text: # Copyright (c) 2019 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 . import core, dygraph
  import sys
  import six
  import warnings
  import numpy as np
  import threading
  import paddle
  from .framework import Program, Variable, program_guard, default_main_program, default_startup_program, in_dygraph_mode, cpu_places
  from .executor import global_scope
  from .data_feeder import DataFeeder, BatchedTensorProvider
  from .layers.io import monkey_patch_reader_methods, _copy_reader_var_, double_buffer
  from .unique_name import UniqueNameGenerator
  import logging
  from .dataset import DatasetBase, InMemoryDataset
  
  __all__ = ['PyReader', 'DataLoader']
  
  data_loader_unique_name_generator = UniqueNameGenerator()
  
  
  def _convert_places(places):
      if not isinstance(places, (list, tuple)):
          places = [places]
  
      ret = []
      for p in places:
          if not isinstance(p, core.Place):
              tmp = core.Place()
              tmp.set_place(p)
              p = tmp
  
          ret.append(p)
      return ret
  
  
  class DataLoaderBase(object):
      def __init__(self):
          self._places = None
  
      def __call__(self):
          return self
  
      def next(self):
          '''
          Get the next item in the DataLoader object. This method    
          should not be called by users directly. It is used for
          implementing iterator protocol of Python 2.x inside
          PaddlePaddle framework.
          '''
          return self.__next__()
  
      def __iter__(self):
          raise NotImplementedError()
  
      def __next__(self):
          raise NotImplementedError()
  
  
  class DataLoader(object):
      @staticmethod
      def from_generator(feed_list=None,
                         capacity=None,
                         use_double_buffer=True,
                         iterable=True,
                         return_list=False):
          """
          Create a DataLoader object for loading data from Python generator. 
          Data would be prefetched using Python thread and be pushed
          into a queue asynchronously.
  
          The created DataLoader object provides 3 methods to set the data source
          :code:`set_sample_generator` , :code:`set_sample_list_generator` and 
          :code:`set_batch_generator` . Please see the following example codes
          to know their usages.
  
          If iterable = True, the created DataLoader object is a Python generator
          object, which is iterable using for-range loop.
  
          If iterable = False, the created DataLoader object provides 
          :code:`start()` and :code:`reset()` method to control the data reading
          process. This mode is designed to be compatible with the 
          :code:`fluid.layers.py_reader` interface. Users can migrate the codes   
          from :code:`fluid.layers.py_reader` to :code:`fluid.io.DataLoader` 
          easily when using iterable=False. 
  
          Args:  
              feed_list (list(Variable)|tuple(Variable)): feed variable list.
                  The variables should be created by :code:`fluid.data()`.
              capacity (int): capacity of the queue maintained in DataLoader.
                  The unit is batch number. Set larger capacity if your reader 
                  is fast. 
              use_double_buffer (bool): whether to use double_buffer_reader. 
                  If use_double_buffer=True, the DataLoader would prefetch next 
                  batch data asynchronously, so it would speed up data feeding 
                  and occupies a little more CPU or GPU memory, i.e., the memory
                  of one batch input data. 
              iterable (bool): whether the created DataLoader is iterable. 
              return_list (bool): whether the return value on each device is 
                  presented as a list. It is only valid when iterable=True. 
                  If return_list=False, the return value on each device would 
                  be a dict of str -> LoDTensor, where the key of the dict is 
                  the name of each feeded variables. If return_list=True, the 
                  return value on each device would be a list(LoDTensor). It is
                  recommended to use return_list=False in static graph mode and
                  use return_list=True in dygraph mode.   
  
          Returns:
              loader (DataLoader): the created DataLoader object.
  
          Examples:
              
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  import numpy as np
  
                  BATCH_NUM = 10 
                  BATCH_SIZE = 16
                  EPOCH_NUM = 4
  
                  CLASS_NUM = 10
  
                  ITERABLE = True # whether the created DataLoader object is iterable
                  USE_GPU = False # whether to use GPU
  
                  DATA_FORMAT = 'batch_generator' # data format of data source user provides 
  
                  def simple_net(image, label):
                      fc_tmp = fluid.layers.fc(image, size=CLASS_NUM)
                      cross_entropy = fluid.layers.softmax_with_cross_entropy(image, label)
                      loss = fluid.layers.reduce_mean(cross_entropy)
                      sgd = fluid.optimizer.SGD(learning_rate=1e-3)
                      sgd.minimize(loss)
                      return loss
  
                  def get_random_images_and_labels(image_shape, label_shape):
                      image = np.random.random(size=image_shape).astype('float32')
                      label = np.random.random(size=label_shape).astype('int64')
                      return image, label
  
