def _deserialize_tensor_value(
value_proto: executor_pb2.Value) -> _DeserializeReturnType:
"""Deserializes a tensor value from `executor_pb2.Value`.
Args:
value_proto: An instance of `executor_pb2.Value`.
Returns:
A tuple `(value, type_spec)`, where `value` is a Numpy array that represents
the deserialized value, and `type_spec` is an instance of `tff.TensorType`
that represents its type.
Raises:
TypeError: If the arguments are of the wrong types.
ValueError: If the value is malformed.
"""
which_value = value_proto.WhichOneof('value')
if which_value != 'tensor':
raise ValueError('Not a tensor value: {}'.format(which_value))
# TODO(b/134543154): Find some way of creating the `TensorProto` using a
# proper public interface rather than creating a dummy value that we will
# overwrite right away.
tensor_proto = tf.make_tensor_proto(values=0)
if not value_proto.tensor.Unpack(tensor_proto):
raise ValueError('Unable to unpack the received tensor value.')
tensor_value = tf.make_ndarray(tensor_proto)
value_type = computation_types.TensorType(
dtype=tf.dtypes.as_dtype(tensor_proto.dtype),
shape=tf.TensorShape(tensor_proto.tensor_shape))
return tensor_value, value_type
def deserialize_value(
value_proto: executor_pb2.Value) -> _DeserializeReturnType:
"""Deserializes a value (of any type) from `executor_pb2.Value`.
Args:
value_proto: An instance of `executor_pb2.Value`.
Returns:
A tuple `(value, type_spec)`, where `value` is a deserialized
representation of the transmitted value (e.g., Numpy array, or a
`pb.Computation` instance), and `type_spec` is an instance of
`tff.TensorType` that represents its type.
Raises:
TypeError: If the arguments are of the wrong types.
ValueError: If the value is malformed.
"""
py_typecheck.check_type(value_proto, executor_pb2.Value)
which_value = value_proto.WhichOneof('value')
if which_value == 'tensor':
return _deserialize_tensor_value(value_proto)
elif which_value == 'computation':
return _deserialize_computation(value_proto)
elif which_value == 'sequence':
return _deserialize_sequence_value(value_proto.sequence)
elif which_value == 'struct':
return _deserialize_struct_value(value_proto)
elif which_value == 'federated':
return _deserialize_federated_value(value_proto)
else:
raise ValueError(
'Unable to deserialize a value of type {}.'.format(which_value))
def deserialize_value(
value_proto: executor_pb2.Value,
type_hint: Optional[computation_types.Type] = None
) -> _DeserializeReturnType:
"""Deserializes a value (of any type) from `executor_pb2.Value`.
Args:
value_proto: An instance of `executor_pb2.Value`.
type_hint: A `comptuations_types.Type` that hints at what the value type
should be for executors that only return values.
Returns:
A tuple `(value, type_spec)`, where `value` is a deserialized
representation of the transmitted value (e.g., Numpy array, or a
`pb.Computation` instance), and `type_spec` is an instance of
`tff.TensorType` that represents its type.
Raises:
TypeError: If the arguments are of the wrong types.
ValueError: If the value is malformed.
"""
if not hasattr(value_proto, 'WhichOneof'):
raise TypeError(
'`value_proto` must be a protocol buffer message with a '
'`value` oneof field.')
which_value = value_proto.WhichOneof('value')
if which_value == 'tensor':
return _deserialize_tensor_value(value_proto)
elif which_value == 'computation':
return _deserialize_computation(value_proto)
elif which_value == 'sequence':
return _deserialize_sequence_value(value_proto.sequence, type_hint)
elif which_value == 'struct':
return _deserialize_struct_value(value_proto, type_hint)
elif which_value == 'federated':
return _deserialize_federated_value(value_proto, type_hint)
else:
raise ValueError(
'Unable to deserialize a value of type {}.'.format(which_value))