Timothy Kautz
1 year ago
parent
6cd5c7a760
commit
b7280f4aa9
@ -0,0 +1,29 @@
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// For licensing see accompanying LICENSE.md file.
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// Copyright (C) 2022 Apple Inc. All Rights Reserved.
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import Foundation
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public struct AlphasCumprodCalculation {
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public var sqrtAlphasCumprod: Float
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public var sqrtOneMinusAlphasCumprod: Float
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public init(
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sqrtAlphasCumprod: Float,
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sqrtOneMinusAlphasCumprod: Float
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) {
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self.sqrtAlphasCumprod = sqrtAlphasCumprod
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self.sqrtOneMinusAlphasCumprod = sqrtOneMinusAlphasCumprod
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}
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public init(
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alphasCumprod: [Float],
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timesteps: Int = 1_000,
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steps: Int,
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strength: Float
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) {
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let tEnc = Int(strength * Float(steps))
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let initTimestep = timesteps - timesteps / steps * (steps - tEnc) + 1
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self.sqrtAlphasCumprod = alphasCumprod[initTimestep].squareRoot()
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self.sqrtOneMinusAlphasCumprod = (1 - alphasCumprod[initTimestep]).squareRoot()
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}
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}
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@ -0,0 +1,120 @@
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// For licensing see accompanying LICENSE.md file.
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// Copyright (C) 2022 Apple Inc. All Rights Reserved.
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import Foundation
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import Accelerate
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import CoreML
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@available(iOS 16.0, macOS 13.0, *)
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extension CGImage {
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typealias PixelBufferPFx1 = vImage.PixelBuffer<vImage.PlanarF>
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typealias PixelBufferP8x3 = vImage.PixelBuffer<vImage.Planar8x3>
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typealias PixelBufferIFx3 = vImage.PixelBuffer<vImage.InterleavedFx3>
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typealias PixelBufferI8x3 = vImage.PixelBuffer<vImage.Interleaved8x3>
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public enum ShapedArrayError: String, Swift.Error {
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case wrongNumberOfChannels
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case incorrectFormatsConvertingToShapedArray
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case vImageConverterNotInitialized
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}
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public static func fromShapedArray(_ array: MLShapedArray<Float32>) throws -> CGImage {
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// array is [N,C,H,W], where C==3
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let channelCount = array.shape[1]
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guard channelCount == 3 else {
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throw ShapedArrayError.wrongNumberOfChannels
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}
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let height = array.shape[2]
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let width = array.shape[3]
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// Normalize each channel into a float between 0 and 1.0
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let floatChannels = (0..<channelCount).map { i in
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// Normalized channel output
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let cOut = PixelBufferPFx1(width: width, height:height)
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// Reference this channel in the array and normalize
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array[0][i].withUnsafeShapedBufferPointer { ptr, _, strides in
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let cIn = PixelBufferPFx1(data: .init(mutating: ptr.baseAddress!),
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width: width, height: height,
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byteCountPerRow: strides[0]*4)
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// Map [-1.0 1.0] -> [0.0 1.0]
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cIn.multiply(by: 0.5, preBias: 1.0, postBias: 0.0, destination: cOut)
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}
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return cOut
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}
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// Convert to interleaved and then to UInt8
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let floatImage = PixelBufferIFx3(planarBuffers: floatChannels)
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let uint8Image = PixelBufferI8x3(width: width, height: height)
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floatImage.convert(to:uint8Image) // maps [0.0 1.0] -> [0 255] and clips
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// Convert to uint8x3 to RGB CGImage (no alpha)
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let bitmapInfo = CGBitmapInfo(rawValue: CGImageAlphaInfo.none.rawValue)
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let cgImage = uint8Image.makeCGImage(cgImageFormat:
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.init(bitsPerComponent: 8,
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bitsPerPixel: 3*8,
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colorSpace: CGColorSpaceCreateDeviceRGB(),
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bitmapInfo: bitmapInfo)!)!
