I have added an argument init_latest. It is similar to restart_latest but it only loads the model state_dict from the checkpoint. Thus, the training can start with a previous model but use a fresh new optimizer and lr_scheduler. In practice, it will also be useful in active learning as the later iterations can use previous models.
Moreover, it will be much better to make restart_latest accept a checkpoint path instead of using the checkpoint in the training directory. However, this implementation will make a big change thus it may be implemented in the future if possible.
Hi MACE team,
I have added an argument
init_latest. It is similar torestart_latestbut it only loads the model state_dict from the checkpoint. Thus, the training can start with a previous model but use a fresh new optimizer and lr_scheduler. In practice, it will also be useful in active learning as the later iterations can use previous models.Moreover, it will be much better to make
restart_latestaccept a checkpoint path instead of using the checkpoint in the training directory. However, this implementation will make a big change thus it may be implemented in the future if possible.All the best, Jiayan