# Average#

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

Helper class to compute arithmetic average of a single variable.

• update must receive output of the form x.

• x can be a number or torch.Tensor.

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 an 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 summed up and added to the accumulator: accumulator += x.sum(dim=0)

Examples:

evaluator = ...

custom_var_mean = Average(output_transform=lambda output: output['custom_var'])
custom_var_mean.attach(evaluator, 'mean_custom_var')

state = evaluator.run(dataset)
# state.metrics['mean_custom_var'] -> average of output['custom_var']

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, 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.

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.