text: # Copyright 2021 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  """learning rate generator"""
  import math
  import numpy as np
  
  def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
      """
      generate learning rate array
  
      Args:
         global_step(int): total steps of the training
         lr_init(float): init learning rate
         lr_end(float): end learning rate
         lr_max(float): max learning rate
         warmup_epochs(int): number of warmup epochs
         total_epochs(int): total epoch of training
         steps_per_epoch(int): steps of one epoch
  
      Returns:
         np.array, learning rate array
      """
      lr_each_step = []
      total_steps = steps_per_epoch * total_epochs
      warmup_steps = steps_per_epoch * warmup_epochs
      for i in range(total_steps):
          if i < warmup_steps:
              lr = lr_init + (lr_max - lr_init) * i / warmup_steps
          else:
              lr = lr_end + \
                  (lr_max - lr_end) * \
                  (1. + math.cos(math.pi * (i - warmup_steps) /
                                 (total_steps - warmup_steps))) / 2.
          if lr < 0.0:
              lr = 0.0
          lr_each_step.append(lr)
      lr_each_step = np.array(lr_each_step).astype(np.float32)
  
      return lr_each_step
  