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

Single variable accumulator helper to compute (arithmetic, geometric, harmonic) average of a single variable.

  • update must receive output of the form x.

  • x can be a number or torch.Tensor.


The class stores input into two public variables: accumulator and num_examples. 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]).

  • op (Callable) – a callable to update accumulator. Method’s signature is (accumulator, output). For example, to compute arithmetic mean value, op = lambda a, x: a + x.

  • 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.

  • skip_unrolling (bool) – specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if y_pred contains multi-ouput as (y_pred_a, y_pred_b) Alternatively, output_transform can be used to handle this.

Changed in version 0.5.1: skip_unrolling argument is added.



Computes the metric based on its accumulated state.


Resets the metric to its initial state.


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


Computes the metric based on its accumulated state.

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


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



NotComputableError – raised when the metric cannot be computed.

required_output_keys: Optional[Tuple] = None#

Resets the metric to its initial state.

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

Return type



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

By default, this is called once for each batch.


output (Union[float, Tensor]) – the is the output from the engine’s process function.

Return type