不从远程Firebase Automl自定义模型检索输出预测列表 [英] Not Retriveing Output Prediction List from Remote Firebase Automl custom model

问题描述

我目前正在尝试构建一个使用Google的autoML功能的应用.我已经训练了一个模型并将其发布在google firebase上，并根据文档将必要的代码集成到了我的应用中:

I am currently trying to build an app that uses google's autoML feature. I have trained a model and published it on google firebase and have integrated the necessary code into my app following the documentation:

https://firebase.google.com/docs/ml-kit/ios/label-images-with-automl

我使用的是远程模型，而不是本地模型.但是，当我尝试运行代码，然后在模拟器中选择图像时，控制台中将输出一个空的预测列表.

I am using a remote model instead of making it local. However when I try running the code, then choose an image in the simulator, an empty list of predictions is output in the console.

我还打开了调试功能，但这并不能帮助我解决错误.这是我在ViewController中运行的代码:

I have also turned on the debugging feature, but this has not helped me fix my error. This is the code I am running in ViewController:

import UIKit
import CoreML
import Vision
import Firebase
import FirebaseMLCommon


var serverImage: UIImage? = nil
var topResult = ""
class ViewController: UIViewController {

@IBOutlet var skinDiseaseImageView: UIImageView!
@IBOutlet var result1Label: UILabel!
@IBOutlet var result1Confidence: UILabel!
@IBOutlet var result2Label: UILabel!
@IBOutlet var result2Confidence: UILabel!
@IBOutlet var result3Label: UILabel!
@IBOutlet var result3Confidence: UILabel!

override func viewDidLoad() {

let initialConditions = ModelDownloadConditions(allowsCellularAccess: true,
                                                                         allowsBackgroundDownloading: true)
let updateConditions = ModelDownloadConditions(allowsCellularAccess: false,
                                               allowsBackgroundDownloading: true)
let remoteModel = RemoteModel(
    name: "skinDiseaseModel",  // The name you assigned in the console.
    allowsModelUpdates: true,
    initialConditions: initialConditions,
    updateConditions: updateConditions
)
ModelManager.modelManager().register(remoteModel)

super.viewDidLoad()


}

override func didReceiveMemoryWarning() {
super.didReceiveMemoryWarning()
}

}


extension ViewController {

@IBAction func selectImage(_ sender: Any) {
    let pickerController = UIImagePickerController()
    pickerController.delegate = self
    pickerController.sourceType = .savedPhotosAlbum
    present(pickerController, animated: true)
}

}


extension ViewController: UIImagePickerControllerDelegate {

func imagePickerController(_ picker: UIImagePickerController,     didFinishPickingMediaWithInfo info: [String : Any]) {
    dismiss(animated: true)

    guard let skinImage =     info[UIImagePickerControllerOriginalImage] as? UIImage else {
        fatalError("Error Retrieving Image Line 95")
    }

    var skinImageToDiagnose = VisionImage(image: skinImage)

    serverImage = skinImage
    skinDiseaseImageView.image = skinImage


let labelerOptions = VisionOnDeviceAutoMLImageLabelerOptions(
    remoteModelName: "skinDiseaseModel",  // Or nil to not use a remote model
    localModelName: nil     // Or nil to not use a bundled model
)
labelerOptions.confidenceThreshold = 0  // Evaluate your model in the Firebase console
// to determine an appropriate value.
let labeler = Vision.vision().onDeviceAutoMLImageLabeler(options: labelerOptions)

var topThreeResults = [String]()
var topThreeConfidences = [String]()
labeler.process(skinImageToDiagnose) { labels, error in
    guard error == nil, let labels = labels
        else {
            print(error)
            return
        }
    //task succeeded
    print("1")
    print(labels)
    var counter = 0
    for label in labels {
        topThreeResults.append(String(describing: label))
        topThreeConfidences.append(String(describing: label.confidence))
        counter = counter + 1
        print("counter")
        if counter == 3 {
            break
        }
    }

}

result1Label.text = topThreeResults[0]
result1Confidence.text = (topThreeConfidences[0] + "%")

result2Label.text = topThreeResults[1]
result2Confidence.text = (topThreeConfidences[1] + "%")

result3Label.text = topThreeResults[2]
result3Confidence.text = (topThreeConfidences[2] + "%")





}

}

这是我收到的错误:

Fatal error: Index out of range
2019-08-31 19:50:19.763469-0700 medicalAppFinal[13776:2281569]            
(lldb) 

我认为索引超出范围问题是由于标签(输出预测)列表在打印后为空.因此，我理解了为什么索引超出范围，但是我不知道为什么在将图像传递到labeler.process()后收到一个空列表.我该如何解决此错误?告诉我您是否需要更多信息

I reasoned that the index out of range problem is due to the list of labels(output predictions) being empty after having printed it. Thus I understand why it is index out of range, but I do not know why I am recieving an empty list after passing in the image into labeler.process() How do I solve this error? Tell me if you need more information

推荐答案

这是一个逻辑错误.这部分代码:

It was a logic error. This part of the code:

result1Label.text = topThreeResults[0]
result1Confidence.text = (topThreeConfidences[0] + "%")

result2Label.text = topThreeResults[1]
result2Confidence.text = (topThreeConfidences[1] + "%")

result3Label.text = topThreeResults[2]
result3Confidence.text = (topThreeConfidences[2] + "%")

应该位于labeler.process()函数内部.否则，它将运行以上代码，甚至没有检索到预测列表，从而导致致命错误.通过将其放入其中，我确保它已检索到预测列表，然后才运行上面的代码以在列表中查找特定值.

should have been inside the labeler.process() function. Otherwise it was running the above code without having even retrieved a list of predictions, thus causing the fatal error. By placing it inside, I ensure it has retrieved the list of predictions and only then runs the above code to find specific values in the list.

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