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GeometricAverage#

class ignite.metrics.GeometricAverage(output_transform=<function GeometricAverage.<lambda>>, device=device(type='cpu'))[source]#

Helper class to compute geometric average of a single variable.

  • update must receive output of the form x.

  • x can be a positive number or a positive torch.Tensor, such that torch.log(x) is not nan.

Note

Number of samples is updated following the rule:

  • +1 if input is a number

  • +1 if input is a 1D torch.Tensor

  • +batch_size if input is a ND torch.Tensor. Batch size is the first dimension (shape[0]).

For input x being an ND torch.Tensor with N > 1, the first dimension is seen as the number of samples and is aggregated and added to the accumulator: accumulator *= prod(x, dim=0)

Parameters

  • 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.

  • 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.

Methods

compute

Computes the metric based on it's accumulated state.
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.