Prune metrics base classes 2/n (#6530)

* base class

* extensions

* chlog

* _stable_1d_sort

* _check_same_shape

* _input_format_classification_one_hot

* utils

* to_onehot

* select_topk

* to_categorical

* get_num_classes

* reduce

* class_reduce

* tests

CHANGELOG.md

@@ -65,7 +65,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `trainer.running_sanity_check` in favor of `trainer.sanity_checking` ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))
 
 
-- Deprecated metrics in favor of `torchmetrics` ([#6505](https://github.com/PyTorchLightning/pytorch-lightning/pull/6505))
+- Deprecated metrics in favor of `torchmetrics` ([#6505](https://github.com/PyTorchLightning/pytorch-lightning/pull/6505),
+
+    [#6530](https://github.com/PyTorchLightning/pytorch-lightning/pull/6530),
+
+)
 
 
 ### Removed

pl_examples/basic_examples/conv_sequential_example.py

@@ -27,11 +27,11 @@
 import torch.nn as nn
 import torch.nn.functional as F
 import torchvision
+from torchmetrics.functional import accuracy
 
 import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
 from pytorch_lightning import Trainer
-from torchmetrics.functional import accuracy
 from pytorch_lightning.plugins import RPCSequentialPlugin
 from pytorch_lightning.utilities import _BOLTS_AVAILABLE, _FAIRSCALE_PIPE_AVAILABLE
 

pytorch_lightning/accelerators/gpu.py

@@ -1,6 +1,6 @@
 import logging
 import os
-from typing import TYPE_CHECKING, Any
+from typing import Any, TYPE_CHECKING
 
 import torch
 

pytorch_lightning/metrics/classification/helpers.py

@@ -15,8 +15,8 @@
 
 import numpy as np
 import torch
+from torchmetrics.utilities.data import select_topk, to_onehot
 
-from pytorch_lightning.metrics.utils import select_topk, to_onehot
 from pytorch_lightning.utilities import LightningEnum
 
 

pytorch_lightning/metrics/compositional.py

@@ -1,14 +1,30 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 Callable, Union
 
 import torch
+from torchmetrics import Metric
+from torchmetrics.metric import CompositionalMetric as __CompositionalMetric
 
-from pytorch_lightning.metrics.metric import Metric
+from pytorch_lightning.utilities import rank_zero_warn
 
 
-class CompositionalMetric(Metric):
-    """Composition of two metrics with a specific operator
-    which will be executed upon metric's compute
+class CompositionalMetric(__CompositionalMetric):
+    r"""
+    This implementation refers to :class:`~torchmetrics.metric.CompositionalMetric`.
 
+    .. warning:: This metric is deprecated, use ``torchmetrics.metric.CompositionalMetric``. Will be removed in v1.5.0.
     """
 
     def __init__(
@@ -17,76 +33,8 @@ def __init__(
         metric_a: Union[Metric, int, float, torch.Tensor],
         metric_b: Union[Metric, int, float, torch.Tensor, None],
     ):
-        """
-
-        Args:
-            operator: the operator taking in one (if metric_b is None)
-                or two arguments. Will be applied to outputs of metric_a.compute()
-                and (optionally if metric_b is not None) metric_b.compute()
-            metric_a: first metric whose compute() result is the first argument of operator
-            metric_b: second metric whose compute() result is the second argument of operator.
-                For operators taking in only one input, this should be None
-        """
-        super().__init__()
-
-        self.op = operator
-
-        if isinstance(metric_a, torch.Tensor):
-            self.register_buffer("metric_a", metric_a)
-        else:
-            self.metric_a = metric_a
-
-        if isinstance(metric_b, torch.Tensor):
-            self.register_buffer("metric_b", metric_b)
-        else:
-            self.metric_b = metric_b
-
-    def _sync_dist(self, dist_sync_fn=None):
-        # No syncing required here. syncing will be done in metric_a and metric_b
-        pass
-
-    def update(self, *args, **kwargs):
-        if isinstance(self.metric_a, Metric):
-            self.metric_a.update(*args, **self.metric_a._filter_kwargs(**kwargs))
-
-        if isinstance(self.metric_b, Metric):
-            self.metric_b.update(*args, **self.metric_b._filter_kwargs(**kwargs))
-
-    def compute(self):
-
-        # also some parsing for kwargs?
-        if isinstance(self.metric_a, Metric):
-            val_a = self.metric_a.compute()
-        else:
-            val_a = self.metric_a
-
-        if isinstance(self.metric_b, Metric):
-            val_b = self.metric_b.compute()
-        else:
-            val_b = self.metric_b
-
-        if val_b is None:
-            return self.op(val_a)
-
-        return self.op(val_a, val_b)
-
-    def reset(self):
-        if isinstance(self.metric_a, Metric):
-            self.metric_a.reset()
-
-        if isinstance(self.metric_b, Metric):
-            self.metric_b.reset()
-
-    def persistent(self, mode: bool = False):
-        if isinstance(self.metric_a, Metric):
-            self.metric_a.persistent(mode=mode)
-        if isinstance(self.metric_b, Metric):
-            self.metric_b.persistent(mode=mode)
-
-    def __repr__(self):
-        repr_str = (
-            self.__class__.__name__
-            + f"(\n  {self.op.__name__}(\n    {repr(self.metric_a)},\n    {repr(self.metric_b)}\n  )\n)"
+        rank_zero_warn(
+            "This `Metric` was deprecated since v1.3.0 in favor of `torchmetrics.Metric`."
+            " It will be removed in v1.5.0", DeprecationWarning
         )
-
-        return repr_str
+        super().__init__(operator=operator, metric_a=metric_a, metric_b=metric_b)

