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 CutOut op in DE
  """
  import numpy as np
  
  import mindspore.dataset as ds
  import mindspore.dataset.transforms.py_transforms
  import mindspore.dataset.vision.c_transforms as c
  import mindspore.dataset.vision.py_transforms as f
  from mindspore import log as logger
  from util import visualize_image, visualize_list, diff_mse, save_and_check_md5, \
      config_get_set_seed, config_get_set_num_parallel_workers
  
  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"
  
  GENERATE_GOLDEN = False
  
  
  def test_cut_out_op(plot=False):
      """
      Test Cutout
      """
      logger.info("test_cut_out")
  
      # First dataset
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
  
      transforms_1 = [
          f.Decode(),
          f.ToTensor(),
          f.RandomErasing(value='random')
      ]
      transform_1 = mindspore.dataset.transforms.py_transforms.Compose(transforms_1)
      data1 = data1.map(operations=transform_1, input_columns=["image"])
  
      # Second dataset
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      decode_op = c.Decode()
      cut_out_op = c.CutOut(80)
  
      transforms_2 = [
          decode_op,
          cut_out_op
      ]
  
      data2 = data2.map(operations=transforms_2, input_columns=["image"])
  
      num_iter = 0
      for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
                              data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
          num_iter += 1
          image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
          # C image doesn't require transpose
          image_2 = item2["image"]
  
          logger.info("shape of image_1: {}".format(image_1.shape))
          logger.info("shape of image_2: {}".format(image_2.shape))
  
          logger.info("dtype of image_1: {}".format(image_1.dtype))
          logger.info("dtype of image_2: {}".format(image_2.dtype))
  
          mse = diff_mse(image_1, image_2)
          if plot:
              visualize_image(image_1, image_2, mse)
  
  
  def test_cut_out_op_multicut(plot=False):
      """
      Test Cutout
      """
      logger.info("test_cut_out")
  
      # First dataset
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
  
      transforms_1 = [
          f.Decode(),
          f.ToTensor(),
      ]
      transform_1 = mindspore.dataset.transforms.py_transforms.Compose(transforms_1)
      data1 = data1.map(operations=transform_1, input_columns=["image"])
  
      # Second dataset
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      decode_op = c.Decode()
      cut_out_op = c.CutOut(80, num_patches=10)
  
      transforms_2 = [
          decode_op,
          cut_out_op
      ]
  
      data2 = data2.map(operations=transforms_2, input_columns=["image"])
  
      num_iter = 0
      image_list_1, image_list_2 = [], []
      for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
                              data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
          num_iter += 1
          image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
          # C image doesn't require transpose
          image_2 = item2["image"]
          image_list_1.append(image_1)
          image_list_2.append(image_2)
  
          logger.info("shape of image_1: {}".format(image_1.shape))
          logger.info("shape of image_2: {}".format(image_2.shape))
  
          logger.info("dtype of image_1: {}".format(image_1.dtype))
          logger.info("dtype of image_2: {}".format(image_2.dtype))
      if plot:
          visualize_list(image_list_1, image_list_2)
  
  
  def test_cut_out_md5():
      """
      Test Cutout with md5 check
      """
      logger.info("test_cut_out_md5")
      original_seed = config_get_set_seed(2)
      original_num_parallel_workers = config_get_set_num_parallel_workers(1)
  
      # First dataset
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      decode_op = c.Decode()
      cut_out_op = c.CutOut(100)
      data1 = data1.map(operations=decode_op, input_columns=["image"])
      data1 = data1.map(operations=cut_out_op, input_columns=["image"])
  
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      transforms = [
          f.Decode(),
          f.ToTensor(),
          f.Cutout(100)
      ]
      transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
      data2 = data2.map(operations=transform, input_columns=["image"])
  
      # Compare with expected md5 from images
      filename1 = "cut_out_01_c_result.npz"
      save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
      filename2 = "cut_out_01_py_result.npz"
      save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN)
  
      # Restore config
      ds.config.set_seed(original_seed)
      ds.config.set_num_parallel_workers(original_num_parallel_workers)
  
  
  def test_cut_out_comp(plot=False):
      """
      Test Cutout with c++ and python op comparison
      """
      logger.info("test_cut_out_comp")
  
      # First dataset
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
  
      transforms_1 = [
          f.Decode(),
          f.ToTensor(),
          f.Cutout(200)
      ]
      transform_1 = mindspore.dataset.transforms.py_transforms.Compose(transforms_1)
      data1 = data1.map(operations=transform_1, input_columns=["image"])
  
      # Second dataset
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
  
      transforms_2 = [
          c.Decode(),
          c.CutOut(200)
      ]
  
      data2 = data2.map(operations=transforms_2, input_columns=["image"])
  
      num_iter = 0
      image_list_1, image_list_2 = [], []
      for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
                              data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
          num_iter += 1
          image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
          # C image doesn't require transpose
          image_2 = item2["image"]
          image_list_1.append(image_1)
          image_list_2.append(image_2)
  
          logger.info("shape of image_1: {}".format(image_1.shape))
          logger.info("shape of image_2: {}".format(image_2.shape))
  
          logger.info("dtype of image_1: {}".format(image_1.dtype))
          logger.info("dtype of image_2: {}".format(image_2.dtype))
      if plot:
          visualize_list(image_list_1, image_list_2, visualize_mode=2)
  
  
  if __name__ == "__main__":
      test_cut_out_op(plot=True)
      test_cut_out_op_multicut(plot=True)
      test_cut_out_md5()
      test_cut_out_comp(plot=True)
  