Simplify deprecations

docs/source/conf.py

@@ -331,6 +331,7 @@ def package_list_from_file(file):
     'comet-ml': 'comet_ml',
     'neptune-client': 'neptune',
     'hydra-core': 'hydra',
+    'pyDeprecate': 'deprecate',
 }
 MOCK_PACKAGES = []
 if SPHINX_MOCK_REQUIREMENTS:

pytorch_lightning/callbacks/model_checkpoint.py

@@ -30,7 +30,7 @@
 import yaml
 
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.utilities import rank_zero_info, rank_zero_only, rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_deprecation, rank_zero_info, rank_zero_only, rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.warnings import WarningCache
@@ -258,9 +258,9 @@ def save_checkpoint(self, trainer, unused: Optional = None):
         to handle correct behaviour in distributed training, i.e., saving only on rank 0.
         """
         if unused is not None:
-            rank_zero_warn(
+            rank_zero_deprecation(
                 "`ModelCheckpoint.save_checkpoint` signature has changed in v1.3. The `pl_module` parameter"
-                " has been removed. Support for the old signature will be removed in v1.5", DeprecationWarning
+                " has been removed. Support for the old signature will be removed in v1.5"
             )
 
         global_step = trainer.global_step
@@ -371,27 +371,27 @@ def __init_triggers(
 
         # period takes precedence over every_n_val_epochs for backwards compatibility
         if period is not None:
-            rank_zero_warn(
+            rank_zero_deprecation(
                 'Argument `period` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.'
-                ' Please use `every_n_val_epochs` instead.', DeprecationWarning
+                ' Please use `every_n_val_epochs` instead.'
             )
             self._every_n_val_epochs = period
 
         self._period = self._every_n_val_epochs
 
     @property
     def period(self) -> Optional[int]:
-        rank_zero_warn(
+        rank_zero_deprecation(
             'Property `period` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.'
-            ' Please use `every_n_val_epochs` instead.', DeprecationWarning
+            ' Please use `every_n_val_epochs` instead.'
         )
         return self._period
 
     @period.setter
     def period(self, value: Optional[int]) -> None:
-        rank_zero_warn(
+        rank_zero_deprecation(
             'Property `period` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.'
-            ' Please use `every_n_val_epochs` instead.', DeprecationWarning
+            ' Please use `every_n_val_epochs` instead.'
         )
         self._period = value
 

pytorch_lightning/core/lightning.py

@@ -38,7 +38,7 @@
 from pytorch_lightning.core.optimizer import LightningOptimizer
 from pytorch_lightning.core.saving import ALLOWED_CONFIG_TYPES, ModelIO, PRIMITIVE_TYPES
 from pytorch_lightning.core.step_result import Result
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_deprecation, rank_zero_warn
 from pytorch_lightning.utilities.apply_func import apply_to_collection, convert_to_tensors
 from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -1226,9 +1226,8 @@ def training_step(...):
                 opt_a.step()
         """
         if optimizer is not None:
-            rank_zero_warn(
-                "`optimizer` argument to `manual_backward` is deprecated in v1.2 and will be removed in v1.4",
-                DeprecationWarning
+            rank_zero_deprecation(
+                "`optimizer` argument to `manual_backward` is deprecated in v1.2 and will be removed in v1.4"
             )
 
         # make sure we're using manual opt

pytorch_lightning/metrics/__init__.py

@@ -11,7 +11,6 @@
 # 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 warnings import warn
 
 from pytorch_lightning.metrics.classification import (  # noqa: F401
     Accuracy,
@@ -39,8 +38,9 @@
     R2Score,
     SSIM,
 )
+from pytorch_lightning.utilities import rank_zero_deprecation
 
-warn(
+rank_zero_deprecation(
     "`pytorch_lightning.metrics.*` module has been renamed to `torchmetrics.*` and split off to its own package"
-    " (https://github.com/PyTorchLightning/metrics) since v1.3 and will be removed in v1.5", DeprecationWarning
+    " (https://github.com/PyTorchLightning/metrics) since v1.3 and will be removed in v1.5"
 )

pytorch_lightning/metrics/classification/accuracy.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import Accuracy as _Accuracy
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class Accuracy(_Accuracy):
 
-    @deprecated(target=_Accuracy, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_Accuracy)
     def __init__(
         self,
         threshold: float = 0.5,

pytorch_lightning/metrics/classification/auc.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import AUC as _AUC
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class AUC(_AUC):
 
-    @deprecated(target=_AUC, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_AUC)
     def __init__(
         self,
         reorder: bool = False,

pytorch_lightning/metrics/classification/auroc.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import AUROC as _AUROC
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class AUROC(_AUROC):
 
-    @deprecated(target=_AUROC, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_AUROC)
     def __init__(
         self,
         num_classes: Optional[int] = None,

pytorch_lightning/metrics/classification/average_precision.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import AveragePrecision as _AveragePrecision
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class AveragePrecision(_AveragePrecision):
 
-    @deprecated(target=_AveragePrecision, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_AveragePrecision)
     def __init__(
         self,
         num_classes: Optional[int] = None,

pytorch_lightning/metrics/classification/confusion_matrix.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import ConfusionMatrix as _ConfusionMatrix
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class ConfusionMatrix(_ConfusionMatrix):
 
-    @deprecated(target=_ConfusionMatrix, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_ConfusionMatrix)
     def __init__(
         self,
         num_classes: int,

pytorch_lightning/metrics/classification/f_beta.py

@@ -16,12 +16,12 @@
 from torchmetrics import F1 as _F1
 from torchmetrics import FBeta as _FBeta
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class FBeta(_FBeta):
 
