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.
  # ============================================================================
  """
  create train or eval dataset.
  """
  import os
  import mindspore.common.dtype as mstype
  import mindspore.dataset.engine as de
  import mindspore.dataset.transforms.vision.c_transforms as C
  import mindspore.dataset.transforms.c_transforms as C2
  
  
  def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=32):
      """
      create a train or eval dataset
  
      Args:
          dataset_path(string): the path of dataset.
          do_train(bool): whether dataset is used for train or eval.
          repeat_num(int): the repeat times of dataset. Default: 1
          batch_size(int): the batch size of dataset. Default: 32
  
      Returns:
          dataset
      """
      if platform == "Ascend":
          rank_size = int(os.getenv("RANK_SIZE"))
          rank_id = int(os.getenv("RANK_ID"))
          if rank_size == 1:
              ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
          else:
              ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
                                           num_shards=rank_size, shard_id=rank_id)
      elif platform == "GPU":
          if do_train:
              from mindspore.communication.management import get_rank, get_group_size
              ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
                                           num_shards=get_group_size(), shard_id=get_rank())
          else:
              ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
      else:
          raise ValueError("Unsupport platform.")
  
      resize_height = config.image_height
      resize_width = config.image_width
      buffer_size = 1000
  
      # define map operations
      decode_op = C.Decode()
      resize_crop_op = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
      horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)
  
      resize_op = C.Resize(256)
      center_crop = C.CenterCrop(resize_width)
      rescale_op = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
      normalize_op = C.Normalize(mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
      change_swap_op = C.HWC2CHW()
  
      if do_train:
          trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
      else:
          trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]
  
      type_cast_op = C2.TypeCast(mstype.int32)
  
      ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
      ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
  
      # apply shuffle operations
      ds = ds.shuffle(buffer_size=buffer_size)
  
      # apply batch operations
      ds = ds.batch(batch_size, drop_remainder=True)
  
      # apply dataset repeat operation
      ds = ds.repeat(repeat_num)
  
      return ds
  