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.

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 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 (Callable) – a callable that is used to transform the 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 (Union[str, 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

metric = InceptionScore()
metric.attach(default_evaluator, "is")
y = torch.rand(10, 3, 299, 299)
state = default_evaluator.run([y])
print(state.metrics["is"])

metric = InceptionScore(num_features=1, feature_extractor=default_model)
metric.attach(default_evaluator, "is")
y = torch.zeros(10, 4)
state = default_evaluator.run([y])
print(state.metrics["is"])

1.0

New in version 0.4.6.

Methods

`compute`	Computes the metric based on it's accumulated state.
`reset`	Resets the metric to it's initial state.
`update`	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 when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()[source]#

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type: None

update(output)[source]#

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters: output (Tensor) – the is the output from the engine’s process function.
Return type: None