def compute_validation_metrics(self) -> workload.Response:
metrics = self.estimator.evaluate(input_fn=self.val_spec.input_fn,
steps=self.val_spec.steps,
hooks=self.val_spec.hooks)
if self.hvd_config.use:
metrics = self.average_metrics(metrics)
if self.is_chief:
logging.debug(f"Averaged validation metrics: {metrics}.")
estimator._cleanup_after_validation_step(self.estimator._model_dir,
self.hvd_config.use,
self.is_chief)
if not self.is_chief:
return workload.Skipped()
return {"validation_metrics": metrics}
def compute_validation_metrics(self) -> workload.Response:
metrics = self.estimator.evaluate(input_fn=self.eval_spec.input_fn,
steps=self.eval_spec.steps,
hooks=self.eval_spec.hooks)
if self.hvd_config.use:
metrics = self.average_metrics(metrics)
if self.is_chief:
logging.debug(f"Averaged validation metrics: {metrics}.")
estimator._cleanup_after_validation_step(
pathlib.Path(self.estimator._model_dir), self.is_chief)
# Reset the per-evaluation set of allgather ops in the context.
self.context.experimental._reset_allgather_ops()
if not self.is_chief:
return workload.Skipped()
return {"validation_metrics": metrics}
def compute_validation_metrics(self) -> workload.Response:
steps = self.eval_spec.steps if not self.env.test_mode else 1
metrics = self.estimator.evaluate(input_fn=self.eval_spec.input_fn,
steps=steps,
hooks=self.eval_spec.hooks)
if self.context.distributed.size > 1:
metrics = self.average_metrics(metrics)
if self.is_chief:
logging.debug(f"Averaged validation metrics: {metrics}.")
estimator._cleanup_after_validation_step(
pathlib.Path(self.estimator._model_dir), self.is_chief)
# Reset the per-evaluation set of allgather ops in the context.
self.context._reset_allgather_ops()
if not self.is_chief:
return {}
return {"validation_metrics": metrics}
