Python中使用object_detection.utils.learning_schedulesmanual_stepping()函数手动调整学习率的方法

object_detection.utils.learning_schedules.manual_stepping()函数是TensorFlow Object Detection API提供的一个用于手动调整学习率的函数。该函数可以在训练过程中根据步数来调整学习率，可以根据自己的需求来设置学习率的变化策略。

该函数的定义如下：

def manual_stepping(global_step, boundaries, rates, warmup=False):
    """Manually stepped learning rate schedule.
  
    This function provides fine grained control over learning rates.  One must
    specify a sequence of learning rates as well as a set of integer steps
    at which the current learning rate must transition to the next.  For
    example, if boundaries = [5, 10] and rates = [.1, .01, .001], then the
    learning rate returned by this function is .1 for global_step=0,...,4,
    .01 for global_step=5...9, and .001 for global_step=10 and onward.
  
    Args:
      global_step: int64 (scalar) tensor representing global step.
      boundaries: a list of global steps at which to switch learning
        rates.  A value less than or equal to global_step will trigger
        the corresponding learning rate.
      rates: a list of learning rates corresponding to intervals between
        the boundaries.  The length of this list must be exactly
        len(boundaries) + 1.
      warmup: whether to linearly interpolate learning rate for steps in
        [0, boundaries[0]].
  
    Returns:
      a scalar float tensor representing learning rate
  
    Raises:
      ValueError: if one of the following checks fails:
        1. boundaries is a strictly increasing list.
        2. len(rates) == len(boundaries) + 1
        3. rates[x] <= rates[x+1], for all x in [0, len(rates) - 2]
    """
    if any([b < 0 for b in boundaries]) or any(
        [a >= b for a, b in zip(boundaries[:-1], boundaries[1:])]):
        raise ValueError('boundaries must be a increasing list of positive integers')
    if any([a >= b for a, b in zip(rates[:-1], rates[1:])]):
        raise ValueError('Learning rates must be in non-decreasing order')
    if len(rates) != len(boundaries) + 1:
        raise ValueError('Number of provided learning rates must exceed '
                         'number of boundary points by exactly 1.')

使用方法：

1. 首先需要准备一个表示全局步数的int64的标量张量global_step。

2. 然后需要指定一个列表boundaries，这个列表中包含了学习率变化的步数点，必须是递增的正整数。例如，如果指定boundaries = [5, 10]，表示在第5步和第10步时学习率会发生变化。

3. 接着需要指定一个列表rates，这个列表中包含了学习率在不同阶段的取值。列表的长度必须为len(boundaries) + 1。例如，如果指定rates = [.1, .01, .001]，表示在第1阶段学习率为0.1，在第2阶段学习率为0.01，在第3阶段及以后学习率为0.001。

4. 最后，可以选择是否开启预热（warmup）设置，如果开启预热设置，则在[0, boundaries[0]]的步数范围内，学习率会进行线性插值。

下面是一个示例代码，演示了如何使用object_detection.utils.learning_schedules.manual_stepping()函数来手动调整学习率：

import tensorflow as tf
from object_detection.utils import learning_schedules

global_step = tf.Variable(0, trainable=False)

boundaries = [4, 8, 12]
rates = [0.1, 0.01, 0.001, 0.0001]

learning_rate = learning_schedules.manual_stepping(global_step, boundaries, rates)

optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)

在上述代码中，我们创建了一个表示全局步数的global_step标量张量，并将其设置为不可训练。然后，我们定义了学习率的变化策略：在第4、8和12步时学习率会发生变化，分别是从0.1到0.01，从0.01到0.001，从0.001到0.0001。最后，我们使用定义好的学习率来创建一个优化器，并使用这个优化器来最小化损失函数。

这样，在训练过程中，每当全局步数达到了指定的边界值时，学习率会自动变化，从而实现了手动调整学习率的功能。