def _next_internal(self):
"""Returns a nested structure of `tf.Tensor`s containing the next element.
"""
with ops.device(self._device):
if self._buffer_resource_handle is not None:
ret = prefetching_ops.function_buffering_resource_get_next(
function_buffer_resource=self._buffer_resource_handle,
output_types=self._flat_output_types)
else:
# TODO(ashankar): Consider removing this ops.device() contextmanager
# and instead mimic ops placement in graphs: Operations on resource
# handles execute on the same device as where the resource is placed.
# NOTE(mrry): Here we use the "_sync" variant of `iterator_get_next`
# because in eager mode this code will run synchronously on the calling
# thread. Therefore we do not need to make a defensive context switch
# to a background thread, and can achieve a small constant performance
# boost by invoking the iterator synchronously.
ret = gen_dataset_ops.iterator_get_next_sync(
self._resource,
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
return sparse.deserialize_sparse_tensors(
nest.pack_sequence_as(self._output_types, ret), self._output_types,
self._output_shapes, self._output_classes)
def _next_internal(self):
"""Returns a nested structure of `tf.Tensor`s containing the next element.
"""
with ops.device(self._device):
if self._buffer_resource_handle is not None:
ret = prefetching_ops.function_buffering_resource_get_next(
function_buffer_resource=self._buffer_resource_handle,
output_types=self._flat_output_types)
else:
# TODO(ashankar): Consider removing this ops.device() contextmanager
# and instead mimic ops placement in graphs: Operations on resource
# handles execute on the same device as where the resource is placed.
# NOTE(mrry): Here we use the "_sync" variant of `iterator_get_next`
# because in eager mode this code will run synchronously on the calling
# thread. Therefore we do not need to make a defensive context switch
# to a background thread, and can achieve a small constant performance
# boost by invoking the iterator synchronously.
ret = gen_dataset_ops.iterator_get_next_sync(
self._resource,
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
return sparse.deserialize_sparse_tensors(
nest.pack_sequence_as(self._output_types, ret), self._output_types,
self._output_shapes, self._output_classes)
def _next_internal(self):
"""Returns a nested structure of `tf.Tensor`s containing the next element.
"""
if not context.executing_eagerly():
with ops.device(self._device):
ret = gen_dataset_ops.iterator_get_next(
self._iterator_resource,
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
return structure.from_compatible_tensor_list(self._element_spec, ret)
# This runs in sync mode as iterators use an error status to communicate
# that there is no more data to iterate over.
# TODO(b/77291417): Fix
with context.execution_mode(context.SYNC):
with ops.device(self._device):
# TODO(ashankar): Consider removing this ops.device() contextmanager
# and instead mimic ops placement in graphs: Operations on resource
# handles execute on the same device as where the resource is placed.
# NOTE(mrry): Here we use the "_sync" variant of `iterator_get_next`
# because in eager mode this code will run synchronously on the calling
# thread. Therefore we do not need to make a defensive context switch
# to a background thread, and can achieve a small constant performance
# boost by invoking the iterator synchronously.
ret = gen_dataset_ops.iterator_get_next_sync(
self._iterator_resource,
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
try:
# Fast path for the case `self._structure` is not a nested structure.
return self._element_spec._from_compatible_tensor_list(ret) # pylint: disable=protected-access
except AttributeError:
return structure.from_compatible_tensor_list(self._element_spec, ret)
def _next_internal(self):
"""Returns a nested structure of `tf.Tensor`s containing the next element.
"""
if not context.executing_eagerly():
with ops.device(self._device):
ret = gen_dataset_ops.iterator_get_next(
self._iterator_resource,
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
return self._structure._from_compatible_tensor_list(ret) # pylint: disable=protected-access
# This runs in sync mode as iterators use an error status to communicate
# that there is no more data to iterate over.
# TODO(b/77291417): Fix
with context.execution_mode(context.SYNC):
with ops.device(self._device):
# TODO(ashankar): Consider removing this ops.device() contextmanager
# and instead mimic ops placement in graphs: Operations on resource
# handles execute on the same device as where the resource is placed.
# NOTE(mrry): Here we use the "_sync" variant of `iterator_get_next`
# because in eager mode this code will run synchronously on the calling
# thread. Therefore we do not need to make a defensive context switch
# to a background thread, and can achieve a small constant performance
# boost by invoking the iterator synchronously.
ret = gen_dataset_ops.iterator_get_next_sync(
self._iterator_resource,
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
return self._structure._from_compatible_tensor_list(ret) # pylint: disable=protected-access
def _next_internal(self):
"""Returns a nested structure of `tf.Tensor`s containing the next element.
"""
# This runs in sync mode as iterators use an error status to communicate
# that there is no more data to iterate over.
# TODO(b/77291417): Fix
with context.execution_mode(context.SYNC):
with ops.device(self._device):
# TODO(ashankar): Consider removing this ops.device() contextmanager
# and instead mimic ops placement in graphs: Operations on resource
# handles execute on the same device as where the resource is placed.
# NOTE(mrry): Here we use the "_sync" variant of `iterator_get_next`
# because in eager mode this code will run synchronously on the calling
# thread. Therefore we do not need to make a defensive context switch
# to a background thread, and can achieve a small constant performance
# boost by invoking the iterator synchronously.
ret = gen_dataset_ops.iterator_get_next_sync(
self._resource,
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
return sparse.deserialize_sparse_tensors(
nest.pack_sequence_as(self._output_types, ret), self._output_types,
self._output_shapes, self._output_classes)
