// Copyright (c) 2017, Apple Inc. All rights reserved.
//
// Use of this source code is governed by a BSD-3-clause license that can be
// found in LICENSE.txt or at https://opensource.org/licenses/BSD-3-Clause
/*
* A Core ML model consists of a specification version
* and a model description,
* and can be any one of the following types:
*
* Neural Networks
* - `NeuralNetwork`
*
* Regressors
* - ``GLMRegressor``
* - ``SupportVectorRegressor``
* - ``TreeEnsembleRegressor``
* - ``NeuralNetworkRegressor``
* - ``BayesianProbitRegressor``
*
* Classifiers
* - `NeuralNetworkClassifier`
* - `TreeEnsembleClassifier`
* - `GLMClassifier`
* - `SupportVectorClassifier`
* - `KNearestNeighborsClassifier`
*
* Other models
* - `CustomModel`
* - `TextClassifier`
* - `WordTagger`
* - `Gazetteer`
* - `WordEmbedding`
* - `VisionFeaturePrint`
* - `LinkedModel`
* - `SoundAnalysisPreprocessing`
* - `ItemSimilarityRecommender`
* - `ClassConfidenceThresholding`
*
* Feature Engineering
* - `Imputer`
* - `Scaler`
* - `Normalizer`
* - `OneHotEncoder`
* - `CategoricalMapping`
* - `FeatureVectorizer`
* - `DictVectorizer`
* - `ArrayFeatureExtractor`
* - `NonMaximumSuppression`
*
* Pipelines
* - `PipelineClassifier`
* - `PipelineRegressor`
* - `Pipeline`
*
* Simple Mathematical Functions
* - `Identity`
*/
syntax = "proto3";
option optimize_for = LITE_RUNTIME;
import public "VisionFeaturePrint.proto";
import public "AudioFeaturePrint.proto";
import public "TextClassifier.proto";
import public "WordTagger.proto";
import public "Gazetteer.proto";
import public "WordEmbedding.proto";
import public "ArrayFeatureExtractor.proto";
import public "BayesianProbitRegressor.proto";
import public "CategoricalMapping.proto";
import public "CustomModel.proto";
import public "DictVectorizer.proto";
import public "FeatureTypes.proto";
import public "FeatureVectorizer.proto";
import public "GLMRegressor.proto";
import public "GLMClassifier.proto";
import public "NearestNeighbors.proto";
import public "Identity.proto";
import public "Imputer.proto";
import public "MIL.proto";
import public "NeuralNetwork.proto";
import public "Normalizer.proto";
import public "OneHotEncoder.proto";
import public "Scaler.proto";
import public "NonMaximumSuppression.proto";
import public "SVM.proto";
import public "TreeEnsemble.proto";
import public "Parameters.proto";
import public "ItemSimilarityRecommender.proto";
import public "SoundAnalysisPreprocessing.proto";
import public "LinkedModel.proto";
import public "ClassConfidenceThresholding.proto";
package CoreML.Specification;
/*
* A pipeline consisting of one or more models.
*/
message Pipeline {
repeated Model models = 1;
// Optional names given for each model
// If not supplied it defaults to ["model0",..., "model"(models.size()-1)]
// These names can be used to disambiguate the scope / domain of a parameter
repeated string names = 2;
}
/*
* A classifier pipeline.
*/
message PipelineClassifier {
Pipeline pipeline = 1;
}
/*
* A regressor pipeline.
*/
message PipelineRegressor {
Pipeline pipeline = 1;
}
/*
* A feature description,
* consisting of a name, short description, and type.
*/
message FeatureDescription {
string name = 1;
string shortDescription = 2;
FeatureType type = 3;
}
/*
* Model metadata,
* consisting of a short description, a version string,
* an author, a license, and any other user defined
* key/value meta data.
*/
message Metadata {
string shortDescription = 1;
string versionString = 2;
string author = 3;
string license = 4;
map<string, string> userDefined = 100;
}
/*
* A description of a model,
* consisting of descriptions of its input and output features.
* Both regressor and classifier models require the name of the
* primary predicted output feature (``predictedFeatureName``).
* Classifier models can specify the output feature containing
* probabilities for the predicted classes
* (``predictedProbabilitiesName``).
*/
message ModelDescription {
repeated FeatureDescription input = 1;
repeated FeatureDescription output = 10;
// [Required for regressor and classifier models]: the name
// to give to an output feature containing the prediction.
string predictedFeatureName = 11;
// [Optional for classifier models]: the name to give to an
// output feature containing a dictionary mapping class
// labels to their predicted probabilities. If not specified,
// the dictionary will not be returned by the model.
string predictedProbabilitiesName = 12;
repeated FeatureDescription trainingInput = 50;
Metadata metadata = 100;
}
message SerializedModel {
// Identifier whose content describes the model type of the serialized
// protocol buffer message.
string identifier = 1;
// Must be a valid serialized protocol buffer of the above specified type.
bytes model = 2;
}
/*
* A Core ML model,
* consisting of a specification version,
* a model description, and a model type.
*
* Core ML model compatibility is indicated by
* a monotonically increasing specification version number,
* which is incremented anytime a backward-incompatible change is made
* (this is functionally equivalent to the MAJOR version number
* described by `Semantic Versioning 2.0.0 <http://semver.org/>`_).
