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Source code for ignite.metrics.entropy

from typing import Sequence

import torch
import torch.nn.functional as F

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

__all__ = ["Entropy"]


[docs]class Entropy(Metric): r"""Calculates the mean of `entropy <https://en.wikipedia.org/wiki/Entropy_(information_theory)>`_. .. math:: H = \frac{1}{N} \sum_{i=1}^N \sum_{c=1}^C -p_{i,c} \log p_{i,c}, \quad p_{i,c} = \frac{\exp(z_{i,c})}{\sum_{c'=1}^C \exp(z_{i,c'})} where :math:`p_{i,c}` is the prediction probability of :math:`i`-th data belonging to the class :math:`c`. - ``update`` must receive output of the form ``(y_pred, y)`` while ``y`` is not used in this metric. - ``y_pred`` is expected to be the unnormalized logits for each class. :math:`(B, C)` (classification) or :math:`(B, C, ...)` (e.g., image segmentation) shapes are allowed. Args: 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:: metric = Entropy() metric.attach(default_evaluator, 'entropy') y_true = torch.tensor([0, 1, 2]) # not considered in the Entropy metric. y_pred = torch.tensor([ [ 0.0000, 0.6931, 1.0986], [ 1.3863, 1.6094, 1.6094], [ 0.0000, -2.3026, -2.3026] ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['entropy']) .. testoutput:: 0.8902875582377116 """ _state_dict_all_req_keys = ("_sum_of_entropies", "_num_examples")
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_entropies = torch.tensor(0.0, device=self._device) self._num_examples = 0
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred = output[0].detach() if y_pred.ndim >= 3: num_classes = y_pred.shape[1] # (B, C, ...) -> (B, ..., C) -> (B*..., C) # regarding as B*... predictions y_pred = y_pred.movedim(1, -1).reshape(-1, num_classes) elif y_pred.ndim == 1: raise ValueError(f"y_pred must be in the shape of (B, C) or (B, C, ...), got {y_pred.shape}.") prob = F.softmax(y_pred, dim=1) log_prob = F.log_softmax(y_pred, dim=1) entropy_sum = -torch.sum(prob * log_prob) self._sum_of_entropies += entropy_sum.to(self._device) self._num_examples += y_pred.shape[0]
[docs] @sync_all_reduce("_sum_of_entropies", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("Entropy must have at least one example before it can be computed.") return self._sum_of_entropies.item() / self._num_examples

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