tensorflow Protocol Buffer
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____tz_zs
Protocol Buffer 是谷歌开发的一种语言无关、平台无关、可扩展的序列化结构数据格式，在 tensorflow 中可以理解为处理结构化数据的工具，用于序列化结构化数据。
关于Protocol Buffer的介绍： https://developers.google.com/protocol-buffers/docs/overview
Protocol Buffer的编码方式： https://developers.google.com/protocol-buffers/docs/encoding
Protocol Bufferdf 定义数据格式的文件一般保存在.proto文件中。每一个 message 代表了一类结构化数据，message 里面定义了每一个属性的类型和名字，Protocol Buffer 里属性的类型可以是像布尔型、整数型、实数型、字符型这些基本类型，也可以是另一个 message。可用 required（可选的）、required（必须的）、repeated（可重复的）说明属性是否可选的、必须的或者可重复的。
以json格式导出MetaGraph元图的数据
元图是由 MetaGraphDef Protocol Buffer 定义的一类结构化数据
# -*- coding: utf-8 -*-"""@author: tz_zs以json格式导出MetaGraph元图的数据"""import tensorflow as tfv1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1")v2 = tf.Variable(tf.constant(2.0, shape=[1]), name="v2")result = v1 + v2saver = tf.train.Saver()saver.export_meta_graph("/path/to/model.ckpt.json", as_text=True)meta_info_def { stripped_op_list { op { name: "Add" input_arg { name: "x" type_attr: "T" } input_arg { name: "y" type_attr: "T" } output_arg { name: "z" type_attr: "T" } attr { name: "T" type: "type" allowed_values { list { type: DT_HALF type: DT_FLOAT type: DT_DOUBLE type: DT_UINT8 type: DT_INT8 type: DT_INT16 type: DT_INT32 type: DT_INT64 type: DT_COMPLEX64 type: DT_COMPLEX128 type: DT_STRING } } } } op { name: "Assign" input_arg { name: "ref" type_attr: "T" is_ref: true } input_arg { name: "value" type_attr: "T" } output_arg { name: "output_ref" type_attr: "T" is_ref: true } attr { name: "T" type: "type" } attr { name: "validate_shape" type: "bool" default_value { b: true } } attr { name: "use_locking" type: "bool" default_value { b: true } } allows_uninitialized_input: true } op { name: "Const" output_arg { name: "output" type_attr: "dtype" } attr { name: "value" type: "tensor" } attr { name: "dtype" type: "type" } } op { name: "Identity" input_arg { name: "input" type_attr: "T" } output_arg { name: "output" type_attr: "T" } attr { name: "T" type: "type" } } op { name: "NoOp" } op { name: "RestoreV2" input_arg { name: "prefix" type: DT_STRING } input_arg { name: "tensor_names" type: DT_STRING } input_arg { name: "shape_and_slices" type: DT_STRING } output_arg { name: "tensors" type_list_attr: "dtypes" } attr { name: "dtypes" type: "list(type)" has_minimum: true minimum: 1 } } op { name: "SaveV2" input_arg { name: "prefix" type: DT_STRING } input_arg { name: "tensor_names" type: DT_STRING } input_arg { name: "shape_and_slices" type: DT_STRING } input_arg { name: "tensors" type_list_attr: "dtypes" } attr { name: "dtypes" type: "list(type)" has_minimum: true minimum: 1 } } op { name: "VariableV2" output_arg { name: "ref" type_attr: "dtype" is_ref: true } attr { name: "shape" type: "shape" } attr { name: "dtype" type: "type" } attr { name: "container" type: "string" default_value { s: "" } } attr { name: "shared_name" type: "string" default_value { s: "" } } is_stateful: true } } tensorflow_version: "1.2.1" tensorflow_git_version: "b\'unknown\'"}graph_def { node { name: "Const" op: "Const" attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "value" value { tensor { dtype: DT_FLOAT tensor_shape { dim { size: 1 } } float_val: 1.0 } } } } node { name: "v1" op: "VariableV2" attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "container" value { s: "" } } attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 1 } } } } attr { key: "shared_name" value { s: "" } } } node { name: "v1/Assign" op: "Assign" input: "v1" input: "Const" attr { key: "T" value { type: DT_FLOAT } } attr { key: "_class" value { list { s: "loc:@v1" } } } attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "use_locking" value { b: true } } attr { key: "validate_shape" value { b: true } } } node { name: "v1/read" op: "Identity" input: "v1" attr { key: "T" value { type: DT_FLOAT } } attr { key: "_class" value { list { s: "loc:@v1" } } } attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } } node { name: "Const_1" op: "Const" attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "value" value { tensor { dtype: DT_FLOAT tensor_shape { dim { size: 1 } } float_val: 2.0 } } } } node { name: "v2" op: "VariableV2" attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "container" value { s: "" } } attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 1 } } } } attr { key: "shared_name" value { s: "" } } } node { name: "v2/Assign" op: "Assign" input: "v2" input: "Const_1" attr { key: "T" value { type: DT_FLOAT } } attr { key: "_class" value { list { s: "loc:@v2" } } } attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "use_locking" value { b: true } } attr { key: "validate_shape" value { b: true } } } node { name: "v2/read" op: "Identity" input: "v2" attr { key: "T" value { type: DT_FLOAT } } attr { key: "_class" value { list { s: "loc:@v2" } } } attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } } node { name: "add" op: "Add" input: "v1/read" input: "v2/read" attr { key: "T" value { type: DT_FLOAT } } attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } } node { name: "save/Const" op: "Const" attr { key: "_output_shapes" value { list { shape { } } } } attr { key: "dtype" value { type: DT_STRING } } attr { key: "value" value { tensor { dtype: DT_STRING tensor_shape { } string_val: "model" } } } } node { name: "save/SaveV2/tensor_names" op: "Const" attr { key: "_output_shapes" value { list { shape { dim { size: 2 } } } } } attr { key: "dtype" value { type: DT_STRING } } attr { key: "value" value { tensor { dtype: DT_STRING tensor_shape { dim { size: 2 } } string_val: "v1" string_val: "v2" } } } } node { name: "save/SaveV2/shape_and_slices" op: "Const" attr { key: "_output_shapes" value { list { shape { dim { size: 2 } } } } } attr { key: "dtype" value { type: DT_STRING } } attr { key: "value" value { tensor { dtype: DT_STRING tensor_shape { dim { size: 2 } } string_val: "" string_val: "" } } } } node { name: "save/SaveV2" op: "SaveV2" input: "save/Const" input: "save/SaveV2/tensor_names" input: "save/SaveV2/shape_and_slices" input: "v1" input: "v2" attr { key: "dtypes" value { list { type: DT_FLOAT type: DT_FLOAT } } } } node { name: "save/control_dependency" op: "Identity" input: "save/Const" input: "^save/SaveV2" attr { key: "T" value { type: DT_STRING } } attr { key: "_class" value { list { s: "loc:@save/Const" } } } attr { key: "_output_shapes" value { list { shape { } } } } } node { name: "save/RestoreV2/tensor_names" op: "Const" attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "dtype" value { type: DT_STRING } } attr { key: "value" value { tensor { dtype: DT_STRING tensor_shape { dim { size: 1 } } string_val: "v1" } } } } node { name: "save/RestoreV2/shape_and_slices" op: "Const" attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "dtype" value { type: DT_STRING } } attr { key: "value" value { tensor { dtype: DT_STRING tensor_shape { dim { size: 1 } } string_val: "" } } } } node { name: "save/RestoreV2" op: "RestoreV2" input: "save/Const" input: "save/RestoreV2/tensor_names" input: "save/RestoreV2/shape_and_slices" attr { key: "_output_shapes" value { list { shape { unknown_rank: true } } } } attr { key: "dtypes" value { list { type: DT_FLOAT } } } } node { name: "save/Assign" op: "Assign" input: "v1" input: "save/RestoreV2" attr { key: "T" value { type: DT_FLOAT } } attr { key: "_class" value { list { s: "loc:@v1" } } } attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "use_locking" value { b: true } } attr { key: "validate_shape" value { b: true } } } node { name: "save/RestoreV2_1/tensor_names" op: "Const" attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "dtype" value { type: DT_STRING } } attr { key: "value" value { tensor { dtype: DT_STRING tensor_shape { dim { size: 1 } } string_val: "v2" } } } } node { name: "save/RestoreV2_1/shape_and_slices" op: "Const" attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "dtype" value { type: DT_STRING } } attr { key: "value" value { tensor { dtype: DT_STRING tensor_shape { dim { size: 1 } } string_val: "" } } } } node { name: "save/RestoreV2_1" op: "RestoreV2" input: "save/Const" input: "save/RestoreV2_1/tensor_names" input: "save/RestoreV2_1/shape_and_slices" attr { key: "_output_shapes" value { list { shape { unknown_rank: true } } } } attr { key: "dtypes" value { list { type: DT_FLOAT } } } } node { name: "save/Assign_1" op: "Assign" input: "v2" input: "save/RestoreV2_1" attr { key: "T" value { type: DT_FLOAT } } attr { key: "_class" value { list { s: "loc:@v2" } } } attr { key: "_output_shapes" value { list { shape { dim { size: 1 } } } } } attr { key: "use_locking" value { b: true } } attr { key: "validate_shape" value { b: true } } } node { name: "save/restore_all" op: "NoOp" input: "^save/Assign" input: "^save/Assign_1" } versions { producer: 22 }}saver_def { filename_tensor_name: "save/Const:0" save_tensor_name: "save/control_dependency:0" restore_op_name: "save/restore_all" max_to_keep: 5 keep_checkpoint_every_n_hours: 10000.0 version: V2}collection_def { key: "trainable_variables" value { bytes_list { value: "\n\004v1:0\022\tv1/Assign\032\tv1/read:0" value: "\n\004v2:0\022\tv2/Assign\032\tv2/read:0" } }}collection_def { key: "variables" value { bytes_list { value: "\n\004v1:0\022\tv1/Assign\032\tv1/read:0" value: "\n\004v2:0\022\tv2/Assign\032\tv2/read:0" } }}tensorflow 元图的 Protocol Buffer 数据格式的定义文件 meta_graph.proto
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto
