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313 lines
12 KiB
Swift
313 lines
12 KiB
Swift
//
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// AgeNetLegacy.swift
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//
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// This file was automatically generated and should not be edited.
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//
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import CoreML
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/// Model Prediction Input Type
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@available(macOS 10.15, iOS 13.0, tvOS 13.0, watchOS 6.0, visionOS 1.0, *)
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class AgeNetLegacyInput : MLFeatureProvider {
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/// input as color (kCVPixelFormatType_32BGRA) image buffer, 112 pixels wide by 112 pixels high
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var input: CVPixelBuffer
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var featureNames: Set<String> { ["input"] }
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func featureValue(for featureName: String) -> MLFeatureValue? {
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if featureName == "input" {
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return MLFeatureValue(pixelBuffer: input)
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}
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return nil
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}
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init(input: CVPixelBuffer) {
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self.input = input
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}
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convenience init(inputWith input: CGImage) throws {
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self.init(input: try MLFeatureValue(cgImage: input, pixelsWide: 112, pixelsHigh: 112, pixelFormatType: kCVPixelFormatType_32ARGB, options: nil).imageBufferValue!)
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}
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convenience init(inputAt input: URL) throws {
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self.init(input: try MLFeatureValue(imageAt: input, pixelsWide: 112, pixelsHigh: 112, pixelFormatType: kCVPixelFormatType_32ARGB, options: nil).imageBufferValue!)
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}
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func setInput(with input: CGImage) throws {
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self.input = try MLFeatureValue(cgImage: input, pixelsWide: 112, pixelsHigh: 112, pixelFormatType: kCVPixelFormatType_32ARGB, options: nil).imageBufferValue!
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}
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func setInput(with input: URL) throws {
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self.input = try MLFeatureValue(imageAt: input, pixelsWide: 112, pixelsHigh: 112, pixelFormatType: kCVPixelFormatType_32ARGB, options: nil).imageBufferValue!
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}
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}
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/// Model Prediction Output Type
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@available(macOS 10.15, iOS 13.0, tvOS 13.0, watchOS 6.0, visionOS 1.0, *)
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class AgeNetLegacyOutput : MLFeatureProvider {
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/// Source provided by CoreML
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private let provider : MLFeatureProvider
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/// Identity as multidimensional array of floats
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var Identity: MLMultiArray {
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provider.featureValue(for: "Identity")!.multiArrayValue!
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}
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/// Identity as multidimensional array of floats
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@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, visionOS 1.0, *)
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var IdentityShapedArray: MLShapedArray<Float> {
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MLShapedArray<Float>(Identity)
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}
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var featureNames: Set<String> {
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provider.featureNames
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}
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func featureValue(for featureName: String) -> MLFeatureValue? {
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provider.featureValue(for: featureName)
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}
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init(Identity: MLMultiArray) {
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self.provider = try! MLDictionaryFeatureProvider(dictionary: ["Identity" : MLFeatureValue(multiArray: Identity)])
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}
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init(features: MLFeatureProvider) {
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self.provider = features
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}
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}
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/// Class for model loading and prediction
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@available(macOS 10.15, iOS 13.0, tvOS 13.0, watchOS 6.0, visionOS 1.0, *)
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class AgeNetLegacy {
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let model: MLModel
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/// URL of model assuming it was installed in the same bundle as this class
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class var urlOfModelInThisBundle : URL {
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let bundle = Bundle(for: self)
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return bundle.url(forResource: "AgeNetLegacy", withExtension:"mlmodelc")!
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}
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/**
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Construct AgeNetLegacy instance with an existing MLModel object.
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Usually the application does not use this initializer unless it makes a subclass of AgeNetLegacy.
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Such application may want to use `MLModel(contentsOfURL:configuration:)` and `AgeNetLegacy.urlOfModelInThisBundle` to create a MLModel object to pass-in.
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- parameters:
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- model: MLModel object
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*/
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init(model: MLModel) {
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self.model = model
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}
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/**
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Construct AgeNetLegacy instance by automatically loading the model from the app's bundle.
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*/
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@available(*, deprecated, message: "Use init(configuration:) instead and handle errors appropriately.")
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convenience init() {
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try! self.init(contentsOf: type(of:self).urlOfModelInThisBundle)
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}
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/**
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Construct a model with configuration
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- parameters:
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- configuration: the desired model configuration
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- throws: an NSError object that describes the problem
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*/
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convenience init(configuration: MLModelConfiguration) throws {
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try self.init(contentsOf: type(of:self).urlOfModelInThisBundle, configuration: configuration)
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}
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/**
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Construct AgeNetLegacy instance with explicit path to mlmodelc file
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- parameters:
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- modelURL: the file url of the model
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- throws: an NSError object that describes the problem
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*/
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convenience init(contentsOf modelURL: URL) throws {
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try self.init(model: MLModel(contentsOf: modelURL))
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}
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/**
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Construct a model with URL of the .mlmodelc directory and configuration
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- parameters:
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- modelURL: the file url of the model
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- configuration: the desired model configuration
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- throws: an NSError object that describes the problem
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*/
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convenience init(contentsOf modelURL: URL, configuration: MLModelConfiguration) throws {
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try self.init(model: MLModel(contentsOf: modelURL, configuration: configuration))
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}
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/**
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Construct AgeNetLegacy instance asynchronously with optional configuration.
