From bc16dc09647641ef4e72cbd9e046bcf3fcc9dfee Mon Sep 17 00:00:00 2001 From: Liu Lantao Date: Sat, 24 Dec 2016 12:24:46 +0800 Subject: [PATCH] MPSCNNHelloWorld: Version 1.1, 2016-11-17 Fixes a typo that affected performance This sample is a port of the open source library, TensorFlow trained networks trained on MNIST Dataset (http://yann.lecun.com/exdb/mnist/) via inference using Metal Performance Shaders. 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.../AppIcon.appiconset/mnist-main-76.png | Bin 0 -> 6145 bytes .../AppIcon.appiconset/mnist-main-80-2.png | Bin 0 -> 6653 bytes .../AppIcon.appiconset/mnist-main-80.png | Bin 0 -> 6653 bytes .../AppIcon.appiconset/mnist-main-87.png | Bin 0 -> 7536 bytes .../Assets.xcassets/Contents.json | 6 + .../Base.lproj/LaunchScreen.storyboard | 27 ++ .../Base.lproj/Main.storyboard | 96 ++++ .../MPSCNNHelloWorld/DrawView.swift | 93 ++++ .../MPSCNNHelloWorld/GetMNISTData.swift | 73 +++ MPSCNNHelloWorld/MPSCNNHelloWorld/Info.plist | 47 ++ .../MPSCNNHelloWorld/MNISTDeepCNN.swift | 147 ++++++ .../MPSCNNHelloWorld/MNISTSingleLayer.swift | 157 ++++++ .../MPSCNNHelloWorld-Bridging-Header.h | 14 + .../MPSCNNHelloWorld/SlimMPSCNN.swift | 215 +++++++++ .../MPSCNNHelloWorld/ViewController.swift | 165 +++++++ MPSCNNHelloWorld/MPSCNNHelloWorld/atomics.h | 19 + MPSCNNHelloWorld/MPSCNNHelloWorld/atomics.m | 19 + .../deep_weights/binaries/bias_conv1.dat | 2 + .../deep_weights/binaries/bias_conv2.dat | 2 + .../deep_weights/binaries/bias_fc1.dat | Bin 0 -> 4096 bytes .../deep_weights/binaries/bias_fc2.dat | 1 + .../deep_weights/binaries/weights_conv1.dat | Bin 0 -> 3200 bytes .../deep_weights/binaries/weights_conv2.dat | Bin 0 -> 204800 bytes .../deep_weights/binaries/weights_fc1.dat | Bin 0 -> 12845056 bytes .../deep_weights/binaries/weights_fc2.dat | Bin 0 -> 40960 bytes .../mnistData/t10k-images-idx3-ubyte.data | Bin 0 -> 7840016 bytes .../mnistData/t10k-labels-idx1-ubyte.data | Bin 0 -> 10008 bytes .../mnistData/train-images-idx3-ubyte.data | Bin 0 -> 47040016 bytes .../mnistData/train-labels-idx1-ubyte.data | Bin 0 -> 60008 bytes .../single_layer_weights/bias_NN.dat | 1 + .../single_layer_weights/weights_NN.dat | Bin 0 -> 31360 bytes MPSCNNHelloWorld/README.md | 30 ++ 45 files changed, 1706 insertions(+) create mode 100644 MPSCNNHelloWorld/LICENSE.txt create mode 100755 MPSCNNHelloWorld/MPSCNNHelloWorld.xcodeproj/project.pbxproj create mode 100755 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b/MPSCNNHelloWorld/LICENSE.txt new file mode 100644 index 00000000..0e1a0580 --- /dev/null +++ b/MPSCNNHelloWorld/LICENSE.txt @@ -0,0 +1,42 @@ +Sample code project: MPSCNNHelloWorld: Simple Digit Detection Convolution Neural Networks (CNN) +Version: 1.1 + +IMPORTANT: This Apple software is supplied to you by Apple +Inc. ("Apple") in consideration of your agreement to the following +terms, and your use, installation, modification or redistribution of +this Apple software constitutes acceptance of these terms. If you do +not agree with these terms, please do not use, install, modify or +redistribute this Apple software. + +In consideration of your agreement to abide by the following terms, and +subject to these terms, Apple grants you a personal, non-exclusive +license, under Apple's copyrights in this original Apple software (the +"Apple Software"), to use, reproduce, modify and redistribute the Apple +Software, with or without modifications, in source and/or binary forms; +provided that if you redistribute the Apple Software in its entirety and +without modifications, you must retain this notice and the following +text and disclaimers in all such redistributions of the Apple Software. +Neither the name, trademarks, service marks or logos of Apple Inc. may +be used to endorse or promote products derived from the Apple Software +without specific prior written permission from Apple. Except as +expressly stated in this notice, no other rights or licenses, express or +implied, are granted by Apple herein, including but not limited to any +patent rights that may be infringed by your derivative works or by other +works in which the Apple Software may be