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Source code for ignite.metrics.recall

from typing import Callable, Sequence, Union

import torch

from ignite.metrics.metric import reinit__is_reduced
from ignite.metrics.precision import _BasePrecisionRecall
from ignite.utils import to_onehot

__all__ = ["Recall"]


[docs]class Recall(_BasePrecisionRecall): r"""Calculates recall for binary and multiclass data. .. math:: \text{Recall} = \frac{ TP }{ TP + FN } where :math:`\text{TP}` is true positives and :math:`\text{FN}` is false negatives. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y_pred` must be in the following shape (batch_size, num_categories, ...) or (batch_size, ...). - `y` must be in the following shape (batch_size, ...). In binary and multilabel cases, the elements of `y` and `y_pred` should have 0 or 1 values. Thresholding of predictions can be done as below: .. code-block:: python def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y recall = Recall(output_transform=thresholded_output_transform) In multilabel cases, average parameter should be True. However, if user would like to compute F1 metric, for example, average parameter should be False. This can be done as shown below: .. code-block:: python precision = Precision(average=False) recall = Recall(average=False) F1 = precision * recall * 2 / (precision + recall + 1e-20) F1 = MetricsLambda(lambda t: torch.mean(t).item(), F1) .. warning:: In multilabel cases, if average is False, current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM. Args: 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. average: if True, precision is computed as the unweighted average (across all classes in multiclass case), otherwise, returns a tensor with the precision (for each class in multiclass case). is_multilabel: flag to use in multilabel case. By default, value is False. If True, average parameter should be True and the average is computed across samples, instead of classes. 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. """ def __init__( self, output_transform: Callable = lambda x: x, average: bool = False, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ): super(Recall, self).__init__( output_transform=output_transform, average=average, is_multilabel=is_multilabel, device=device )
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: self._check_shape(output) self._check_type(output) y_pred, y = output[0].detach(), output[1].detach() if self._type == "binary": y_pred = y_pred.view(-1) y = y.view(-1) elif self._type == "multiclass": num_classes = y_pred.size(1) if y.max() + 1 > num_classes: raise ValueError( f"y_pred contains less classes than y. Number of predicted classes is {num_classes}" f" and element in y has invalid class = {y.max().item() + 1}." ) y = to_onehot(y.view(-1), num_classes=num_classes) indices = torch.argmax(y_pred, dim=1).view(-1) y_pred = to_onehot(indices, num_classes=num_classes) elif self._type == "multilabel": # if y, y_pred shape is (N, C, ...) -> (C, N x ...) num_classes = y_pred.size(1) y_pred = torch.transpose(y_pred, 1, 0).reshape(num_classes, -1) y = torch.transpose(y, 1, 0).reshape(num_classes, -1) # Convert from int cuda/cpu to double on self._device y_pred = y_pred.to(dtype=torch.float64, device=self._device) y = y.to(dtype=torch.float64, device=self._device) correct = y * y_pred actual_positives = y.sum(dim=0) if correct.sum() == 0: true_positives = torch.zeros_like(actual_positives) else: true_positives = correct.sum(dim=0) if self._type == "multilabel": if not self._average: self._true_positives = torch.cat([self._true_positives, true_positives], dim=0) # type: torch.Tensor self._positives = torch.cat([self._positives, actual_positives], dim=0) # type: torch.Tensor else: self._true_positives += torch.sum(true_positives / (actual_positives + self.eps)) self._positives += len(actual_positives) else: self._true_positives += true_positives self._positives += actual_positives self._updated = True

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