Path: blob/master/modules/dnn/src/onnx/opencv-onnx.proto
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//
// WARNING: This file is automatically generated! Please edit onnx.in.proto.
//
// Copyright (c) Facebook Inc. and Microsoft Corporation.
// Licensed under the MIT license.
syntax = "proto2";
package opencv_onnx;
// Overview
//
// ONNX is an open specification that is comprised of the following components:
//
// 1) A definition of an extensible computation graph model.
// 2) Definitions of standard data types.
// 3) Definitions of built-in operators.
//
// This document describes the syntax of models and their computation graphs,
// as well as the standard data types. Together, they are referred to as the ONNX
// Intermediate Representation, or 'IR' for short.
//
// The normative semantic specification of the ONNX IR is found in docs/IR.md.
// Definitions of the built-in neural network operators may be found in docs/Operators.md.
// Notes
//
// Release
//
// We are still in the very early stage of defining ONNX. The current
// version of ONNX is a starting point. While we are actively working
// towards a complete spec, we would like to get the community involved
// by sharing our working version of ONNX.
//
// Protobuf compatibility
//
// To simplify framework compatibility, ONNX is defined using the subset of protobuf
// that is compatible with both protobuf v2 and v3. This means that we do not use any
// protobuf features that are only available in one of the two versions.
//
// Here are the most notable contortions we have to carry out to work around
// these limitations:
//
// - No 'map' (added protobuf 3.0). We instead represent mappings as lists
// of key-value pairs, where order does not matter and duplicates
// are not allowed.
// Versioning
//
// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
//
// To be compatible with both proto2 and proto3, we will use a version number
// that is not defined by the default value but an explicit enum number.
enum Version {
// proto3 requires the first enum value to be zero.
// We add this just to appease the compiler.
_START_VERSION = 0;
// The version field is always serialized and we will use it to store the
// version that the graph is generated from. This helps us set up version
// control.
// For the IR, we are using simple numbers starting with with 0x00000001,
// which was the version we published on Oct 10, 2017.
IR_VERSION_2017_10_10 = 0x0000000000000001;
// IR_VERSION 2 published on Oct 30, 2017
// - Added type discriminator to AttributeProto to support proto3 users
IR_VERSION_2017_10_30 = 0x0000000000000002;
// IR VERSION 3 published on Nov 3, 2017
// - For operator versioning:
// - Added new message OperatorSetIdProto
// - Added opset_import in ModelProto
// - For vendor extensions, added domain in NodeProto
IR_VERSION = 0x0000000000000003;
}
// Attributes
//
// A named attribute containing either singular float, integer, string, graph,
// and tensor values, or repeated float, integer, string, graph, and tensor values.
// An AttributeProto MUST contain the name field, and *only one* of the
// following content fields, effectively enforcing a C/C++ union equivalent.
message AttributeProto {
// Note: this enum is structurally identical to the OpSchema::AttrType
// enum defined in schema.h. If you rev one, you likely need to rev the other.
enum AttributeType {
UNDEFINED = 0;
FLOAT = 1;
INT = 2;
STRING = 3;
TENSOR = 4;
GRAPH = 5;
FLOATS = 6;
INTS = 7;
STRINGS = 8;
TENSORS = 9;
GRAPHS = 10;
}
// The name field MUST be present for this version of the IR.
optional string name = 1; // namespace Attribute
// if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
// In this case, this AttributeProto does not contain data, and it's a reference of attribute
// in parent scope.
// NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
optional string ref_attr_name = 21;
// A human-readable documentation for this attribute. Markdown is allowed.
optional string doc_string = 13;
// The type field MUST be present for this version of the IR.
// For 0.0.1 versions of the IR, this field was not defined, and
// implementations needed to use has_field hueristics to determine
// which value field was in use. For IR_VERSION 0.0.2 or later, this
// field MUST be set and match the f|i|s|t|... field in use. This
// change was made to accomodate proto3 implementations.
optional AttributeType type = 20; // discriminator that indicates which field below is in use
// Exactly ONE of the following fields must be present for this version of the IR
optional float f = 2; // float
optional int64 i = 3; // int
optional bytes s = 4; // UTF-8 string
optional TensorProto t = 5; // tensor value
optional GraphProto g = 6; // graph
// Do not use field below, it's deprecated.
