text: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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.
  
  from __future__ import absolute_import
  import os.path as osp
  import random
  import copy
  import paddlex.utils.logging as logging
  from paddlex.utils import path_normalization
  from .dataset import Dataset
  from .dataset import get_encoding
  from .dataset import is_pic
  
  
  class SegDataset(Dataset):
      """读取语义分割任务数据集，并对样本进行相应的处理。
  
      Args:
          data_dir (str): 数据集所在的目录路径。
          file_list (str): 描述数据集图片文件和对应标注文件的文件路径（文本内每行路径为相对data_dir的相对路）。
          label_list (str): 描述数据集包含的类别信息文件路径。默认值为None。
          transforms (list): 数据集中每个样本的预处理/增强算子。
          num_workers (int): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。
          buffer_size (int): 数据集中样本在预处理过程中队列的缓存长度，以样本数为单位。默认为100。
          parallel_method (str): 数据集中样本在预处理过程中并行处理的方式，支持'thread'
              线程和'process'进程两种方式。默认为'process'（Windows和Mac下会强制使用thread，该参数无效）。
          shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
      """
  
      def __init__(self,
                   data_dir,
                   file_list,
                   label_list=None,
                   transforms=None,
                   num_workers='auto',
                   buffer_size=100,
                   parallel_method='process',
                   shuffle=False):
          super(SegDataset, self).__init__(
              transforms=transforms,
              num_workers=num_workers,
              buffer_size=buffer_size,
              parallel_method=parallel_method,
              shuffle=shuffle)
          self.file_list = list()
          self.labels = list()
          self._epoch = 0
  
          if label_list is not None:
              with open(label_list, encoding=get_encoding(label_list)) as f:
                  for line in f:
                      item = line.strip()
                      self.labels.append(item)
          with open(file_list, encoding=get_encoding(file_list)) as f:
              for line in f:
                  items = line.strip().split()
                  items[0] = path_normalization(items[0])
                  items[1] = path_normalization(items[1])
                  if not is_pic(items[0]):
                      continue
                  full_path_im = osp.join(data_dir, items[0])
                  full_path_label = osp.join(data_dir, items[1])
                  if not osp.exists(full_path_im):
                      raise IOError('The image file {} is not exist!'.format(
                          full_path_im))
                  if not osp.exists(full_path_label):
                      raise IOError('The image file {} is not exist!'.format(
                          full_path_label))
                  self.file_list.append([full_path_im, full_path_label])
          self.num_samples = len(self.file_list)
          logging.info("{} samples in file {}".format(
              len(self.file_list), file_list))
  
      def iterator(self):
          self._epoch += 1
          self._pos = 0
          files = copy.deepcopy(self.file_list)
          if self.shuffle:
              random.shuffle(files)
          files = files[:self.num_samples]
          self.num_samples = len(files)
          for f in files:
              label_path = f[1]
              sample = [f[0], None, label_path]
              yield sample
  