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from typing import Callable, Union
import torch
import ignite.distributed as idist
from ignite.engine import Engine, Events
from ignite.handlers.timing import Timer
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
class Frequency(Metric):
"""Provides metrics for the number of examples processed per second.
Args:
output_transform: a callable that is used to transform the
:class:`~ignite.engine.engine.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: 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: 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.
Examples:
For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
.. code-block:: python
# 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:
.. code-block:: python
# 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]
.. versionchanged:: 0.5.1
``skip_unrolling`` argument is added.
"""
def __init__(
self,
output_transform: Callable = lambda x: x,
device: Union[str, torch.device] = torch.device("cpu"),
skip_unrolling: bool = False,
) -> None:
super(Frequency, self).__init__(output_transform=output_transform, device=device, skip_unrolling=skip_unrolling)
@reinit__is_reduced
def reset(self) -> None:
self._timer = Timer()
self._acc = 0
self._n = 0
self._elapsed = 0.0
super(Frequency, self).reset() # type: ignore
@reinit__is_reduced
def update(self, output: int) -> None:
self._acc += output
self._n = self._acc
self._elapsed = self._timer.value()
@sync_all_reduce("_n", "_elapsed")
def compute(self) -> float:
time_divisor = 1.0
if idist.get_world_size() > 1:
time_divisor *= idist.get_world_size()
# Returns the average processed objects per second across all workers
return self._n / self._elapsed * time_divisor
def completed(self, engine: Engine, name: str) -> None:
engine.state.metrics[name] = int(self.compute())
# TODO: see issue https://github.com/pytorch/ignite/issues/1405
def attach( # type: ignore
self, engine: Engine, name: str, event_name: Events = Events.ITERATION_COMPLETED
) -> None:
engine.add_event_handler(Events.EPOCH_STARTED, self.started)
engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)
engine.add_event_handler(event_name, self.completed, name)
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