def _deserialize_sequence_value(
sequence_value_proto: executor_pb2.Value.Sequence
) -> _DeserializeReturnType:
"""Deserializes a `tf.data.Dataset`.
Args:
sequence_value_proto: `Sequence` protocol buffer message.
Returns:
A tuple of `(tf.data.Dataset, tff.Type)`.
"""
which_value = sequence_value_proto.WhichOneof('value')
if which_value == 'zipped_saved_model':
ds = _deserialize_dataset(sequence_value_proto.zipped_saved_model)
else:
raise NotImplementedError(
'Deserializing Sequences enocded as {!s} has not been implemented'
.format(which_value))
element_type = type_serialization.deserialize_type(
sequence_value_proto.element_type)
# If a serialized dataset had elements of nested structes of tensors (e.g.
# `dict`, `OrderedDict`), the deserialized dataset will return `dict`,
# `tuple`, or `namedtuple` (loses `collections.OrderedDict` in a conversion).
#
# Since the dataset will only be used inside TFF, we wrap the dictionary
# coming from TF in an `OrderedDict` when necessary (a type that both TF and
# TFF understand), using the field order stored in the TFF type stored during
# serialization.
ds = tensorflow_utils.coerce_dataset_elements_to_tff_type_spec(
ds, element_type)
return ds, computation_types.SequenceType(element=element_type)
def _deserialize_sequence_value(
sequence_value_proto: executor_pb2.Value.Sequence
) -> _DeserializeReturnType:
"""Deserializes a `tf.data.Dataset`.
Args:
sequence_value_proto: `Sequence` protocol buffer message.
Returns:
A tuple of `(tf.data.Dataset, tff.Type)`.
"""
element_type = type_serialization.deserialize_type(
sequence_value_proto.element_type)
which_value = sequence_value_proto.WhichOneof('value')
if which_value == 'zipped_saved_model':
warnings.warn(
'Deserializng a sequence value that was encoded as a zipped SavedModel.'
' This is a deprecated path, please update the binary that is '
'serializing the sequences.', DeprecationWarning)
ds = _deserialize_dataset_from_zipped_saved_model(
sequence_value_proto.zipped_saved_model)
ds = tensorflow_utils.coerce_dataset_elements_to_tff_type_spec(
ds, element_type)
elif which_value == 'serialized_graph_def':
ds = _deserialize_dataset_from_graph_def(
sequence_value_proto.serialized_graph_def, element_type)
else:
raise NotImplementedError(
'Deserializing Sequences enocded as {!s} has not been implemented'.
format(which_value))
return ds, computation_types.SequenceType(element=element_type)
def _deserialize_sequence_value(
sequence_value_proto: executor_pb2.Value.Sequence,
type_hint: Optional[computation_types.Type] = None
) -> _DeserializeReturnType:
"""Deserializes a `tf.data.Dataset`.
Args:
sequence_value_proto: `Sequence` protocol buffer message.
type_hint: A `computation_types.Type` that hints at what the value type
should be for executors that only return values. If the
`sequence_value_proto.element_type` field was not set, the `type_hint` is
used instead.
Returns:
A tuple of `(tf.data.Dataset, tff.Type)`.
"""
if sequence_value_proto.HasField('element_type'):
element_type = type_serialization.deserialize_type(
sequence_value_proto.element_type)
elif type_hint is not None:
element_type = type_hint.element
else:
raise ValueError(
'Cannot deserialize a sequence Value proto that without one of '
'`element_type` proto field or `element_type_hint`')
which_value = sequence_value_proto.WhichOneof('value')
if which_value == 'zipped_saved_model':
warnings.warn(
'Deserializng a sequence value that was encoded as a zipped SavedModel.'
' This is a deprecated path, please update the binary that is '
'serializing the sequences.', DeprecationWarning)
ds = _deserialize_dataset_from_zipped_saved_model(
sequence_value_proto.zipped_saved_model)
ds = tensorflow_utils.coerce_dataset_elements_to_tff_type_spec(
ds, element_type)
elif which_value == 'serialized_graph_def':
ds = _deserialize_dataset_from_graph_def(
sequence_value_proto.serialized_graph_def, element_type)
else:
raise NotImplementedError(
'Deserializing Sequences enocded as {!s} has not been implemented'.
format(which_value))
return ds, computation_types.SequenceType(element=element_type)