prune metric: accuracy 4/n

CHANGELOG.md

@@ -75,6 +75,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
     [#6547](https://github.com/PyTorchLightning/pytorch-lightning/pull/6547),
 
+    [#6515](https://github.com/PyTorchLightning/pytorch-lightning/pull/6515),
+
 )
 
 

pytorch_lightning/metrics/classification/accuracy.py

@@ -13,92 +13,14 @@
 # limitations under the License.
 from typing import Any, Callable, Optional
 
-import torch
-from torchmetrics import Metric
+from torchmetrics import Accuracy as _Accuracy
 
-from pytorch_lightning.metrics.functional.accuracy import _accuracy_compute, _accuracy_update
+from pytorch_lightning.utilities.deprecation import deprecated
 
 
-class Accuracy(Metric):
-    r"""
-    Computes `Accuracy <https://en.wikipedia.org/wiki/Accuracy_and_precision>`__:
-
-    .. math::
-        \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
-
-    Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
-    tensor of predictions.
-
-    For multi-class and multi-dimensional multi-class data with probability predictions, the
-    parameter ``top_k`` generalizes this metric to a Top-K accuracy metric: for each sample the
-    top-K highest probability items are considered to find the correct label.
-
-    For multi-label and multi-dimensional multi-class inputs, this metric computes the "global"
-    accuracy by default, which counts all labels or sub-samples separately. This can be
-    changed to subset accuracy (which requires all labels or sub-samples in the sample to
-    be correctly predicted) by setting ``subset_accuracy=True``.
-
-    Args:
-        threshold:
-            Threshold probability value for transforming probability predictions to binary
-            (0,1) predictions, in the case of binary or multi-label inputs.
-        top_k:
-            Number of highest probability predictions considered to find the correct label, relevant
-            only for (multi-dimensional) multi-class inputs with probability predictions. The
-            default value (``None``) will be interpreted as 1 for these inputs.
-
-            Should be left at default (``None``) for all other types of inputs.
-        subset_accuracy:
-            Whether to compute subset accuracy for multi-label and multi-dimensional
-            multi-class inputs (has no effect for other input types).
-
-            - For multi-label inputs, if the parameter is set to ``True``, then all labels for
-              each sample must be correctly predicted for the sample to count as correct. If it
-              is set to ``False``, then all labels are counted separately - this is equivalent to
-              flattening inputs beforehand (i.e. ``preds = preds.flatten()`` and same for ``target``).
-
-            - For multi-dimensional multi-class inputs, if the parameter is set to ``True``, then all
-              sub-sample (on the extra axis) must be correct for the sample to be counted as correct.
-              If it is set to ``False``, then all sub-samples are counter separately - this is equivalent,
-              in the case of label predictions, to flattening the inputs beforehand (i.e.
-              ``preds = preds.flatten()`` and same for ``target``). Note that the ``top_k`` parameter
-              still applies in both cases, if set.
-
-        compute_on_step:
-            Forward only calls ``update()`` and return ``None`` if this is set to ``False``.
-        dist_sync_on_step:
-            Synchronize metric state across processes at each ``forward()``
-            before returning the value at the step
-        process_group:
-            Specify the process group on which synchronization is called.
-            default: ``None`` (which selects the entire world)
-        dist_sync_fn:
-            Callback that performs the allgather operation on the metric state. When ``None``, DDP
-            will be used to perform the allgather
-
-    Raises:
-        ValueError:
-            If ``threshold`` is not between ``0`` and ``1``.
-        ValueError:
-            If ``top_k`` is not an ``integer`` larger than ``0``.
-
-    Example:
-
-        >>> from pytorch_lightning.metrics import Accuracy
-        >>> target = torch.tensor([0, 1, 2, 3])
-        >>> preds = torch.tensor([0, 2, 1, 3])
-        >>> accuracy = Accuracy()
-        >>> accuracy(preds, target)
-        tensor(0.5000)
-
-        >>> target = torch.tensor([0, 1, 2])
-        >>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
-        >>> accuracy = Accuracy(top_k=2)
-        >>> accuracy(preds, target)
-        tensor(0.6667)
-
-    """
+class Accuracy(_Accuracy):
 
