Shortcuts

Source code for ignite.metrics.confusion_matrix

import numbers
from typing import Callable, Optional, Sequence, Tuple, Union

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
from ignite.metrics.metrics_lambda import MetricsLambda

__all__ = ["ConfusionMatrix", "mIoU", "IoU", "DiceCoefficient", "cmAccuracy", "cmPrecision", "cmRecall", "JaccardIndex"]


[docs]class ConfusionMatrix(Metric): """Calculates confusion matrix for multi-class data. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y_pred` must contain logits and has the following shape (batch_size, num_classes, ...). If you are doing binary classification, see Note for an example on how to get this. - `y` should have the following shape (batch_size, ...) and contains ground-truth class indices with or without the background class. During the computation, argmax of `y_pred` is taken to determine predicted classes. Args: num_classes: Number of classes, should be > 1. See notes for more details. average: confusion matrix values averaging schema: None, "samples", "recall", "precision". Default is None. If `average="samples"` then confusion matrix values are normalized by the number of seen samples. If `average="recall"` then confusion matrix values are normalized such that diagonal values represent class recalls. If `average="precision"` then confusion matrix values are normalized such that diagonal values represent class precisions. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Note: The confusion matrix is formatted such that columns are predictions and rows are targets. For example, if you were to plot the matrix, you could correctly assign to the horizontal axis the label "predicted values" and to the vertical axis the label "actual values". Note: In case of the targets `y` in `(batch_size, ...)` format, target indices between 0 and `num_classes` only contribute to the confusion matrix and others are neglected. For example, if `num_classes=20` and target index equal 255 is encountered, then it is filtered out. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: 1 metric = ConfusionMatrix(num_classes=3) metric.attach(default_evaluator, 'cm') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['cm']) .. testoutput:: 1 tensor([[1, 1, 0], [0, 2, 0], [0, 1, 0]]) If you are doing binary classification with a single output unit, you may have to transform your network output, so that you have one value for each class. E.g. you can transform your network output into a one-hot vector with: .. testcode:: 2 def binary_one_hot_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred).round().long() y_pred = ignite.utils.to_onehot(y_pred, 2) y = y.long() return y_pred, y metric = ConfusionMatrix(num_classes=2, output_transform=binary_one_hot_output_transform) metric.attach(default_evaluator, 'cm') y_true = torch.tensor([0, 1, 0, 1, 0]) y_pred = torch.tensor([0, 0, 1, 1, 0]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['cm']) .. testoutput:: 2 tensor([[2, 1], [1, 1]]) """ def __init__( self, num_classes: int, average: Optional[str] = None, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): if average is not None and average not in ("samples", "recall", "precision"): raise ValueError("Argument average can None or one of 'samples', 'recall', 'precision'") if num_classes <= 1: raise ValueError("Argument num_classes needs to be > 1") self.num_classes = num_classes self._num_examples = 0 self.average = average super(ConfusionMatrix, self).__init__(output_transform=output_transform, device=device)
[docs] @reinit__is_reduced def reset(self) -> None: self.confusion_matrix = torch.zeros(self.num_classes, self.num_classes, dtype=torch.int64, device=self._device) self._num_examples = 0
def _check_shape(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if y_pred.ndimension() < 2: raise ValueError( f"y_pred must have shape (batch_size, num_classes (currently set to {self.num_classes}), ...), " f"but given {y_pred.shape}" ) if y_pred.shape[1] != self.num_classes: raise ValueError(f"y_pred does not have correct number of classes: {y_pred.shape[1]} vs {self.num_classes}") if not (y.ndimension() + 1 == y_pred.ndimension()): raise ValueError( f"y_pred must have shape (batch_size, num_classes (currently set to {self.num_classes}), ...) " "and y must have shape of (batch_size, ...), " f"but given {y.shape} vs {y_pred.shape}." ) y_shape = y.shape y_pred_shape = y_pred.shape # type: Tuple[int, ...] if y.ndimension() + 1 == y_pred.ndimension(): y_pred_shape = (y_pred_shape[0],) + y_pred_shape[2:] if y_shape != y_pred_shape: raise ValueError("y and y_pred must have compatible shapes.")
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: self._check_shape(output) y_pred, y = output[0].detach(), output[1].detach() self._num_examples += y_pred.shape[0] # target is (batch_size, ...) y_pred = torch.argmax(y_pred, dim=1).flatten() y = y.flatten() target_mask = (y >= 0) & (y < self.num_classes) y = y[target_mask] y_pred = y_pred[target_mask] indices = self.num_classes * y + y_pred m = torch.bincount(indices, minlength=self.num_classes ** 2).reshape(self.num_classes, self.num_classes) self.confusion_matrix += m.to(self.confusion_matrix)
[docs] @sync_all_reduce("confusion_matrix", "_num_examples") def compute(self) -> torch.Tensor: if self._num_examples == 0: raise NotComputableError("Confusion matrix must have at least one example before it can be computed.") if self.average: self.confusion_matrix = self.confusion_matrix.float() if self.average == "samples": return self.confusion_matrix / self._num_examples else: return self.normalize(self.confusion_matrix, self.average) return self.confusion_matrix
[docs] @staticmethod def normalize(matrix: torch.Tensor, average: str) -> torch.Tensor: """Normalize given `matrix` with given `average`.""" if average == "recall": return matrix / (matrix.sum(dim=1).unsqueeze(1) + 1e-15) elif average == "precision": return matrix / (matrix.sum(dim=0) + 1e-15) else: raise ValueError("Argument average should be one of 'samples', 'recall', 'precision'")
