Prune metric: helpers and inputs 3/n

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/functional/accuracy.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/auroc.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/confusion_matrix.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/hamming_distance.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/precision_recall.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/stat_scores.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/trainer/connectors/env_vars_connector.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

tests/metrics/classification/test_accuracy.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

tests/metrics/classification/test_hamming_distance.py

@@ -1,9 +1,9 @@
 import pytest
 import torch
 from sklearn.metrics import hamming_loss as sk_hamming_loss
+from torchmetrics.classification.checks import _input_format_classification
 
 from pytorch_lightning.metrics import HammingDistance
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification
 from pytorch_lightning.metrics.functional import hamming_distance
 from tests.metrics.classification.inputs import _input_binary, _input_binary_prob
 from tests.metrics.classification.inputs import _input_multiclass as _input_mcls

tests/metrics/classification/test_inputs.py

@@ -1,9 +1,10 @@
 import pytest
 import torch
 from torch import rand, randint
+from torchmetrics.classification.checks import _input_format_classification
 from torchmetrics.utilities.data import select_topk, to_onehot
+from torchmetrics.utilities.enums import DataType
 
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification, DataType
 from tests.metrics.classification.inputs import _input_binary as _bin
 from tests.metrics.classification.inputs import _input_binary_prob as _bin_prob
 from tests.metrics.classification.inputs import _input_multiclass as _mc

tests/metrics/classification/test_precision_recall.py

@@ -5,9 +5,9 @@
 import pytest
 import torch
 from sklearn.metrics import precision_score, recall_score
+from torchmetrics.classification.checks import _input_format_classification
 
 from pytorch_lightning.metrics import Metric, Precision, Recall
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification
 from pytorch_lightning.metrics.functional import precision, precision_recall, recall
 from tests.metrics.classification.inputs import _input_binary, _input_binary_prob
 from tests.metrics.classification.inputs import _input_multiclass as _input_mcls

tests/metrics/classification/test_stat_scores.py

@@ -5,9 +5,9 @@
 import pytest
 import torch
 from sklearn.metrics import multilabel_confusion_matrix
+from torchmetrics.classification.checks import _input_format_classification
 
 from pytorch_lightning.metrics import StatScores
-from pytorch_lightning.metrics.classification.helpers import _input_format_classification
 from pytorch_lightning.metrics.functional import stat_scores
 from tests.metrics.classification.inputs import _input_binary, _input_binary_prob, _input_multiclass
 from tests.metrics.classification.inputs import _input_multiclass_prob as _input_mccls_prob

tests/trainer/test_lr_finder.py

@@ -277,6 +277,7 @@ def test_lr_find_with_bs_scale(tmpdir):
     """ Test that lr_find runs with batch_size_scaling """
 
     class BoringModelTune(BoringModel):
+
         def __init__(self, learning_rate=0.1, batch_size=2):
             super().__init__()
             self.save_hyperparameters()