incorporated. + +The Apple Software is provided by Apple on an "AS IS" basis. APPLE +MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION +THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS +FOR A PARTICULAR PURPOSE, REGARDING THE APPLE SOFTWARE OR ITS USE AND +OPERATION ALONE OR IN COMBINATION WITH YOUR PRODUCTS. + +IN NO EVENT SHALL APPLE BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL +OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) ARISING IN ANY WAY OUT OF THE USE, REPRODUCTION, +MODIFICATION AND/OR DISTRIBUTION OF THE APPLE SOFTWARE, HOWEVER CAUSED +AND WHETHER UNDER THEORY OF CONTRACT, TORT (INCLUDING NEGLIGENCE), +STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + +Copyright (C) 2016 Apple Inc. 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All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + Application delegate for the App +*/ + +import UIKit + +@UIApplicationMain +class AppDelegate: UIResponder, UIApplicationDelegate { + + var window: UIWindow? + +} + diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/Assets.xcassets/AppIcon.appiconset/Contents.json b/MPSCNNHelloWorld/MPSCNNHelloWorld/Assets.xcassets/AppIcon.appiconset/Contents.json new file mode 100755 index 00000000..94d32ea9 --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/Assets.xcassets/AppIcon.appiconset/Contents.json @@ -0,0 +1,86 @@ +{ + "images" : [ + { + "size" : "29x29", + "idiom" : "iphone", + "filename" : "mnist-main-58.png", + "scale" : "2x" + }, + { + "size" : "29x29", + "idiom" : "iphone", + "filename" : "mnist-main-87.png", + "scale" : "3x" + }, + { + "size" : "40x40", + "idiom" : "iphone", + "filename" : "mnist-main-80.png", + "scale" : "2x" + }, + { + "size" : "40x40", + "idiom" : "iphone", + "filename" : 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zGhweJ((9#@!XEp8<+UnT>HQEq(plYR0#;l}uiN6e5+!e^>e{FyX-_EWQ)D(VpK-B* zl)xy|&f<<7)E}Jd3}$4-(N~PXM6<5 zK#l8nCOL3y3Zaxzy1$dLgeJ1>FX8%0000 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/Base.lproj/Main.storyboard b/MPSCNNHelloWorld/MPSCNNHelloWorld/Base.lproj/Main.storyboard new file mode 100755 index 00000000..b7f65e6a --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/Base.lproj/Main.storyboard @@ -0,0 +1,96 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/DrawView.swift b/MPSCNNHelloWorld/MPSCNNHelloWorld/DrawView.swift new file mode 100755 index 00000000..a05c60fd --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/DrawView.swift @@ -0,0 +1,93 @@ +/* + Copyright (C) 2016 Apple Inc. All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + This file has routines for drwaing and detecting user touches (input digit) +*/ + +import UIKit + +/** + This class is used to handle the drawing in the DigitView so we can get user input digit, + This class doesn't really have an MPS or Metal going in it, it is just used to get user input + */ +class DrawView: UIView { + + // some parameters of how thick a line to draw 15 seems to work + // and we have white drawings on black background just like MNIST needs its input + var linewidth = CGFloat(15) { didSet { setNeedsDisplay() } } + var color = UIColor.white { didSet { setNeedsDisplay() } } + + // we will keep touches made by user in view in these as a record so we can draw them. + var lines: [Line] = [] + var lastPoint: CGPoint! + + override func touchesBegan(_ touches: Set, with event: UIEvent?) { + lastPoint = touches.first!.location(in: self) + } + + override func touchesMoved(_ touches: Set, with event: UIEvent?) { + let newPoint = touches.first!.location(in: self) + // keep all lines drawn by user as touch in record so we can draw them in view + lines.append(Line(start: lastPoint, end: newPoint)) + lastPoint = newPoint + // make a draw call + setNeedsDisplay() + } + + override func draw(_ rect: CGRect) { + super.draw(rect) + + let drawPath = UIBezierPath() + drawPath.lineCapStyle = .round + + for line in lines{ + drawPath.move(to: line.start) + drawPath.addLine(to: line.end) + } + + drawPath.lineWidth = linewidth + color.set() + drawPath.stroke() + } + + + /** + This function gets the pixel data of the view so we can put it in