Frequency#

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

Provides metrics for the number of examples processed per second.

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 (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-output as (y_pred_a, y_pred_b) Alternatively, output_transform can be used to handle this.

Examples

For more information on how metric works with Engine, visit Attach Engine API.

# Compute number of tokens processed
wps_metric = Frequency(output_transform=lambda x: x['ntokens'])
wps_metric.attach(trainer, name='wps')
# Logging with TQDM
ProgressBar(persist=True).attach(trainer, metric_names=['wps'])
# Progress bar will look like
# Epoch [2/10]: [12/24]  50%|█████      , wps=400 [00:17<1:23]

To compute examples processed per second every 50th iteration:

# Compute number of tokens processed
wps_metric = Frequency(output_transform=lambda x: x['ntokens'])
wps_metric.attach(trainer, name='wps', event_name=Events.ITERATION_COMPLETED(every=50))
# Logging with TQDM
ProgressBar(persist=True).attach(trainer, metric_names=['wps'])
# Progress bar will look like
# Epoch [2/10]: [50/100]  50%|█████      , wps=400 [00:17<00:35]

Changed in version 0.5.1: skip_unrolling argument is added.

Methods

`attach`	Attaches current metric to provided engine.
`completed`	Helper method to compute metric's value and put into the engine.
`compute`	Computes the metric based on its accumulated state.
`reset`	Resets the metric to its initial state.
`update`	Updates the metric's state using the passed batch output.

attach(engine, name, event_name=Events.ITERATION_COMPLETED)[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:

engine (Engine) – the engine to which the metric must be attached
name (str) – the name of the metric to attach
usage – the usage of the metric. Valid string values should be ignite.metrics.metric.EpochWise.usage_name (default) or ignite.metrics.metric.BatchWise.usage_name.
event_name (Events) –

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)

completed(engine, name)[source]#

Helper method to compute metric’s value and put into the engine. It is automatically attached to the engine with attach(). If metrics’ value is torch tensor, it is explicitly sent to CPU device.

Parameters:

engine (Engine) – the engine to which the metric must be attached
name (str) – the name of the metric used as key in dict engine.state.metrics

Return type:

None

Changed in version 0.4.3: Added dict in metrics results.

Changed in version 0.4.5: metric’s value is put on CPU if torch tensor.

compute()[source]#

Computes the metric based on its 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.

reset()[source]#

Resets the metric to its 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 (int) – the is the output from the engine’s process function.
Return type:: None

Frequency#

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