Source code for ignite.metrics.gan.inception_score

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"]

[docs]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`__. __ 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 =[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 =[y]) print(state.metrics["is"]) .. testoutput:: 1.0 .. versionadded:: 0.4.6 """ 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, )
[docs] @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()
[docs] @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 = 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
[docs] @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 = 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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