99 lines
4.1 KiB
Python
99 lines
4.1 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
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import torch
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from torchmetrics.classification.checks import _input_format_classification
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from torchmetrics.utilities.enums import DataType
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from pytorch_lightning.utilities import rank_zero_warn
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def _confusion_matrix_update(
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preds: torch.Tensor, target: torch.Tensor, num_classes: int, threshold: float = 0.5
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) -> torch.Tensor:
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preds, target, mode = _input_format_classification(preds, target, threshold)
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if mode not in (DataType.BINARY, DataType.MULTILABEL):
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preds = preds.argmax(dim=1)
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target = target.argmax(dim=1)
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unique_mapping = (target.view(-1) * num_classes + preds.view(-1)).to(torch.long)
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bins = torch.bincount(unique_mapping, minlength=num_classes**2)
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confmat = bins.reshape(num_classes, num_classes)
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return confmat
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def _confusion_matrix_compute(confmat: torch.Tensor, normalize: Optional[str] = None) -> torch.Tensor:
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allowed_normalize = ('true', 'pred', 'all', 'none', None)
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assert normalize in allowed_normalize, \
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f"Argument average needs to one of the following: {allowed_normalize}"
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confmat = confmat.float()
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if normalize is not None and normalize != 'none':
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if normalize == 'true':
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cm = confmat / confmat.sum(axis=1, keepdim=True)
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elif normalize == 'pred':
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cm = confmat / confmat.sum(axis=0, keepdim=True)
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elif normalize == 'all':
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cm = confmat / confmat.sum()
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nan_elements = cm[torch.isnan(cm)].nelement()
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if nan_elements != 0:
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cm[torch.isnan(cm)] = 0
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rank_zero_warn(f'{nan_elements} nan values found in confusion matrix have been replaced with zeros.')
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return cm
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return confmat
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def confusion_matrix(
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preds: torch.Tensor,
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target: torch.Tensor,
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num_classes: int,
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normalize: Optional[str] = None,
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threshold: float = 0.5
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) -> torch.Tensor:
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"""
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Computes the confusion matrix. Works with binary, multiclass, and multilabel data.
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Accepts probabilities from a model output or integer class values in prediction.
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Works with multi-dimensional preds and target.
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If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument
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to convert into integer labels. This is the case for binary and multi-label probabilities.
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If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
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Args:
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preds: (float or long tensor), Either a ``(N, ...)`` tensor with labels or
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``(N, C, ...)`` where C is the number of classes, tensor with labels/probabilities
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target: ``target`` (long tensor), tensor with shape ``(N, ...)`` with ground true labels
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num_classes: Number of classes in the dataset.
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normalize: Normalization mode for confusion matrix. Choose from
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- ``None`` or ``'none'``: no normalization (default)
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- ``'true'``: normalization over the targets (most commonly used)
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- ``'pred'``: normalization over the predictions
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- ``'all'``: normalization over the whole matrix
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threshold:
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Threshold value for binary or multi-label probabilities. default: 0.5
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Example:
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>>> from pytorch_lightning.metrics.functional import confusion_matrix
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>>> target = torch.tensor([1, 1, 0, 0])
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>>> preds = torch.tensor([0, 1, 0, 0])
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>>> confusion_matrix(preds, target, num_classes=2)
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tensor([[2., 0.],
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[1., 1.]])
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"""
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confmat = _confusion_matrix_update(preds, target, num_classes, threshold)
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return _confusion_matrix_compute(confmat, normalize)
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