected argument `log_prob` to be bool but got {log_prob}")

src/torchmetrics/regression/r2.py

@@ -17,7 +17,6 @@
 
 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
 
@@ -108,21 +107,11 @@ class R2Score(Metric):
 
     def __init__(
         self,
-        num_outputs: Optional[int] = None,
         adjusted: int = 0,
         multioutput: str = "uniform_average",
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
-
-        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.")
         self.adjusted = adjusted

tests/unittests/regression/test_r2.py

@@ -60,28 +60,28 @@ def _multi_target_ref_wrapper(preds, target, adjusted, multioutput):
 @pytest.mark.parametrize("adjusted", [0, 5, 10])
 @pytest.mark.parametrize("multioutput", ["raw_values", "uniform_average", "variance_weighted"])
 @pytest.mark.parametrize(
-    "preds, target, ref_metric, num_outputs",
+    "preds, target, ref_metric",
     [
-        (_single_target_inputs.preds, _single_target_inputs.target, _single_target_ref_wrapper, 1),
-        (_multi_target_inputs.preds, _multi_target_inputs.target, _multi_target_ref_wrapper, NUM_TARGETS),
+        (_single_target_inputs.preds, _single_target_inputs.target, _single_target_ref_wrapper),
+        (_multi_target_inputs.preds, _multi_target_inputs.target, _multi_target_ref_wrapper),
     ],
 )
 class TestR2Score(MetricTester):
     """Test class for `R2Score` metric."""
 
     @pytest.mark.parametrize("ddp", [pytest.param(True, marks=pytest.mark.DDP), False])
-    def test_r2(self, adjusted, multioutput, preds, target, ref_metric, num_outputs, ddp):
+    def test_r2(self, adjusted, multioutput, preds, target, ref_metric, ddp):
         """Test class implementation of metric."""
         self.run_class_metric_test(
             ddp,
             preds,
             target,
             R2Score,
             partial(ref_metric, adjusted=adjusted, multioutput=multioutput),
-            metric_args={"adjusted": adjusted, "multioutput": multioutput, "num_outputs": num_outputs},
+            metric_args={"adjusted": adjusted, "multioutput": multioutput},
         )
 
-    def test_r2_functional(self, adjusted, multioutput, preds, target, ref_metric, num_outputs):
+    def test_r2_functional(self, adjusted, multioutput, preds, target, ref_metric):
         """Test functional implementation of metric."""
         self.run_functional_metric_test(
             preds,
@@ -91,35 +91,23 @@ def test_r2_functional(self, adjusted, multioutput, preds, target, ref_metric, n
             metric_args={"adjusted": adjusted, "multioutput": multioutput},
         )
 
-    def test_r2_differentiability(self, adjusted, multioutput, preds, target, ref_metric, num_outputs):
+    def test_r2_differentiability(self, adjusted, multioutput, preds, target, ref_metric):
         """Test the differentiability of the metric, according to its `is_differentiable` attribute."""
         self.run_differentiability_test(
-            preds=preds,
-            target=target,
-            metric_module=partial(R2Score, num_outputs=num_outputs),
-            metric_functional=r2_score,
-            metric_args={"adjusted": adjusted, "multioutput": multioutput},
+            preds, target, R2Score, r2_score, {"adjusted": adjusted, "multioutput": multioutput}
         )
 
-    def test_r2_half_cpu(self, adjusted, multioutput, preds, target, ref_metric, num_outputs):
+    def test_r2_half_cpu(self, adjusted, multioutput, preds, target, ref_metric):
         """Test dtype support of the metric on CPU."""
         self.run_precision_test_cpu(
-            preds,
-            target,
-            partial(R2Score, num_outputs=num_outputs),
-            r2_score,
-            {"adjusted": adjusted, "multioutput": multioutput},
+            preds, target, R2Score, r2_score, {"adjusted": adjusted, "multioutput": multioutput}
         )
 
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")
-    def test_r2_half_gpu(self, adjusted, multioutput, preds, target, ref_metric, num_outputs):
+    def test_r2_half_gpu(self, adjusted, multioutput, preds, target, ref_metric):
         """Test dtype support of the metric on GPU."""
         self.run_precision_test_gpu(
-            preds,
-            target,
-            partial(R2Score, num_outputs=num_outputs),
-            r2_score,
-            {"adjusted": adjusted, "multioutput": multioutput},
+            preds, target, R2Score, r2_score, {"adjusted": adjusted, "multioutput": multioutput}
         )