EXC_BAD_ACCESS (code=1, address=0x0) when calling model.prediction()

Hi, I try the activity classifier and want to show the results with an app on a IPhone SE.

I try the example analog https://apple.github.io/turicreate/docs/userguide/activity_classifier/export_coreml.html

When calling the prediction function: ...

   func performModelPrediction () -> String? {

... EXC_BAD_ACCESS (code=1, address=0x0) was thrown in line:

        let modelPrediction = try! activityClassificationModel.prediction( 
                                ^^^^^^^^here was the Error shown

  I can trak it down to the source of the mlmodel:

    func prediction(input: MyActivityClassifier2Input, options: MLPredictionOptions) throws -> MyActivityClassifier2Output {

        let outFeatures = try model.prediction(from: input, options:options)

        return MyActivityClassifier2Output(features: outFeatures)

    }

I created the model with ml creator, working with Xcode 13 beta.

What I am doing wrong ? Do you have any hints ? Is there a better example for activity classifier ?

Don't hesitate to ask for further details. -Hans

here my code:

//

//  ViewController.swift

//  motionstor4yboard

//

//  Created by Hans Regler on 19.07.21.

//



import Foundation

import UIKit

import CoreML

import CoreMotion



class ViewController: UIViewController

{

    override func viewDidLoad() {

        debugPrint("info: start viewDidLoad ... ")

        super.viewDidLoad()

        // Do any additional setup after loading the view, typically from a nib.

                // Connect data:



        self.startDeviceMotion()

    }

    

// Define some ML Model constants for the recurrent network

  struct ModelConstants {

    static let numOfFeatures = 6

    // Must be the same value you used while training

    static let predictionWindowSize = 100

    static let sensorsUpdateInterval = 1.0 / 10.0

    static let stateInLength = 400



  }

    

// Initialize the model, layers, and sensor data arrays

    let activityClassificationModel = MyActivityClassifier2()



    var currentIndexInPredictionWindow = 0

    let accelDataX = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)

    let accelDataY = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)

    let accelDataZ = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)



    let gyroDataX = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)

    let gyroDataY = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)

    let gyroDataZ = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)



    var stateOutput = try! MLMultiArray(shape:[ModelConstants.stateInLength as NSNumber], dataType: MLMultiArrayDataType.double)



    // Initialize CoreMotion Manager

  let motionManager = CMMotionManager()

    







func startDeviceMotion() {

//         guard motionManager.isDeviceMotionAvailable else {

    guard motionManager.isAccelerometerAvailable, motionManager.isGyroAvailable else {

            debugPrint("Core Motion Data Unavailable!")

            return

        }

    

    motionManager.accelerometerUpdateInterval = TimeInterval(ModelConstants.sensorsUpdateInterval)

    motionManager.gyroUpdateInterval = TimeInterval(ModelConstants.sensorsUpdateInterval)



    motionManager.startAccelerometerUpdates(to: .main) { accelerometerData, error in

        guard let accelerometerData = accelerometerData else {

            print("Error: accelerometerData = accelerometerData")

            return }



        // Add the current data sample to the data array

        self.addAccelSampleToDataArray(accelSample: accelerometerData)

    }



       

    }

    

func addAccelSampleToDataArray (accelSample: CMAccelerometerData) {

    // Add the current accelerometer reading to the data array

    accelDataX[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.x as NSNumber

    accelDataY[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.y as NSNumber

    accelDataZ[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.z as NSNumber



    // Update the index in the prediction window data array

    currentIndexInPredictionWindow += 1



    // If the data array is full, call the prediction method to get a new model prediction.

    // We assume here for simplicity that the Gyro data was added to the data arrays as well.

    if (currentIndexInPredictionWindow == ModelConstants.predictionWindowSize) {



        if let predictedActivity = performModelPrediction() {



            // Use the predicted activity here

            // ...



            // Start a new prediction window

            currentIndexInPredictionWindow = 0

        }

    }

}

    

    func performModelPrediction () -> String? {

        // Perform model prediction

        let modelPrediction = try! activityClassificationModel.prediction(

            acceleration_x: accelDataX,

            acceleration_y: accelDataY,

            acceleration_z: accelDataZ,

            rotation_x: gyroDataX,

            rotation_y: gyroDataY,

            rotation_z: gyroDataZ,

            stateIn: stateOutput)



        // Update the state vector

        stateOutput = modelPrediction.stateOut



        // Return the predicted activity - the activity with the highest probability

        return modelPrediction.label

    }