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from typing import Callable, Optional, Union
import torch
from ignite.exceptions import NotComputableError
from ignite.metrics.gan.utils import _BaseInceptionMetric, InceptionModel
# These decorators helps with distributed settings
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
__all__ = ["InceptionScore"]
class InceptionScore(_BaseInceptionMetric):
r"""Calculates Inception Score.
.. math::
\text{IS(G)} = \exp(\frac{1}{N}\sum_{i=1}^{N} D_{KL} (p(y|x^{(i)} \parallel \hat{p}(y))))
where :math:`p(y|x)` is the conditional probability of image being the given object and
:math:`p(y)` is the marginal probability that the given image is real, `G` refers to the
generated image and :math:`D_{KL}` refers to KL Divergence of the above mentioned probabilities.
More details can be found in `Barratt et al. 2018`__.
__ https://arxiv.org/pdf/1801.01973.pdf
Args:
num_features: number of features predicted by the model or number of classes of the model. Default
value is 1000.
feature_extractor: a torch Module for predicting the probabilities from the input data.
It returns a tensor of shape (batch_size, num_features).
If neither ``num_features`` nor ``feature_extractor`` are defined, by default we use an ImageNet
pretrained Inception Model. If only ``num_features`` is defined but ``feature_extractor`` is not
defined, ``feature_extractor`` is assigned Identity Function.
Please note that the class object will be implicitly converted to device mentioned in the
``device`` argument.
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``.
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.
.. note::
The default Inception model requires the `torchvision` module to be installed.
Examples:
For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
.. include:: defaults.rst
:start-after: :orphan:
.. code-block:: python
metric = InceptionScore()
metric.attach(default_evaluator, "is")
y = torch.rand(10, 3, 299, 299)
state = default_evaluator.run([y])
print(state.metrics["is"])
.. testcode::
metric = InceptionScore(num_features=1, feature_extractor=default_model)
metric.attach(default_evaluator, "is")
y = torch.zeros(10, 4)
state = default_evaluator.run([y])
print(state.metrics["is"])
.. testoutput::
1.0
.. versionadded:: 0.4.6
"""
_state_dict_all_req_keys = ("_num_examples", "_prob_total", "_total_kl_d")
def __init__(
self,
num_features: Optional[int] = None,
feature_extractor: Optional[torch.nn.Module] = None,
output_transform: Callable = lambda x: x,
device: Union[str, torch.device] = torch.device("cpu"),
) -> None:
if num_features is None and feature_extractor is None:
num_features = 1000
feature_extractor = InceptionModel(return_features=False, device=device)
self._eps = 1e-16
super(InceptionScore, self).__init__(
num_features=num_features,
feature_extractor=feature_extractor,
output_transform=output_transform,
device=device,
)
@reinit__is_reduced
def reset(self) -> None:
self._num_examples = 0
self._prob_total = torch.zeros(self._num_features, dtype=torch.float64, device=self._device)
self._total_kl_d = torch.zeros(self._num_features, dtype=torch.float64, device=self._device)
super(InceptionScore, self).reset() # type: ignore
@reinit__is_reduced
def update(self, output: torch.Tensor) -> None:
probabilities = self._extract_features(output)
prob_sum = torch.sum(probabilities, 0, dtype=torch.float64)
log_prob = torch.log(probabilities + self._eps)
if log_prob.dtype != probabilities.dtype:
log_prob = log_prob.to(probabilities)
kl_sum = torch.sum(probabilities * log_prob, 0, dtype=torch.float64)
self._num_examples += probabilities.shape[0]
self._prob_total += prob_sum
self._total_kl_d += kl_sum
@sync_all_reduce("_num_examples", "_prob_total", "_total_kl_d")
def compute(self) -> float:
if self._num_examples == 0:
raise NotComputableError("InceptionScore must have at least one example before it can be computed.")
mean_probs = self._prob_total / self._num_examples
log_mean_probs = torch.log(mean_probs + self._eps)
if log_mean_probs.dtype != self._prob_total.dtype:
log_mean_probs = log_mean_probs.to(self._prob_total)
excess_entropy = self._prob_total * log_mean_probs
avg_kl_d = torch.sum(self._total_kl_d - excess_entropy) / self._num_examples
return torch.exp(avg_kl_d).item()
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