Resume training from a finished-training model will results in a new incorrect checkpoint
To verify a model if a model has finished training. I ran the training script again.
However, when I try to evaluate with the model, I got error that:
pytorch_lightning.utilities.exceptions.MisconfigurationException:
            you restored a checkpoint with current_epoch=11
            but the Trainer(max_epochs=10)

After digging a little bit, it seems that since the model has finished training, it will directly go to on_train_end, and save the model checkpoint with currect_epoch+1.
I am wondering if the design of saving checkpoints with "next epoch" is reasonable? Why not add the epoch number after resuming?