[docs]def IoU(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: r"""Calculates Intersection over Union using :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. .. math:: \text{J}(A, B) = \frac{ \lvert A \cap B \rvert }{ \lvert A \cup B \rvert } Args: cm: instance of confusion matrix metric ignore_index: index to ignore, e.g. background index Returns: MetricsLambda Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: cm = ConfusionMatrix(num_classes=3) metric = IoU(cm) metric.attach(default_evaluator, 'iou') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['iou']) .. testoutput:: tensor([0.5000, 0.5000, 0.0000], dtype=torch.float64) """ if not isinstance(cm, ConfusionMatrix): raise TypeError(f"Argument cm should be instance of ConfusionMatrix, but given {type(cm)}") if not (cm.average in (None, "samples")): raise ValueError("ConfusionMatrix should have average attribute either None or 'samples'") if ignore_index is not None: if not (isinstance(ignore_index, numbers.Integral) and 0 <= ignore_index < cm.num_classes): raise ValueError(f"ignore_index should be non-negative integer, but given {ignore_index}") # Increase floating point precision and pass to CPU cm = cm.to(torch.double) iou = cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag() + 1e-15) # type: MetricsLambda if ignore_index is not None: ignore_idx = ignore_index # type: int # used due to typing issues with mympy def ignore_index_fn(iou_vector: torch.Tensor) -> torch.Tensor: if ignore_idx >= len(iou_vector): raise ValueError(f"ignore_index {ignore_idx} is larger than the length of IoU vector {len(iou_vector)}") indices = list(range(len(iou_vector))) indices.remove(ignore_idx) return iou_vector[indices] return MetricsLambda(ignore_index_fn, iou) else: return iou
[docs]def mIoU(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: """Calculates mean Intersection over Union using :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric ignore_index: index to ignore, e.g. background index Returns: MetricsLambda Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: cm = ConfusionMatrix(num_classes=3) metric = mIoU(cm, ignore_index=0) metric.attach(default_evaluator, 'miou') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['miou']) .. testoutput:: 0.24999... """ iou = IoU(cm=cm, ignore_index=ignore_index).mean() # type: MetricsLambda return iou
def cmAccuracy(cm: ConfusionMatrix) -> MetricsLambda: """Calculates accuracy using :class:`~ignite.metrics.metric.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.to(torch.double) accuracy = cm.diag().sum() / (cm.sum() + 1e-15) # type: MetricsLambda return accuracy def cmPrecision(cm: ConfusionMatrix, average: bool = True) -> MetricsLambda: """Calculates precision using :class:`~ignite.metrics.metric.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric average: if True metric value is averaged over all classes Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.to(torch.double) precision = cm.diag() / (cm.sum(dim=0) + 1e-15) # type: MetricsLambda if average: mean = precision.mean() # type: MetricsLambda return mean return precision def cmRecall(cm: ConfusionMatrix, average: bool = True) -> MetricsLambda: """ Calculates recall using :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric average: if True metric value is averaged over all classes Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.to(torch.double) recall = cm.diag() / (cm.sum(dim=1) + 1e-15) # type: MetricsLambda if average: mean = recall.mean() # type: MetricsLambda return mean return recall
[docs]def DiceCoefficient(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: """Calculates Dice Coefficient for a given :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric ignore_index: index to ignore, e.g. background index Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: cm = ConfusionMatrix(num_classes=3) metric = DiceCoefficient(cm, ignore_index=0) metric.attach(default_evaluator, 'dice') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['dice']) .. testoutput:: tensor([0.6667, 0.0000], dtype=torch.float64) """ if not isinstance(cm, ConfusionMatrix): raise TypeError(f"Argument cm should be instance of ConfusionMatrix, but given {type(cm)}") if ignore_index is not None: if not (isinstance(ignore_index, numbers.Integral) and 0 <= ignore_index < cm.num_classes): raise ValueError(f"ignore_index should be non-negative integer, but given {ignore_index}") # Increase floating point precision and pass to CPU cm = cm.to(torch.double) dice = 2.0 * cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) + 1e-15) # type: MetricsLambda if ignore_index is not None: ignore_idx = ignore_index # type: int # used due to typing issues with mympy def ignore_index_fn(dice_vector: torch.Tensor) -> torch.Tensor: if ignore_idx >= len(dice_vector): raise ValueError( f"ignore_index {ignore_idx} is larger than the length of Dice vector {len(dice_vector)}" ) indices = list(range(len(dice_vector))) indices.remove(ignore_idx) return dice_vector[indices] return MetricsLambda(ignore_index_fn, dice) else: return dice
[docs]def JaccardIndex(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: r"""Calculates the Jaccard Index using :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. Implementation is based on :meth:`~ignite.metrics.IoU`. .. math:: \text{J}(A, B) = \frac{ \lvert A \cap B \rvert }{ \lvert A \cup B \rvert } Args: cm: instance of confusion matrix metric ignore_index: index to ignore, e.g. background index Returns: MetricsLambda Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: cm = ConfusionMatrix(num_classes=3) metric = JaccardIndex(cm, ignore_index=0) metric.attach(default_evaluator, 'jac') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['jac']) .. testoutput:: tensor([0.5000, 0.0000], dtype=torch.float64) """ return IoU(cm, ignore_index)

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 10/03/2022, 1:29:13 PM.

Built with Sphinx using a theme provided by Read the Docs.