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.
  # ==============================================================================
  
  import numpy as np
  import matplotlib.pyplot as plt
  from mindspore import log as logger
  import mindspore.dataset.engine as de
  import mindspore.dataset.transforms.vision.py_transforms as F
  
  DATA_DIR = "../data/dataset/testImageNetData/train/"
  
  def visualize(image_original, image_ua):
      """
      visualizes the image using DE op and Numpy op
      """
      num = len(image_ua)
      for i in range(num):
          plt.subplot(2, num, i + 1)
          plt.imshow(image_original[i])
          plt.title("Original image")
  
          plt.subplot(2, num, i + num + 1)
          plt.imshow(image_ua[i])
          plt.title("DE UniformAugment image")
  
      plt.show()
      
  
  def test_uniform_augment(plot=False, num_ops=2):
      """
      Test UniformAugment
      """
      logger.info("Test UniformAugment")
      
      # Original Images
      ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
      
      transforms_original = F.ComposeOp([F.Decode(),
                                         F.Resize((224,224)),
                                         F.ToTensor()])    
      
      ds_original = ds.map(input_columns="image",
                           operations=transforms_original())
      
      ds_original = ds_original.batch(512)
              
      for idx, (image,label) in enumerate(ds_original):
          if idx == 0:
              images_original = np.transpose(image, (0, 2,3,1))
          else:
              images_original = np.append(images_original,
                                          np.transpose(image, (0, 2,3,1)),
                                          axis=0)    
  
      # UniformAugment Images
      ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
      
      transform_list = [F.RandomRotation(45),
                        F.RandomColor(),
                        F.RandomSharpness(),
                        F.Invert(),
                        F.AutoContrast(),
                        F.Equalize()]
      
      transforms_ua = F.ComposeOp([F.Decode(),
                                   F.Resize((224,224)),
                                   F.UniformAugment(transforms=transform_list, num_ops=num_ops),
                                   F.ToTensor()])    
      
      ds_ua = ds.map(input_columns="image",
                     operations=transforms_ua())
      
      ds_ua = ds_ua.batch(512)    
        
      for idx, (image,label) in enumerate(ds_ua):
          if idx == 0:
              images_ua = np.transpose(image, (0, 2,3,1))
          else:
              images_ua = np.append(images_ua,
                                    np.transpose(image, (0, 2,3,1)),
                                    axis=0)
      
      num_samples = images_original.shape[0]
      mse = np.zeros(num_samples)
      for i in range(num_samples):
          mse[i] = np.mean((images_ua[i]-images_original[i])**2)
      logger.info("MSE= {}".format(str(np.mean(mse))))
      
      if plot:
          visualize(images_original, images_ua)
          
  
  if __name__ == "__main__":
      test_uniform_augment(num_ops=1)
      
  