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FID#

class ignite.metrics.FID(num_features=None, feature_extractor=None, output_transform=<function FID.<lambda>>, device=device(type='cpu'))[source]#

Calculates Frechet Inception Distance.

FID=μ1μ2+Tr(σ1+σ22σ1σ2)\text{FID} = |\mu_{1} - \mu_{2}| + \text{Tr}(\sigma_{1} + \sigma_{2} - {2}\sqrt{\sigma_1*\sigma_2})

where μ1\mu_1 and σ1\sigma_1 refer to the mean and covariance of the train data and μ2\mu_2 and σ2\sigma_2 refer to the mean and covariance of the test data.

More details can be found in Heusel et al. 2002

In addition, a faster and online computation approach can be found in Chen et al. 2014

Remark:

This implementation is inspired by pytorch_fid package which can be found here

Note

The default Inception model requires the torchvision module to be installed. FID also requires scipy library for matrix square root calculations.

Parameters
  • num_features (Optional[int]) – number of features predicted by the model or the reduced feature vector of the image. Default value is 2048.

  • feature_extractor (Optional[torch.nn.modules.module.Module]) – a torch Module for extracting the features 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 model 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, y) or {'y_pred': y_pred, 'y': y}.

  • device (Union[str, torch.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.

Return type

None

Examples

metric = FID()
metric.attach(default_evaluator, "fid")
y_true = torch.rand(10, 3, 299, 299)
y_pred = torch.rand(10, 3, 299, 299)
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["fid"])
metric = FID(num_features=1, feature_extractor=default_model)
metric.attach(default_evaluator, "fid")
y_true = torch.ones(10, 4)
y_pred = torch.ones(10, 4)
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["fid"])
0.0

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 (Sequence[torch.Tensor]) – the is the output from the engine’s process function.

Return type

None