Prune metric: helpers and inputs 3/n (#6547)

* _basic_input_validation

* _check_shape_and_type_consistency

* _check_num_classes_binary

* _check_num_classes_mc

* _check_num_classes_ml

* _check_top_k

* _check_classification_inputs

* _input_format_classification

* _reduce_stat_scores

* DataType

* rest

* flake8

* chlog

Author: Borda

CHANGELOG.md

@@ -68,6 +68,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated metrics in favor of `torchmetrics` ([#6505](https://github.com/PyTorchLightning/pytorch-lightning/pull/6505),
 
     [#6530](https://github.com/PyTorchLightning/pytorch-lightning/pull/6530),
+  
+    [#6547](https://github.com/PyTorchLightning/pytorch-lightning/pull/6547),
 
 )
 

pytorch_lightning/metrics/classification/helpers.py

@@ -14,8 +14,8 @@
 from typing import Optional, Tuple
 
 import torch
-
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification, DataType
+from torchmetrics.classification.checks import _input_format_classification
+from torchmetrics.utilities.enums import DataType
 
 
 def _accuracy_update(

pytorch_lightning/metrics/functional/accuracy.py

@@ -15,8 +15,9 @@
 from typing import Optional, Sequence, Tuple
 
 import torch
+from torchmetrics.classification.checks import _input_format_classification
+from torchmetrics.utilities.enums import DataType
 
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification, DataType
 from pytorch_lightning.metrics.functional.auc import auc
 from pytorch_lightning.metrics.functional.roc import roc
 from pytorch_lightning.utilities import LightningEnum

pytorch_lightning/metrics/functional/auroc.py

@@ -14,8 +14,9 @@
 from typing import Optional
 
 import torch
+from torchmetrics.classification.checks import _input_format_classification
+from torchmetrics.utilities.enums import DataType
 
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification, DataType
 from pytorch_lightning.utilities import rank_zero_warn
 
 

pytorch_lightning/metrics/functional/confusion_matrix.py

@@ -14,8 +14,7 @@
 from typing import Tuple, Union
 
 import torch
-
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification
+from torchmetrics.classification.checks import _input_format_classification
 
 
 def _hamming_distance_update(

pytorch_lightning/metrics/functional/hamming_distance.py

@@ -14,8 +14,8 @@
 from typing import Optional
 
 import torch
+from torchmetrics.classification.stat_scores import _reduce_stat_scores
 
-from pytorch_lightning.metrics.classification.helpers import _reduce_stat_scores
 from pytorch_lightning.metrics.functional.stat_scores import _stat_scores_update
 from pytorch_lightning.utilities import rank_zero_warn
 

pytorch_lightning/metrics/functional/precision_recall.py

@@ -14,8 +14,7 @@
 from typing import Optional, Tuple
 
 import torch
-
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification
+from torchmetrics.classification.checks import _input_format_classification
 
 
 def _del_column(tensor: torch.Tensor, index: int):

pytorch_lightning/metrics/functional/stat_scores.py

@@ -23,6 +23,7 @@ def _defaults_from_env_vars(fn: Callable) -> Callable:
     Decorator for :class:`~pytorch_lightning.trainer.trainer.Trainer` methods for which
     input arguments should be moved automatically to the correct device.
     """
+
     @wraps(fn)
     def insert_env_defaults(self, *args, **kwargs):
         cls = self.__class__  # get the class

pytorch_lightning/trainer/connectors/env_vars_connector.py

@@ -4,9 +4,10 @@
 import pytest
 import torch
 from sklearn.metrics import accuracy_score as sk_accuracy
+from torchmetrics.classification.checks import _input_format_classification
+from torchmetrics.utilities.enums import DataType
 
 from pytorch_lightning.metrics import Accuracy
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification, DataType
 from pytorch_lightning.metrics.functional import accuracy
 from tests.metrics.classification.inputs import _input_binary, _input_binary_prob
 from tests.metrics.classification.inputs import _input_multiclass as _input_mcls