syntax = "proto3";package tensorflow;option cc_enable_arenas = true;option java_outer_classname = "MetaGraphProtos";option java_multiple_files = true;option java_package = "org.tensorflow.framework";import "google/protobuf/any.proto";import "tensorflow/core/framework/graph.proto";import "tensorflow/core/framework/op_def.proto";import "tensorflow/core/framework/tensor_shape.proto";import "tensorflow/core/framework/types.proto";import "tensorflow/core/protobuf/saver.proto";// NOTE: This protocol buffer is evolving, and will go through revisions in the// coming months.//// Protocol buffer containing the following which are necessary to restart// training, run inference. It can be used to serialize/de-serialize memory// objects necessary for running computation in a graph when crossing the// process boundary. It can be used for long term storage of graphs,// cross-language execution of graphs, etc.// MetaInfoDef// GraphDef// SaverDef// CollectionDef// TensorInfo// SignatureDefmessage MetaGraphDef { // Meta information regarding the graph to be exported. To be used by users // of this protocol buffer to encode information regarding their meta graph. message MetaInfoDef { // User specified Version string. Can be the name of the model and revision, // steps this model has been trained to, etc. string meta_graph_version = 1; // A copy of the OpDefs used by the producer of this graph_def. // Descriptions and Ops not used in graph_def are stripped out. OpList stripped_op_list = 2; // A serialized protobuf. Can be the time this meta graph is created, or // modified, or name of the model. google.protobuf.Any any_info = 3; // User supplied tag(s) on the meta_graph and included graph_def. // // MetaGraphDefs should be tagged with their capabilities or use-cases. // Examples: "train", "serve", "gpu", "tpu", etc. // These tags enable loaders to access the MetaGraph(s) appropriate for a // specific use-case or runtime environment. repeated string tags = 4; // The __version__ string of the tensorflow build used to write this graph. // This will be populated by the framework, which will overwrite any user // supplied value. string tensorflow_version = 5; // The __git_version__ string of the tensorflow build used to write this // graph. This will be populated by the framework, which will overwrite any // user supplied value. string tensorflow_git_version = 6; } MetaInfoDef meta_info_def = 1; // GraphDef. GraphDef graph_def = 2; // SaverDef. SaverDef saver_def = 3; // collection_def: Map from collection name to collections. // See CollectionDef section for details. map<string, CollectionDef> collection_def = 4; // signature_def: Map from user supplied key for a signature to a single // SignatureDef. map<string, SignatureDef> signature_def = 5; // Asset file def to be used with the defined graph. repeated AssetFileDef asset_file_def = 6;}// CollectionDef should cover most collections.// To add a user-defined collection, do one of the following:// 1. For simple data types, such as string, int, float:// tf.add_to_collection("your_collection_name", your_simple_value)// strings will be stored as bytes_list.//// 2. For Protobuf types, there are three ways to add them:// 1) tf.add_to_collection("your_collection_name",// your_proto.SerializeToString())//// collection_def {// key: "user_defined_bytes_collection"// value {// bytes_list {// value: "queue_name: \"test_queue\"\n"// }// }// }//// or//// 2) tf.add_to_collection("your_collection_name", str(your_proto))//// collection_def {// key: "user_defined_string_collection"// value {// bytes_list {// value: "\n\ntest_queue"// }// }// }//// or//// 3) any_buf = any_pb2.Any()// tf.add_to_collection("your_collection_name",// any_buf.Pack(your_proto))//// collection_def {// key: "user_defined_any_collection"// value {// any_list {// value {// type_url: "type.googleapis.com/tensorflow.QueueRunnerDef"// value: "\n\ntest_queue"// }// }// }// }//// 3. For Python objects, implement to_proto() and from_proto(), and register// them in the following manner:// ops.register_proto_function("your_collection_name",// proto_type,// to_proto=YourPythonObject.to_proto,// from_proto=YourPythonObject.from_proto)// These functions will be invoked to serialize and de-serialize the// collection. For example,// ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES,// proto_type=variable_pb2.VariableDef,// to_proto=Variable.to_proto,// from_proto=Variable.from_proto)message CollectionDef { // NodeList is used for collecting nodes in graph. For example // collection_def { // key: "summaries" // value { // node_list { // value: "input_producer/ScalarSummary:0" // value: "shuffle_batch/ScalarSummary:0" // value: "ImageSummary:0" // } // } message NodeList { repeated string value = 1; } // BytesList is used for collecting strings and serialized protobufs. For // example: // collection_def { // key: "trainable_variables" // value { // bytes_list { // value: "\n\017conv1/weights:0\022\024conv1/weights/Assign // \032\024conv1/weights/read:0" // value: "\n\016conv1/biases:0\022\023conv1/biases/Assign\032 // \023conv1/biases/read:0" // } // } // } message BytesList { repeated bytes value = 1; } // Int64List is used for collecting int, int64 and long values. message Int64List { repeated int64 value = 1 [packed = true]; } // FloatList is used for collecting float values. message FloatList { repeated float value = 1 [packed = true]; } // AnyList is used for collecting Any protos. message AnyList { repeated google.protobuf.Any value = 1; } oneof kind { NodeList node_list = 1; BytesList bytes_list = 2; Int64List int64_list = 3; FloatList float_list = 4; AnyList any_list = 5; }}// Information about a Tensor necessary for feeding or retrieval.message TensorInfo { // For sparse tensors, The COO encoding stores a triple of values, indices, // and shape. message CooSparse { // The shape of the values Tensor is [?]. Its dtype must be the dtype of // the SparseTensor as a whole, given in the enclosing TensorInfo. string values_tensor_name = 1; // The indices Tensor must have dtype int64 and shape [?, ?]. string indices_tensor_name = 2; // The dynamic logical shape represented by the SparseTensor is recorded in // the Tensor referenced here. It must have dtype int64 and shape [?]. string dense_shape_tensor_name = 3; } oneof encoding { // For dense `Tensor`s, the name of the tensor in the graph. string name = 1; // There are many possible encodings of sparse matrices // (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow // uses only the COO encoding. This is supported and documented in the // SparseTensor Python class. CooSparse coo_sparse = 4; } DataType dtype = 2; // The static shape should be recorded here, to the extent that it can // be known in advance. In the case of a SparseTensor, this field describes // the logical shape of the represented tensor (aka dense_shape). TensorShapeProto tensor_shape = 3;}// SignatureDef defines the signature of a computation supported by a TensorFlow// graph.//// For example, a model with two loss computations, sharing a single input,// might have the following signature_def map.//// Note that across the two SignatureDefs "loss_A" and "loss_B", the input key,// output key, and method_name are identical, and will be used by system(s) that// implement or rely upon this particular loss method. The output tensor names// differ, demonstrating how different outputs can exist for the same method.//// signature_def {// key: "loss_A"// value {// inputs {// key: "input"// value {// name: "input:0"// dtype: DT_STRING// tensor_shape: ...// }// }// outputs {// key: "loss_output"// value {// name: "loss_output_A:0"// dtype: DT_FLOAT// tensor_shape: ...// }// }// }// ...// method_name: "some/package/compute_loss"// }// signature_def {// key: "loss_B"// value {// inputs {// key: "input"// value {// name: "input:0"// dtype: DT_STRING// tensor_shape: ...// }// }// outputs {// key: "loss_output"// value {// name: "loss_output_B:0"// dtype: DT_FLOAT// tensor_shape: ...// }// }// }// ...// method_name: "some/package/compute_loss"// }message SignatureDef { // Named input parameters. map<string, TensorInfo> inputs = 1; // Named output parameters. map<string, TensorInfo> outputs = 2; // Extensible method_name information enabling third-party users to mark a // SignatureDef as supporting a particular method. This enables producers and // consumers of SignatureDefs, e.g. a model definition library and a serving // library to have a clear hand-off regarding the semantics of a computation. // // Note that multiple SignatureDefs in a single MetaGraphDef may have the same // method_name. This is commonly used to support multi-headed computation, // where a single graph computation may return multiple results. string method_name = 3;}// An asset file def for a single file or a set of sharded files with the same// name.message AssetFileDef { // The tensor to bind the asset filename to. TensorInfo tensor_info = 1; // The filename within an assets directory. Note: does not include the path // prefix, i.e. directories. For an asset at /tmp/path/vocab.txt, the filename // would be "vocab.txt". string filename = 2;}阅读全文
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