Sync our torchmetrics wrappers after the 0.4 release (#8205)

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

pytorch_lightning/metrics/classification/f_beta.py

@@ -21,7 +21,7 @@ from pytorch_lightning.metrics.utils import deprecated_metrics, void
 
 class FBeta(_FBeta):
 
-    @deprecated_metrics(target=_FBeta)
+    @deprecated_metrics(target=_FBeta, args_mapping={"multilabel": None})
     def __init__(
         self,
         num_classes: int,
@@ -44,7 +44,7 @@ class FBeta(_FBeta):
 
 class F1(_F1):
 
-    @deprecated_metrics(target=_F1)
+    @deprecated_metrics(target=_F1, args_mapping={"multilabel": None})
     def __init__(
         self,
         num_classes: int,

pytorch_lightning/metrics/classification/precision_recall.py

@@ -21,7 +21,7 @@ from pytorch_lightning.metrics.utils import deprecated_metrics, void
 
 class Precision(_Precision):
 
-    @deprecated_metrics(target=_Precision)
+    @deprecated_metrics(target=_Precision, args_mapping={"multilabel": None, "is_multiclass": None})
     def __init__(
         self,
         num_classes: Optional[int] = None,
@@ -49,7 +49,7 @@ class Precision(_Precision):
 
 class Recall(_Recall):
 
-    @deprecated_metrics(target=_Recall)
+    @deprecated_metrics(target=_Recall, args_mapping={"multilabel": None, "is_multiclass": None})
     def __init__(
         self,
         num_classes: Optional[int] = None,

pytorch_lightning/metrics/classification/stat_scores.py

@@ -20,7 +20,7 @@ from pytorch_lightning.metrics.utils import deprecated_metrics, void
 
 class StatScores(_StatScores):
 
-    @deprecated_metrics(target=_StatScores)
+    @deprecated_metrics(target=_StatScores, args_mapping={"is_multiclass": None})
     def __init__(
         self,
         threshold: float = 0.5,

pytorch_lightning/metrics/functional/f_beta.py

@@ -20,7 +20,7 @@ from torchmetrics.functional import fbeta as _fbeta
 from pytorch_lightning.metrics.utils import deprecated_metrics, void
 
 
-@deprecated_metrics(target=_fbeta)
+@deprecated_metrics(target=_fbeta, args_mapping={"multilabel": None})
 def fbeta(
     preds: torch.Tensor,
     target: torch.Tensor,
@@ -37,7 +37,7 @@ def fbeta(
     return void(preds, target, num_classes, beta, threshold, average, multilabel)
 
 
-@deprecated_metrics(target=_f1)
+@deprecated_metrics(target=_f1, args_mapping={"multilabel": None})
 def f1(
     preds: torch.Tensor,
     target: torch.Tensor,

pytorch_lightning/metrics/functional/precision_recall.py

@@ -21,7 +21,7 @@ from torchmetrics.functional import recall as _recall
 from pytorch_lightning.metrics.utils import deprecated_metrics, void
 
 
-@deprecated_metrics(target=_precision)
+@deprecated_metrics(target=_precision, args_mapping={"is_multiclass": None})
 def precision(
     preds: torch.Tensor,
     target: torch.Tensor,
@@ -40,7 +40,7 @@ def precision(
     return void(preds, target, average, mdmc_average, ignore_index, num_classes, threshold, top_k, is_multiclass)
 
 
-@deprecated_metrics(target=_recall)
+@deprecated_metrics(target=_recall, args_mapping={"is_multiclass": None})
 def recall(
     preds: torch.Tensor,
     target: torch.Tensor,
@@ -59,7 +59,7 @@ def recall(
     return void(preds, target, average, mdmc_average, ignore_index, num_classes, threshold, top_k, is_multiclass)
 
 
-@deprecated_metrics(target=_precision_recall)
+@deprecated_metrics(target=_precision_recall, args_mapping={"is_multiclass": None})
 def precision_recall(
     preds: torch.Tensor,
     target: torch.Tensor,

pytorch_lightning/metrics/functional/stat_scores.py

@@ -19,7 +19,7 @@ from torchmetrics.functional import stat_scores as _stat_scores
 from pytorch_lightning.metrics.utils import deprecated_metrics, void
 
 
-@deprecated_metrics(target=_stat_scores)
+@deprecated_metrics(target=_stat_scores, args_mapping={"is_multiclass": None})
 def stat_scores(
     preds: torch.Tensor,
     target: torch.Tensor,

pytorch_lightning/metrics/utils.py

@@ -65,7 +65,7 @@ def select_topk(prob_tensor: torch.Tensor, topk: int = 1, dim: int = 1) -> torch
     return void(prob_tensor, topk, dim)
 
 
-@deprecated_metrics(target=_to_categorical)
+@deprecated_metrics(target=_to_categorical, args_mapping={"tensor": "x"})
 def to_categorical(tensor: torch.Tensor, argmax_dim: int = 1) -> torch.Tensor:
     """
     .. deprecated::

requirements.txt

@@ -7,7 +7,7 @@ tqdm>=4.41.0
 PyYAML>=5.1,<=5.4.1
 fsspec[http]>=2021.05.0, !=2021.06.0
 tensorboard>=2.2.0, !=2.5.0  # 2.5.0 GPU CI error: 'Couldn't build proto file into descriptor pool!'
-torchmetrics>=0.4.0rc1
+torchmetrics>=0.4.0
 pyDeprecate==0.3.1
 packaging>=17.0
 typing-extensions  # TypedDict support for python<3.8