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.
  # ============================================================================
  """postprocess."""
  import os
  import numpy as np
  from mindspore import Tensor
  from src.edcn import AUCMetric
  from src.model_utils.config import config, train_config
  
  
  def get_acc():
      ''' get accuracy '''
      auc_metric = AUCMetric()
      files = os.listdir(config.label_path)
      batch_size = train_config.batch_size
  
      for f in files:
          rst_file = os.path.join(config.result_path, f.split('.')[0] + '_0.bin')
          label_file = os.path.join(config.label_path, f)
  
          logit = Tensor(np.fromfile(rst_file, np.float32).reshape(batch_size, 1))
          label = Tensor(np.fromfile(label_file, np.float32).reshape(batch_size, 1))
  
          res = []
          res.append(logit)
          res.append(logit)
          res.append(label)
  
          auc_metric.update(*res)
      auc = auc_metric.eval()
      print("auc : {}".format(auc))
  
  if __name__ == '__main__':
      get_acc()
  