# VariableAccumulation#

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

Note

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]).

Parameters
• 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, 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. 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.

required_output_keys: Optional[Tuple] = None#
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 (Union[float, torch.Tensor]) – the is the output from the engine’s process function.

Return type

None