Merge branch 'master' into ci/upload-failure

CHANGELOG.md

@@ -85,9 +85,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
-- Removed `multiclass_roc` and `multiclass_precision_recall_curve`, use `roc` and `precision_recall_curve` instead ([#4549](https://github.com/PyTorchLightning/pytorch-lightning/pull/4549))
 - Tuner algorithms will be skipped if `fast_dev_run=True` ([#3903](https://github.com/PyTorchLightning/pytorch-lightning/pull/3903))
-- WandbLogger does not force wandb `reinit` arg to True anymore and creates a run only when needed ([#4648](https://github.com/PyTorchLightning/pytorch-lightning/pull/4648))
+- `WandbLogger` does not force wandb `reinit` arg to True anymore and creates a run only when needed ([#4648](https://github.com/PyTorchLightning/pytorch-lightning/pull/4648))
 - Changed `automatic_optimization` to be a model attribute ([#4602](https://github.com/PyTorchLightning/pytorch-lightning/pull/4602))
 - Changed `Simple Profiler` report to order by percentage time spent + num calls ([#4880](https://github.com/PyTorchLightning/pytorch-lightning/pull/4880))
 - Simplify optimization Logic ([#4984](https://github.com/PyTorchLightning/pytorch-lightning/pull/4984))
@@ -105,6 +104,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Removed
 
 - Removed `reorder` parameter of the `auc` metric ([#5004](https://github.com/PyTorchLightning/pytorch-lightning/pull/5004))
+- Removed `multiclass_roc` and `multiclass_precision_recall_curve`, use `roc` and `precision_recall_curve` instead ([#4549](https://github.com/PyTorchLightning/pytorch-lightning/pull/4549))
 
 ### Fixed
 

pytorch_lightning/metrics/classification/__init__.py

@@ -14,7 +14,7 @@
 from pytorch_lightning.metrics.classification.accuracy import Accuracy
 from pytorch_lightning.metrics.classification.average_precision import AveragePrecision
 from pytorch_lightning.metrics.classification.confusion_matrix import ConfusionMatrix
-from pytorch_lightning.metrics.classification.f_beta import FBeta, F1
+from pytorch_lightning.metrics.classification.f_beta import FBeta, Fbeta, F1
 from pytorch_lightning.metrics.classification.precision_recall import Precision, Recall
 from pytorch_lightning.metrics.classification.precision_recall_curve import PrecisionRecallCurve
 from pytorch_lightning.metrics.classification.roc import ROC

pytorch_lightning/metrics/classification/f_beta.py

@@ -20,6 +20,7 @@
     _fbeta_compute
 )
 from pytorch_lightning.metrics.metric import Metric
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 class FBeta(Metric):
@@ -131,6 +132,34 @@ def compute(self) -> torch.Tensor:
                               self.actual_positives, self.beta, self.average)
 
 
+# todo: remove in v1.2
+class Fbeta(FBeta):
+    r"""
+    Computes `F-score <https://en.wikipedia.org/wiki/F-score>`_
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.classification.f_beta.FBeta`
+    """
+    def __init__(
+        self,
+        num_classes: int,
+        beta: float = 1.0,
+        threshold: float = 0.5,
+        average: str = "micro",
+        multilabel: bool = False,
+        compute_on_step: bool = True,
+        dist_sync_on_step: bool = False,
+        process_group: Optional[Any] = None,
+    ):
+        rank_zero_warn(
+            "This `Fbeta` was deprecated in v1.0.x in favor of"
+            " `from pytorch_lightning.metrics.classification.f_beta import FBeta`."
+            " It will be removed in v1.2.0", DeprecationWarning
+        )
+        super().__init__(
+            num_classes, beta, threshold, average, multilabel, compute_on_step, dist_sync_on_step, process_group
+        )
+
+
 class F1(FBeta):
     """
     Computes F1 metric. F1 metrics correspond to a harmonic mean of the

pytorch_lightning/metrics/functional/__init__.py

@@ -17,13 +17,18 @@
     auc,
     auroc,
     dice_score,
+    f1_score,
+    fbeta_score,
+    get_num_classes,
+    iou,
     multiclass_auroc,
     precision,
     precision_recall,
     recall,
     stat_scores,
     stat_scores_multiple_classes,
-    iou,
+    to_categorical,
+    to_onehot,
 )
 from pytorch_lightning.metrics.functional.confusion_matrix import confusion_matrix
 # TODO: unify metrics between class and functional, add below