[个人笔记]torch.optim.lr_scheduler.ReduceLROnPlateau

最近需要使用torch.optim.lr_scheduler.ReduceLROnPlateau,但是没有看过相关论文,网上有很多相关的资料在threshold环节都很模糊.
我对这个API主要有两个问题:

1. 应用ReduceLROnPlateau, 是如何改变学习率的(或者说,新的学习率是放在optimizer还是lr_scheduler里的)?
2. threshold的"显著"到底指的是什么?

下面贴上torch.optim.lr_scheduler.ReduceLROnPlateau的源代码,想直接看结论可以滑动到文章最底部.

class ReduceLROnPlateau(object):
    """Reduce learning rate when a metric has stopped improving.
    Models often benefit from reducing the learning rate by a factor
    of 2-10 once learning stagnates. This scheduler reads a metrics
    quantity and if no improvement is seen for a 'patience' number
    of epochs, the learning rate is reduced.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        mode (str): One of `min`, `max`. In `min` mode, lr will
            be reduced when the quantity monitored has stopped
            decreasing; in `max` mode it will be reduced when the
            quantity monitored has stopped increasing. Default: 'min'.
        factor (float): Factor by which the learning rate will be
            reduced. new_lr = lr * factor. Default: 0.1.
        patience (int): Number of epochs with no improvement after
            which learning rate will be reduced. For example, if
            `patience = 2`, then we will ignore the first 2 epochs
            with no improvement, and will only decrease the LR after the
            3rd epoch if the loss still hasn't improved then.
            Default: 10.
        threshold (float): Threshold for measuring the new optimum,
            to only focus on significant changes. Default: 1e-4.
        threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
            dynamic_threshold = best * ( 1 + threshold ) in 'max'
            mode or best * ( 1 - threshold ) in `min` mode.
            In `abs` mode, dynamic_threshold = best + threshold in
            `max` mode or best - threshold in `min` mode. Default: 'rel'.
        cooldown (int): Number of epochs to wait before resuming
            normal operation after lr has been reduced. Default: 0.
        min_lr (float or list): A scalar or a list of scalars. A
            lower bound on the learning rate of all param groups
            or each group respectively. Default: 0.
        eps (float): Minimal decay applied to lr. If the difference
            between new and old lr is smaller than eps, the update is
            ignored. Default: 1e-8.
        verbose (bool): If ``True``, prints a message to stdout for
            each update. Default: ``False``.

    Example:
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
        >>> scheduler = ReduceLROnPlateau(optimizer, 'min')
        >>> for epoch in range(10):
        >>>     train(...)
        >>>     val_loss = validate(...)
        >>>     # Note that step should be called after validate()
        >>>     scheduler.step(val_loss)
    """

    def __init__(self, optimizer, mode='min', factor=0.1, patience=10,
                 threshold=1e-4, threshold_mode='rel', cooldown=0,
                 min_lr=0, eps=1e-8, verbose=False):

        if factor >= 1.0:
            raise ValueError('Factor should be < 1.0.')
        self.factor = factor

        # Attach optimizer
        if not isinstance(optimizer, Optimizer):
            raise TypeError('{} is no