115 lines
4.3 KiB
Python
115 lines
4.3 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 Any, Optional
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import torch
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from pytorch_lightning.metrics.functional.confusion_matrix import _confusion_matrix_compute, _confusion_matrix_update
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from pytorch_lightning.metrics.metric import Metric
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class ConfusionMatrix(Metric):
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"""
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Computes the `confusion matrix
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<https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix>`_. Works with binary,
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multiclass, and multilabel data. Accepts probabilities from a model output or
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integer class values in prediction. Works with multi-dimensional preds and
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target.
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Note:
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This metric produces a multi-dimensional output, so it can not be directly logged.
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Forward accepts
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- ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
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- ``target`` (long tensor): ``(N, ...)``
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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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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 probabilites. default: 0.5
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compute_on_step:
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Forward only calls ``update()`` and return None if this is set to False. default: True
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dist_sync_on_step:
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Synchronize metric state across processes at each ``forward()``
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before returning the value at the step. default: False
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process_group:
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Specify the process group on which synchronization is called. default: None (which selects the entire world)
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Example:
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>>> from pytorch_lightning.metrics import ConfusionMatrix
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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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>>> confmat = ConfusionMatrix(num_classes=2)
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>>> confmat(preds, target)
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tensor([[2., 0.],
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[1., 1.]])
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"""
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def __init__(
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self,
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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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compute_on_step: bool = True,
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dist_sync_on_step: bool = False,
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process_group: Optional[Any] = None,
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):
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super().__init__(
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compute_on_step=compute_on_step,
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dist_sync_on_step=dist_sync_on_step,
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process_group=process_group,
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)
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self.num_classes = num_classes
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self.normalize = normalize
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self.threshold = threshold
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allowed_normalize = ('true', 'pred', 'all', 'none', None)
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assert self.normalize in allowed_normalize, \
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f"Argument average needs to one of the following: {allowed_normalize}"
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self.add_state("confmat", default=torch.zeros(num_classes, num_classes), dist_reduce_fx="sum")
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def update(self, preds: torch.Tensor, target: torch.Tensor):
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"""
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Update state with predictions and targets.
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Args:
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preds: Predictions from model
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target: Ground truth values
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"""
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confmat = _confusion_matrix_update(preds, target, self.num_classes, self.threshold)
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self.confmat += confmat
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def compute(self) -> torch.Tensor:
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"""
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Computes confusion matrix
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"""
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return _confusion_matrix_compute(self.confmat, self.normalize)
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