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"]


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}``.

    Args:
        k: the k in “top-k”.
        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.
            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
        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,
        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