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return cgImage
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}
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public var plannerRGBShapedArray: MLShapedArray<Float32> {
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get throws {
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guard
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var sourceFormat = vImage_CGImageFormat(cgImage: self),
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var mediumFormat = vImage_CGImageFormat(
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bitsPerComponent: 8 * MemoryLayout<UInt8>.size,
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bitsPerPixel: 8 * MemoryLayout<UInt8>.size * 4,
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colorSpace: CGColorSpaceCreateDeviceRGB(),
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bitmapInfo: CGBitmapInfo(rawValue: CGImageAlphaInfo.first.rawValue)),
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let width = vImagePixelCount(exactly: self.width),
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let height = vImagePixelCount(exactly: self.height)
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else {
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throw ShapedArrayError.incorrectFormatsConvertingToShapedArray
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}
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var sourceImageBuffer = try vImage_Buffer(cgImage: self)
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var mediumDesination = try vImage_Buffer(width: Int(width), height: Int(height), bitsPerPixel: mediumFormat.bitsPerPixel)
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let converter = vImageConverter_CreateWithCGImageFormat(
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&sourceFormat,
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&mediumFormat,
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nil,
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vImage_Flags(kvImagePrintDiagnosticsToConsole),
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nil)
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guard let converter = converter?.takeRetainedValue() else {
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throw ShapedArrayError.vImageConverterNotInitialized
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}
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vImageConvert_AnyToAny(converter, &sourceImageBuffer, &mediumDesination, nil, vImage_Flags(kvImagePrintDiagnosticsToConsole))
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var destinationA = try vImage_Buffer(width: Int(width), height: Int(height), bitsPerPixel: 8 * UInt32(MemoryLayout<Float>.size))
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var destinationR = try vImage_Buffer(width: Int(width), height: Int(height), bitsPerPixel: 8 * UInt32(MemoryLayout<Float>.size))
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var destinationG = try vImage_Buffer(width: Int(width), height: Int(height), bitsPerPixel: 8 * UInt32(MemoryLayout<Float>.size))
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var destinationB = try vImage_Buffer(width: Int(width), height: Int(height), bitsPerPixel: 8 * UInt32(MemoryLayout<Float>.size))
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var minFloat: [Float] = [-1.0, -1.0, -1.0, -1.0]
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var maxFloat: [Float] = [1.0, 1.0, 1.0, 1.0]
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vImageConvert_ARGB8888toPlanarF(&mediumDesination, &destinationA, &destinationR, &destinationG, &destinationB, &maxFloat, &minFloat, .zero)
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let redData = Data(bytes: destinationR.data, count: Int(width) * Int(height) * MemoryLayout<Float>.size)
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let greenData = Data(bytes: destinationG.data, count: Int(width) * Int(height) * MemoryLayout<Float>.size)
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let blueData = Data(bytes: destinationB.data, count: Int(width) * Int(height) * MemoryLayout<Float>.size)
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let imageData = redData + greenData + blueData
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let shapedArray = MLShapedArray<Float32>(data: imageData, shape: [1, 3, 512, 512])
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return shapedArray
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}
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}
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}
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@ -0,0 +1,80 @@
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// For licensing see accompanying LICENSE.md file.
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// Copyright (C) 2022 Apple Inc. All Rights Reserved.
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import Foundation
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import CoreML
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@available(iOS 16.0, macOS 13.0, *)
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/// Encoder, currently supports image2image
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public struct Encoder {
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public enum Error: String, Swift.Error {
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case latentOutputNotValid
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case batchLatentOutputEmpty
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}
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/// VAE encoder model + post math and adding noise from schedular
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var model: MLModel
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/// Create decoder from Core ML model
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///
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/// - Parameters
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/// - model: Core ML model for VAE decoder
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public init(model: MLModel) {
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self.model = model
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}
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/// Prediction queue
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let queue = DispatchQueue(label: "encoder.predict")
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/// Batch encode latent samples into images
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/// - Parameters:
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/// - image: image used for image2image
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/// - diagonalNoise: random noise for `DiagonalGaussianDistribution` operation
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/// - noise: random noise for initial latent space based on strength argument
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/// - alphasCumprodStep: calculations using the scheduler traditionally calculated in the pipeline in pyTorch Diffusers library.
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/// - Returns: The encoded latent space as MLShapedArray
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public func encode(
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image: CGImage,
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diagonalNoise: MLShapedArray<Float32>,
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noise: MLShapedArray<Float32>,
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alphasCumprodStep: AlphasCumprodCalculation
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) throws -> MLShapedArray<Float32> {
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let sample = try image.plannerRGBShapedArray
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let sqrtAlphasCumprod = MLShapedArray(scalars: [alphasCumprodStep.sqrtAlphasCumprod], shape: [1, 1])
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let sqrtOneMinusAlphasCumprod = MLShapedArray(scalars: [alphasCumprodStep.sqrtOneMinusAlphasCumprod], shape: [1, 1])
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let dict: [String: Any] = [
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"sample": MLMultiArray(sample),
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"diagonalNoise": MLMultiArray(diagonalNoise),
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"noise": MLMultiArray(noise),
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"sqrtAlphasCumprod": MLMultiArray(sqrtAlphasCumprod),
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"sqrtOneMinusAlphasCumprod": MLMultiArray(sqrtOneMinusAlphasCumprod),
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]
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let featureProvider = try MLDictionaryFeatureProvider(dictionary: dict)
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let batch = MLArrayBatchProvider(array: [featureProvider])
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// Batch predict with model
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let results = try queue.sync { try model.predictions(fromBatch: batch) }
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let batchLatents: [MLShapedArray<Float32>] = try (0..<results.count).compactMap { i in
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let result = results.features(at: i)
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guard
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let outputName = result.featureNames.first,
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let output = result.featureValue(for: outputName)?.multiArrayValue
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else {
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throw Error.latentOutputNotValid
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}
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print("output.shape: \(output.shape)")
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return MLShapedArray(output)
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}
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guard let latents = batchLatents.first else {
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throw Error.batchLatentOutputEmpty
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}
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return latents
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}
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}
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