text: # Copyright 2019 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.
  # ==============================================================================
  """
  Testing the one_hot op in DE
  """
  import mindspore.dataset.transforms.vision.c_transforms as vision
  import mindspore.dataset.transforms.c_transforms as data_trans
  import numpy as np
  
  import mindspore.dataset as ds
  from mindspore import log as logger
  
  DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
  SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
  
  
  def one_hot(index, depth):
      """
      Apply the one_hot
      """
      arr = np.zeros([1, depth], dtype=np.int32)
      arr[0, index] = 1
      return arr
  
  
  def test_one_hot():
      """
      Test one_hot
      """
      logger.info("Test one_hot")
  
      depth = 10
  
      # First dataset
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
      one_hot_op = data_trans.OneHot(depth)
      data1 = data1.map(input_columns=["label"], operations=one_hot_op, columns_order=["label"])
  
      # Second dataset
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["label"], shuffle=False)
  
      num_iter = 0
      for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
          assert len(item1) == len(item2)
          label1 = item1["label"]
          label2 = one_hot(item2["label"][0], depth)
          mse = np.sum(label1 - label2)
          logger.info("DE one_hot: {}, Numpy one_hot: {}, diff: {}".format(label1, label2, mse))
          num_iter += 1
  
  
  if __name__ == "__main__":
      test_one_hot()
  