60f86864a6da5a746f9ae51f73ccfdeb29e6b35f,utils.py,Trainer,save,#Trainer#Any#,59

Before Change

        model_folder = "checkpoints/"
        model_out_path = model_folder + "model_epoch_{}.pth".format(epoch)
        state = {"epoch": epoch, "weight": self.model.state_dict()}
        if not os.path.exists(model_folder):
            os.makedirs(model_folder)
        torch.save(state, model_out_path)


// copied from pytorch"s master

After Change

                self.save(ep)

    def save(self, epoch, **kwargs):
        if self.save_dir is not None:
            model_out_path = Path(self.save_dir)
            state = {"epoch": epoch, "weight": self.model.state_dict()}
            if not model_out_path.exists():
                model_out_path.exists()
            torch.save(state, model_out_path / "model_epoch_{}.pth".format(epoch))


// copied from pytorch"s master
class _LRScheduler(object):
    def __init__(self, optimizer, last_epoch=-1):
        if not isinstance(optimizer, Optimizer):
            raise TypeError("{} is not an Optimizer".format(

Instances