Shortcuts

RunningAverage#

class ignite.metrics.RunningAverage(src=None, alpha=0.98, output_transform=None, epoch_bound=True, device=None)[source]#

Compute running average of a metric or the output of process function.

Parameters
  • src (Optional[ignite.metrics.metric.Metric]) – input source: an instance of Metric or None. The latter corresponds to engine.state.output which holds the output of process function.

  • alpha (float) – running average decay factor, default 0.98

  • output_transform (Optional[Callable]) – a function to use to transform the output if src is None and corresponds the output of process function. Otherwise it should be None.

  • epoch_bound (bool) – whether the running average should be reset after each epoch (defaults to True).

  • device (Optional[Union[str, torch.device]]) – specifies which device updates are accumulated on. Should be None when src is an instance of Metric, as the running average will use the src’s device. Otherwise, defaults to CPU. Only applicable when the computed value from the metric is a tensor.

Examples

alpha = 0.98
acc_metric = RunningAverage(Accuracy(output_transform=lambda x: [x[1], x[2]]), alpha=alpha)
acc_metric.attach(trainer, 'running_avg_accuracy')

avg_output = RunningAverage(output_transform=lambda x: x[0], alpha=alpha)
avg_output.attach(trainer, 'running_avg_loss')

@trainer.on(Events.ITERATION_COMPLETED)
def log_running_avg_metrics(engine):
    print("running avg accuracy:", engine.state.metrics['running_avg_accuracy'])
    print("running avg loss:", engine.state.metrics['running_avg_loss'])

Methods

attach

Attaches current metric to provided engine.

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.

attach(engine, name, _usage=<ignite.metrics.metric.EpochWise object>)[source]#

Attaches current metric to provided engine. On the end of engine’s run, engine.state.metrics dictionary will contain computed metric’s value under provided name.

Parameters
Return type

None

Examples

metric = ...
metric.attach(engine, "mymetric")

assert "mymetric" in engine.run(data).metrics

assert metric.is_attached(engine)

Example with usage:

metric = ...
metric.attach(engine, "mymetric", usage=BatchWise.usage_name)

assert "mymetric" in engine.run(data).metrics

assert metric.is_attached(engine, usage=BatchWise.usage_name)
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 (Sequence) – the is the output from the engine’s process function.

Return type

None