                  # If the data generator yields one sample each time,
                  # use DataLoader.set_sample_generator to set the data source.
                  def sample_generator_creator(): 
                      def __reader__():
                          for _ in range(BATCH_NUM * BATCH_SIZE):
                              image, label = get_random_images_and_labels([784], [1])
                              yield image, label
  
                      return __reader__
  
                  # If the data generator yield list of samples each time,
                  # use DataLoader.set_sample_list_generator to set the data source.
                  def sample_list_generator_creator():
                      def __reader__():
                          for _ in range(BATCH_NUM): 
                              sample_list = []
                              for _ in range(BATCH_SIZE):
                                  image, label = get_random_images_and_labels([784], [1])
                                  sample_list.append([image, label])
  
                              yield sample_list
  
                      return __reader__ 
  
                  # If the data generator yields a batch each time, 
                  # use DataLoader.set_batch_generator to set the data source.
                  def batch_generator_creator():
                      def __reader__():
                          for _ in range(BATCH_NUM):
                              batch_image, batch_label = get_random_images_and_labels([BATCH_SIZE, 784], [BATCH_SIZE, 1]) 
                              yield batch_image, batch_label
  
                      return __reader__
  
                  # If DataLoader is iterable, use for loop to train the network 
                  def train_iterable(exe, prog, loss, loader):
                      for _ in range(EPOCH_NUM):
                          for data in loader():
                              exe.run(prog, feed=data, fetch_list=[loss])
  
                  # If DataLoader is not iterable, use start() and reset() method to control the process 
                  def train_non_iterable(exe, prog, loss, loader):
                      for _ in range(EPOCH_NUM):
                          loader.start() # call DataLoader.start() before each epoch starts
                          try:
                              while True:
                                  exe.run(prog, fetch_list=[loss])
                          except fluid.core.EOFException:
                              loader.reset() # call DataLoader.reset() after catching EOFException 
  
                  def set_data_source(loader, places):
                      if DATA_FORMAT == 'sample_generator':
                          loader.set_sample_generator(sample_generator_creator(), batch_size=BATCH_SIZE, drop_last=True, places=places)
                      elif DATA_FORMAT == 'sample_list_generator':
                          loader.set_sample_list_generator(sample_list_generator_creator(), places=places)
                      elif DATA_FORMAT == 'batch_generator':
                          loader.set_batch_generator(batch_generator_creator(), places=places)
                      else:
                          raise ValueError('Unsupported data format')
  
                  image = fluid.data(name='image', shape=[None, 784], dtype='float32')
                  label = fluid.data(name='label', shape=[None, 1], dtype='int64')
  
                  # Define DataLoader 
                  loader = fluid.io.DataLoader.from_generator(feed_list=[image, label], capacity=16, iterable=ITERABLE)
  
                  # Define network
                  loss = simple_net(image, label)
  
                  # Set data source of DataLoader
                  #
                  # If DataLoader is iterable, places must be given and the number of places must be the same with device number.  
                  #  - If you are using GPU, call `fluid.cuda_places()` to get all GPU places. 
                  #  - If you are using CPU, call `fluid.cpu_places()` to get all CPU places. 
                  # 
                  # If DataLoader is not iterable, places can be None.
                  places = fluid.cuda_places() if USE_GPU else fluid.cpu_places()
                  set_data_source(loader, places)
  
                  exe = fluid.Executor(places[0])
                  exe.run(fluid.default_startup_program())
  
                  prog = fluid.CompiledProgram(fluid.default_main_program()).with_data_parallel(loss_name=loss.name)
  
                  if loader.iterable:
                      train_iterable(exe, prog, loss, loader)
                  else:
                      train_non_iterable(exe, prog, loss, loader)
  
  
                  '''
                  Users can use return_list = True in dygraph mode. 
                  '''
                  with fluid.dygraph.guard(places[0]):
                      loader = fluid.io.DataLoader.from_generator(capacity=2, return_list=True)
                      set_data_source(loader, places[0]) 
                      for image, label in loader():
                          relu = fluid.layers.relu(image)
                          assert image.shape == [BATCH_SIZE, 784] 
                          assert label.shape == [BATCH_SIZE, 1]
                          assert relu.shape == [BATCH_SIZE, 784]
          """
          return GeneratorLoader(feed_list, capacity, use_double_buffer, iterable,
                                 return_list)
  
      @staticmethod
      def from_dataset(dataset, places, drop_last=True):
          """
          Create an iterable DataLoader object for loading data from Dataset.    
          Dataset is only supported in Linux system currently.
  
          Args:
              dataset (InMemoryDataset|QueueDataset): the dataset object.
              places (list(CUDAPlace)|list(CPUPlace)): places where the result 
                  data should be converted.   
              drop_last (bool): whether to drop the last batch whose sample 
                  number is less than batch size. If drop_last = True, they
                  would be dropped. If drop_last = False, they would be kept. 
  
          Returns:
              loader (DataLoader): the created DataLoader object, which can be 
                  treated as a Python generator.   
  
          Examples:
  
              .. code-block:: python
  
                  import paddle.fluid as fluid
  
                  image = fluid.data(name='image', shape=[None, 784], dtype='float32')
                  label = fluid.data(name='label', shape=[None, 1], dtype='int64')
  
                  dataset = fluid.DatasetFactory().create_dataset("QueueDataset")
                  dataset.set_batch_size(32)
                  dataset.set_filelist(['a.txt', 'b.txt', 'c.txt'])
                  dataset.set_use_var([image, label])
                  dataset.set_pipe_command('cat') 
  
                  loader = fluid.io.DataLoader.from_dataset(dataset, fluid.cpu_places())
          """
          return DatasetLoader(dataset, places, drop_last)
  
  
  class GeneratorLoader(DataLoaderBase):
      def __init__(self,
                   feed_list=None,
                   capacity=None,
                   use_double_buffer=True,
                   iterable=True,
                   return_list=False):
          self._tensor_reader = None
          self._places = None
          self._thread = None
          self._feed_list = feed_list
          if not capacity:
              raise ValueError("Please give value to capacity.")
          # force to use iterable mode under dygraph mode
          if in_dygraph_mode():
              if not iterable:
                  warnings.warn(
                      "Please NOTE: dygraph can support iterable mode only. Change to iterable mode."
                  )
              self._iterable = True
              if not return_list:
                  warnings.warn(
                      "Please NOTE: dygraph can support return as list only. Change to return as list."
                  )
              self._return_list = True
          else:
              self._iterable = iterable
              self._return_list = return_list
              if not self._feed_list:
                  raise Exception("Feed list must be given under static mode.")
          self._use_double_buffer = use_double_buffer
          self._capacity = capacity
          if not self._iterable:
              self._init_non_iterable()
  
      def _wait_thread_ends(self):
          # Get self._thread first to prevent data race, because __thread_main__
          # would set self._thread be None at the end
          thread = self._thread
          if thread is not None and self._iterable:
              self._queue.close()
              thread.join()
  
      def _init_iterable(self):
          self._wait_thread_ends()
          if in_dygraph_mode():
              self._var_names = []
              self._shapes = []
              self._dtypes = []
              self._need_check_feed = []
          else:
              self._var_names = [v.name for v in self._feed_list]
              self._shapes = [v.shape for v in self._feed_list]
              self._dtypes = [v.dtype for v in self._feed_list]
              self._need_check_feed = [
                  v.desc.need_check_feed() for v in self._feed_list
              ]
          self._queue = core.init_lod_tensor_blocking_queue(core.Variable(),
                                                            self._capacity)
          self._reader = core.create_py_reader(
              self.queue, self._var_names, self._shapes, self._dtypes,
              self._need_check_feed, self._places, self._use_double_buffer)
  
      def _init_non_iterable(self):
          lod_levels = []
          dtypes = []
          shape_concat = []
          ranks = []
          shapes = []
          need_check_feed = []
  
          for feed_data in self._feed_list:
              dtypes.append(feed_data.dtype)
              shape_concat.extend(feed_data.shape)
              ranks.append(len(feed_data.shape))
              shapes.append(feed_data.shape)
              lod_levels.append(feed_data.lod_level)
              need_check_feed.append(int(feed_data.desc.need_check_feed()))
  
          queue_name = data_loader_unique_name_generator(
              'lod_tensor_blocking_queue')
          reader_name = data_loader_unique_name_generator('create_py_reader')
          double_buffer_name = data_loader_unique_name_generator('double_buffer')
  
          var = global_scope().var(queue_name)
          self._queue = core.init_lod_tensor_blocking_queue(var, self._capacity)
  
          startup_blk = default_startup_program().current_block()
          startup_var = startup_blk.create_var(name=reader_name)
  
          dtype_int = [int(t) for t in dtypes]
          startup_blk.append_op(
              type='create_py_reader',
              inputs={'blocking_queue': [queue_name]},
              outputs={'Out': [startup_var]},
              attrs={
                  'shape_concat': shape_concat,
                  'lod_levels': lod_levels,
                  'dtypes': dtype_int,
                  'need_check_feed': need_check_feed,
                  'ranks': ranks
              })
  
          startup_var.desc.set_dtypes(dtypes)
          startup_var.persistable = True
  
          main_prog_var = _copy_reader_var_(
              default_main_program().current_block(), startup_var)
  
          main_prog_var.stop_gradient = True
          main_prog_var.persistable = True
  
          reader = monkey_patch_reader_methods(main_prog_var)
          if self._use_double_buffer:
              double_buffer_reader = double_buffer(
                  reader, name=double_buffer_name)
              # we return a double buffer reader. However, the reset method comes from
              # py_reader.
              double_buffer_reader.reset = reader.reset
              reader = double_buffer_reader
  
          self._reader = reader
  
          default_main_program().current_block().append_op(
              type='read',
              inputs={'Reader': [self._reader]},
              outputs={'Out': self._feed_list})
  
      @property
      def queue(self):
          return self._queue
  
      @property
      def iterable(self):
          return self._iterable
  
      def __iter__(self):
          assert self.iterable, "DataLoader is not iterable"
          assert self._tensor_reader is not None, \
              "Data source of DataLoader has not set yet"
  
          self._init_iterable()
          self._start()
          return self
  
      def __next__(self):
          try:
              if in_dygraph_mode():
                  return self._reader.read_next_var_list()
              else:
                  if self._return_list:
                      return self._reader.read_next_list()
                  else:
                      return self._reader.read_next()
          except StopIteration:
              self._queue.close()
              self._reset()
              six.reraise(*sys.exc_info())
  
      def start(self):
          if not in_dygraph_mode():
              assert not self._iterable, "start() cannot be called when DataLoader is iterable"
              self._start()
  
      def reset(self):
          if not in_dygraph_mode():
              assert not self._iterable, "reset() cannot be called when DataLoader is iterable"
              self._reset()
  
      @classmethod
      def _check_input_array(cls, item):
          arr = np.array(item)
          if arr.dtype == np.object:
              raise TypeError((
                  "\n\tFaild to convert input data to a regular ndarray :\n\t* Usually "
                  "this means the input data contains nested lists with different lengths. "
                  "\n\t* Check the reader function passed to 'decorate_batch_generator'"
                  " to locate the data causes this issue.\n\t* Please consider using "
                  "'fluid.create_lod_tensor' to convert it to a LoD-Tensor."))
  
      def _start(self):
          def __thread_main__():
              try:
                  for tensors in self._tensor_reader():
                      array = core.LoDTensorArray()
                      for item in tensors:
                          if not isinstance(item, core.LoDTensor):
                              self._check_input_array(item)
                              tmp = core.LoDTensor()
                              tmp.set(item, core.CPUPlace())
                              item = tmp
  
                          array.append(item)
  
                      if not self._queue.push(array):
                          break
  
                  self._queue.close()
                  self._thread = None
              except Exception as ex:
                  self._queue.kill()
                  self._thread = None
                  logging.warn('Your reader has raised an exception!')
                  six.reraise(*sys.exc_info())
  
          self._thread = threading.Thread(target=__thread_main__)
          self._thread.daemon = True
          self._thread.start()
  
      def _reset(self):
          self._reader.reset()
          thread = self._thread
          if thread is not None:
              thread.join()
  
      def set_sample_generator(self,
                               reader,
                               batch_size,
                               drop_last=True,
                               places=None):
          assert batch_size > 0, "batch_size must be larger than 0"
          if in_dygraph_mode():
              self.set_sample_list_generator(
                  paddle.batch(
                      reader, batch_size=batch_size, drop_last=drop_last),
                  places=places)
          else:
              has_lod = False
              for f in self._feed_list:
                  if f.lod_level != 0:
                      has_lod = True
                      break
  
              if has_lod:
                  self.set_sample_list_generator(
                      paddle.batch(
                          reader, batch_size=batch_size, drop_last=drop_last),
                      places=places)
              else:
                  reader = BatchedTensorProvider(
                      feed_list=self._feed_list,
                      place=core.CPUPlace(),
                      batch_size=batch_size,
                      generator=reader,
                      drop_last=drop_last)
                  self.set_batch_generator(reader, places=places)
          return self
  
      def set_sample_list_generator(self, reader, places=None):
          if in_dygraph_mode():
  
              def __tensor_reader_impl__():
                  for batch in reader():
                      slots = []
                      for items in batch:
                          for i, item in enumerate(items):
                              if len(slots) < len(items):
                                  slots.append([item])
                              else:
                                  slots[i].append(item)
                      yield slots
          else:
              with program_guard(Program(), Program()):
                  feeder = DataFeeder(
                      feed_list=self._feed_list, place=core.CPUPlace())
                  paddle_reader = feeder.decorate_reader(
                      reader, multi_devices=False)
  
              def __tensor_reader_impl__():
                  for slots in paddle_reader():
                      yield [slots[var.name] for var in self._feed_list]
  
          self.set_batch_generator(__tensor_reader_impl__, places)
          return self
  
      def set_batch_generator(self, reader, places=None):
          self._tensor_reader = reader
          if self._iterable:
              assert places is not None, "Places cannot be None when DataLoader is iterable"
              self._places = _convert_places(places)
              if in_dygraph_mode():
                  assert len(self._places) == 1, \
                      "Number of places must be 1 in dygraph mode"
          else:
              if places is not None:
                  logging.info(
                      'places would be ommited when DataLoader is not iterable')
          return self
  
  
  class PyReader(DataLoaderBase):
      """
      Create a reader object for data feeding in Python. 
      Data would be prefetched using Python thread and be pushed
      into a queue asynchronously. Data in the queue would be extracted 
      automatically when `Executor.run(...)` is called.
  
      Args:  
          feed_list (list(Variable)|tuple(Variable)): feed variable list.
              The variables should be created by :code:`fluid.layers.data()`.
          capacity (int): capacity of the queue maintained in PyReader.
              The unit is batch number. Set larger capacity if your reader 
              is fast. 
          use_double_buffer (bool): whether to use double_buffer_reader. 
              If use_double_buffer=True, PyReader would prefetch next 
              batch data asynchronously, so it would speed up data feeding 
              and occupies a little more CPU or GPU memory, i.e., the memory
              of one batch input data. 
          iterable (bool): whether the created PyReader is iterable. 
          return_list (bool): whether the return value on each device is 
              presented as a list. It is only valid when iterable=True. 
              If return_list=False, the return value on each device would 
              be a dict of str -> LoDTensor, where the key of the dict is 
              the name of each feeded variables. If return_list=True, the 
              return value on each device would be a list(LoDTensor). It is
              recommended to use return_list=False in static graph mode and
              use return_list=True in dygraph mode. 
  
      Returns:
          the created reader object.
  
      Return type:
          reader(Reader)
  
      Examples:
          1. If iterable = False, the created PyReader object is almost the
             same as :code:`fluid.layers.py_reader()`. Operators would be 
             inserted into the program. User should call :code:`start()` 
             before each epoch and catch :code:`fluid.core.EOFException`
             thrown by :code:`Executor.run()` when epoch ends. Once the 
             exception is caught, user should call :code:`reset()` to reset 
             the reader manually.
  
          .. code-block:: python
  
             import paddle
             import paddle.fluid as fluid
             import numpy as np
  
             EPOCH_NUM = 3
             ITER_NUM = 5
             BATCH_SIZE = 3
             
             def network(image, label):
                 # User-defined network, here is an example of softmax regression.
                 predict = fluid.layers.fc(input=image, size=10, act='softmax')           
                 return fluid.layers.cross_entropy(input=predict, label=label)
  
             def reader_creator_random_image_and_label(height, width):
                 def reader():
                     for i in range(ITER_NUM):
                         fake_image = np.random.uniform(low=0,
                                                        high=255,
                                                        size=[height, width])
                         fake_label = np.ones([1])
                         yield fake_image, fake_label
                 return reader
  
             image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
             label = fluid.data(name='label', shape=[None, 1], dtype='int64')
  
             reader = fluid.io.PyReader(feed_list=[image, label],
                                        capacity=4,
                                        iterable=False)
  
             user_defined_reader = reader_creator_random_image_and_label(784, 784)
             reader.decorate_sample_list_generator(
                 paddle.batch(user_defined_reader, batch_size=BATCH_SIZE))
             loss = network(image, label)
             executor = fluid.Executor(fluid.CPUPlace())
             executor.run(fluid.default_startup_program())
             for i in range(EPOCH_NUM):
                 reader.start()
                 while True:
                     try:
                         executor.run(feed=None)
                     except fluid.core.EOFException:
                         reader.reset()
                         break
  
   
          2. If iterable=True, the created PyReader object is decoupled with
             the program. No operator would be inserted into the program. 
             In this case, the created reader is a Python generator, which 
             is iterable. User should feed the data yielded from PyReader 
             object into :code:`Executor.run(feed=...)`.  
  
          .. code-block:: python
  
             import paddle
             import paddle.fluid as fluid
             import numpy as np
  
             EPOCH_NUM = 3
             ITER_NUM = 5
             BATCH_SIZE = 10
  
             def network(image, label):
                 # User-defined network, here is an example of softmax regression.
                 predict = fluid.layers.fc(input=image, size=10, act='softmax')           
                 return fluid.layers.cross_entropy(input=predict, label=label)
  
             def reader_creator_random_image(height, width):
                 def reader():
                     for i in range(ITER_NUM):
                         fake_image = np.random.uniform(low=0, high=255, size=[height, width])
                         fake_label = np.ones([1])
                         yield fake_image, fake_label 
                 return reader
  
             image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
             label = fluid.data(name='label', shape=[None, 1], dtype='int64')
             reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True, return_list=False)
  
             user_defined_reader = reader_creator_random_image(784, 784)
             reader.decorate_sample_list_generator(
                 paddle.batch(user_defined_reader, batch_size=BATCH_SIZE),
                     fluid.core.CPUPlace())
             
             loss = network(image, label)
             executor = fluid.Executor(fluid.CPUPlace())
             executor.run(fluid.default_startup_program())
             
             for _ in range(EPOCH_NUM):
                 for data in reader():
                     executor.run(feed=data, fetch_list=[loss])
  
  
          3. If return_list=True, the return values would be presented as list instead of dict. 
             This is usually used in dygraph mode.
  
          .. code-block:: python
  
             import paddle
             import paddle.fluid as fluid
             import numpy as np
  
             ITER_NUM = 5
             BATCH_SIZE = 10
  
             def reader_creator_random_image(height, width):
                 def reader():
                     for i in range(ITER_NUM):
                         yield np.random.uniform(low=0, high=255, size=[height, width]), \
                             np.random.random_integers(low=0, high=9, size=[1])
                 return reader
  
             place = fluid.CPUPlace()
             with fluid.dygraph.guard(place):
                 py_reader = fluid.io.PyReader(capacity=2, return_list=True)
                 user_defined_reader = reader_creator_random_image(784, 784)
                 py_reader.decorate_sample_list_generator(
                     paddle.batch(user_defined_reader, batch_size=BATCH_SIZE),
                     place)
                 for image, label in py_reader():
                     relu = fluid.layers.relu(image)
      """
  
      def __init__(self,
                   feed_list=None,
                   capacity=None,
                   use_double_buffer=True,
                   iterable=True,
                   return_list=False):
          self._loader = DataLoader.from_generator(
              feed_list, capacity, use_double_buffer, iterable, return_list)
  
      @property
      def queue(self):
          return self._loader.queue
  
      @property
      def iterable(self):
          return self._loader.iterable
  
      def __iter__(self):
          return self._loader.__iter__()
  
      def __next__(self):
          return self._loader.__next__()
  
      def start(self):
          '''
          Start the data feeding thread. 
          Can only call when the reader object is not iterable.  
          
  	Example:
  	    .. code-block:: python
      
                  import paddle
                  import paddle.fluid as fluid
                  import numpy as np
  
                  BATCH_SIZE = 10
  
                  def generator():
                      for i in range(5):
                          yield np.random.uniform(low=0, high=255, size=[784, 784]),
  
                  image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
                  reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=False)
                  reader.decorate_sample_list_generator(
                      paddle.batch(generator, batch_size=BATCH_SIZE))
  
                  executor = fluid.Executor(fluid.CPUPlace())
                  executor.run(fluid.default_startup_program())
                  for i in range(3):
                      reader.start()
                      while True:
                          try:
                              executor.run(feed=None)
                          except fluid.core.EOFException:
                              reader.reset()
                              break
  
  	    '''
          self._loader.start()
  
      def reset(self):
          '''
          Reset the reader object when :code:`fluid.core.EOFException` raises. 
          Can only call when the reader object is not iterable.
          
          Example:
              .. code-block:: python
  
                  import paddle
                  import paddle.fluid as fluid
                  import numpy as np
  
                  BATCH_SIZE = 10
  
                  def generator():
                      for i in range(5):
                          yield np.random.uniform(low=0, high=255, size=[784, 784]),
  
                  image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
                  reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=False)
                  reader.decorate_sample_list_generator(
                      paddle.batch(generator, batch_size=BATCH_SIZE))
  
                  executor = fluid.Executor(fluid.CPUPlace())
                  executor.run(fluid.default_startup_program())
                  for i in range(3):
                      reader.start()
                      while True:
                          try:
                              executor.run(feed=None)
                          except fluid.core.EOFException:
                              reader.reset()
                              break        
  
          '''
          self._loader.reset()
  
      def decorate_sample_generator(self,
                                    sample_generator,
                                    batch_size,
                                    drop_last=True,
                                    places=None):
          '''
          Set the data source of the PyReader object.
          
          The provided :code:`sample_generator` should be a Python generator,
          which yields list(numpy.ndarray)-typed data of each sample.
  
          :code:`places` must be set when the PyReader object is iterable.
  
          If all inputs have no lods, this method is faster than 
          :code:`decorate_sample_list_generator(paddle.batch(sample_generator, ...))` .
  
          Args:
              sample_generator (generator): Python generator that yields
                  list(numpy.ndarray)-typed sample data.
              batch_size (int): batch size. Must be larger than 0.
              drop_last (bool): Whether to drop the last batch when sample number
                  is less than batch_size. 
              places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
                  be provided when PyReader is iterable.
  
          Example:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  import numpy as np
  
                  EPOCH_NUM = 3
                  ITER_NUM = 15
                  BATCH_SIZE = 3
          
                  def network(image, label):
                      # User-defined network, here is an example of softmax regression.
                      predict = fluid.layers.fc(input=image, size=10, act='softmax')           
                      return fluid.layers.cross_entropy(input=predict, label=label)
  
                  def random_image_and_label_generator(height, width):
                      def generator():
                          for i in range(ITER_NUM):
                              fake_image = np.random.uniform(low=0,
                                                             high=255,
                                                             size=[height, width])
                              fake_label = np.array([1])
                              yield fake_image, fake_label
                      return generator
  
                  image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
                  label = fluid.data(name='label', shape=[None, 1], dtype='int64')
                  reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)
  
                  user_defined_generator = random_image_and_label_generator(784, 784)
                  reader.decorate_sample_generator(user_defined_generator,
                                                   batch_size=BATCH_SIZE,
                                                   places=[fluid.CPUPlace()])
                  loss = network(image, label)
                  executor = fluid.Executor(fluid.CPUPlace())
                  executor.run(fluid.default_startup_program())
  
                  for _ in range(EPOCH_NUM):
                      for data in reader():
                          executor.run(feed=data, fetch_list=[loss])
      
          '''
          self._loader.set_sample_generator(sample_generator, batch_size,
                                            drop_last, places)
  
      def decorate_sample_list_generator(self, reader, places=None):
          '''
          Set the data source of the PyReader object. 
  
          The provided :code:`reader` should be a Python generator,
          which yields list(numpy.ndarray) typed batched data. 
          
          :code:`places` must be set when the PyReader object is iterable.
  
          Args:
              reader (generator): Python generator that yields 
                  list(numpy.ndarray)-typed batched data. 
              places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
                  be provided when PyReader is iterable.
          
          Example:
              .. code-block:: python
  
                  import paddle
                  import paddle.fluid as fluid
                  import numpy as np
  
                  EPOCH_NUM = 3
                  ITER_NUM = 15
                  BATCH_SIZE = 3
  
                  def network(image, label):
                      # User-defined network, here is an example of softmax regression.
                      predict = fluid.layers.fc(input=image, size=10, act='softmax')           
                      return fluid.layers.cross_entropy(input=predict, label=label)
  
                  def random_image_and_label_generator(height, width):
                      def generator():
                          for i in range(ITER_NUM):
                              fake_image = np.random.uniform(low=0,
                                                             high=255,
                                                             size=[height, width])
                              fake_label = np.ones([1])
                              yield fake_image, fake_label
                      return generator
  
                  image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
                  label = fluid.data(name='label', shape=[None, 1], dtype='int64')
                  reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)
  
                  user_defined_generator = random_image_and_label_generator(784, 784)
                  reader.decorate_sample_list_generator(
                      paddle.batch(user_defined_generator, batch_size=BATCH_SIZE),
                      fluid.core.CPUPlace())
                  
                  loss = network(image, label)
                  executor = fluid.Executor(fluid.core.CPUPlace())
                  executor.run(fluid.default_startup_program())
  
                  for _ in range(EPOCH_NUM):
                      for data in reader():
                          executor.run(feed=data, fetch_list=[loss])
                   
          '''
          self._loader.set_sample_list_generator(reader, places)
  
      def decorate_batch_generator(self, reader, places=None):
          '''
          Set the data source of the PyReader object.
  
          The provided :code:`reader` should be a Python generator,
          which yields numpy.ndarray-typed or LoDTensor-typed batched data.
  
          :code:`places` must be set when the PyReader object is iterable.
  
          Args:
              reader (generator): Python generator that yields LoDTensor-typed
                  batched data.
              places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
                  be provided when PyReader is iterable.
  
          Example:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  import numpy as np
  
                  EPOCH_NUM = 3
                  ITER_NUM = 15
                  BATCH_SIZE = 3
                 
                  def network(image, label):
                      # User-defined network, here is an example of softmax regression.
                      predict = fluid.layers.fc(input=image, size=10, act='softmax')           
                      return fluid.layers.cross_entropy(input=predict, label=label)
  
                  def random_image_and_label_generator(height, width):
                      def generator():
                          for i in range(ITER_NUM):
                              batch_image = np.random.uniform(low=0,
                                                              high=255,
                                                              size=[BATCH_SIZE, height, width])
                              batch_label = np.ones([BATCH_SIZE, 1])
                              batch_image = batch_image.astype('float32')
                              batch_label = batch_label.astype('int64')
                              yield batch_image, batch_label
                      return generator
  
                  image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
                  label = fluid.data(name='label', shape=[None, 1], dtype='int64')
                  reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)
  
                  user_defined_generator = random_image_and_label_generator(784, 784)
                  reader.decorate_batch_generator(user_defined_generator, fluid.CPUPlace())
                  
                  loss = network(image, label)
                  executor = fluid.Executor(fluid.CPUPlace())
                  executor.run(fluid.default_startup_program())
  
                  for _ in range(EPOCH_NUM):
                      for data in reader():
                          executor.run(feed=data, fetch_list=[loss])
  
          '''
          self._loader.set_batch_generator(reader, places)
  
  
  class DatasetLoader(DataLoaderBase):
      def __init__(self, dataset, places, drop_last):
          assert isinstance(dataset,
                            DatasetBase), "dataset must be type of DatasetBase"
          assert not in_dygraph_mode(
          ), "DatasetLoader is not supported in dygraph mode yet"
  
          thread_num = len(places)
  
          assert len(dataset.filelist) >= thread_num, \
              "Filelist number of dataset {} must be not less than place number {}".format(len(dataset.filelist), thread_num)
  
          if dataset.thread_num != 0 and dataset.thread_num != thread_num:
              logging.warn('thread_num {} which is set in Dataset is ignored'.
                           format(dataset.thread_num))
  
          dataset.set_thread(thread_num)
  
          if isinstance(dataset,
                        InMemoryDataset) and dataset.queue_num > thread_num:
              logging.warn("queue_num {} which is set in Dataset is ignored".
                           format(dataset.queue_num))
              dataset.set_queue_num(thread_num)
  
          self._dataset = dataset
          use_slots = [
              slot.name for slot in dataset.proto_desc.multi_slot_desc.slots
              if slot.is_used
          ]
  
          self._iterable_dataset = core.IterableDatasetWrapper(
              dataset.dataset, use_slots,
              _convert_places(places), dataset.proto_desc.batch_size, drop_last)
  
      def __iter__(self):
          self._dataset._finish_to_run()
          self._dataset._prepare_to_run()
          self._iterable_dataset._start()
          return self
  
      def __next__(self):
          return self._iterable_dataset._next()
  