text: # Copyright 2020 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.
  # ============================================================================
  '''
  ##############train models#################
  python train.py
  '''
  import os
  from mindspore import context, nn
  from mindspore.train import Model
  from mindspore.common import set_seed
  from mindspore.train.loss_scale_manager import FixedLossScaleManager
  from mindspore.train.serialization import load_checkpoint, load_param_into_net
  from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
  
  from src.model_utils.device_adapter import get_device_id
  from src.model_utils.config import config
  from src.model_utils.moxing_adapter import moxing_wrapper
  from src.dataset import create_dataset
  from src.musictagger import MusicTaggerCNN
  from src.loss import BCELoss
  
  
  def modelarts_pre_process():
      pass
  
  
  @moxing_wrapper(pre_process=modelarts_pre_process)
  def train(model, dataset_direct, filename, columns_list, num_consumer=4,
            batch=16, epoch=50, save_checkpoint_steps=2172, keep_checkpoint_max=50,
            prefix="model", directory='./'):
      """
      train network
      """
      config_ck = CheckpointConfig(save_checkpoint_steps=save_checkpoint_steps,
                                   keep_checkpoint_max=keep_checkpoint_max)
      ckpoint_cb = ModelCheckpoint(prefix=prefix,
                                   directory=directory,
                                   config=config_ck)
      data_train = create_dataset(dataset_direct, filename, batch, columns_list,
                                  num_consumer)
  
  
      model.train(epoch, data_train, callbacks=[ckpoint_cb, \
          LossMonitor(per_print_times=181), TimeMonitor()], dataset_sink_mode=True)
  
  
  if __name__ == "__main__":
      set_seed(1)
  
      config.checkpoint_path = os.path.abspath(config.checkpoint_path)
      context.set_context(device_target=config.device_target, mode=context.GRAPH_MODE)
      if config.device_target == 'Ascend':
          context.set_context(device_id=get_device_id())
  
      network = MusicTaggerCNN(in_classes=[1, 128, 384, 768, 2048],
                               kernel_size=[3, 3, 3, 3, 3],
                               padding=[0] * 5,
                               maxpool=[(2, 4), (4, 5), (3, 8), (4, 8)],
                               has_bias=True)
  
      if config.pre_trained:
          param_dict = load_checkpoint(config.checkpoint_path + '/' +
                                       config.model_name)
          load_param_into_net(network, param_dict)
  
      net_loss = BCELoss()
  
      network.set_train(True)
      net_opt = nn.Adam(params=network.trainable_params(),
                        learning_rate=config.lr,
                        loss_scale=config.loss_scale)
  
      loss_scale_manager = FixedLossScaleManager(loss_scale=config.loss_scale,
                                                 drop_overflow_update=False)
      net_model = Model(network, net_loss, net_opt, loss_scale_manager=loss_scale_manager)
  
      train(model=net_model,
            dataset_direct=config.data_dir,
            filename=config.train_filename,
            columns_list=['feature', 'label'],
            num_consumer=config.num_consumer,
            batch=config.batch_size,
            epoch=config.epoch_size,
            save_checkpoint_steps=config.save_step,
            keep_checkpoint_max=config.keep_checkpoint_max,
            prefix=config.prefix,
            directory=config.checkpoint_path) #  + "_{}".format(get_device_id())
      print("train success")
  