+    @deprecated(target=_Accuracy, ver_deprecate="1.3.0", ver_remove="1.5.0")
     def __init__(
         self,
         threshold: float = 0.5,
@@ -109,44 +31,9 @@ def __init__(
         process_group: Optional[Any] = None,
         dist_sync_fn: Callable = None,
     ):
-        super().__init__(
-            compute_on_step=compute_on_step,
-            dist_sync_on_step=dist_sync_on_step,
-            process_group=process_group,
-            dist_sync_fn=dist_sync_fn,
-        )
-
-        self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
-        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
-
-        if not 0 < threshold < 1:
-            raise ValueError(f"The `threshold` should be a float in the (0,1) interval, got {threshold}")
-
-        if top_k is not None and (not isinstance(top_k, int) or top_k <= 0):
-            raise ValueError(f"The `top_k` should be an integer larger than 0, got {top_k}")
-
-        self.threshold = threshold
-        self.top_k = top_k
-        self.subset_accuracy = subset_accuracy
-
-    def update(self, preds: torch.Tensor, target: torch.Tensor):
-        """
-        Update state with predictions and targets.
-
-        Args:
-            preds: Predictions from model (probabilities, or labels)
-            target: Ground truth labels
         """
+        This implementation refers to :class:`~torchmetrics.Accuracy`.
 
-        correct, total = _accuracy_update(
-            preds, target, threshold=self.threshold, top_k=self.top_k, subset_accuracy=self.subset_accuracy
-        )
-
-        self.correct += correct
-        self.total += total
-
-    def compute(self) -> torch.Tensor:
-        """
-        Computes accuracy based on inputs passed in to ``update`` previously.
+        .. deprecated::
+            Use :class:`~torchmetrics.Accuracy`. Will be removed in v1.5.0.
         """
-        return _accuracy_compute(self.correct, self.total)

pytorch_lightning/metrics/functional/accuracy.py

@@ -11,112 +11,23 @@
 # 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 Optional, Tuple
+from typing import Optional
 
 import torch
-from torchmetrics.classification.checks import _input_format_classification
-from torchmetrics.utilities.enums import DataType
+from torchmetrics.functional import accuracy as _accuracy
 
-
-def _accuracy_update(
-    preds: torch.Tensor, target: torch.Tensor, threshold: float, top_k: Optional[int], subset_accuracy: bool
-) -> Tuple[torch.Tensor, torch.Tensor]:
-
-    preds, target, mode = _input_format_classification(preds, target, threshold=threshold, top_k=top_k)
-
-    if mode == DataType.MULTILABEL and top_k:
-        raise ValueError("You can not use the `top_k` parameter to calculate accuracy for multi-label inputs.")
-
-    if mode == DataType.BINARY or (mode == DataType.MULTILABEL and subset_accuracy):
-        correct = (preds == target).all(dim=1).sum()
-        total = torch.tensor(target.shape[0], device=target.device)
-    elif mode == DataType.MULTILABEL and not subset_accuracy:
-        correct = (preds == target).sum()
-        total = torch.tensor(target.numel(), device=target.device)
-    elif mode == DataType.MULTICLASS or (mode == DataType.MULTIDIM_MULTICLASS and not subset_accuracy):
-        correct = (preds * target).sum()
-        total = target.sum()
-    elif mode == DataType.MULTIDIM_MULTICLASS and subset_accuracy:
-        sample_correct = (preds * target).sum(dim=(1, 2))
-        correct = (sample_correct == target.shape[2]).sum()
-        total = torch.tensor(target.shape[0], device=target.device)
-
-    return correct, total
-
-
-def _accuracy_compute(correct: torch.Tensor, total: torch.Tensor) -> torch.Tensor:
-    return correct.float() / total
+from pytorch_lightning.utilities.deprecation import deprecated
 