// optional ValueProto v = 12; // value - subsumes everything but graph
repeated float floats = 7; // list of floats
repeated int64 ints = 8; // list of ints
repeated bytes strings = 9; // list of UTF-8 strings
repeated TensorProto tensors = 10; // list of tensors
repeated GraphProto graphs = 11; // list of graph
}
// Defines information on value, including the name, the type, and
// the shape of the value.
message ValueInfoProto {
// This field MUST be present in this version of the IR.
optional string name = 1; // namespace Value
// This field MUST be present in this version of the IR.
optional TypeProto type = 2;
// A human-readable documentation for this value. Markdown is allowed.
optional string doc_string = 3;
}
// Nodes
//
// Computation graphs are made up of a DAG of nodes, which represent what is
// commonly called a "layer" or "pipeline stage" in machine learning frameworks.
//
// For example, it can be a node of type "Conv" that takes in an image, a filter
// tensor and a bias tensor, and produces the convolved output.
message NodeProto {
repeated string input = 1; // namespace Value
repeated string output = 2; // namespace Value
// An optional identifier for this node in a graph.
// This field MAY be absent in ths version of the IR.
optional string name = 3; // namespace Node
// The symbolic identifier of the Operator to execute.
optional string op_type = 4; // namespace Operator
// The domain of the OperatorSet that specifies the operator named by op_type.
optional string domain = 7; // namespace Domain
// Additional named attributes.
repeated AttributeProto attribute = 5;
// A human-readable documentation for this node. Markdown is allowed.
optional string doc_string = 6;
}
// Models
//
// ModelProto is a top-level file/container format for bundling a ML model and
// associating its computation graph with metadata.
//
// The semantics of the model are described by the associated GraphProto.
message ModelProto {
// The version of the IR this model targets. See Version enum above.
// This field MUST be present.
optional int64 ir_version = 1;
// The OperatorSets this model relies on.
// All ModelProtos MUST have at least one entry that
// specifies which version of the ONNX OperatorSet is
// being imported.
//
// All nodes in the ModelProto's graph will bind against the operator
// with the same-domain/same-op_type operator with the HIGHEST version
// in the referenced operator sets.
repeated OperatorSetIdProto opset_import = 8;
// The name of the framework or tool used to generate this model.
// This field SHOULD be present to indicate which implementation/tool/framework
// emitted the model.
optional string producer_name = 2;
// The version of the framework or tool used to generate this model.
// This field SHOULD be present to indicate which implementation/tool/framework
// emitted the model.
optional string producer_version = 3;
// Domain name of the model.
// We use reverse domain names as name space indicators. For example:
// `com.facebook.fair` or `com.microsoft.cognitiveservices`
//
// Together with `model_version` and GraphProto.name, this forms the unique identity of
// the graph.
optional string domain = 4;
// The version of the graph encoded. See Version enum below.
optional int64 model_version = 5;
// A human-readable documentation for this model. Markdown is allowed.
optional string doc_string = 6;
// The parameterized graph that is evaluated to execute the model.
optional GraphProto graph = 7;
// Named metadata values; keys should be distinct.
repeated StringStringEntryProto metadata_props = 14;
};
// StringStringEntryProto follows the pattern for cross-proto-version maps.
// See https://developers.google.com/protocol-buffers/docs/proto3#maps
message StringStringEntryProto {
optional string key = 1;
optional string value= 2;
};
// Graphs
//
// A graph defines the computational logic of a model and is comprised of a parameterized
// list of nodes that form a directed acyclic graph based on their inputs and outputs.
// This is the equivalent of the "network" or "graph" in many deep learning
// frameworks.
message GraphProto {
// The nodes in the graph, sorted topologically.
repeated NodeProto node = 1;
// The name of the graph.
optional string name = 2; // namespace Graph
// A list of named tensor values, used to specify constant inputs of the graph.
// Each TensorProto entry must have a distinct name (within the list) that
// also appears in the input list.
repeated TensorProto initializer = 5;
// A human-readable documentation for this graph. Markdown is allowed.
optional string doc_string = 10;
// The inputs and outputs of the graph.
repeated ValueInfoProto input = 11;
repeated ValueInfoProto output = 12;
// Information for the values in the graph. The ValueInfoProto.name's
// must be distinct. It is optional for a value to appear in value_info list.
repeated ValueInfoProto value_info = 13;
// DO NOT USE the following fields, they were deprecated from earlier versions.
// repeated string input = 3;
// repeated string output = 4;
// optional int64 ir_version = 6;
// optional int64 producer_version = 7;
// optional string producer_tag = 8;
// optional string domain = 9;
}
// Tensors
//
// A serialized tensor value.
message TensorProto {
enum DataType {
UNDEFINED = 0;
// Basic types.
