就是取前几个epoch的weight的平均值，可以缓解微调时的灾难性遗忘（因为新数据引导，模型权重逐渐，偏离训练时学到的数据分布，忘记之前学好的先验知识）

class EMA():
    def __init__(self, model, decay):
        self.model = model
        self.decay = decay  # decay rate
        self.shadow = {}  # old weight
        self.backup = {}  # new weight
 
    def register(self):  # deep copy weight for init
        for name, param in self.model.named_parameters():
            if param.requires_grad:
                self.shadow[name] = param.data.clone()
 
    def update(self):  # ema：average weight for train
        for name, param in self.model.named_parameters():
            if param.requires_grad:
                assert name in self.shadow
                new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[name]
                self.shadow[name] = new_average.clone()
 
    def apply_shadow(self):  # load old weight for eval begin
        for name, param in self.model.named_parameters():
            if param.requires_grad:
                assert name in self.shadow
                self.backup[name] = param.data
                param.data = self.shadow[name]
 
    def restore(self):  # load new weight for eval end
        for name, param in self.model.named_parameters():
            if param.requires_grad:
                assert name in self.backup
                param.data = self.backup[name]
        self.backup = {}
 
# 初始化
ema = EMA(model, 0.999)
ema.register()
 
# 训练过程中，更新完参数后，同步update shadow weights
def train():
    optimizer.step()
    ema.update()
 
# eval前，apply shadow weights；eval之后，恢复原来模型的参数
def evaluate():
    ema.apply_shadow()
    # evaluate
    ema.restore()