Shortcuts

Inception Score

Module Interface

class torchmetrics.image.inception.InceptionScore(feature='logits_unbiased', splits=10, normalize=False, **kwargs)[source]

Calculate the Inception Score (IS) which is used to access how realistic generated images are. It is defined as.

IS = exp(\mathbb{E}_x KL(p(y | x ) || p(y)))

where KL(p(y | x) || p(y)) is the KL divergence between the conditional distribution p(y|x) and the margianl distribution p(y). 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 inception ref1.

Using the default feature extraction (Inception v3 using the original weights from inception ref2), the input is expected to be mini-batches of 3-channel RGB images of shape (3 x H x W). If argument normalize is True images are expected to be dtype float and have values in the [0, 1] range, else if normalize is set to False images are expected to have dtype uint8 and take values in the [0, 255] range. 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 as pip install torchmetrics[image] or pip install torch-fidelity

As input to forward and update the metric accepts the following input

  • imgs (Tensor): tensor with images feed to the feature extractor

As output of forward and compute the metric returns the following output

  • fid (Tensor): float scalar tensor with mean FID value over samples

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 where N is the batch size and d is the feature size.

  • splits (int) – integer determining how many splits the inception score calculation should be split among

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises
  • ValueError – If feature is set to an str or int and torch-fidelity is not installed

  • ValueError – If feature is set to an str or int and not one of ('logits_unbiased', 64, 192, 768, 2048)

  • TypeError – If feature is not an str, int or torch.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.

Read the Docs v: latest
Versions
latest
stable
v0.11.1
v0.11.0
v0.10.3
v0.10.2
v0.10.1
v0.10.0
v0.9.3
v0.9.2
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
Downloads
pdf
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.