def execute_training_loop(self, trn, dev, devices, epochs, logger,
patience, save_dir, optimizer,
gradient_accumulation, **kwargs):
optimizer, scheduler, transformer_optimizer, transformer_scheduler = optimizer
criterion = self.build_criterion()
best_e, best_metric = 0, self.build_metric()
timer = CountdownTimer(epochs)
history = History()
ratio_width = len(
f'{len(trn) // gradient_accumulation}/{len(trn) // gradient_accumulation}'
)
for epoch in range(1, epochs + 1):
# train one epoch and update the parameters
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn,
optimizer,
scheduler,
criterion,
epoch,
logger,
history,
transformer_optimizer,
transformer_scheduler,
gradient_accumulation=gradient_accumulation,
eval_trn=self.config.eval_trn)
loss, dev_metric = self.evaluate_dataloader(
dev, criterion, ratio_width=ratio_width, logger=logger)
timer.update()
# logger.info(f"{'Dev' + ' ' * ratio_width} loss: {loss:.4f} {dev_metric}")
# save the model if it is the best so far
report = f"{timer.elapsed_human} / {timer.total_time_human} ETA: {timer.eta_human}"
if dev_metric > best_metric:
best_e, best_metric = epoch, dev_metric
self.save_weights(save_dir)
report += ' ([red]saved[/red])'
else:
if patience != epochs:
report += f' ({epoch - best_e}/{patience})'
else:
report += f' ({epoch - best_e})'
logger.info(report)
if patience is not None and epoch - best_e >= patience:
logger.info(
f'LAS has stopped improving for {patience} epochs, early stop.'
)
break
timer.stop()
if not best_e:
self.save_weights(save_dir)
elif best_e != epoch:
self.load_weights(save_dir)
logger.info(
f"Max score of dev is {best_metric.score:.2%} at epoch {best_e}")
logger.info(
f"Average time of each epoch is {timer.elapsed_average_human}")
logger.info(f"{timer.elapsed_human} elapsed")
def execute_training_loop(self,
trn: DataLoader,
dev: DataLoader,
epochs,
criterion,
optimizer,
metric,
save_dir,
logger: logging.Logger,
devices,
ratio_width=None,
patience=5,
teacher=None,
kd_criterion=None,
eval_trn=True,
**kwargs):
best_epoch, best_metric = 0, -1
timer = CountdownTimer(epochs)
history = History()
for epoch in range(1, epochs + 1):
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn,
criterion,
optimizer,
metric,
logger,
history=history,
ratio_width=ratio_width,
eval_trn=eval_trn,
**self.config)
loss, dev_metric = self.evaluate_dataloader(
dev, criterion, logger=logger, ratio_width=ratio_width)
timer.update()
report = f"{timer.elapsed_human} / {timer.total_time_human} ETA: {timer.eta_human}"
if dev_metric > best_metric:
best_epoch, best_metric = epoch, dev_metric
self.save_weights(save_dir)
report += ' [red](saved)[/red]'
else:
report += f' ({epoch - best_epoch})'
if epoch - best_epoch >= patience:
report += ' early stop'
logger.info(report)
if epoch - best_epoch >= patience:
break
if not best_epoch:
self.save_weights(save_dir)
elif best_epoch != epoch:
self.load_weights(save_dir)
logger.info(f"Max score of dev is {best_metric} at epoch {best_epoch}")
logger.info(
f"Average time of each epoch is {timer.elapsed_average_human}")
logger.info(f"{timer.elapsed_human} elapsed")
return best_metric
def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epochs, criterion, optimizer, metric, save_dir,
logger: logging.Logger, devices, ratio_width=None, dev_data=None, eval_after=None,
**kwargs):
best_epoch, best_metric = 0, -1
if isinstance(eval_after, float):
eval_after = int(epochs * eval_after)
timer = CountdownTimer(epochs)
history = History()
for epoch in range(1, epochs + 1):
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn, criterion, optimizer, metric, logger, history=history, ratio_width=ratio_width,
**self.config)
if epoch > eval_after:
dev_metric = self.evaluate_dataloader(dev, criterion, logger=logger, ratio_width=ratio_width,
output=os.path.join(save_dir, 'dev.pred.txt'),
input=dev_data, use_fast=True)
timer.update()
report = f"{timer.elapsed_human} / {timer.total_time_human} ETA: {timer.eta_human}"
if epoch > eval_after:
if dev_metric > best_metric:
best_epoch, best_metric = epoch, dev_metric
self.save_weights(save_dir)
report += ' [red](saved)[/red]'
else:
report += f' ({epoch - best_epoch})'
# if epoch - best_epoch >= patience:
# report += ' early stop'
logger.info(report)
# if epoch - best_epoch >= patience:
# break
if not best_epoch:
self.save_weights(save_dir)
elif best_epoch != epoch:
self.load_weights(save_dir)
logger.info(f"Max score of dev is {best_metric} at epoch {best_epoch}")
logger.info(f"Average time of each epoch is {timer.elapsed_average_human}")
logger.info(f"{timer.elapsed_human} elapsed")
return best_metric