def train_one_epoch(
trainer: Trainer,
epoch_count: int) -> Tuple[Dict[str, float], Dict[str, float]]:
train_metrics: Dict[str, float] = {}
val_metrics: Dict[str, float] = {}
this_epoch_val_metric: float = None
metrics: Dict[str, float] = {}
train_metrics = trainer._train_epoch(epoch_count)
if trainer._validation_data is not None:
with torch.no_grad():
# We have a validation set, so compute all the metrics on it.
val_loss, num_batches = trainer._validation_loss()
val_metrics = training_util.get_metrics(trainer.model,
val_loss,
num_batches,
reset=True)
this_epoch_val_metric = val_metrics[trainer._validation_metric]
for key, value in train_metrics.items():
metrics["training_" + key] = value
for key, value in val_metrics.items():
metrics["validation_" + key] = value
if trainer._serialization_dir:
dump_metrics(
os.path.join(trainer._serialization_dir,
f"metrics_epoch_{epoch_count}.json"), metrics)
# The Scheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
if trainer._learning_rate_scheduler:
trainer._learning_rate_scheduler.step(this_epoch_val_metric,
epoch_count)
if trainer._momentum_scheduler:
trainer._momentum_scheduler.step(this_epoch_val_metric, epoch_count)
#trainer._save_checkpoint(epoch_count)
return train_metrics, val_metrics
