tensorflow/core/framework/tensor.proto

syntax = "proto3";

package tensorflow;

import "tensorflow/core/framework/resource_handle.proto";
import "tensorflow/core/framework/tensor_shape.proto";
import "tensorflow/core/framework/types.proto";

option cc_enable_arenas = true;
option java_outer_classname = "TensorProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.framework";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto";

// Protocol buffer representing a tensor.
message TensorProto {
  // Data type of the tensor.
  DataType dtype = 1;

  // Shape of the tensor.  TODO(touts): sort out the 0-rank issues.
  TensorShapeProto tensor_shape = 2;

  // Only one of the representations below is set, one of "tensor_contents" and
  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
  // contain repeated fields it would require another extra set of messages.

  // Version number.
  //
  // In version 0, if the "repeated xxx" representations contain only one
  // element, that element is repeated to fill the shape.  This makes it easy
  // to represent a constant Tensor with a single value.
  int32 version_number = 3;

  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
  // can be used for all tensor types. The purpose of this representation is to
  // reduce serialization overhead during RPC call by avoiding serialization of
  // many repeated small items.
  bytes tensor_content = 4;

  // Type specific representations that make it easy to create tensor protos in
  // all languages.  Only the representation corresponding to "dtype" can
  // be set.  The values hold the flattened representation of the tensor in
  // row major order.

  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
  // have some pointless zero padding for each value here.
  repeated int32 half_val = 13 [packed = true];

  // DT_FLOAT.
  repeated float float_val = 5 [packed = true];

  // DT_DOUBLE.
  repeated double double_val = 6 [packed = true];

  // DT_INT32, DT_INT16, DT_UINT16, DT_INT8, DT_UINT8.
  repeated int32 int_val = 7 [packed = true];

  // DT_STRING
  repeated bytes string_val = 8;

  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
  // and imaginary parts of i-th single precision complex.
  repeated float scomplex_val = 9 [packed = true];

  // DT_INT64
  repeated int64 int64_val = 10 [packed = true];

  // DT_BOOL
  repeated bool bool_val = 11 [packed = true];

  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
  // and imaginary parts of i-th double precision complex.
  repeated double dcomplex_val = 12 [packed = true];

  // DT_RESOURCE
  repeated ResourceHandleProto resource_handle_val = 14;

  // DT_VARIANT
  repeated VariantTensorDataProto variant_val = 15;

  // DT_UINT32
  repeated uint32 uint32_val = 16 [packed = true];

  // DT_UINT64
  repeated uint64 uint64_val = 17 [packed = true];

  // DT_FLOAT8_*, use variable-sized set of bytes
  // (i.e. the equivalent of repeated uint8, if such a thing existed).
  bytes float8_val = 18;
}

// Protocol buffer representing the serialization format of DT_VARIANT tensors.
message VariantTensorDataProto {
  // Name of the type of objects being serialized.
  string type_name = 1;
  // Portions of the object that are not Tensors.
  bytes metadata = 2;
  // Tensors contained within objects being serialized.
  repeated TensorProto tensors = 3;
}