*
* Specification Versions : OS Availability (Core ML Version)
*
* 1 : iOS 11, macOS 10.13, tvOS 11, watchOS 4 (Core ML 1)
* - Feedforward & Recurrent Neural Networks
* - General Linear Models
* - Tree Ensembles
* - Support Vector Machines
* - Pipelines
* - Feature Engineering
*
* 2 : iOS 11.2, macOS 10.13.2, tvOS 11.2, watchOS 4.2 (Core ML 1.2)
* - Custom Layers for Neural Networks
* - Float 16 support for Neural Network layers
*
* 3 : iOS 12, macOS 10.14, tvOS 12, watchOS 5 (Core ML 2)
* - Flexible shapes and image sizes
* - Categorical sequences
* - Core ML Vision Feature Print, Text Classifier, Word Tagger
* - Non Max Suppression
* - Crop and Resize Bilinear NN layers
* - Custom Models
*
* 4 : iOS 13, macOS 10.15, tvOS 13, watchOS 6 (Core ML 3)
* - Updatable models
* - Exact shape / general rank mapping for neural networks
* - Large expansion of supported neural network layers
* - Generalized operations
* - Control flow
* - Dynamic layers
* - See NeuralNetwork.proto
* - Nearest Neighbor Classifier
* - Sound Analysis Prepreocessing
* - Recommender
* - Linked Model
* - NLP Gazeteer
* - NLP WordEmbedding
*
* 5 : iOS 14, macOS 11, tvOS 14, watchOS 7 (Core ML 4)
* - Model Deployment
* - Model Encryption
* - Unified converter API with PyTorch and Tensorflow 2 Support in coremltools
* 4
* - MIL builder for neural networks and composite ops in coremltools 4
* - New layers in neural network:
* - CumSum
* - OneHot
* - ClampedReLu
* - ArgSort
* - SliceBySize
* - Convolution3D
* - Pool3D
* - Bilinear Upsample with align corners and fractional factors
* - PixelShuffle
* - MatMul with int8 weights and int8 activations
* - Concat interleave
* - See NeuralNetwork.proto
* - Enhanced Xcode model view with interactive previews
* - Enhanced Xcode Playground support for Core ML models
*
* 6 : iOS 15, macOS 12, tvOS 15, watchOS 8 (Core ML 5)
* - Core ML Audio Feature Print
* - new type of model: mlprogram (MILSpec.Program)
*
* 7 : iOS 16, macOS 13, tvOS 16, watchOS 9 (Core ML 6)
* - FLOAT16 array data type
* - GRAYSCALE_FLOAT16 image color space.
*
* 8 : iOS 17, macOS 14, tvOS 17, watchOS 10 (Core ML 7)
* - iOS 17 ops
* - Scene print v2
* - ClassConfidenceThresholding model
*/
message Model {
int32 specificationVersion = 1;
ModelDescription description = 2;
/*
* Following model types support on-device update:
*
* - NeuralNetworkClassifier
* - NeuralNetworkRegressor
* - NeuralNetwork
* - KNearestNeighborsClassifier
*/
bool isUpdatable = 10;
// start at 200 here
// model specific parameters:
oneof Type {
// pipeline starts at 200
PipelineClassifier pipelineClassifier = 200;
PipelineRegressor pipelineRegressor = 201;
Pipeline pipeline = 202;
// regressors start at 300
GLMRegressor glmRegressor = 300;
SupportVectorRegressor supportVectorRegressor = 301;
TreeEnsembleRegressor treeEnsembleRegressor = 302;
NeuralNetworkRegressor neuralNetworkRegressor = 303;
BayesianProbitRegressor bayesianProbitRegressor = 304;
// classifiers start at 400
GLMClassifier glmClassifier = 400;
SupportVectorClassifier supportVectorClassifier = 401;
TreeEnsembleClassifier treeEnsembleClassifier = 402;
NeuralNetworkClassifier neuralNetworkClassifier = 403;
KNearestNeighborsClassifier kNearestNeighborsClassifier = 404;
// generic models start at 500
NeuralNetwork neuralNetwork = 500;
ItemSimilarityRecommender itemSimilarityRecommender = 501;
MILSpec.Program mlProgram = 502;
// Custom and linked models
CustomModel customModel = 555;
LinkedModel linkedModel = 556;
// Precision Recall Curve 'container''
ClassConfidenceThresholding classConfidenceThresholding = 560;
// feature engineering starts at 600
OneHotEncoder oneHotEncoder = 600;
Imputer imputer = 601;
FeatureVectorizer featureVectorizer = 602;
DictVectorizer dictVectorizer = 603;
Scaler scaler = 604;
CategoricalMapping categoricalMapping = 606;
Normalizer normalizer = 607;
ArrayFeatureExtractor arrayFeatureExtractor = 609;
NonMaximumSuppression nonMaximumSuppression = 610;
// simple mathematical functions used for testing start at 900
Identity identity = 900;
// reserved until 1000
// CoreML provided models
CoreMLModels.TextClassifier textClassifier = 2000;
CoreMLModels.WordTagger wordTagger = 2001;
CoreMLModels.VisionFeaturePrint visionFeaturePrint = 2002;
CoreMLModels.SoundAnalysisPreprocessing soundAnalysisPreprocessing = 2003;
CoreMLModels.Gazetteer gazetteer = 2004;
CoreMLModels.WordEmbedding wordEmbedding = 2005;
CoreMLModels.AudioFeaturePrint audioFeaturePrint = 2006;
// Reserved private messages start at 3000
// These messages are subject to change with no notice or support.
SerializedModel serializedModel = 3000;
}
}
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