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. """ ds_variant = gen_dataset_ops.unwrap_dataset_variant(wrap_ds_variant) resource = gen_dataset_ops.anonymous_iterator( **self._input_dataset._flat_structure) # pylint: disable=protected-access 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_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( 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.executing_eagerly(): raise RuntimeError( "{} objects can only be used when eager execution is enabled, use " "tf.data.Dataset.make_initializable_iterator or " "tf.data.Dataset.make_one_shot_iterator for graph construction" .format(type(self))) self._device = context.context().device_name with ops.device("/cpu:0"): ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access self._output_classes = dataset.output_classes self._output_types = dataset.output_types self._output_shapes = dataset.output_shapes self._flat_output_types = nest.flatten( sparse.as_dense_types(self._output_types, self._output_classes)) self._flat_output_shapes = nest.flatten( sparse.as_dense_shapes(self._output_shapes, self._output_classes)) with ops.colocate_with(ds_variant): self._resource = gen_dataset_ops.anonymous_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=self._device)
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.executing_eagerly(): raise RuntimeError( "{} objects can only be used when eager execution is enabled, use " "tf.data.Dataset.make_initializable_iterator or " "tf.data.Dataset.make_one_shot_iterator for graph construction". format(type(self))) self._device = context.context().device_name with ops.device("/cpu:0"): ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access self._output_classes = dataset.output_classes self._output_types = dataset.output_types self._output_shapes = dataset.output_shapes self._flat_output_types = nest.flatten( sparse.as_dense_types(self._output_types, self._output_classes)) self._flat_output_shapes = nest.flatten( sparse.as_dense_shapes(self._output_shapes, self._output_classes)) with ops.colocate_with(ds_variant): self._resource = gen_dataset_ops.anonymous_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=self._device)