JacopoMangiavacchi

struct DragPinchView: View {
    @State var currentOffset = CGSize.zero
    @State var currentScale: CGFloat = 1.0
    @State var gestureOffset = CGSize.zero
    @State var gestureScale: CGFloat = 1.0

    var body: some View {
        let dragGesture = DragGesture()
            .onChanged { value in gestureOffset = value.translation }
            .onEnded { _ in

JacopoMangiavacchi

import torch
import torchvision

class VGGPerceptualLoss(torch.nn.Module):
    def __init__(self, resize=True):
        super(VGGPerceptualLoss, self).__init__()
        blocks = []
        blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval())

JacopoMangiavacchi

import Foundation

public extension String {
    
    /**
     Enables passing in negative indices to access characters
     starting from the end and going backwards.
     if num is negative, then it is added to the
     length of the string to retrieve the true index.
     */

JacopoMangiavacchi

private func evaluateGraph(log: (String) -> Void) {
    let testingSample = testDataX!.count / imageSize
    let testingBatches = testingSample / batchSize


    inferenceGraph = MLCInferenceGraph(graphObjects: [graph])
    inferenceGraph.addInputs(["image" : inputTensor])
    inferenceGraph.compile(options: [], device: device)

    // TESTING LOOP FOR A FULL EPOCH ON TESTING DATA

JacopoMangiavacchi

private func execTrainingLoop(log: (String) -> Void) {
    let trainingSample = trainingDataX!.count / imageSize
    let trainingBatches = trainingSample / batchSize

    for epoch in 0..<epochs {
        var epochMatch = 0

        for batch in 0..<trainingBatches {
            let xData = trainingDataX!.withUnsafeBufferPointer { pointer in
                MLCTensorData(immutableBytesNoCopy: pointer.baseAddress!.advanced(by: batch * imageSize * batchSize),

JacopoMangiavacchi

private func buildTrainingGraph() {
    trainingGraph = MLCTrainingGraph(graphObjects: [graph],
                                         lossLayer: MLCLossLayer(descriptor: MLCLossDescriptor(type: .softmaxCrossEntropy,
                                                                                               reductionType: .mean)),
                                         optimizer: MLCAdamOptimizer(descriptor: MLCOptimizerDescriptor(learningRate: 0.001,
                                                                                                       gradientRescale: 1.0,
                                                                                                    regularizationType: .none,
                                                                                                    regularizationScale: 0.0),
                                                                     beta1: 0.9,
                                                                     beta2: 0.999,

JacopoMangiavacchi

private func initializeTensors() {
    device = MLCDevice(type: .cpu)!

    inputTensor = MLCTensor(descriptor: MLCTensorDescriptor(shape: [batchSize, imageSize, 1, 1], dataType: .float32)!)

    dense1WeightsTensor = MLCTensor(descriptor: MLCTensorDescriptor(shape: [1, imageSize*dense1LayerOutputSize, 1, 1], dataType: .float32)!,
                                        randomInitializerType: .glorotUniform)
    dense1BiasesTensor = MLCTensor(descriptor: MLCTensorDescriptor(shape: [1, dense1LayerOutputSize, 1, 1], dataType: .float32)!,
                                       randomInitializerType: .glorotUniform)
    dense2WeightsTensor = MLCTensor(descriptor: MLCTensorDescriptor(shape: [1, dense1LayerOutputSize*numberOfClasses, 1, 1], dataType: .float32)!,

JacopoMangiavacchi

// Load in memory and split is not performant
private func getFileLine(filePath: String, process: (String) -> Void) {
    guard let filePointer:UnsafeMutablePointer<FILE> = fopen(filePath,"r") else {
        preconditionFailure("Could not open file at \(filePath)")
    }

    defer {
        fclose(filePointer)
    }

JacopoMangiavacchi

private func readDataSet(fileName: String, updateStatus: @escaping (Int) -> Void) -> ([Float], [Float]) { //}(MLCTensor, MLCTensor) {
    guard let filePath = Bundle.main.path(forResource: fileName, ofType: "csv") else {
        fatalError("CSV file not found")
    }

    let serialQueue = DispatchQueue(label: "MNIST.serial.queue.\(fileName)")

    var count = 0
    var X = [Float]()
    var Y = [Float]()

JacopoMangiavacchi

import Foundation
import MLCompute
import PlaygroundSupport

let page = PlaygroundPage.current
page.needsIndefiniteExecution = true

let tensor1 = MLCTensor(descriptor: MLCTensorDescriptor(shape: [6, 1], dataType: .float32)!)
let tensor2 = MLCTensor(descriptor: MLCTensorDescriptor(shape: [6, 1], dataType: .float32)!)
let tensor3 = MLCTensor(descriptor: MLCTensorDescriptor(shape: [6, 1], dataType: .float32)!)