Shortcuts

Source code for ignite.metrics.frequency

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


[docs]class Frequency(Metric): """Provides metrics for the number of examples processed per second. Examples: .. 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] """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ) -> None: super(Frequency, self).__init__(output_transform=output_transform, device=device)
[docs] @reinit__is_reduced def reset(self) -> None: self._timer = Timer() self._acc = 0 self._n = 0 self._elapsed = 0.0 super(Frequency, self).reset()
[docs] @reinit__is_reduced def update(self, output: int) -> None: self._acc += output self._n = self._acc self._elapsed = self._timer.value()
[docs] @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
[docs] 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
[docs] 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)

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 08/16/2022, 6:36:26 AM.

Built with Sphinx using a theme provided by Read the Docs.