Python gen_dataset_ops.iterator_to_string_handle函数代码示例

  def __init__(self, iterator_resource, initializer, output_types,
               output_shapes, output_classes):
    """Creates a new iterator from the given iterator resource.

    Note: Most users will not call this initializer directly, and will
    instead use `Dataset.make_initializable_iterator()` or
    `Dataset.make_one_shot_iterator()`.

    Args:
      iterator_resource: A `tf.resource` scalar `tf.Tensor` representing the
        iterator.
      initializer: A `tf.Operation` that should be run to initialize this
        iterator.
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this dataset.
      output_shapes: A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset.
      output_classes: A nested structure of Python `type` object corresponding
        to each
        component of an element of this iterator.
    """
    self._iterator_resource = iterator_resource
    self._initializer = initializer
    self._output_classes = output_classes
    self._output_types = output_types
    self._output_shapes = output_shapes
    self._string_handle = gen_dataset_ops.iterator_to_string_handle(
        self._iterator_resource)
    self._get_next_call_count = 0

  def __init__(self,
               input_dataset,
               device,
               buffer_size):
    with ops.device("/device:CPU:0"):
      super(_PrefetchToDeviceEagerIterator, self).__init__(input_dataset)
      input_iterator_handle = gen_dataset_ops.iterator_to_string_handle(
          self._resource)

    self._device = device

    @function.defun(input_signature=[tensor_spec.TensorSpec([], dtypes.string)])
    def _prefetch_fn(handle):
      """Prefetches one element from `input_iterator`."""
      remote_iterator = iterator_ops.Iterator.from_string_handle(
          handle, self.output_types, self.output_shapes, self.output_classes)
      ret = remote_iterator.get_next()
      return nest.flatten(sparse.serialize_sparse_tensors(ret))

    self._prefetch_fn = _prefetch_fn._get_concrete_function_internal()  # pylint: disable=protected-access

    with ops.device(device):
      self._buffering_resource = function_buffering_resource(
          f=self._prefetch_fn,
          output_types=self._flat_output_types,
          target_device=ged_ops.experimental_iterator_get_device(
              self._resource),
          string_arg=input_iterator_handle,
          buffer_size=buffer_size,
          shared_name=iterator_ops._generate_shared_name(
              "function_buffer_resource"))

  def __init__(self, iterator_resource, initializer, output_types,
               output_shapes, output_classes):
    """Creates a new iterator from the given iterator resource.

    Note: Most users will not call this initializer directly, and will
    instead use `Dataset.make_initializable_iterator()` or
    `Dataset.make_one_shot_iterator()`.

    Args:
      iterator_resource: A `tf.resource` scalar `tf.Tensor` representing the
        iterator.
      initializer: A `tf.Operation` that should be run to initialize this
        iterator.
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this iterator.
      output_shapes: A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this iterator.
      output_classes: A nested structure of Python `type` objects corresponding
        to each component of an element of this iterator.
    """
    self._iterator_resource = iterator_resource
    self._initializer = initializer

    if (output_types is None or output_shapes is None
        or output_classes is None):
      raise ValueError("If `structure` is not specified, all of "
                       "`output_types`, `output_shapes`, and `output_classes`"
                       " must be specified.")
    self._structure = structure_lib.convert_legacy_structure(
        output_types, output_shapes, output_classes)

    self._string_handle = gen_dataset_ops.iterator_to_string_handle(
        self._iterator_resource)
    self._get_next_call_count = 0
    ops.add_to_collection(GLOBAL_ITERATORS, self._iterator_resource)

  def __init__(self, dataset):
    """Creates a new iterator over the given dataset.

    For example:
    ```python
    dataset = tf.data.Dataset.range(4)
    for x in Iterator(dataset):
      print(x)
    ```

    Tensors produced will be placed on the device on which this iterator object
    was created.

    Args:
      dataset: A `tf.data.Dataset` object.

    Raises:
      TypeError: If `dataset` is an unsupported type.
      RuntimeError: When invoked without eager execution enabled.
    """
    if isinstance(dataset, prefetching_ops._PrefetchToDeviceDataset):  # pylint: disable=protected-access
      raise TypeError(
          "`tf.contrib.data.prefetch_to_device()` is not compatible with "
          "`tf.contrib.eager.Iterator`. Use `for ... in dataset:` to iterate "
          "over the dataset instead.")

    super(Iterator, self).__init__(dataset)
    if not context.context().device_spec.device_type:
      is_remote_device = False
    else:
      is_remote_device = context.context().device_spec.device_type != "CPU"
    self._buffer_resource_handle = None
    if is_remote_device:
      with ops.device("/device:CPU:0"):
        iter_string_handle = gen_dataset_ops.iterator_to_string_handle(
            self._resource)