 
+@deprecated(target=_accuracy, ver_deprecate="1.3.0", ver_remove="1.5.0")
 def accuracy(
     preds: torch.Tensor,
     target: torch.Tensor,
     threshold: float = 0.5,
     top_k: Optional[int] = None,
     subset_accuracy: bool = False,
 ) -> torch.Tensor:
-    r"""Computes `Accuracy <https://en.wikipedia.org/wiki/Accuracy_and_precision>`_:
-
-    .. math::
-        \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
-
-    Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
-    tensor of predictions.
-
-    For multi-class and multi-dimensional multi-class data with probability predictions, the
-    parameter ``top_k`` generalizes this metric to a Top-K accuracy metric: for each sample the
-    top-K highest probability items are considered to find the correct label.
-
-    For multi-label and multi-dimensional multi-class inputs, this metric computes the "global"
-    accuracy by default, which counts all labels or sub-samples separately. This can be
-    changed to subset accuracy (which requires all labels or sub-samples in the sample to
-    be correctly predicted) by setting ``subset_accuracy=True``.
-
-    Args:
-        preds: Predictions from model (probabilities, or labels)
-        target: Ground truth labels
-        threshold:
-            Threshold probability value for transforming probability predictions to binary
-            (0,1) predictions, in the case of binary or multi-label inputs.
-        top_k:
-            Number of highest probability predictions considered to find the correct label, relevant
-            only for (multi-dimensional) multi-class inputs with probability predictions. The
-            default value (``None``) will be interpreted as 1 for these inputs.
-
-            Should be left at default (``None``) for all other types of inputs.
-        subset_accuracy:
-            Whether to compute subset accuracy for multi-label and multi-dimensional
-            multi-class inputs (has no effect for other input types).
-
-            - For multi-label inputs, if the parameter is set to ``True``, then all labels for
-              each sample must be correctly predicted for the sample to count as correct. If it
-              is set to ``False``, then all labels are counted separately - this is equivalent to
-              flattening inputs beforehand (i.e. ``preds = preds.flatten()`` and same for ``target``).
-
-            - For multi-dimensional multi-class inputs, if the parameter is set to ``True``, then all
-              sub-sample (on the extra axis) must be correct for the sample to be counted as correct.
-              If it is set to ``False``, then all sub-samples are counter separately - this is equivalent,
-              in the case of label predictions, to flattening the inputs beforehand (i.e.
-              ``preds = preds.flatten()`` and same for ``target``). Note that the ``top_k`` parameter
-              still applies in both cases, if set.
-
-    Raises:
-        ValueError:
-            If ``top_k`` parameter is set for ``multi-label`` inputs.
-
-    Example:
-
-        >>> from pytorch_lightning.metrics.functional import accuracy
-        >>> target = torch.tensor([0, 1, 2, 3])
-        >>> preds = torch.tensor([0, 2, 1, 3])
-        >>> accuracy(preds, target)
-        tensor(0.5000)
-
-        >>> target = torch.tensor([0, 1, 2])
-        >>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
-        >>> accuracy(preds, target, top_k=2)
-        tensor(0.6667)
     """
-
-    correct, total = _accuracy_update(preds, target, threshold, top_k, subset_accuracy)
-    return _accuracy_compute(correct, total)
+    .. deprecated::
+        Use :func:`torchmetrics.functional.accuracy`. Will be removed in v1.5.0.
+    """

tests/deprecated_api/test_remove_1-5_metrics.py

@@ -17,10 +17,11 @@
 import torch
 
 from pytorch_lightning.metrics import Accuracy, MetricCollection
+from pytorch_lightning.metrics.functional.accuracy import accuracy
 from pytorch_lightning.metrics.utils import get_num_classes, select_topk, to_categorical, to_onehot
 
 
-def test_v1_5_0_metrics_utils():
+def test_v1_5_metrics_utils():
     x = torch.tensor([1, 2, 3])
     with pytest.deprecated_call(match="It will be removed in v1.5.0"):
         assert torch.equal(to_onehot(x), torch.Tensor([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]).to(int))
@@ -37,12 +38,24 @@ def test_v1_5_0_metrics_utils():
         assert torch.equal(to_categorical(x), torch.Tensor([1, 0]).to(int))
 
 
-def test_v1_5_0_metrics_collection():
+def test_v1_5_metric_accuracy():
+    accuracy.warned = False
+    with pytest.deprecated_call(match='It will be removed in v1.5.0'):
+        assert accuracy(preds=torch.tensor([0, 1]), target=torch.tensor([0, 1])) == torch.tensor(1.)
+
+    Accuracy.__init__.warned = False
+    with pytest.deprecated_call(match='It will be removed in v1.5.0'):
+        Accuracy()
+
+
+def test_v1_5_metrics_collection():
     target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
     preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
+
+    MetricCollection.__init__.warned = False
     with pytest.deprecated_call(
         match="`pytorch_lightning.metrics.metric.MetricCollection` was deprecated since v1.3.0 in favor"
               " of `torchmetrics.collections.MetricCollection`. It will be removed in v1.5.0."
     ):
         metrics = MetricCollection([Accuracy()])
-    assert metrics(preds, target) == {'Accuracy': torch.Tensor([0.1250])[0]}
+    assert metrics(preds, target) == {'Accuracy': torch.tensor(0.1250)}