pytorch_lightning/metrics/functional/auc.py

@@ -14,8 +14,7 @@
 from typing import Tuple
 
 import torch
-
-from pytorch_lightning.metrics.utils import _stable_1d_sort
+from torchmetrics.utilities.data import _stable_1d_sort
 
 
 def _auc_update(x: torch.Tensor, y: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:

pytorch_lightning/metrics/functional/classification.py

@@ -15,11 +15,12 @@
 from typing import Callable, Optional, Sequence, Tuple
 
 import torch
+from torchmetrics.utilities import class_reduce, reduce
+from torchmetrics.utilities.data import get_num_classes, to_categorical
 
 from pytorch_lightning.metrics.functional.auc import auc as __auc
 from pytorch_lightning.metrics.functional.auroc import auroc as __auroc
 from pytorch_lightning.metrics.functional.iou import iou as __iou
-from pytorch_lightning.metrics.utils import class_reduce, get_num_classes, reduce, to_categorical
 from pytorch_lightning.utilities import rank_zero_warn
 
 

pytorch_lightning/metrics/functional/explained_variance.py

@@ -14,8 +14,7 @@
 from typing import Sequence, Tuple, Union
 
 import torch
-
-from pytorch_lightning.metrics.utils import _check_same_shape
+from torchmetrics.utilities.checks import _check_same_shape
 
 
 def _explained_variance_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:

pytorch_lightning/metrics/functional/f_beta.py

@@ -14,8 +14,8 @@
 from typing import Tuple
 
 import torch
-
-from pytorch_lightning.metrics.utils import _input_format_classification_one_hot, class_reduce
+from torchmetrics.utilities import class_reduce
+from torchmetrics.utilities.checks import _input_format_classification_one_hot
 
 
 def _fbeta_update(

pytorch_lightning/metrics/functional/iou.py

@@ -14,9 +14,10 @@
 from typing import Optional
 
 import torch
+from torchmetrics.utilities import reduce
+from torchmetrics.utilities.data import get_num_classes
 
 from pytorch_lightning.metrics.functional.confusion_matrix import _confusion_matrix_update
-from pytorch_lightning.metrics.utils import get_num_classes, reduce
 
 
 def _iou_from_confmat(

pytorch_lightning/metrics/functional/mean_absolute_error.py

@@ -14,8 +14,7 @@
 from typing import Tuple
 
 import torch
-
-from pytorch_lightning.metrics.utils import _check_same_shape
+from torchmetrics.utilities.checks import _check_same_shape
 
 
 def _mean_absolute_error_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:

pytorch_lightning/metrics/functional/mean_relative_error.py

@@ -14,8 +14,7 @@
 from typing import Tuple
 
 import torch
-
-from pytorch_lightning.metrics.utils import _check_same_shape
+from torchmetrics.utilities.checks import _check_same_shape
 
 
 def _mean_relative_error_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:

pytorch_lightning/metrics/functional/mean_squared_error.py

@@ -14,8 +14,7 @@
 from typing import Tuple
 
 import torch
-
-from pytorch_lightning.metrics.utils import _check_same_shape
+from torchmetrics.utilities.checks import _check_same_shape
 
 
 def _mean_squared_error_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:

pytorch_lightning/metrics/functional/mean_squared_log_error.py

@@ -14,8 +14,7 @@
 from typing import Tuple
 
 import torch
-
-from pytorch_lightning.metrics.utils import _check_same_shape
+from torchmetrics.utilities.checks import _check_same_shape
 
 
 def _mean_squared_log_error_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:

pytorch_lightning/metrics/functional/psnr.py

@@ -14,9 +14,9 @@
 from typing import Optional, Tuple, Union
 
 import torch
+from torchmetrics.utilities import reduce
 
-from pytorch_lightning import utilities
-from pytorch_lightning.metrics import utils
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 def _psnr_compute(
@@ -28,7 +28,7 @@ def _psnr_compute(
 ) -> torch.Tensor:
     psnr_base_e = 2 * torch.log(data_range) - torch.log(sum_squared_error / n_obs)
     psnr = psnr_base_e * (10 / torch.log(torch.tensor(base)))
-    return utils.reduce(psnr, reduction=reduction)
+    return reduce(psnr, reduction=reduction)
 
 
 def _psnr_update(preds: torch.Tensor,
@@ -97,7 +97,7 @@ def psnr(
 
     """
     if dim is None and reduction != 'elementwise_mean':
-        utilities.rank_zero_warn(f'The `reduction={reduction}` will not have any effect when `dim` is None.')
+        rank_zero_warn(f'The `reduction={reduction}` will not have any effect when `dim` is None.')
 
     if data_range is None:
         if dim is not None:

pytorch_lightning/metrics/functional/r2score.py

@@ -14,8 +14,8 @@
 from typing import Tuple
 
 import torch
+from torchmetrics.utilities.checks import _check_same_shape
 
-from pytorch_lightning.metrics.utils import _check_same_shape
 from pytorch_lightning.utilities import rank_zero_warn
 
 

pytorch_lightning/metrics/functional/ssim.py

@@ -15,8 +15,8 @@
 
 import torch
 from torch.nn import functional as F
-
-from pytorch_lightning.metrics.utils import _check_same_shape, reduce
+from torchmetrics.utilities import reduce
+from torchmetrics.utilities.checks import _check_same_shape
 
 
 def _gaussian(kernel_size: int, sigma: int, dtype: torch.dtype, device: torch.device):