Removed Deprecated compute_on_step from Classification (#962)

* Removed Deprecated  from Classification
* chlog

Co-authored-by: Jirka <jirka.borovec@seznam.cz>

CHANGELOG.md

@@ -35,7 +35,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Removed
 
--
+- Removed deprecated `compute_on_step` argument ([#962](https://github.com/PyTorchLightning/metrics/pull/962))
 
 
 -

torchmetrics/classification/accuracy.py

@@ -126,12 +126,6 @@ class Accuracy(StatScores):
  ``preds = preds.flatten()`` and same for ``target``). Note that the ``top_k`` parameter
  still applies in both cases, if set.
 
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
-
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
  Raises:
@@ -175,7 +169,6 @@ def __init__(
  top_k: Optional[int] = None,
  multiclass: Optional[bool] = None,
  subset_accuracy: bool = False,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
  allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
@@ -190,7 +183,6 @@ def __init__(
  num_classes=num_classes,
  multiclass=multiclass,
  ignore_index=ignore_index,
- compute_on_step=compute_on_step,
  **kwargs,
  )
 

torchmetrics/classification/auc.py

@@ -11,7 +11,7 @@
 # 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 typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 from torch import Tensor
 
@@ -32,11 +32,6 @@ class AUC(Metric):
  reorder: AUC expects its first input to be sorted. If this is not the case,
  setting this argument to ``True`` will use a stable sorting algorithm to
  sort the input in descending order
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  """
@@ -47,10 +42,9 @@ class AUC(Metric):
  def __init__(
  self,
  reorder: bool = False,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
 
  self.reorder = reorder
 

torchmetrics/classification/auroc.py

@@ -63,11 +63,6 @@ class AUROC(Metric):
  max_fpr:
  If not ``None``, calculates standardized partial AUC over the
  range ``[0, max_fpr]``. Should be a float between 0 and 1.
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -113,10 +108,9 @@ def __init__(
  pos_label: Optional[int] = None,
  average: Optional[str] = "macro",
  max_fpr: Optional[float] = None,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
 
  self.num_classes = num_classes
  self.pos_label = pos_label

torchmetrics/classification/avg_precision.py

@@ -57,12 +57,6 @@ class AveragePrecision(Metric):
  - ``'none'`` or ``None``: Calculate the metric for each class separately, and return
  the metric for every class.
 
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
-
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
  Example (binary case):
@@ -93,10 +87,9 @@ def __init__(
  num_classes: Optional[int] = None,
  pos_label: Optional[int] = None,
  average: Optional[str] = "macro",
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
 
  self.num_classes = num_classes
  self.pos_label = pos_label

torchmetrics/classification/binned_precision_recall.py

@@ -11,7 +11,7 @@
 # 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 typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -61,11 +61,6 @@ class BinnedPrecisionRecallCurve(Metric):
  thresholds: list or tensor with specific thresholds or a number of bins from linear sampling.
  It is used for computation will lead to more detailed curve and accurate estimates,
  but will be slower and consume more memory.
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -122,10 +117,9 @@ def __init__(
  self,
  num_classes: int,
  thresholds: Union[int, Tensor, List[float], None] = None,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
 
  self.num_classes = num_classes
  if isinstance(thresholds, int):
@@ -203,11 +197,6 @@ class BinnedAveragePrecision(BinnedPrecisionRecallCurve):
  thresholds: list or tensor with specific thresholds or a number of bins from linear sampling.
  It is used for computation will lead to more detailed curve and accurate estimates,
  but will be slower and consume more memory
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -258,11 +247,6 @@ class BinnedRecallAtFixedPrecision(BinnedPrecisionRecallCurve):
  thresholds: list or tensor with specific thresholds or a number of bins from linear sampling.
  It is used for computation will lead to more detailed curve and accurate estimates,
  but will be slower and consume more memory
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -295,10 +279,9 @@ def __init__(
  num_classes: int,
  min_precision: float,
  thresholds: Union[int, Tensor, List[float], None] = None,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(num_classes=num_classes, thresholds=thresholds, compute_on_step=compute_on_step, **kwargs)
+ super().__init__(num_classes=num_classes, thresholds=thresholds, **kwargs)
  self.min_precision = min_precision
 
  def compute(self) -> Tuple[Tensor, Tensor]: # type: ignore

torchmetrics/classification/calibration_error.py

@@ -11,7 +11,7 @@
 # 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 typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 import torch
 from torch import Tensor
@@ -52,11 +52,6 @@ class CalibrationError(Metric):
  norm: Norm used to compare empirical and expected probability bins.
  Defaults to "l1", or Expected Calibration Error.
  debias: Applies debiasing term, only implemented for l2 norm. Defaults to True.
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  """
@@ -69,11 +64,10 @@ def __init__(
  self,
  n_bins: int = 15,
  norm: str = "l1",
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ):
 
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
 
  if norm not in self.DISTANCES:
  raise ValueError(f"Norm {norm} is not supported. Please select from l1, l2, or max. ")

torchmetrics/classification/cohen_kappa.py

@@ -56,12 +56,6 @@ class labels.
  Threshold for transforming probability or logit predictions to binary ``(0,1)`` predictions, in the case
  of binary or multi-label inputs. Default value of ``0.5`` corresponds to input being probabilities.
 
