InceptionScore#
- class ignite.metrics.InceptionScore(num_features=None, feature_extractor=None, output_transform=<function InceptionScore.<lambda>>, device=device(type='cpu'))[source]#
Calculates Inception Score.
$\text{IS(G)} = \exp(\frac{1}{N}\sum_{i=1}^{N} D_{KL} (p(y|x^{(i)} \parallel \hat{p}(y))))$where $p(y|x)$ is the conditional probability of image being the given object and $p(y)$ is the marginal probability that the given image is real, G refers to the generated image and $D_{KL}$ refers to KL Divergence of the above mentioned probabilities.
More details can be found in Barratt et al. 2018.
Note
The default Inception model requires the torchvision module to be installed.
- Parameters
num_features (Optional[int]) – number of features predicted by the model or number of classes of the model. Default value is 1000.
feature_extractor (Optional[Module]) – 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
norfeature_extractor
are defined, by default we use an ImageNet pretrained Inception Model. If onlynum_features
is defined butfeature_extractor
is not defined,feature_extractor
is assigned Identity Function. Please note that the class object will be implicitly converted to device mentioned in thedevice
argument.output_transform (Callable) – a callable that is used to transform the
Engine
’sprocess_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 asy_pred
.device (Union[str, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
update
arguments ensures theupdate
method is non-blocking. By default, CPU.
Example
from ignite.metric.gan import InceptionScore import torch images = torch.rand(10, 3, 299, 299) m = InceptionScore() m.update(images) print(m.compute())
New in version 0.4.6.
Methods
Computes the metric based on it's accumulated state.
Resets the metric to it's initial state.
Updates the metric's state using the passed batch output.
- compute()[source]#
Computes the metric based on it’s accumulated state.
By default, this is called at the end of each epoch.
- Returns
- the actual quantity of interest. However, if a
Mapping
is returned, it will be (shallow) flattened into engine.state.metrics whencompleted()
is called. - Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed.