Python io_ops.ReaderBase方法代码示例

# 需要导入模块: from tensorflow.python.ops import io_ops [as 别名]
# 或者: from tensorflow.python.ops.io_ops import ReaderBase [as 别名]
def single_pass_read(data_sources, reader_class, reader_kwargs=None,
                     scope=None):
  """Reads sequentially the data_sources using the reader, doing a single pass.

  Args:
    data_sources: a list/tuple of files or the location of the data, i.e.
      /path/to/train@128, /path/to/train* or /tmp/.../train*
    reader_class: one of the io_ops.ReaderBase subclasses ex: TFRecordReader.
    reader_kwargs: an optional dict, of kwargs for the reader.
    scope: Optional name scope for the ops.

  Returns:
    key, value: a tuple of keys and values from the data_source.
  """
  data_files = get_data_files(data_sources)
  with ops.name_scope(scope, 'single_pass_read'):
    filename_queue = tf_input.string_input_producer(
        data_files, num_epochs=1, shuffle=False, capacity=1, name='filenames')
    reader_kwargs = reader_kwargs or {}
    return reader_class(**reader_kwargs).read(filename_queue) 

# 需要导入模块: from tensorflow.python.ops import io_ops [as 别名]
# 或者: from tensorflow.python.ops.io_ops import ReaderBase [as 别名]
def single_pass_read(data_sources,
                     reader_class,
                     reader_kwargs=None):
  """Reads sequentially the data_sources using the reader, doing a single pass.

  Args:
    data_sources: a list/tuple of files or the location of the data, i.e.
      /path/to/train@128, /path/to/train* or /tmp/.../train*
    reader_class: one of the io_ops.ReaderBase subclasses ex: TFRecordReader.
    reader_kwargs: an optional dict, of kwargs for the reader.

  Returns:
    key, value: a tuple of keys and values from the data_source.
  """
  data_files = get_data_files(data_sources)
  with ops.name_scope('single_pass_read'):
    filename_queue = tf_input.string_input_producer(data_files,
                                                    num_epochs=1,
                                                    shuffle=False,
                                                    capacity=1)
    reader_kwargs = reader_kwargs or {}
    return reader_class(**reader_kwargs).read(filename_queue) 

# 需要导入模块: from tensorflow.python.ops import io_ops [as 别名]
# 或者: from tensorflow.python.ops.io_ops import ReaderBase [as 别名]
def __init__(self,
               reader_class,
               common_queue,
               num_readers=4,
               reader_kwargs=None):
    """ParallelReader creates num_readers instances of the reader_class.

    Each instance is created by calling the `reader_class` function passing
    the arguments specified in `reader_kwargs` as in:
      reader_class(**read_kwargs)

    When you read from a ParallelReader, with its `read()` method,
    you just dequeue examples from the `common_queue`.

    The readers will read different files in parallel, asynchronously enqueueing
    their output into `common_queue`. The `common_queue.dtypes` must be
    [tf.string, tf.string]

    Because each reader can read from a different file, the examples in the
    `common_queue` could be from different files. Due to the asynchronous
    reading there is no guarantee that all the readers will read the same
    number of examples.

    If the `common_queue` is a shuffling queue, then the examples are shuffled.

    Usage:
      common_queue = tf.RandomShuffleQueue(
          capacity=256,
          min_after_dequeue=128,
          dtypes=[tf.string, tf.string])
      p_reader = ParallelReader(tf.TFRecordReader, common_queue)

      common_queue = tf.FIFOQueue(
          capacity=256,
          dtypes=[tf.string, tf.string])
      p_reader = ParallelReader(readers, common_queue, num_readers=2)


    Args:
      reader_class: one of the io_ops.ReaderBase subclasses ex: TFRecordReader
      common_queue: a Queue to hold (key, value pairs) with `dtypes` equal to
        [tf.string, tf.string]. Must be one of the data_flow_ops.Queues
        instances, ex. `tf.FIFOQueue()`, `tf.RandomShuffleQueue()`, ...
      num_readers: a integer, number of instances of reader_class to create.
      reader_kwargs: an optional dict of kwargs to create the readers.

    Raises:
      TypeError: if `common_queue.dtypes` is not [tf.string, tf.string].
    """
    if len(common_queue.dtypes) != 2:
      raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')
    for dtype in common_queue.dtypes:
      if not dtype.is_compatible_with(tf_dtypes.string):
        raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')

    reader_kwargs = reader_kwargs or {}
    self._readers = [reader_class(**reader_kwargs) for _ in range(num_readers)]
    self._common_queue = common_queue 

# 需要导入模块: from tensorflow.python.ops import io_ops [as 别名]
# 或者: from tensorflow.python.ops.io_ops import ReaderBase [as 别名]
def __init__(self,
               reader_class,
               common_queue,
               num_readers=4,
               reader_kwargs=None):
    """ParallelReader creates num_readers instances of the reader_class.

    Each instance is created by calling the `reader_class` function passing
    the arguments specified in `reader_kwargs` as in:
      reader_class(**read_kwargs)

    When you read from a ParallelReader, with its `read()` method,
    you just dequeue examples from the `common_queue`.

    The readers will read different files in parallel, asynchronously enqueueing
    their output into `common_queue`. The `common_queue.dtypes` must be
    [tf.string, tf.string]

    Because each reader can read from a different file, the examples in the
    `common_queue` could be from different files. Due to the asynchronous
    reading there is no guarantee that all the readers will read the same
    number of examples.

    If the `common_queue` is a shuffling queue, then the examples are shuffled.

    Usage:
      common_queue = tf.queue.RandomShuffleQueue(
          capacity=256,
          min_after_dequeue=128,
          dtypes=[tf.string, tf.string])
      p_reader = ParallelReader(tf.compat.v1.TFRecordReader, common_queue)

      common_queue = tf.queue.FIFOQueue(
          capacity=256,
          dtypes=[tf.string, tf.string])
      p_reader = ParallelReader(readers, common_queue, num_readers=2)


    Args:
      reader_class: one of the io_ops.ReaderBase subclasses ex: TFRecordReader
      common_queue: a Queue to hold (key, value pairs) with `dtypes` equal to
        [tf.string, tf.string]. Must be one of the data_flow_ops.Queues
        instances, ex. `tf.queue.FIFOQueue()`, `tf.queue.RandomShuffleQueue()`,
        ...
      num_readers: a integer, number of instances of reader_class to create.
      reader_kwargs: an optional dict of kwargs to create the readers.

    Raises:
      TypeError: if `common_queue.dtypes` is not [tf.string, tf.string].
    """
    if len(common_queue.dtypes) != 2:
      raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')
    for dtype in common_queue.dtypes:
      if not dtype.is_compatible_with(tf_dtypes.string):
        raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')

    reader_kwargs = reader_kwargs or {}
    self._readers = [reader_class(**reader_kwargs) for _ in range(num_readers)]
    self._common_queue = common_queue