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 Pad op in DE
  """
  import numpy as np
  
  import mindspore.dataset as ds
  import mindspore.dataset.transforms.vision.c_transforms as c_vision
  import mindspore.dataset.transforms.vision.py_transforms as py_vision
  from mindspore import log as logger
  from util import diff_mse, save_and_check_md5
  
  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_pad_op():
      """
      Test Pad op
      """
      logger.info("test_random_color_jitter_op")
  
      # First dataset
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      decode_op = c_vision.Decode()
  
      pad_op = c_vision.Pad((100, 100, 100, 100))
      ctrans = [decode_op,
                pad_op,
                ]
  
      data1 = data1.map(input_columns=["image"], operations=ctrans)
  
      # Second dataset
      transforms = [
          py_vision.Decode(),
          py_vision.Pad(100),
          py_vision.ToTensor(),
      ]
      transform = py_vision.ComposeOp(transforms)
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      data2 = data2.map(input_columns=["image"], operations=transform())
  
      for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
          c_image = item1["image"]
          py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
  
          logger.info("shape of c_image: {}".format(c_image.shape))
          logger.info("shape of py_image: {}".format(py_image.shape))
  
          logger.info("dtype of c_image: {}".format(c_image.dtype))
          logger.info("dtype of py_image: {}".format(py_image.dtype))
  
          mse = diff_mse(c_image, py_image)
          logger.info("mse is {}".format(mse))
          assert mse < 0.01
  
  
  
  def test_pad_grayscale():
      """
      Tests that the pad works for grayscale images
      """
  
      # Note: image.transpose performs channel swap to allow py transforms to
      # work with c transforms
      transforms = [
          py_vision.Decode(),
          py_vision.Grayscale(1),
          py_vision.ToTensor(),
          (lambda image: (image.transpose(1, 2, 0) * 255).astype(np.uint8))
      ]
  
      transform = py_vision.ComposeOp(transforms)
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      data1 = data1.map(input_columns=["image"], operations=transform())
  
      # if input is grayscale, the output dimensions should be single channel
      pad_gray = c_vision.Pad(100, fill_value=(20, 20, 20))
      data1 = data1.map(input_columns=["image"], operations=pad_gray)
      dataset_shape_1 = []
      for item1 in data1.create_dict_iterator():
          c_image = item1["image"]
          dataset_shape_1.append(c_image.shape)
  
      # Dataset for comparison
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      decode_op = c_vision.Decode()
  
      # we use the same padding logic
      ctrans = [decode_op, pad_gray]
      dataset_shape_2 = []
  
      data2 = data2.map(input_columns=["image"], operations=ctrans)
  
      for item2 in data2.create_dict_iterator():
          c_image = item2["image"]
          dataset_shape_2.append(c_image.shape)
  
      for shape1, shape2 in zip(dataset_shape_1, dataset_shape_2):
          # validate that the first two dimensions are the same
          # we have a little inconsistency here because the third dimension is 1 after py_vision.Grayscale
          assert shape1[0:1] == shape2[0:1]
  
  
  def test_pad_md5():
      """
      Test Pad with md5 check
      """
      logger.info("test_pad_md5")
  
      # First dataset
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      decode_op = c_vision.Decode()
      pad_op = c_vision.Pad(150)
      ctrans = [decode_op,
                pad_op,
                ]
  
      data1 = data1.map(input_columns=["image"], operations=ctrans)
  
      # Second dataset
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      pytrans = [
          py_vision.Decode(),
          py_vision.Pad(150),
          py_vision.ToTensor(),
      ]
      transform = py_vision.ComposeOp(pytrans)
      data2 = data2.map(input_columns=["image"], operations=transform())
      # Compare with expected md5 from images
      filename1 = "pad_01_c_result.npz"
      save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
      filename2 = "pad_01_py_result.npz"
      save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN)
  
  def test_pad_exception():
      try:
          data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
          pad_op = c_vision.Pad(150)
          data1 = data1.map(input_columns=["image"], operations=pad_op)
          for _ in data1.create_dict_iterator():
              pass
          assert False
      except RuntimeError as e:
          assert "Pad error: invalid image shape, only support 3 channels image" in str(e)
  
  
  if __name__ == "__main__":
      test_pad_op()
      test_pad_grayscale()
      test_pad_md5()
      test_pad_exception()
  