# Source code for pytorch_lightning.metrics.functional.accuracy

```
# 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 Optional, Tuple
import torch
from pytorch_lightning.metrics.classification.helpers import _input_format_classification, DataType
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
[docs]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``.
Accepts all input types listed in :ref:`extensions/metrics:input types`.
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.
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)
```