de75ff9237213fba6478fdbbaaff8e97749eab62,utils.py,Trainer,save,#Trainer#,59

Before Change

                self.save(ep)

    def save(self, epoch, **kwargs):
        if self.save_dir:
            name = f"weight-{epoch}-" + "-".join([f"{k}_{v}" for k, v in kwargs.items()]) + ".pkl"
            torch.save({"weight": self.model.state_dict()},
                       os.path.join(self.save_dir, name))


// 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(

After Change

    def save(self, epoch, **kwargs):
        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
class _LRScheduler(object):

Instances