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Source code for ignite.metrics.metrics_lambda

import itertools

from ignite.metrics.metric import Metric, reinit__is_reduced
from ignite.engine import Events


[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 dependencies would be automatically attached. 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) """ def __init__(self, f, *args, **kwargs): self.function = f self.args = args self.kwargs = kwargs super(MetricsLambda, self).__init__(device='cpu') @reinit__is_reduced def reset(self): for i in itertools.chain(self.args, self.kwargs.values()): if isinstance(i, Metric): i.reset() @reinit__is_reduced def update(self, output): # 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): 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): for index, metric in enumerate(itertools.chain(self.args, self.kwargs.values())): if isinstance(metric, MetricsLambda): metric._internal_attach(engine) elif isinstance(metric, Metric): if not engine.has_event_handler(metric.started, Events.EPOCH_STARTED): engine.add_event_handler(Events.EPOCH_STARTED, metric.started) if not engine.has_event_handler(metric.iteration_completed, Events.ITERATION_COMPLETED): engine.add_event_handler(Events.ITERATION_COMPLETED, metric.iteration_completed) def attach(self, engine, name): # recursively attach all its dependencies self._internal_attach(engine) # attach only handler on EPOCH_COMPLETED engine.add_event_handler(Events.EPOCH_COMPLETED, self.completed, name)

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