39be9adca1
We reference a bunch of things in the MetaGraph/GraphDef, so it makes sense to add it there rather than to the SavedModel directly. This is in preparation for non-experimental tf.saved_model.save/load symbols. We don't yet have an exposed symbol for loading object-based SavedModels, so this CL won't break anyone (despite moving around the proto and not checking the old location). RFC: https://github.com/tensorflow/community/pull/34 PiperOrigin-RevId: 234887195
437 lines
12 KiB
Python
437 lines
12 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Module that encodes (decodes) nested structures into (from) protos.
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The intended use is to serialize everything needed to restore a `Function` that
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was saved into a SavedModel. This may include concrete function inputs and
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outputs, signatures, function specs, etc.
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Example use:
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coder = nested_structure_coder.StructureCoder()
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# Encode into proto.
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signature_proto = coder.encode_structure(function.input_signature)
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# Decode into a Python object.
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restored_signature = coder.decode_proto(signature_proto)
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import functools
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import six
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from tensorflow.core.protobuf import struct_pb2
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.util import compat
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class NotEncodableError(Exception):
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"""Error raised when a coder cannot encode an object."""
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class StructureCoder(object):
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"""Encoder and decoder for nested structures into protos."""
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_codecs = []
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@classmethod
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def register_codec(cls, x):
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cls._codecs.append(x)
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@classmethod
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def _get_encoders(cls):
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return [(c.can_encode, c.do_encode) for c in cls._codecs]
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@classmethod
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def _get_decoders(cls):
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return [(c.can_decode, c.do_decode) for c in cls._codecs]
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def _map_structure(self, pyobj, coders):
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for can, do in coders:
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if can(pyobj):
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recursion_fn = functools.partial(self._map_structure, coders=coders)
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return do(pyobj, recursion_fn)
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raise NotEncodableError(
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"No encoder for object [%s] of type [%s]." % (str(pyobj), type(pyobj)))
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def encode_structure(self, nested_structure):
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"""Encodes nested structures composed of encodable types into a proto.
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Args:
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nested_structure: Structure to encode.
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Returns:
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Encoded proto.
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Raises:
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NotEncodableError: For values for which there are no encoders.
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"""
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return self._map_structure(nested_structure, self._get_encoders())
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def can_encode(self, nested_structure):
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"""Determines whether a nested structure can be encoded into a proto.
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Args:
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nested_structure: Structure to encode.
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Returns:
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True if the nested structured can be encoded.
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"""
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try:
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self.encode_structure(nested_structure)
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except NotEncodableError:
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return False
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return True
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def decode_proto(self, proto):
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"""Decodes proto representing a nested structure.
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Args:
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proto: Proto to decode.
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Returns:
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Decoded structure.
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Raises:
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NotEncodableError: For values for which there are no encoders.
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"""
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return self._map_structure(proto, self._get_decoders())
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class _ListCodec(object):
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"""Codec for lists."""
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def can_encode(self, pyobj):
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return isinstance(pyobj, list)
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def do_encode(self, list_value, encode_fn):
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encoded_list = struct_pb2.StructuredValue()
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encoded_list.list_value.CopyFrom(struct_pb2.ListValue())
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for element in list_value:
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encoded_list.list_value.values.add().CopyFrom(encode_fn(element))
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return encoded_list
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def can_decode(self, value):
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return value.HasField("list_value")
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def do_decode(self, value, decode_fn):
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return [decode_fn(element) for element in value.list_value.values]
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StructureCoder.register_codec(_ListCodec())
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def _is_tuple(obj):
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return not _is_named_tuple(obj) and isinstance(obj, tuple)
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def _is_named_tuple(instance):
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"""Returns True iff `instance` is a `namedtuple`.
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Args:
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instance: An instance of a Python object.
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Returns:
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True if `instance` is a `namedtuple`.
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"""
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if not isinstance(instance, tuple):
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return False
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return (hasattr(instance, "_fields") and
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isinstance(instance._fields, collections.Sequence) and
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all(isinstance(f, six.string_types) for f in instance._fields))
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class _TupleCodec(object):
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"""Codec for tuples."""
