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import torch
import torch.nn.functional as F
from packaging.version import Version
from ignite.exceptions import NotComputableError
from ignite.metrics.kl_divergence import KLDivergence
from ignite.metrics.metric import sync_all_reduce
__all__ = ["JSDivergence"]
TORCH_VERSION_GE_160 = Version(torch.__version__) >= Version("1.6.0")
class JSDivergence(KLDivergence):
r"""Calculates the mean of `Jensen-Shannon (JS) divergence
<https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence>`_.
.. math::
\begin{align*}
D_\text{JS}(\mathbf{p}_i \| \mathbf{q}_i) &= \frac{1}{2} D_\text{KL}(\mathbf{p}_i \| \mathbf{m}_i)
+ \frac{1}{2} D_\text{KL}(\mathbf{q}_i \| \mathbf{m}_i), \\
\mathbf{m}_i &= \frac{1}{2}(\mathbf{p}_i + \mathbf{q}_i), \\
D_\text{KL}(\mathbf{p}_i \| \mathbf{q}_i) &= \sum_{c=1}^C p_{i,c} \log \frac{p_{i,c}}{q_{i,c}}.
\end{align*}
where :math:`\mathbf{p}_i` and :math:`\mathbf{q}_i` are the ground truth and prediction probability tensors,
and :math:`D_\text{KL}` is the KL-divergence.
- ``update`` must receive output of the form ``(y_pred, y)``.
- ``y_pred`` and ``y`` are 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 = JSDivergence()
metric.attach(default_evaluator, 'js-div')
y_true = torch.tensor([
[ 0.0000, -2.3026, -2.3026],
[ 1.3863, 1.6094, 1.6094],
[ 0.0000, 0.6931, 1.0986]
])
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['js-div'])
.. testoutput::
0.16266516844431558
.. versionchanged:: 0.5.1
``skip_unrolling`` argument is added.
"""
def _update(self, y_pred: torch.Tensor, y: torch.Tensor) -> None:
y_pred_prob = F.softmax(y_pred, dim=1)
y_prob = F.softmax(y, dim=1)
m_prob = (y_pred_prob + y_prob) / 2
m_log = m_prob.log()
if TORCH_VERSION_GE_160:
# log_target option can be used from 1.6.0
y_pred_log = F.log_softmax(y_pred, dim=1)
y_log = F.log_softmax(y, dim=1)
self._sum_of_kl += (
F.kl_div(m_log, y_pred_log, log_target=True, reduction="sum")
+ F.kl_div(m_log, y_log, log_target=True, reduction="sum")
).to(self._device)
else:
# y_pred and y are expected to be probabilities
self._sum_of_kl += (
F.kl_div(m_log, y_pred_prob, reduction="sum") + F.kl_div(m_log, y_prob, reduction="sum")
).to(self._device)
@sync_all_reduce("_sum_of_kl", "_num_examples")
def compute(self) -> float:
if self._num_examples == 0:
raise NotComputableError("JSDivergence must have at least one example before it can be computed.")
return self._sum_of_kl.item() / (self._num_examples * 2)
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