Inception Score¶
Module Interface¶
- class torchmetrics.image.inception.InceptionScore(feature='logits_unbiased', splits=10, **kwargs)[source]
Calculates the Inception Score (IS) which is used to access how realistic generated images are. It is defined as.
where is the KL divergence between the conditional distribution and the margianl distribution . Both the conditional and marginal distribution is calculated from features extracted from the images. The score is calculated on random splits of the images such that both a mean and standard deviation of the score are returned. The metric was originally proposed in [1].
Using the default feature extraction (Inception v3 using the original weights from [2]), the input is expected to be mini-batches of 3-channel RGB images of shape (3 x H x W) with dtype uint8. All images will be resized to 299 x 299 which is the size of the original training data.
Note
using this metric with the default feature extractor requires that
torch-fidelity
is installed. Either install aspip install torchmetrics[image]
orpip install torch-fidelity
- Parameters
feature¶ (
Union
[str
,int
,Module
]) –Either an str, integer or
nn.Module
:an str or integer will indicate the inceptionv3 feature layer to choose. Can be one of the following: ‘logits_unbiased’, 64, 192, 768, 2048
an
nn.Module
for using a custom feature extractor. Expects that its forward method returns an[N,d]
matrix whereN
is the batch size andd
is the feature size.
splits¶ (
int
) – integer determining how many splits the inception score calculation should be split amongkwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
References
[1] Improved Techniques for Training GANs Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen https://arxiv.org/abs/1606.03498
[2] GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter https://arxiv.org/abs/1706.08500
- Raises
ValueError – If
feature
is set to anstr
orint
andtorch-fidelity
is not installedValueError – If
feature
is set to anstr
orint
and not one of['logits_unbiased', 64, 192, 768, 2048]
TypeError – If
feature
is not anstr
,int
ortorch.nn.Module
Example
>>> import torch >>> _ = torch.manual_seed(123) >>> from torchmetrics.image.inception import InceptionScore >>> inception = InceptionScore() >>> # generate some images >>> imgs = torch.randint(0, 255, (100, 3, 299, 299), dtype=torch.uint8) >>> inception.update(imgs) >>> inception.compute() (tensor(1.0544), tensor(0.0117))
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- compute()[source]
Override this method to compute the final metric value from state variables synchronized across the distributed backend.