def _save_checkpoint(self, scheduler, epoch: int, offset: int) -> str:
args = self.args
model_to_save = get_model_obj(self.reader)
cp = os.path.join(args.output_dir,
args.checkpoint_file_name + '.' + str(epoch) + ('.' + str(offset) if offset > 0 else ''))
meta_params = get_encoder_params_state(args)
state = CheckpointState(model_to_save.state_dict(), self.optimizer.state_dict(), scheduler.state_dict(), offset,
epoch, meta_params
)
torch.save(state._asdict(), cp)
return cp
def _save_checkpoint(self, scheduler, epoch: int, offset: int) -> str:
cfg = self.cfg
model_to_save = get_model_obj(self.biencoder)
cp = os.path.join(cfg.output_dir, cfg.checkpoint_file_name + "." + str(epoch))
meta_params = get_encoder_params_state_from_cfg(cfg)
state = CheckpointState(
model_to_save.get_state_dict(),
self.optimizer.state_dict(),
scheduler.state_dict(),
offset,
epoch,
meta_params,
)
torch.save(state._asdict(), cp)
logger.info("Saved checkpoint at %s", cp)
return cp
def _save_checkpoint(self, scheduler, epoch: int, offset: int) -> str:
args = self.args
model_to_save = get_model_obj(self.biencoder)
cp = os.path.join(
args.output_dir,
args.checkpoint_file_name + "." + str(epoch) +
("." + str(offset) if offset > 0 else ""),
)
meta_params = get_encoder_params_state(args)
state = CheckpointState(
model_to_save.state_dict(),
self.optimizer.state_dict(),
scheduler.state_dict(),
offset,
epoch,
meta_params,
)
torch.save(state._asdict(), cp)
logger.info("Saved checkpoint at %s", cp)
return cp