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def can_encode(self, pyobj):
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return _is_tuple(pyobj)
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def do_encode(self, tuple_value, encode_fn):
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encoded_tuple = struct_pb2.StructuredValue()
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encoded_tuple.tuple_value.CopyFrom(struct_pb2.TupleValue())
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for element in tuple_value:
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encoded_tuple.tuple_value.values.add().CopyFrom(encode_fn(element))
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return encoded_tuple
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def can_decode(self, value):
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return value.HasField("tuple_value")
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def do_decode(self, value, decode_fn):
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return tuple(decode_fn(element) for element in value.tuple_value.values)
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StructureCoder.register_codec(_TupleCodec())
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class _DictCodec(object):
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"""Codec for dicts."""
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def can_encode(self, pyobj):
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return isinstance(pyobj, dict)
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def do_encode(self, dict_value, encode_fn):
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encoded_dict = struct_pb2.StructuredValue()
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encoded_dict.dict_value.CopyFrom(struct_pb2.DictValue())
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for key, value in dict_value.items():
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encoded_dict.dict_value.fields[key].CopyFrom(encode_fn(value))
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return encoded_dict
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def can_decode(self, value):
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return value.HasField("dict_value")
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def do_decode(self, value, decode_fn):
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return {key: decode_fn(val) for key, val in value.dict_value.fields.items()}
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StructureCoder.register_codec(_DictCodec())
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class _NamedTupleCodec(object):
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"""Codec for namedtuples.
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Encoding and decoding a namedtuple reconstructs a namedtuple with a different
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actual Python type, but with same `typename` and `fields`.
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"""
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def can_encode(self, pyobj):
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return _is_named_tuple(pyobj)
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def do_encode(self, named_tuple_value, encode_fn):
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encoded_named_tuple = struct_pb2.StructuredValue()
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encoded_named_tuple.named_tuple_value.CopyFrom(struct_pb2.NamedTupleValue())
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encoded_named_tuple.named_tuple_value.name = \
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named_tuple_value.__class__.__name__
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for key in named_tuple_value._fields:
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pair = encoded_named_tuple.named_tuple_value.values.add()
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pair.key = key
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pair.value.CopyFrom(encode_fn(named_tuple_value._asdict()[key]))
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return encoded_named_tuple
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def can_decode(self, value):
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return value.HasField("named_tuple_value")
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def do_decode(self, value, decode_fn):
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key_value_pairs = value.named_tuple_value.values
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items = [(pair.key, decode_fn(pair.value)) for pair in key_value_pairs]
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named_tuple_type = collections.namedtuple(value.named_tuple_value.name,
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[item[0] for item in items])
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return named_tuple_type(**dict(items))
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StructureCoder.register_codec(_NamedTupleCodec())
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class _Float64Codec(object):
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"""Codec for floats."""
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def can_encode(self, pyobj):
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return isinstance(pyobj, float)
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def do_encode(self, float64_value, encode_fn):
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del encode_fn
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value = struct_pb2.StructuredValue()
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value.float64_value = float64_value
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return value
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def can_decode(self, value):
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return value.HasField("float64_value")
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def do_decode(self, value, decode_fn):
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del decode_fn
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return value.float64_value
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StructureCoder.register_codec(_Float64Codec())
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class _Int64Codec(object):
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"""Codec for Python integers (limited to 64 bit values)."""
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def can_encode(self, pyobj):
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return not isinstance(pyobj, bool) and isinstance(pyobj, int)
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def do_encode(self, int_value, encode_fn):
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del encode_fn
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value = struct_pb2.StructuredValue()
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value.int64_value = int_value
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return value
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def can_decode(self, value):
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return value.HasField("int64_value")
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def do_decode(self, value, decode_fn):
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del decode_fn
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return int(value.int64_value)
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StructureCoder.register_codec(_Int64Codec())
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class _StringCodec(object):
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"""Codec for strings.
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See StructuredValue.string_value in proto/struct.proto for more detailed
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explanation.
