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.
  # ============================================================================
  """
  optimizer parameters.
  """
  
  
  def get_param_groups(network):
      """get param groups"""
      decay_params = []
      no_decay_params = []
      for x in network.trainable_params():
          parameter_name = x.name
          if parameter_name.endswith(".bias"):
              # all bias not using weight decay
              no_decay_params.append(x)
          elif parameter_name.endswith(".gamma"):
              # bn weight bias not using weight decay, be carefully for now x not include BN
              no_decay_params.append(x)
          elif parameter_name.endswith(".beta"):
              # bn weight bias not using weight decay, be carefully for now x not include BN
              no_decay_params.append(x)
          else:
              decay_params.append(x)
  
      return [{"params": no_decay_params, "weight_decay": 0.0}, {"params": decay_params}]
  
  
  def get_finetune_param_groups(network, config):
      """get param groups"""
      decay_params = []
      no_decay_params = []
  
      if config.model_name == "resnet50":
          for x in network.trainable_params():
              parameter_name = x.name
              if parameter_name.endswith(".bias"):
                  # all bias not using weight decay
                  no_decay_params.append(x)
              elif parameter_name.endswith(".gamma"):
                  # bn weight bias not using weight decay, be carefully for now x not include BN
                  no_decay_params.append(x)
              elif parameter_name.endswith(".beta"):
                  # bn weight bias not using weight decay, be carefully for now x not include BN
                  no_decay_params.append(x)
              elif parameter_name.startswith("end_point"):
                  decay_params.append(x)
              else:
                  no_decay_params.append(x)
      elif config.model_name == "se_resnext50":
          for x in network.trainable_params():
              parameter_name = x.name
              print(parameter_name)
              if parameter_name.endswith(".bias"):
                  # all bias not using weight decay
                  no_decay_params.append(x)
              elif parameter_name.endswith(".gamma"):
                  # bn weight bias not using weight decay, be carefully for now x not include BN
                  no_decay_params.append(x)
              elif parameter_name.endswith(".beta"):
                  # bn weight bias not using weight decay, be carefully for now x not include BN
                  no_decay_params.append(x)
              elif parameter_name.startswith("fc"):
                  decay_params.append(x)
              else:
                  no_decay_params.append(x)
  
      return [{"params": no_decay_params, "weight_decay": 0.0}, {"params": decay_params}]
  