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)``. 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. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: def process_function(engine, batch): y_pred, y = batch return y_pred, y def one_hot_to_binary_output_transform(output): y_pred, y = output y = torch.argmax(y, dim=1) # one-hot vector to label index vector return y_pred, y engine = Engine(process_function) metric = TopKCategoricalAccuracy( k=2, output_transform=one_hot_to_binary_output_transform) metric.attach(engine, 'top_k_accuracy') preds = torch.tensor([ [0.7, 0.2, 0.05, 0.05], # 1 is in the top 2 [0.2, 0.3, 0.4, 0.1], # 0 is not in the top 2 [0.4, 0.4, 0.1, 0.1], # 0 is in the top 2 [0.7, 0.05, 0.2, 0.05] # 2 is in the top 2 ]) target = torch.tensor([ # targets as one-hot vectors [0, 1, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0] ]) state =[[preds, target]]) print(state.metrics['top_k_accuracy']) .. testoutput:: 0.75 """ _state_dict_all_req_keys = ("_num_correct", "_num_examples") 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
[docs] @reinit__is_reduced def reset(self) -> None: self._num_correct = torch.tensor(0, device=self._device) self._num_examples = 0
[docs] @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]
[docs] @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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