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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"]
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.
skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be
true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)``
Alternatively, ``output_transform`` can be used to handle this.
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
.. versionchanged:: 0.5.1
``skip_unrolling`` argument is added.
"""
_state_dict_all_req_keys = ("_sum_of_entropies", "_num_examples")
@reinit__is_reduced
def reset(self) -> None:
self._sum_of_entropies = torch.tensor(0.0, device=self._device)
self._num_examples = 0
@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)
self._update(prob, log_prob)
def _update(self, prob: torch.Tensor, log_prob: torch.Tensor) -> None:
entropy_sum = -torch.sum(prob * log_prob)
self._sum_of_entropies += entropy_sum.to(self._device)
self._num_examples += prob.shape[0]
@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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