- class ignite.metrics.GeometricAverage(output_transform=<function GeometricAverage.<lambda>>, device=device(type='cpu'))[source]#
Helper class to compute geometric average of a single variable.
updatemust 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.
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).
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)
output_transform (Callable) – a callable that is used to transform the
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, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
updatearguments ensures the
updatemethod is non-blocking. By default, CPU.
Computes the metric based on it's accumulated state.
Computes the metric based on it’s accumulated state.
By default, this is called at the end of each epoch.
- the actual quantity of interest. However, if a
Mappingis returned, it will be (shallow) flattened into engine.state.metrics when
- Return type
NotComputableError – raised when the metric cannot be computed.