Source code for ignite.metrics.top_k_categorical_accuracy

from typing import Callable, Optional, 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=5, output_transform: Callable = lambda x: x, device: Optional[Union[str, torch.device]] = None ): super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device) self._k = k @reinit__is_reduced def reset(self) -> None: self._num_correct = 0 self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence) -> None: y_pred, y = output 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).item() 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 / self._num_examples

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