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Remove coremltools submodule *security vulnerability* and copy the co…
…reml model schema (#10424) * remove coremltools submodule * update cgmanifest * Copy proto files directly from coremltools
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Submodule coremltools
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19 changes: 19 additions & 0 deletions
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onnxruntime/core/providers/coreml/mlmodel_format/ArrayFeatureExtractor.proto
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| // 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 | ||
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| syntax = "proto3"; | ||
| option optimize_for = LITE_RUNTIME; | ||
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| package CoreML.Specification; | ||
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| /** | ||
| * An array feature extractor. | ||
| * | ||
| * Given an index, extracts the value at that index from its array input. | ||
| * Indexes are zero-based. | ||
| */ | ||
| message ArrayFeatureExtractor { | ||
| repeated uint64 extractIndex = 1; | ||
| } |
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onnxruntime/core/providers/coreml/mlmodel_format/BayesianProbitRegressor.proto
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| // 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 | ||
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| syntax = "proto3"; | ||
| option optimize_for = LITE_RUNTIME; | ||
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| package CoreML.Specification; | ||
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| /** | ||
| * A Bayesian probit regressor. | ||
| * | ||
| * The probit regression model is superficially similar to the more commonly known | ||
| * logistic regression, with sampling distribution of the model given by | ||
| * | ||
| * P(y=+1|x,w) = Φ(<w,x>/β) | ||
| * | ||
| * where w are the set of weights, | ||
| * x are the set of features for the given event, | ||
| * β is a model hyper-parameter, and | ||
| * Φ is the link function, defined to be the CDF of the normal distribution. | ||
| * The weights w[i,j] are Gaussian distributed, with mean μ[i,j] and precision 1/(σ[i,j])^2 | ||
| * (where i indexes over features and j indexes over the values for the feature). | ||
| * The parameter β scales the steepness of the inverse link function. | ||
| * | ||
| * (see https://en.wikipedia.org/wiki/Probit_model and https://en.wikipedia.org/wiki/Logistic_regression | ||
| * for more details on probit model and logistic regression, respectively) | ||
| * | ||
| * Input: X | ||
| * x represents a set of features, each taking on a discrete value (note that continuous values | ||
| * would first need to be discretized). x can be represented as a vector where the index i is | ||
| * the feature id and x[i] is the feature value. Alternatively, x can be represented as a matrix | ||
| * with 2 columns where the first column indicates the feature id and the second column contains | ||
| * the feature values, i.e. x[i,0] is the feature id and x[i,1] is the feature value. | ||
| * | ||
| * additional input features: | ||
| * - "optimism": apply a mean shift to the probability, i.e. shift regression mean by o*stdev, | ||
| * where o is the "optimism" parameter (see additional output features) | ||
| * - "samplingScale": for sampling from posterior, multiply standard deviation by this factor | ||
| * - "samplingTruncation": for sampling from posterior, truncate sampling distribution at given multiple of std from mean | ||
| * | ||
| * Output: Y | ||
| * probability P(y|x,w) | ||
| * | ||
| * additional output features: | ||
| * - mean (regression output before applying link function) | ||
| * - variance (regression output variance before applying link function) | ||
| * - pessimistic probability: P(y|x,w) with a mean shift parameterized by "optimism" feature | ||
| * - sampled probability: p ~ P(y|x,w) with standard deviation scaling parametrized by "samplingScale" feature | ||
| * and distribution truncated at multiple of standard deviation, | ||
| * where multiple parameterized by "samplingTruncation" feature. | ||
| * | ||
| */ | ||
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| message BayesianProbitRegressor { | ||
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| /* | ||
| * Parameterization of a Gaussian distribution | ||
| */ | ||
| message Gaussian { | ||
| double mean = 1; | ||
| double precision = 2; // inverse of the variance | ||
| } | ||
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| /* | ||
| * Weight for a specific feature value | ||
| * The weight is represented as a Gaussian distribution | ||
| * with a mean and precision (1/variance) to capture | ||
| * uncertainty in the weight | ||
| */ | ||
| message FeatureValueWeight { | ||
| uint32 featureValue = 1; | ||
| Gaussian featureWeight = 2; | ||
| } | ||
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| /* | ||
| * Feature with associated weights (for different values) | ||
| * Each feature has a set of weights for the (discrete) values | ||
| * it can take | ||
| */ | ||
| message FeatureWeight { | ||
| uint32 featureId = 1; | ||
| repeated FeatureValueWeight weights = 2; | ||
| } | ||
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| uint32 numberOfFeatures = 1; | ||
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| Gaussian bias = 2; // bias term | ||
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| /* | ||
| * Set of features with associated weights | ||
| */ | ||
| repeated FeatureWeight features = 3; // feature weights | ||
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| /* | ||
| * Set this name to be the same as input feature of type multi-array (1D) | ||
| * in the model description you want to use as the regression input | ||
| */ | ||
| string regressionInputFeatureName = 10; | ||
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| /* | ||
| * Set this name to be the same as optional input feature of type double | ||
| * in the model description you want to use as the optimism input | ||
| */ | ||
| string optimismInputFeatureName = 11; | ||
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| /* | ||
| * Set this name to be the same as optional input feature of type double | ||
| * in the model description you want to use as the samplingScale input | ||
| */ | ||
| string samplingScaleInputFeatureName = 12; | ||
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| /* | ||
| * Set this name to be the same as optional input feature of type double | ||
| * in the model description you want to use as the samplingBounds input | ||
| */ | ||
| string samplingTruncationInputFeatureName = 13; | ||
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| /* | ||
| * name of 'mean' output feature | ||
| */ | ||
| string meanOutputFeatureName = 20; | ||
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| /* | ||
| * name of 'variance' output feature | ||
| */ | ||
| string varianceOutputFeatureName = 21; | ||
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| /* | ||
| * name of 'pessimistic' output feature | ||
| */ | ||
| string pessimisticProbabilityOutputFeatureName = 22; | ||
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| /* | ||
| * name of 'sampled' output feature: samples from the scaled posterior probability distribuiton | ||
| */ | ||
| string sampledProbabilityOutputFeatureName = 23; | ||
| } |
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onnxruntime/core/providers/coreml/mlmodel_format/CategoricalMapping.proto
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| // 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 | ||
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| syntax = "proto3"; | ||
| option optimize_for = LITE_RUNTIME; | ||
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| import public "DataStructures.proto"; | ||
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| package CoreML.Specification; | ||
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| /** | ||
| * A categorical mapping. | ||
| * | ||
| * This allows conversion from integers to strings, or from strings to integers. | ||
| */ | ||
| message CategoricalMapping { | ||
| oneof MappingType { | ||
| // Conversion from strings to integers | ||
| StringToInt64Map stringToInt64Map = 1; | ||
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| // Conversion from integer to string | ||
| Int64ToStringMap int64ToStringMap = 2; | ||
| } | ||
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| /** | ||
| * The value returned if an input is not contained in the map above. | ||
| * If one of these is not set, then an error is raised on an unknown input. | ||
| */ | ||
| oneof ValueOnUnknown { | ||
| // Default output when converting from an integer to a string. | ||
| string strValue = 101; | ||
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| // Default output when converting from a string to an integer. | ||
| int64 int64Value = 102; | ||
| } | ||
| } |
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