Source code for ignite.metrics.metrics_lambda

import itertools
from typing import Any, Callable, Union

from ignite.engine import Engine, Events
from ignite.metrics.metric import EpochWise, Metric, MetricUsage, reinit__is_reduced

__all__ = ["MetricsLambda"]

[docs]class MetricsLambda(Metric): """ Apply a function to other metrics to obtain a new metric. The result of the new metric is defined to be the result of applying the function to the result of argument metrics. When update, this metric does not recursively update the metrics it depends on. When reset, all its dependency metrics would be resetted. When attach, all its dependency metrics would be attached automatically (but partially, e.g `is_attached()` will return False). Args: f (callable): the function that defines the computation args (sequence): Sequence of other metrics or something else that will be fed to ``f`` as arguments. Example: .. code-block:: python precision = Precision(average=False) recall = Recall(average=False) def Fbeta(r, p, beta): return torch.mean((1 + beta ** 2) * p * r / (beta ** 2 * p + r + 1e-20)).item() F1 = MetricsLambda(Fbeta, recall, precision, 1) F2 = MetricsLambda(Fbeta, recall, precision, 2) F3 = MetricsLambda(Fbeta, recall, precision, 3) F4 = MetricsLambda(Fbeta, recall, precision, 4) When check if the metric is attached, if one of its dependency metrics is detached, the metric is considered detached too. .. code-block:: python engine = ... precision = Precision(average=False) aP = precision.mean() aP.attach(engine, "aP") assert aP.is_attached(engine) # partially attached assert not precision.is_attached(engine) precision.detach(engine) assert not aP.is_attached(engine) # fully attached assert not precision.is_attached(engine) """ def __init__(self, f: Callable, *args, **kwargs): self.function = f self.args = args self.kwargs = kwargs self.engine = None super(MetricsLambda, self).__init__(device="cpu") @reinit__is_reduced def reset(self) -> None: for i in itertools.chain(self.args, self.kwargs.values()): if isinstance(i, Metric): i.reset() @reinit__is_reduced def update(self, output) -> None: # NB: this method does not recursively update dependency metrics, # which might cause duplicate update issue. To update this metric, # users should manually update its dependencies. pass def compute(self) -> Any: materialized = [i.compute() if isinstance(i, Metric) else i for i in self.args] materialized_kwargs = {k: (v.compute() if isinstance(v, Metric) else v) for k, v in self.kwargs.items()} return self.function(*materialized, **materialized_kwargs) def _internal_attach(self, engine: Engine, usage: MetricUsage) -> None: self.engine = engine for index, metric in enumerate(itertools.chain(self.args, self.kwargs.values())): if isinstance(metric, MetricsLambda): metric._internal_attach(engine, usage) elif isinstance(metric, Metric): # NB : metrics is attached partially # We must not use is_attached() but rather if these events exist if not engine.has_event_handler(metric.started, usage.STARTED): engine.add_event_handler(usage.STARTED, metric.started) if not engine.has_event_handler(metric.iteration_completed, usage.ITERATION_COMPLETED): engine.add_event_handler(usage.ITERATION_COMPLETED, metric.iteration_completed) def attach(self, engine: Engine, name: str, usage: Union[str, MetricUsage] = EpochWise()) -> None: usage = self._check_usage(usage) # recursively attach all its dependencies (partially) self._internal_attach(engine, usage) # attach only handler on EPOCH_COMPLETED engine.add_event_handler(usage.COMPLETED, self.completed, name) def detach(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise()) -> None: usage = self._check_usage(usage) # remove from engine super(MetricsLambda, self).detach(engine, usage) self.engine = None def is_attached(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise()) -> bool: usage = self._check_usage(usage) # check recursively the dependencies return super(MetricsLambda, self).is_attached(engine, usage) and self._internal_is_attached(engine, usage) def _internal_is_attached(self, engine: Engine, usage: MetricUsage) -> bool: # if no engine, metrics is not attached if engine is None: return False # check recursively if metrics are attached is_detached = False for metric in itertools.chain(self.args, self.kwargs.values()): if isinstance(metric, MetricsLambda): if not metric._internal_is_attached(engine, usage): is_detached = True elif isinstance(metric, Metric): if not engine.has_event_handler(metric.started, usage.STARTED): is_detached = True if not engine.has_event_handler(metric.iteration_completed, usage.ITERATION_COMPLETED): is_detached = True return not is_detached

© Copyright 2024, PyTorch-Ignite Contributors. Last updated on 07/17/2024, 10:10:30 AM.

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