swift 加载图像mlmodel的方式

第一种使用Vision框架加载

lazy var classificationRequest: [VNRequest] = {
        do {
            let model = try VNCoreMLModel(for: self.model.model)
            let classificationRequest = VNCoreMLRequest(model: model, completionHandler: self.handleClassification)
            return [ classificationRequest ]
        } catch {
            fatalError("Can't load Vision ML model: \(error)")
        }
    }()
    
    func handleClassification(request: VNRequest, error: Error?) {
       ... ...
    }
    
    func runCoreML(buffer: CVImageBuffer) {
       
        let newbuffer = self.resize(pixelBuffer: buffer)
        
        let classifierRequestHandler = VNImageRequestHandler(cvPixelBuffer: newbuffer!, options: [:])
        do {
            try classifierRequestHandler.perform(self.classificationRequest)
        } catch {
            print(error)
        }

第二种直接使用model

public func predict(buffer: CVImageBuffer){
   
        let newbuffer = self.resize(pixelBuffer: buffer)
        let _input= ModelInput(image: image!.buffer!)

        if let output = try? model.prediction(from:_input) {
            let mlMultiArray = output.net_output
            ... ...
        } else {
            print("error")
            
        }
    }

class ModelInput : MLFeatureProvider {
    /// Input image of scene to be classified as color (kCVPixelFormatType_32BGRA) image buffer, 224 pixels wide by 224 pixels high
    var image: CVPixelBuffer
    
    var featureNames: Set<String> {
        get {
            return ["image"]
        }
    }
    
    func featureValue(for featureName: String) -> MLFeatureValue? {
        if (featureName == "image") {
            return MLFeatureValue(pixelBuffer: image)
        }
        return nil
    }
    
    init(image: CVPixelBuffer) {
        self.image = image
    }
}