- compute_on_step:
- Forward only calls ``update()`` and returns ``None`` if this is set to ``False``.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
-
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
  Example:
@@ -82,10 +76,9 @@ def __init__(
  num_classes: int,
  weights: Optional[str] = None,
  threshold: float = 0.5,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
  self.num_classes = num_classes
  self.weights = weights
  self.threshold = threshold

torchmetrics/classification/confusion_matrix.py

@@ -54,11 +54,6 @@ class ConfusionMatrix(Metric):
  of binary or multi-label inputs. Default value of ``0.5`` corresponds to input being probabilities.
 
  multilabel: determines if data is multilabel or not.
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -99,10 +94,9 @@ def __init__(
  normalize: Optional[str] = None,
  threshold: float = 0.5,
  multilabel: bool = False,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
  self.num_classes = num_classes
  self.normalize = normalize
  self.threshold = threshold

torchmetrics/classification/f_beta.py

@@ -102,12 +102,6 @@ class FBetaScore(StatScores):
  :ref:`documentation section <pages/classification:using the multiclass parameter>`
  for a more detailed explanation and examples.
 
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
-
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
  Raises:
@@ -135,7 +129,6 @@ def __init__(
  ignore_index: Optional[int] = None,
  top_k: Optional[int] = None,
  multiclass: Optional[bool] = None,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
  self.beta = beta
@@ -151,7 +144,6 @@ def __init__(
  num_classes=num_classes,
  multiclass=multiclass,
  ignore_index=ignore_index,
- compute_on_step=compute_on_step,
  **kwargs,
  )
 
@@ -237,12 +229,6 @@ class F1Score(FBetaScore):
  :ref:`documentation section <pages/classification:using the multiclass parameter>`
  for a more detailed explanation and examples.
 
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
-
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
 
@@ -268,7 +254,6 @@ def __init__(
  ignore_index: Optional[int] = None,
  top_k: Optional[int] = None,
  multiclass: Optional[bool] = None,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
  super().__init__(
@@ -280,6 +265,5 @@ def __init__(
  ignore_index=ignore_index,
  top_k=top_k,
  multiclass=multiclass,
- compute_on_step=compute_on_step,
  **kwargs,
  )

torchmetrics/classification/hamming.py

@@ -11,7 +11,7 @@
 # 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 typing import Any, Dict, Optional
+from typing import Any, Dict
 
 import torch
 from torch import Tensor, tensor
@@ -40,11 +40,6 @@ class HammingDistance(Metric):
  threshold:
  Threshold for transforming probability or logit predictions to binary ``(0,1)`` predictions, in the case
  of binary or multi-label inputs. Default value of ``0.5`` corresponds to input being probabilities.
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -69,10 +64,9 @@ class HammingDistance(Metric):
  def __init__(
  self,
  threshold: float = 0.5,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
 
  self.add_state("correct", default=tensor(0), dist_reduce_fx="sum")
  self.add_state("total", default=tensor(0), dist_reduce_fx="sum")

torchmetrics/classification/hinge.py

@@ -54,11 +54,6 @@ class HingeLoss(Metric):
  Which approach to use for multi-class inputs (has no effect in the binary case). ``None`` (default),
  ``MulticlassMode.CRAMMER_SINGER`` or ``"crammer-singer"``, uses the Crammer Singer multi-class hinge loss.
  ``MulticlassMode.ONE_VS_ALL`` or ``"one-vs-all"`` computes the hinge loss in a one-vs-all fashion.
- compute_on_step:
- Forward only calls ``update()`` and returns None if this is set to False.
-
- .. deprecated:: v0.8
- Argument has no use anymore and will be removed v0.9.
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
@@ -101,10 +96,9 @@ def __init__(
  self,
  squared: bool = False,
  multiclass_mode: Optional[Union[str, MulticlassMode]] = None,
- compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
- super().__init__(compute_on_step=compute_on_step, **kwargs)
+ super().__init__(**kwargs)
 
  self.add_state("measure", default=tensor(0.0), dist_reduce_fx="sum")
  self.add_state("total", default=tensor(0), dist_reduce_fx="sum")