-    @deprecated(target=_FBeta, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_FBeta)
     def __init__(
         self,
         num_classes: int,
@@ -43,7 +43,7 @@ def __init__(
 
 class F1(_F1):
 
-    @deprecated(target=_F1, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_F1)
     def __init__(
         self,
         num_classes: int,

pytorch_lightning/metrics/classification/hamming_distance.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import HammingDistance as _HammingDistance
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class HammingDistance(_HammingDistance):
 
-    @deprecated(target=_HammingDistance, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_HammingDistance)
     def __init__(
         self,
         threshold: float = 0.5,

pytorch_lightning/metrics/classification/iou.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import IoU as _IoU
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class IoU(_IoU):
 
-    @deprecated(target=_IoU, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_IoU)
     def __init__(
         self,
         num_classes: int,

pytorch_lightning/metrics/classification/precision_recall.py

@@ -16,12 +16,12 @@
 from torchmetrics import Precision as _Precision
 from torchmetrics import Recall as _Recall
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class Precision(_Precision):
 
-    @deprecated(target=_Precision, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_Precision)
     def __init__(
         self,
         num_classes: Optional[int] = None,
@@ -47,7 +47,7 @@ def __init__(
 
 class Recall(_Recall):
 
-    @deprecated(target=_Recall, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_Recall)
     def __init__(
         self,
         num_classes: Optional[int] = None,

pytorch_lightning/metrics/classification/precision_recall_curve.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import PrecisionRecallCurve as _PrecisionRecallCurve
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class PrecisionRecallCurve(_PrecisionRecallCurve):
 
-    @deprecated(target=_PrecisionRecallCurve, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_PrecisionRecallCurve)
     def __init__(
         self,
         num_classes: Optional[int] = None,

pytorch_lightning/metrics/classification/roc.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import ROC as _ROC
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class ROC(_ROC):
 
-    @deprecated(target=_ROC, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_ROC)
     def __init__(
         self,
         num_classes: Optional[int] = None,

pytorch_lightning/metrics/classification/stat_scores.py

@@ -15,12 +15,12 @@
 
 from torchmetrics import StatScores as _StatScores
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 class StatScores(_StatScores):
 
-    @deprecated(target=_StatScores, ver_deprecate="1.3.0", ver_remove="1.5.0")
+    @deprecated_metrics(target=_StatScores)
     def __init__(
         self,
         threshold: float = 0.5,

pytorch_lightning/metrics/compositional.py

@@ -15,25 +15,21 @@
 
 import torch
 from torchmetrics import Metric
-from torchmetrics.metric import CompositionalMetric as __CompositionalMetric
+from torchmetrics.metric import CompositionalMetric as _CompositionalMetric
 
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
-class CompositionalMetric(__CompositionalMetric):
-    """
-    .. deprecated::
-        Use :class:`torchmetrics.metric.CompositionalMetric`. Will be removed in v1.5.0.
-    """
+class CompositionalMetric(_CompositionalMetric):
 
+    @deprecated_metrics(target=_CompositionalMetric)
     def __init__(
         self,
         operator: Callable,
         metric_a: Union[Metric, int, float, torch.Tensor],
         metric_b: Union[Metric, int, float, torch.Tensor, None],
     ):
-        rank_zero_warn(
-            "This `CompositionalMetric` was deprecated since v1.3.0 in favor of"
-            " `torchmetrics.metric.CompositionalMetric`. It will be removed in v1.5.0", DeprecationWarning
-        )
-        super().__init__(operator=operator, metric_a=metric_a, metric_b=metric_b)
+        """
+        .. deprecated::
+            Use :class:`torchmetrics.metric.CompositionalMetric`. Will be removed in v1.5.0.
+        """

pytorch_lightning/metrics/functional/accuracy.py

@@ -16,10 +16,10 @@
 import torch
 from torchmetrics.functional import accuracy as _accuracy
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
-@deprecated(target=_accuracy, ver_deprecate="1.3.0", ver_remove="1.5.0")
+@deprecated_metrics(target=_accuracy)
 def accuracy(
     preds: torch.Tensor,
     target: torch.Tensor,

pytorch_lightning/metrics/functional/auc.py

@@ -14,10 +14,10 @@
 import torch
 from torchmetrics.functional import auc as _auc
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
-@deprecated(target=_auc, ver_deprecate="1.3.0", ver_remove="1.5.0")
+@deprecated_metrics(target=_auc)
 def auc(x: torch.Tensor, y: torch.Tensor, reorder: bool = False) -> torch.Tensor:
     """
     .. deprecated::

pytorch_lightning/metrics/functional/auroc.py

@@ -16,10 +16,10 @@
 import torch
 from torchmetrics.functional import auroc as _auroc
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
-@deprecated(target=_auroc, ver_deprecate="1.3.0", ver_remove="1.5.0")
+@deprecated_metrics(target=_auroc)
 def auroc(
     preds: torch.Tensor,
     target: torch.Tensor,

pytorch_lightning/metrics/functional/average_precision.py

@@ -16,10 +16,10 @@
 import torch
 from torchmetrics.functional import average_precision as _average_precision
 
-from pytorch_lightning.utilities.deprecation import deprecated
+from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
-@deprecated(target=_average_precision, ver_deprecate="1.3.0", ver_remove="1.5.0")
+@deprecated_metrics(target=_average_precision)
 def average_precision(
     preds: torch.Tensor,
     target: torch.Tensor,