        @function.Defun(dtypes.string)
        def remote_fn(h):
          remote_iterator = iterator_ops.Iterator.from_string_handle(
              h, self.output_types, self.output_shapes, self.output_classes)
          return remote_iterator.get_next()

        remote_fn.add_to_graph(None)
        target = constant_op.constant("/device:CPU:0")
      with ops.device(self._device):
        self._buffer_resource_handle = prefetching_ops.function_buffering_resource(  # pylint: disable=line-too-long
            string_arg=iter_string_handle,
            output_types=self._flat_output_types,
            f=remote_fn,
            target_device=target,
            buffer_size=10,
            container="",
            shared_name=_generate_shared_name(
                "contrib_eager_iterator_function_buffer_resource"))
        self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter(  # pylint: disable=line-too-long
            handle=self._buffer_resource_handle,
            handle_device=self._device)

  def __init__(self, iterator_resource, initializer, output_types,
               output_shapes, output_classes):

    self._iterator_resource = iterator_resource
    self._initializer = initializer
    self._output_classes = output_classes
    self._output_types = output_types
    self._output_shapes = output_shapes
    self._string_handle = gen_dataset_ops.iterator_to_string_handle(
        self._iterator_resource)

  def string_handle(self, name=None):
    """Returns a string-valued `tf.Tensor` that represents this iterator.

    Args:
      name: (Optional.) A name for the created operation.

    Returns:
      A scalar `tf.Tensor` of type `tf.string`.
    """
    return gen_dataset_ops.iterator_to_string_handle(
        self._iterator_resource, name=name)

    def _init_func():
      """Creates an iterator for the input dataset.

      Returns:
        A `string` tensor that encapsulates the iterator created.
      """
      ds_variant = gen_dataset_ops.unwrap_dataset_variant(wrap_ds_variant)
      resource = gen_dataset_ops.anonymous_iterator(
          **dataset_ops.flat_structure(self._input_dataset))
      with ops.control_dependencies(
          [gen_dataset_ops.make_iterator(ds_variant, resource)]):
        return gen_dataset_ops.iterator_to_string_handle(resource)

    def _init_func():
      """Creates an iterator for the input dataset.

      Returns:
        A `string` tensor that encapsulates the iterator created.
      """
      # pylint: disable=protected-access
      ds_variant = self._input_dataset._as_variant_tensor()
      resource = gen_dataset_ops.anonymous_iterator(
          output_types=self._flat_output_types,
          output_shapes=self._flat_output_shapes)
      with ops.control_dependencies(
          [gen_dataset_ops.make_iterator(ds_variant, resource)]):
        return gen_dataset_ops.iterator_to_string_handle(resource)

  def __init__(self, dataset):
    """Creates a new iterator over the given dataset.

    For example:
    ```python
    dataset = tf.data.Dataset.range(4)
    for x in Iterator(dataset):
      print(x)
    ```

    Tensors produced will be placed on the device on which this iterator object
    was created.

    Args:
      dataset: A `tf.data.Dataset` object.

    Raises:
      RuntimeError: When invoked without eager execution enabled.
    """

    if not context.in_eager_mode():
      raise RuntimeError(
          "{} objects can only be used when eager execution is enabled, use "
          "tf.data.Dataset.make_iterator or "
          "tf.data.Dataset.make_one_shot_iterator for graph construction".
          format(type(self)))
    with ops.device("/device:CPU:0"):
      ds_variant = dataset._as_variant_tensor()  # pylint: disable=protected-access
      self._output_types = dataset.output_types
      self._output_shapes = dataset.output_shapes
      self._flat_output_types = nest.flatten(dataset.output_types)
      self._flat_output_shapes = nest.flatten(dataset.output_shapes)
      self._resource = gen_dataset_ops.iterator(
          container="",
          shared_name=_generate_shared_name("eager_iterator"),
          output_types=self._flat_output_types,
          output_shapes=self._flat_output_shapes)
      gen_dataset_ops.make_iterator(ds_variant, self._resource)
      # Delete the resource when this object is deleted
      self._resource_deleter = resource_variable_ops.EagerResourceDeleter(
          handle=self._resource, handle_device="/device:CPU:0")
    self._device = context.context().device_name
    self._buffer_resource_handle = None
    if not context.context().device_spec.device_type:
      is_remote_device = False
    else:
      is_remote_device = context.context().device_spec.device_type != "CPU"
    if is_remote_device:
      with ops.device("/device:CPU:0"):
        iter_string_handle = gen_dataset_ops.iterator_to_string_handle(
            self._resource)

        @function.Defun(dtypes.string)
        def remote_fn(h):
          remote_iterator = iterator_ops.Iterator.from_string_handle(
              h, self._output_types, self._output_shapes)
          return remote_iterator.get_next()

        remote_fn.add_to_graph(None)
        target = constant_op.constant("/device:CPU:0")
      with ops.device(self._device):
        self._buffer_resource_handle = prefetching_ops.function_buffering_resource(
            string_arg=iter_string_handle,
            f=remote_fn,
            target_device=target,
            buffer_size=10,
            thread_pool_size=1,
            container="",
            shared_name=_generate_shared_name("function_buffer_resource"))
        self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter(
            handle=self._buffer_resource_handle, handle_device=self._device)