121 lines
5.5 KiB
Python
121 lines
5.5 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Tuple
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import torch
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from pytorch_lightning.metrics.classification.helpers import _input_format_classification, DataType
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def _accuracy_update(
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preds: torch.Tensor, target: torch.Tensor, threshold: float, top_k: Optional[int], subset_accuracy: bool
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) -> Tuple[torch.Tensor, torch.Tensor]:
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preds, target, mode = _input_format_classification(preds, target, threshold=threshold, top_k=top_k)
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if mode == DataType.MULTILABEL and top_k:
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raise ValueError("You can not use the `top_k` parameter to calculate accuracy for multi-label inputs.")
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if mode == DataType.BINARY or (mode == DataType.MULTILABEL and subset_accuracy):
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correct = (preds == target).all(dim=1).sum()
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total = torch.tensor(target.shape[0], device=target.device)
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elif mode == DataType.MULTILABEL and not subset_accuracy:
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correct = (preds == target).sum()
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total = torch.tensor(target.numel(), device=target.device)
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elif mode == DataType.MULTICLASS or (mode == DataType.MULTIDIM_MULTICLASS and not subset_accuracy):
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correct = (preds * target).sum()
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total = target.sum()
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elif mode == DataType.MULTIDIM_MULTICLASS and subset_accuracy:
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sample_correct = (preds * target).sum(dim=(1, 2))
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correct = (sample_correct == target.shape[2]).sum()
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total = torch.tensor(target.shape[0], device=target.device)
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return correct, total
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def _accuracy_compute(correct: torch.Tensor, total: torch.Tensor) -> torch.Tensor:
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return correct.float() / total
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def accuracy(
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preds: torch.Tensor,
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target: torch.Tensor,
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threshold: float = 0.5,
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top_k: Optional[int] = None,
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subset_accuracy: bool = False,
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) -> torch.Tensor:
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r"""Computes `Accuracy <https://en.wikipedia.org/wiki/Accuracy_and_precision>`_:
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.. math::
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\text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
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Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
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tensor of predictions.
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For multi-class and multi-dimensional multi-class data with probability predictions, the
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parameter ``top_k`` generalizes this metric to a Top-K accuracy metric: for each sample the
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top-K highest probability items are considered to find the correct label.
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For multi-label and multi-dimensional multi-class inputs, this metric computes the "global"
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accuracy by default, which counts all labels or sub-samples separately. This can be
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changed to subset accuracy (which requires all labels or sub-samples in the sample to
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be correctly predicted) by setting ``subset_accuracy=True``.
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Accepts all input types listed in :ref:`extensions/metrics:input types`.
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Args:
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preds: Predictions from model (probabilities, or labels)
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target: Ground truth labels
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threshold:
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Threshold probability value for transforming probability predictions to binary
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(0,1) predictions, in the case of binary or multi-label inputs.
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top_k:
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Number of highest probability predictions considered to find the correct label, relevant
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only for (multi-dimensional) multi-class inputs with probability predictions. The
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default value (``None``) will be interpreted as 1 for these inputs.
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Should be left at default (``None``) for all other types of inputs.
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subset_accuracy:
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Whether to compute subset accuracy for multi-label and multi-dimensional
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multi-class inputs (has no effect for other input types).
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- For multi-label inputs, if the parameter is set to ``True``, then all labels for
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each sample must be correctly predicted for the sample to count as correct. If it
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is set to ``False``, then all labels are counted separately - this is equivalent to
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flattening inputs beforehand (i.e. ``preds = preds.flatten()`` and same for ``target``).
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- For multi-dimensional multi-class inputs, if the parameter is set to ``True``, then all
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sub-sample (on the extra axis) must be correct for the sample to be counted as correct.
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If it is set to ``False``, then all sub-samples are counter separately - this is equivalent,
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in the case of label predictions, to flattening the inputs beforehand (i.e.
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``preds = preds.flatten()`` and same for ``target``). Note that the ``top_k`` parameter
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still applies in both cases, if set.
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Example:
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>>> from pytorch_lightning.metrics.functional import accuracy
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>>> target = torch.tensor([0, 1, 2, 3])
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>>> preds = torch.tensor([0, 2, 1, 3])
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>>> accuracy(preds, target)
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tensor(0.5000)
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>>> target = torch.tensor([0, 1, 2])
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>>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
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>>> accuracy(preds, target, top_k=2)
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tensor(0.6667)
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"""
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correct, total = _accuracy_update(preds, target, threshold, top_k, subset_accuracy)
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return _accuracy_compute(correct, total)
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