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Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
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- parameters:
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- configuration: the desired model configuration
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- handler: the completion handler to be called when the model loading completes successfully or unsuccessfully
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*/
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@available(macOS 11.0, iOS 14.0, tvOS 14.0, watchOS 7.0, visionOS 1.0, *)
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class func load(configuration: MLModelConfiguration = MLModelConfiguration(), completionHandler handler: @escaping (Swift.Result<AgeNetLegacy, Error>) -> Void) {
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load(contentsOf: self.urlOfModelInThisBundle, configuration: configuration, completionHandler: handler)
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}
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/**
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Construct AgeNetLegacy instance asynchronously with optional configuration.
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Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
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- parameters:
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- configuration: the desired model configuration
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*/
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@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, visionOS 1.0, *)
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class func load(configuration: MLModelConfiguration = MLModelConfiguration()) async throws -> AgeNetLegacy {
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try await load(contentsOf: self.urlOfModelInThisBundle, configuration: configuration)
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}
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/**
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Construct AgeNetLegacy instance asynchronously with URL of the .mlmodelc directory with optional configuration.
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Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
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- parameters:
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- modelURL: the URL to the model
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- configuration: the desired model configuration
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- handler: the completion handler to be called when the model loading completes successfully or unsuccessfully
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*/
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@available(macOS 11.0, iOS 14.0, tvOS 14.0, watchOS 7.0, visionOS 1.0, *)
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class func load(contentsOf modelURL: URL, configuration: MLModelConfiguration = MLModelConfiguration(), completionHandler handler: @escaping (Swift.Result<AgeNetLegacy, Error>) -> Void) {
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MLModel.load(contentsOf: modelURL, configuration: configuration) { result in
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switch result {
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case .failure(let error):
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handler(.failure(error))
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case .success(let model):
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handler(.success(AgeNetLegacy(model: model)))
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}
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}
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}
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/**
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Construct AgeNetLegacy instance asynchronously with URL of the .mlmodelc directory with optional configuration.
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Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
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- parameters:
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- modelURL: the URL to the model
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- configuration: the desired model configuration
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*/
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@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, visionOS 1.0, *)
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class func load(contentsOf modelURL: URL, configuration: MLModelConfiguration = MLModelConfiguration()) async throws -> AgeNetLegacy {
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let model = try await MLModel.load(contentsOf: modelURL, configuration: configuration)
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return AgeNetLegacy(model: model)
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}
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/**
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Make a prediction using the structured interface
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It uses the default function if the model has multiple functions.
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- parameters:
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- input: the input to the prediction as AgeNetLegacyInput
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- throws: an NSError object that describes the problem
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- returns: the result of the prediction as AgeNetLegacyOutput
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*/
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func prediction(input: AgeNetLegacyInput) throws -> AgeNetLegacyOutput {
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try prediction(input: input, options: MLPredictionOptions())
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}
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/**
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Make a prediction using the structured interface
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It uses the default function if the model has multiple functions.
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- parameters:
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- input: the input to the prediction as AgeNetLegacyInput
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- options: prediction options
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- throws: an NSError object that describes the problem
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- returns: the result of the prediction as AgeNetLegacyOutput
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*/
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func prediction(input: AgeNetLegacyInput, options: MLPredictionOptions) throws -> AgeNetLegacyOutput {
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let outFeatures = try model.prediction(from: input, options: options)
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return AgeNetLegacyOutput(features: outFeatures)
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}
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/**
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Make an asynchronous prediction using the structured interface
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It uses the default function if the model has multiple functions.
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- parameters:
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- input: the input to the prediction as AgeNetLegacyInput
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- options: prediction options
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- throws: an NSError object that describes the problem
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- returns: the result of the prediction as AgeNetLegacyOutput
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*/
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@available(macOS 14.0, iOS 17.0, tvOS 17.0, watchOS 10.0, visionOS 1.0, *)
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func prediction(input: AgeNetLegacyInput, options: MLPredictionOptions = MLPredictionOptions()) async throws -> AgeNetLegacyOutput {
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let outFeatures = try await model.prediction(from: input, options: options)
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return AgeNetLegacyOutput(features: outFeatures)
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}
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/**
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Make a prediction using the convenience interface
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It uses the default function if the model has multiple functions.
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- parameters:
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- input: color (kCVPixelFormatType_32BGRA) image buffer, 112 pixels wide by 112 pixels high
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- throws: an NSError object that describes the problem
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- returns: the result of the prediction as AgeNetLegacyOutput
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*/
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func prediction(input: CVPixelBuffer) throws -> AgeNetLegacyOutput {
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let input_ = AgeNetLegacyInput(input: input)
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return try prediction(input: input_)
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}
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/**
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Make a batch prediction using the structured interface
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It uses the default function if the model has multiple functions.
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- parameters:
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- inputs: the inputs to the prediction as [AgeNetLegacyInput]
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- options: prediction options
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- throws: an NSError object that describes the problem
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- returns: the result of the prediction as [AgeNetLegacyOutput]
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*/
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func predictions(inputs: [AgeNetLegacyInput], options: MLPredictionOptions = MLPredictionOptions()) throws -> [AgeNetLegacyOutput] {
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let batchIn = MLArrayBatchProvider(array: inputs)
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let batchOut = try model.predictions(from: batchIn, options: options)
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var results : [AgeNetLegacyOutput] = []
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results.reserveCapacity(inputs.count)
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for i in 0..<batchOut.count {
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let outProvider = batchOut.features(at: i)
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let result = AgeNetLegacyOutput(features: outProvider)
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results.append(result)
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}
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return results
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}
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}
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