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"""
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def can_encode(self, pyobj):
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return isinstance(pyobj, str)
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def do_encode(self, string_value, encode_fn):
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del encode_fn
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value = struct_pb2.StructuredValue()
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value.string_value = string_value
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return value
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def can_decode(self, value):
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return value.HasField("string_value")
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def do_decode(self, value, decode_fn):
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del decode_fn
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return compat.as_str(value.string_value)
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StructureCoder.register_codec(_StringCodec())
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class _NoneCodec(object):
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"""Codec for None."""
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def can_encode(self, pyobj):
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return pyobj is None
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def do_encode(self, none_value, encode_fn):
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del encode_fn, none_value
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value = struct_pb2.StructuredValue()
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value.none_value.CopyFrom(struct_pb2.NoneValue())
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return value
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def can_decode(self, value):
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return value.HasField("none_value")
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def do_decode(self, value, decode_fn):
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del decode_fn, value
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return None
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StructureCoder.register_codec(_NoneCodec())
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class _BoolCodec(object):
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"""Codec for booleans."""
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def can_encode(self, pyobj):
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return isinstance(pyobj, bool)
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def do_encode(self, bool_value, encode_fn):
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del encode_fn
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value = struct_pb2.StructuredValue()
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value.bool_value = bool_value
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return value
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def can_decode(self, value):
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return value.HasField("bool_value")
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def do_decode(self, value, decode_fn):
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del decode_fn
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return value.bool_value
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StructureCoder.register_codec(_BoolCodec())
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class _TensorShapeCodec(object):
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"""Codec for `TensorShape`."""
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def can_encode(self, pyobj):
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return isinstance(pyobj, tensor_shape.TensorShape)
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def do_encode(self, tensor_shape_value, encode_fn):
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del encode_fn
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encoded_tensor_shape = struct_pb2.StructuredValue()
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encoded_tensor_shape.tensor_shape_value.CopyFrom(
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tensor_shape_value.as_proto())
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return encoded_tensor_shape
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def can_decode(self, value):
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return value.HasField("tensor_shape_value")
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def do_decode(self, value, decode_fn):
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del decode_fn
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return tensor_shape.TensorShape(value.tensor_shape_value)
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StructureCoder.register_codec(_TensorShapeCodec())
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class _TensorTypeCodec(object):
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"""Codec for `TensorType`."""
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def can_encode(self, pyobj):
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return isinstance(pyobj, dtypes.DType)
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def do_encode(self, tensor_dtype_value, encode_fn):
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del encode_fn
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encoded_tensor_type = struct_pb2.StructuredValue()
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encoded_tensor_type.tensor_dtype_value = tensor_dtype_value.as_datatype_enum
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return encoded_tensor_type
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def can_decode(self, value):
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return value.HasField("tensor_dtype_value")
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def do_decode(self, value, decode_fn):
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del decode_fn
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return dtypes.DType(value.tensor_dtype_value)
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StructureCoder.register_codec(_TensorTypeCodec())
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class _TensorSpecCodec(object):
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"""Codec for `TensorSpec`."""
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def can_encode(self, pyobj):
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return isinstance(pyobj, tensor_spec.TensorSpec)
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def do_encode(self, tensor_spec_value, encode_fn):
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encoded_tensor_spec = struct_pb2.StructuredValue()
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encoded_tensor_spec.tensor_spec_value.CopyFrom(
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struct_pb2.TensorSpecProto(
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shape=encode_fn(tensor_spec_value.shape).tensor_shape_value,
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dtype=encode_fn(tensor_spec_value.dtype).tensor_dtype_value,
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name=tensor_spec_value.name))
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return encoded_tensor_spec
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def can_decode(self, value):
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return value.HasField("tensor_spec_value")
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def do_decode(self, value, decode_fn):
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return tensor_spec.TensorSpec(
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shape=decode_fn(
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struct_pb2.StructuredValue(
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tensor_shape_value=value.tensor_spec_value.shape)),
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dtype=decode_fn(
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struct_pb2.StructuredValue(
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tensor_dtype_value=value.tensor_spec_value.dtype)),
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name=value.tensor_spec_value.name)
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StructureCoder.register_codec(_TensorSpecCodec())
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