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.
  # ============================================================================
  """
  dataset processing.
  """
  import os
  from mindspore.common import dtype as mstype
  import mindspore.dataset as de
  import mindspore.dataset.transforms.c_transforms as C
  import mindspore.dataset.transforms.vision.c_transforms as V_C
  from PIL import Image, ImageFile
  from src.utils.sampler import DistributedSampler
  
  ImageFile.LOAD_TRUNCATED_IMAGES = True
  
  class TxtDataset():
      """
      create txt dataset.
  
      Args:
      Returns:
          de_dataset.
      """
      def __init__(self, root, txt_name):
          super(TxtDataset, self).__init__()
          self.imgs = []
          self.labels = []
          fin = open(txt_name, "r")
          for line in fin:
              img_name, label = line.strip().split(' ')
              self.imgs.append(os.path.join(root, img_name))
              self.labels.append(int(label))
          fin.close()
  
      def __getitem__(self, index):
          img = Image.open(self.imgs[index]).convert('RGB')
          return img, self.labels[index]
  
      def __len__(self):
          return len(self.imgs)
  
  
  def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank, group_size,
                             mode='train',
                             input_mode='folder',
                             root='',
                             num_parallel_workers=None,
                             shuffle=None,
                             sampler=None,
                             class_indexing=None,
                             drop_remainder=True,
                             transform=None,
                             target_transform=None):
      """
      A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
      If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
      are written into a textfile.
  
      Args:
          data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
              Or path of the textfile that contains every image's path of the dataset.
          image_size (str): Size of the input images.
          per_batch_size (int): the batch size of evey step during training.
          max_epoch (int): the number of epochs.
          rank (int): The shard ID within num_shards (default=None).
          group_size (int): Number of shards that the dataset should be divided
              into (default=None).
          mode (str): "train" or others. Default: " train".
          input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
          root (str): the images path for "input_mode="txt"". Default: " ".
          num_parallel_workers (int): Number of workers to read the data. Default: None.
          shuffle (bool): Whether or not to perform shuffle on the dataset
              (default=None, performs shuffle).
          sampler (Sampler): Object used to choose samples from the dataset. Default: None.
          class_indexing (dict): A str-to-int mapping from folder name to index
              (default=None, the folder names will be sorted
              alphabetically and each class will be given a
              unique index starting from 0).
  
      Examples:
          >>> from mindvision.common.datasets.classification import classification_dataset
          >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
          >>> dataset_dir = "/path/to/imagefolder_directory"
          >>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
          >>>                               per_batch_size=64, max_epoch=100,
          >>>                               rank=0, group_size=4)
          >>> # Path of the textfile that contains every image's path of the dataset.
          >>> dataset_dir = "/path/to/dataset/images/train.txt"
          >>> images_dir = "/path/to/dataset/images"
          >>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
          >>>                               per_batch_size=64, max_epoch=100,
          >>>                               rank=0, group_size=4,
          >>>                               input_mode="txt", root=images_dir)
      """
  
      mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
      std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
  
      if transform is None:
          if mode == 'train':
              transform_img = [
                  V_C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
                  V_C.RandomHorizontalFlip(prob=0.5),
                  V_C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
                  V_C.Normalize(mean=mean, std=std),
                  V_C.HWC2CHW()
              ]
          else:
              transform_img = [
                  V_C.Decode(),
                  V_C.Resize((256, 256)),
                  V_C.CenterCrop(image_size),
                  V_C.Normalize(mean=mean, std=std),
                  V_C.HWC2CHW()
              ]
      else:
          transform_img = transform
  
      if target_transform is None:
          transform_label = [C.TypeCast(mstype.int32)]
      else:
          transform_label = target_transform
  
      if input_mode == 'folder':
          de_dataset = de.ImageFolderDatasetV2(data_dir, num_parallel_workers=num_parallel_workers,
                                               shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
                                               num_shards=group_size, shard_id=rank)
      else:
          dataset = TxtDataset(root, data_dir)
          sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
          de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
          de_dataset.set_dataset_size(len(sampler))
  
      de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
      de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
  
      columns_to_project = ["image", "label"]
      de_dataset = de_dataset.project(columns=columns_to_project)
  
      de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
      de_dataset = de_dataset.repeat(max_epoch)
  
      return de_dataset
  