MTLTexture + + - Returns: + Void + */ + func getViewContext() -> CGContext? { + // our network takes in only grayscale images as input + let colorSpace:CGColorSpace = CGColorSpaceCreateDeviceGray() + + // we have 3 channels no alpha value put in the network + let bitmapInfo = CGImageAlphaInfo.none.rawValue + + // this is where our view pixel data will go in once we make the render call + let context = CGContext(data: nil, width: 28, height: 28, bitsPerComponent: 8, bytesPerRow: 28, space: colorSpace, bitmapInfo: bitmapInfo) + + // scale and translate so we have the full digit and in MNIST standard size 28x28 + context!.translateBy(x: 0 , y: 28) + context!.scaleBy(x: 28/self.frame.size.width, y: -28/self.frame.size.height) + + // put view pixel data in context + self.layer.render(in: context!) + + return context + } +} + +/** + 2 points can give a line and this class is just for that purpose, it keeps a record of a line + */ +class Line{ + var start, end: CGPoint + + init(start: CGPoint, end: CGPoint) { + self.start = start + self.end = end + } +} diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/GetMNISTData.swift b/MPSCNNHelloWorld/MPSCNNHelloWorld/GetMNISTData.swift new file mode 100755 index 00000000..cf704ff0 --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/GetMNISTData.swift @@ -0,0 +1,73 @@ +/* + Copyright (C) 2016 Apple Inc. All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + GetMNISTData is used to import the test set from the MNIST dataset +*/ + +import Foundation + +class GetMNISTData { + + var labels = [UInt8]() + var images = [UInt8]() + + var hdrW, hdrB: UnsafeMutableRawPointer? + var fd_b, fd_w: CInt + var sizeBias, sizeWeights: Int + + + init() { + // get the url to this layer's weights and bias + let wtPath = Bundle.main.path(forResource: "t10k-images-idx3-ubyte", ofType: "data") + let bsPath = Bundle.main.path(forResource: "t10k-labels-idx1-ubyte", ofType: "data") + + // find and open file + let URLL = Bundle.main.url(forResource: "t10k-labels-idx1-ubyte", withExtension: "data") + let dataL = NSData(contentsOf: URLL!) + + let URLI = Bundle.main.url(forResource: "t10k-images-idx3-ubyte", withExtension: "data") + let dataI = NSData(contentsOf: URLI!) + + // calculate the size of weights and bias required to be memory mapped into memory + sizeBias = dataL!.length + sizeWeights = dataI!.length + + // open file descriptors in read-only mode to parameter files + fd_w = open(wtPath!, O_RDONLY, S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH) + fd_b = open(bsPath!, O_RDONLY, S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH) + + assert(fd_w != -1, "Error: failed to open output file at \""+wtPath!+"\" errno = \(errno)\n") + assert(fd_b != -1, "Error: failed to open output file at \""+bsPath!+"\" errno = \(errno)\n") + + + // memory map the parameters + hdrW = mmap(nil, Int(sizeWeights), PROT_READ, MAP_FILE | MAP_SHARED, fd_w, 0); + hdrB = mmap(nil, Int(sizeBias), PROT_READ, MAP_FILE | MAP_SHARED, fd_b, 0); + + let i = UnsafePointer(hdrW!.bindMemory(to: UInt8.self, capacity: Int(sizeWeights))) + let l = UnsafePointer(hdrB!.bindMemory(to: UInt8.self, capacity: Int(sizeBias))) + + assert(i != UnsafePointer(bitPattern: -1), "mmap failed with errno = \(errno)") + assert(l != UnsafePointer(bitPattern: -1), "mmap failed with errno = \(errno)") + + + // remove first 16 bytes that contain info data from array + images = Array(UnsafeBufferPointer(start: (i + 16), count: sizeWeights - 16)) + + // remove first 8 bytes that contain file data from our labels array + labels = Array(UnsafeBufferPointer(start: (l + 8), count: sizeBias - 8)) + } + + deinit{ + // unmap files at initialization of MPSCNNFullyConnected, the weights are copied and packed internally we no longer require these + assert(munmap(hdrW, Int(sizeWeights)) == 0, "munmap failed with errno = \(errno)") + assert(munmap(hdrB, Int(sizeBias)) == 0, "munmap failed with errno = \(errno)") + + // close file descriptors + close(fd_w) + close(fd_b) + } + +} diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/Info.plist b/MPSCNNHelloWorld/MPSCNNHelloWorld/Info.plist new file mode 100755 