android-12.0.0_r34/s

syntax = "proto3";

package tensorflow;

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/saved_object_graph.proto";
import "tensorflow/core/protobuf/saver.proto";
import "tensorflow/core/protobuf/struct.proto";

option cc_enable_arenas = true;
option java_outer_classname = "MetaGraphProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.framework";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_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
//   SignatureDef
message 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;

    // A flag to denote whether default-valued attrs have been stripped from
    // the nodes in this graph_def.
    bool stripped_default_attrs = 7;

    // FunctionDef name to aliases mapping.
    map<string, string> function_aliases = 8;
  }
  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;

  // Extra information about the structure of functions and stateful objects.
  SavedObjectGraph object_graph_def = 7;
}

// 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;
  }

  // Generic encoding for composite tensors.
  message CompositeTensor {
    // The serialized TypeSpec for the composite tensor.
    TypeSpecProto type_spec = 1;

    // A TensorInfo for each flattened component tensor.
    repeated TensorInfo components = 2;
  }

  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;
    // Generic encoding for CompositeTensors.
    CompositeTensor composite_tensor = 5;
  }
  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, in a MetaGraphDef message.
//
// 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;
}