FLOAT = 1; // float
UINT8 = 2; // uint8_t
INT8 = 3; // int8_t
UINT16 = 4; // uint16_t
INT16 = 5; // int16_t
INT32 = 6; // int32_t
INT64 = 7; // int64_t
STRING = 8; // string
BOOL = 9; // bool
// Advanced types
FLOAT16 = 10;
DOUBLE = 11;
UINT32 = 12;
UINT64 = 13;
COMPLEX64 = 14; // complex with float32 real and imaginary components
COMPLEX128 = 15; // complex with float64 real and imaginary components
// Future extensions go here.
}
// The shape of the tensor.
repeated int64 dims = 1;
// The data type of the tensor.
optional DataType data_type = 2;
// For very large tensors, we may want to store them in chunks, in which
// case the following fields will specify the segment that is stored in
// the current TensorProto.
message Segment {
optional int64 begin = 1;
optional int64 end = 2;
}
optional Segment segment = 3;
// Tensor content must be organized in row-major order.
//
// Depending on the data_type field, exactly one of the fields below with
// name ending in _data is used to store the elements of the tensor.
// For float and complex64 values
// Complex64 tensors are encoded as a single array of floats,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
repeated float float_data = 4 [packed = true];
// For int32, uint8, int8, uint16, int16, bool, and float16 values
// float16 values must be bit-wise converted to an uint16_t prior
// to writing to the buffer.
// When this field is present, the data_type field MUST be
// INT32, INT16, INT8, UINT16, INT8, BOOL, or FLOAT16
repeated int32 int32_data = 5 [packed = true];
// For strings.
// Each element of string_data is a UTF-8 encoded Unicode
// string. No trailing null, no leading BOM. The protobuf "string"
// scalar type is not used to match ML community conventions.
// When this field is present, the data_type field MUST be STRING
repeated bytes string_data = 6;
// For int64.
// When this field is present, the data_type field MUST be INT64
repeated int64 int64_data = 7 [packed = true];
// Optionally, a name for the tensor.
optional string name = 8; // namespace Value
// A human-readable documentation for this tensor. Markdown is allowed.
optional string doc_string = 12;
// Serializations can either use one of the fields above, or use this
// raw bytes field. The only exception is the string case, where one is
// required to store the content in the repeated bytes string_data field.
//
// When this raw_data field is used to store tensor value, elements MUST
// be stored in as fixed-width, little-endian order.
// Floating-point data types MUST be stored in IEEE 754 format.
// Complex64 elements must be written as two consecutive FLOAT values, real component first.
// Complex128 elements must be written as two consecutive DOUBLE values, real component first.
// Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
//
// Note: the advantage of specific field rather than the raw_data field is
// that in some cases (e.g. int data), protobuf does a better packing via
// variable length storage, and may lead to smaller binary footprint.
// When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
optional bytes raw_data = 9;
// For double
// Complex64 tensors are encoded as a single array of doubles,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
repeated double double_data = 10 [packed = true];
// For uint64 and uint32 values
// When this field is present, the data_type field MUST be
// UINT32 or UINT64
repeated uint64 uint64_data = 11 [packed = true];
}
// Defines a tensor shape. A dimension can be either an integer value
// or a symbolic variable. A symbolic variable represents an unknown
// dimension.
message TensorShapeProto {
message Dimension {
oneof value {
int64 dim_value = 1;
string dim_param = 2; // namespace Shape
};
// Standard denotation can optionally be used to denote tensor
// dimensions with standard semantic descriptions to ensure
// that operations are applied to the correct axis of a tensor.
// Refer to https://github.com/onnx/onnx/blob/master/docs/DimensionDenotation.md#denotation-definition
// for pre-defined dimension denotations.
optional string denotation = 3;
};
repeated Dimension dim = 1;
}
// Types
//
// The standard ONNX data types.
message TypeProto {
message Tensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST be present for this version of the IR.
optional TensorProto.DataType elem_type = 1;
optional TensorShapeProto shape = 2;
}
oneof value {
// The type of a tensor.
Tensor tensor_type = 1;
}
// An optional denotation can be used to denote the whole
// type with a standard semantic description as to what is
// stored inside. Refer to https://github.com/onnx/onnx/blob/master/docs/TypeDenotation.md#type-denotation-definition
// for pre-defined type denotations.
optional string denotation = 6;
}
// Operator Sets
//
// OperatorSets are uniquely identified by a (domain, opset_version) pair.
message OperatorSetIdProto {
// The domain of the operator set being identified.
// The empty string ("") or absence of this field implies the operator
// set that is defined as part of the ONNX specification.
// This field MUST be present in this version of the IR when referring to any other operator set.
optional string domain = 1;
// The version of the operator set being identified.
// This field MUST be present in this version of the IR.
optional int64 version = 2;
}