Source code for ignite.metrics.top_k_categorical_accuracy

from typing import Callable, Sequence, Union

import torch

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

__all__ = ["TopKCategoricalAccuracy"]

[docs]class TopKCategoricalAccuracy(Metric): """ Calculates the top-k categorical accuracy. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. """ def __init__( self, k: int = 5, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device) self._k = k @reinit__is_reduced def reset(self) -> None: self._num_correct = torch.tensor(0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() sorted_indices = torch.topk(y_pred, self._k, dim=1)[1] expanded_y = y.view(-1, 1).expand(-1, self._k) correct = torch.sum(torch.eq(sorted_indices, expanded_y), dim=1) self._num_correct += torch.sum(correct).to(self._device) self._num_examples += correct.shape[0] @sync_all_reduce("_num_correct", "_num_examples") def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError( "TopKCategoricalAccuracy must have at least one example before it can be computed." ) return self._num_correct.item() / self._num_examples

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