Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix return type documentation of inception score #2467

Merged
merged 2 commits into from
Mar 24, 2024
Merged
Changes from 1 commit
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 3 additions & 2 deletions src/torchmetrics/image/inception.py
Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ class InceptionScore(Metric):
IS = exp(\mathbb{E}_x KL(p(y | x ) || p(y)))

where :math:`KL(p(y | x) || p(y))` is the KL divergence between the conditional distribution :math:`p(y|x)`
and the margianl distribution :math:`p(y)`. Both the conditional and marginal distribution is calculated
and the marginal distribution :math:`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`_.
Expand All @@ -59,7 +59,8 @@ class InceptionScore(Metric):

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

- ``fid`` (:class:`~torch.Tensor`): float scalar tensor with mean FID value over samples
- ``inception_mean`` (:class:`~torch.Tensor`): float scalar tensor with mean inception score over subsets
- ``inception_std`` (:class:`~torch.Tensor`): float scalar tensor with standard deviation of inception score over subsets

Args:
feature:
Expand Down
Loading