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Source code for ignite.metrics.recall

from typing import Callable, Optional, 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): """ Calculates recall for binary and multiclass data. - `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, is_multilabel=True) recall = Recall(average=False, is_multilabel=True) 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. .. warning:: In multilabel cases, if average is False, current implementation does not work with distributed computations. Results are not reduced across the GPUs. Computed result corresponds to the local rank's (single GPU) result. Args: output_transform (callable, optional): a callable that is used to transform the :class:`~ignite.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 (bool, optional): 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 (bool, optional) 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 (str of torch.device, optional): unused argument. """ def __init__( self, output_transform: Callable = lambda x: x, average: bool = False, is_multilabel: bool = False, device: Optional[Union[str, torch.device]] = None, ): super(Recall, self).__init__( output_transform=output_transform, average=average, is_multilabel=is_multilabel, device=device ) @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output self._check_shape(output) self._check_type((y_pred, y)) 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( "y_pred contains less classes than y. Number of predicted classes is {}" " and element in y has invalid class = {}.".format(num_classes, 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) y = y.type_as(y_pred) correct = y * y_pred actual_positives = y.sum(dim=0).type(torch.DoubleTensor) # Convert from int cuda/cpu to double cpu if correct.sum() == 0: true_positives = torch.zeros_like(actual_positives) else: true_positives = correct.sum(dim=0) # Convert from int cuda/cpu to double cpu # We need double precision for the division true_positives / actual_positives true_positives = true_positives.type(torch.DoubleTensor) if self._type == "multilabel": if not self._average: self._true_positives = torch.cat([self._true_positives, true_positives], dim=0) self._positives = torch.cat([self._positives, actual_positives], dim=0) 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

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 11/27/2022, 9:04:08 PM.

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