index 00000000..40c6215d --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/Info.plist @@ -0,0 +1,47 @@ + + + + + CFBundleDevelopmentRegion + en + CFBundleExecutable + $(EXECUTABLE_NAME) + CFBundleIdentifier + $(PRODUCT_BUNDLE_IDENTIFIER) + CFBundleInfoDictionaryVersion + 6.0 + CFBundleName + $(PRODUCT_NAME) + CFBundlePackageType + APPL + CFBundleShortVersionString + 1.0 + CFBundleSignature + ???? + CFBundleVersion + 1 + LSRequiresIPhoneOS + + UILaunchStoryboardName + LaunchScreen + UIMainStoryboardFile + Main + UIRequiredDeviceCapabilities + + armv7 + + UISupportedInterfaceOrientations + + UIInterfaceOrientationPortrait + UIInterfaceOrientationLandscapeLeft + UIInterfaceOrientationLandscapeRight + + UISupportedInterfaceOrientations~ipad + + UIInterfaceOrientationPortrait + UIInterfaceOrientationPortraitUpsideDown + UIInterfaceOrientationLandscapeLeft + UIInterfaceOrientationLandscapeRight + + + diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/MNISTDeepCNN.swift b/MPSCNNHelloWorld/MPSCNNHelloWorld/MNISTDeepCNN.swift new file mode 100755 index 00000000..b97719dd --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/MNISTDeepCNN.swift @@ -0,0 +1,147 @@ +/* + Copyright (C) 2016 Apple Inc. All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + This is the deep layer network where we define and encode the correct layers on a command buffer as needed +*/ + +import MetalPerformanceShaders + +/** + + This class has our entire network with all layers to getting the final label + + Resources: + * [Instructions](https://www.tensorflow.org/versions/r0.8/tutorials/mnist/pros/index.html#deep-mnist-for-experts) to run this network on TensorFlow. + + */ +class MNIST_Deep_ConvNN: MNIST_Full_LayerNN{ + // MPSImageDescriptors for different layers outputs to be put in + let c1id = MPSImageDescriptor(channelFormat: MPSImageFeatureChannelFormat.float16, width: 28, height: 28, featureChannels: 32) + let p1id = MPSImageDescriptor(channelFormat: MPSImageFeatureChannelFormat.float16, width: 14, height: 14, featureChannels: 32) + let c2id = MPSImageDescriptor(channelFormat: MPSImageFeatureChannelFormat.float16, width: 14, height: 14, featureChannels: 64) + let p2id = MPSImageDescriptor(channelFormat: MPSImageFeatureChannelFormat.float16, width: 7 , height: 7 , featureChannels: 64) + let fc1id = MPSImageDescriptor(channelFormat: MPSImageFeatureChannelFormat.float16, width: 1 , height: 1 , featureChannels: 1024) + + // MPSImages and layers declared + var c1Image, c2Image, p1Image, p2Image, fc1Image: MPSImage + var conv1, conv2: MPSCNNConvolution + var fc1, fc2: MPSCNNFullyConnected + var pool: MPSCNNPoolingMax + var relu: MPSCNNNeuronReLU + + override init(withCommandQueue commandQueueIn: MTLCommandQueue!) { + // use device for a little while to initialize + let device = commandQueueIn.device + + pool = MPSCNNPoolingMax(device: device, kernelWidth: 2, kernelHeight: 2, strideInPixelsX: 2, strideInPixelsY: 2) + pool.offset = MPSOffset(x: 1, y: 1, z: 0); + pool.edgeMode = MPSImageEdgeMode.clamp + relu = MPSCNNNeuronReLU(device: device, a: 0) + + + + // Initialize MPSImage from descriptors + c1Image = MPSImage(device: device, imageDescriptor: c1id) + p1Image = MPSImage(device: device, imageDescriptor: p1id) + c2Image = MPSImage(device: device, imageDescriptor: c2id) + p2Image = MPSImage(device: device, imageDescriptor: p2id) + fc1Image = MPSImage(device: device, imageDescriptor: fc1id) + + + // setup convolution layers + conv1 = SlimMPSCNNConvolution(kernelWidth: 5, + kernelHeight: 5, + inputFeatureChannels: 1, + outputFeatureChannels: 32, + neuronFilter: relu, + device: device, + kernelParamsBinaryName: "conv1") + + conv2 = SlimMPSCNNConvolution(kernelWidth: 5, + kernelHeight: 5, + inputFeatureChannels: 32, + outputFeatureChannels: 64, + neuronFilter: relu, + device: device, + kernelParamsBinaryName: "conv2") + + + // same as a 1x1 convolution filter to produce 1x1x10 from 1x1x1024 + fc1 = SlimMPSCNNFullyConnected(kernelWidth: 7, + kernelHeight: 7, + inputFeatureChannels: 64, + outputFeatureChannels: 1024, + neuronFilter: nil, + device: device, + kernelParamsBinaryName: "fc1") + + fc2 = SlimMPSCNNFullyConnected(kernelWidth: 1, + kernelHeight: 1, + inputFeatureChannels: 1024, + outputFeatureChannels: 10, + neuronFilter: nil, + device: device, + kernelParamsBinaryName: "fc2") + + super.init(withCommandQueue: commandQueueIn) + } + + + /** + This function encodes all the layers of the network into given commandBuffer, it calls subroutines for each piece of the network + + - Parameters: + - inputImage: Image coming in on which the network will run + - imageNum: If the test set is being used we will get a value between 0 and 9999 for which of the 10,000 images is being evaluated + - correctLabel: The correct label for the inputImage while testing + + - Returns: + Guess of the network as to what the digit is as UInt + */ + override func forward(inputImage: MPSImage? = nil, imageNum: Int = 9999, correctLabel: UInt = 10) -> UInt{ + var label = UInt(99) + + // to deliver optimal performance we leave some resources used in MPSCNN to be released at next call of autoreleasepool, + // so the user can decide the appropriate time to release this + autoreleasepool{ + // Get command buffer to use in MetalPerformanceShaders. + let commandBuffer = commandQueue.makeCommandBuffer() + + // output will be stored in this image + let finalLayer = MPSImage(device: commandBuffer.device, imageDescriptor: did) + + // encode layers to metal commandBuffer + if inputImage == nil { + conv1.encode(commandBuffer: commandBuffer, sourceImage: srcImage, destinationImage: c1Image) + } + else{ + conv1.encode(commandBuffer: commandBuffer, sourceImage: inputImage!, destinationImage: c1Image) + } + + pool.encode (commandBuffer: commandBuffer, sourceImage: c1Image , destinationImage: p1Image) + conv2.encode (commandBuffer: commandBuffer, sourceImage: p1Image , destinationImage: c2Image) + pool.encode (commandBuffer: commandBuffer, sourceImage: c2Image , destinationImage: p2Image) + fc1.encode (commandBuffer: commandBuffer, sourceImage: p2Image , destinationImage: fc1Image) + fc2.encode (commandBuffer: commandBuffer, sourceImage: fc1Image , destinationImage: dstImage) + softmax.encode(commandBuffer: commandBuffer, sourceImage: dstImage , destinationImage: finalLayer) + + // add a completion handler to get the correct label the moment GPU is done and compare it to the correct output or return it + commandBuffer.addCompletedHandler { commandBuffer in + label = self.getLabel(finalLayer: finalLayer) + if(correctLabel == label){ + __atomic_increment() + } + } + + // commit commandbuffer to run on GPU and wait for completion + commandBuffer.commit() + if imageNum == 9999 { + commandBuffer.waitUntilCompleted() + } + + } + return label + } +} diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/MNISTSingleLayer.swift b/MPSCNNHelloWorld/MPSCNNHelloWorld/MNISTSingleLayer.swift new file mode 100755 index 00000000..0c1673f2 --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/MNISTSingleLayer.swift @@ -0,0 +1,157 @@ +/* + Copyright (C) 2016 Apple Inc. All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + This is the single layer network where we define and encode the correct layers on a command buffer as needed +*/ + +import MetalPerformanceShaders +import Accelerate + +/** + + This class has our entire network with all layers to getting the final label + + Resources: + * [Instructions](https://www.tensorflow.org/versions/r0.8/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners) to run this network on TensorFlow. + + */ +class MNIST_Full_LayerNN{ + + // MPSImageDescriptors for different layers outputs to be put in + let sid = MPSImageDescriptor(channelFormat: MPSImageFeatureChannelFormat.unorm8, width: 28, height: 28, featureChannels: 1) + let did = MPSImageDescriptor(channelFormat: MPSImageFeatureChannelFormat.float16, width: 1, height: 1, featureChannels: 10) + + // MPSImages and layers declared + var srcImage, dstImage : MPSImage + var layer: MPSCNNFullyConnected + var softmax : MPSCNNSoftMax + var commandQueue : MTLCommandQueue + var device : MTLDevice + + init(withCommandQueue commandQueueIn: MTLCommandQueue!){ + + // CommandQueue to be kept around + commandQueue = commandQueueIn + device = commandQueueIn.device + + // Initialize MPSImage from descriptors + srcImage = MPSImage(device: device, imageDescriptor: sid) + dstImage = MPSImage(device: device, imageDescriptor: did) + + + // setup convolution layer (which is a fully-connected layer) + // cliprect, offset is automatically set + layer = SlimMPSCNNFullyConnected(kernelWidth: 28, + kernelHeight: 28, + inputFeatureChannels : 1, + outputFeatureChannels: 10, + neuronFilter: nil, + device: device, + kernelParamsBinaryName: "NN") + + // prepare softmax layer to be applied at the end to get a clear label + softmax = MPSCNNSoftMax(device: device) + + } + + /** + This function encodes all the layers of the network into given commandBuffer, it calls subroutines for each piece of the network + + - Parameters: + - inputImage: Image coming in on which the network will run + - imageNum: If the test set is being used we will get a value between 0 and 9999 for which of the 10,000 images is being evaluated + - correctLabel: The correct label for the inputImage while testing + + - Returns: + Guess of the network as to what the digit is as UInt + */ + func forward(inputImage: MPSImage? = nil, imageNum: Int = 9999, correctLabel: UInt = 10) -> UInt { + var label = UInt(99) + + // to deliver optimal performance we leave some resources used in MPSCNN to be released at next call of autoreleasepool, + // so the user can decide the appropriate time to release this + autoreleasepool{ + // Get command buffer to use in MetalPerformanceShaders. + let commandBuffer = commandQueue.makeCommandBuffer() + + // output will be stored in this image + let finalLayer = MPSImage(device: commandBuffer.device, imageDescriptor: did) + + // encode layers to metal commandBuffer + if inputImage == nil { + layer.encode(commandBuffer: commandBuffer, sourceImage: srcImage, destinationImage: dstImage) + } + else{ + layer.encode(commandBuffer: commandBuffer, sourceImage: inputImage!, destinationImage: dstImage) + } + softmax.encode(commandBuffer: commandBuffer, sourceImage: dstImage, destinationImage: finalLayer) + + // add a completion handler to get the correct label the moment GPU is done and compare it to the correct output or return it + commandBuffer.addCompletedHandler { commandBuffer in + label = self.getLabel(finalLayer: finalLayer) + if(correctLabel == label){ + __atomic_increment() + } + } + + // commit commandbuffer to run on GPU and wait for completion + commandBuffer.commit() + if imageNum == 9999 || inputImage == nil { + commandBuffer.waitUntilCompleted() + } + + } + return label + } + + /** + This function reads the output probabilities from finalLayer to CPU, sorts them and gets the label with heighest probability + + - Parameters: + - finalLayer: output image of the network this has probabilities of each digit + + - Returns: + Guess of the network as to what the digit is as UInt + */ + func getLabel(finalLayer: MPSImage) -> UInt { + // even though we have 10 labels outputed the MTLTexture format used is RGBAFloat16 thus 3 slices will have 3*4 = 12 outputs + var result_half_array = [UInt16](repeating: 6, count: 12) + var result_float_array = [Float](repeating: 0.3, count: 10) + for i in 0...2 { + finalLayer.texture.getBytes(&(result_half_array[4*i]), + bytesPerRow: MemoryLayout.size*1*4, + bytesPerImage: MemoryLayout.size*1*1*4, + from: MTLRegion(origin: MTLOrigin(x: 0, y: 0, z: 0), + size: MTLSize(width: 1, height: 1, depth: 1)), + mipmapLevel: 0, + slice: i) + } + + // we use vImage to convert our data to float16, Metal GPUs use float16 and swift float is 32-bit + var fullResultVImagebuf = vImage_Buffer(data: &result_float_array, height: 1, width: 10, rowBytes: 10*4) + var halfResultVImagebuf = vImage_Buffer(data: &result_half_array , height: 1, width: 10, rowBytes: 10*2) + + if vImageConvert_Planar16FtoPlanarF(&halfResultVImagebuf, &fullResultVImagebuf, 0) != kvImageNoError { + print("Error in vImage") + } + + // poll all labels for probability and choose the one with max probability to return + var max:Float = 0 + var mostProbableDigit = 10 + for i in 0...9 { + if(max < result_float_array[i]){ + max = result_float_array[i] + mostProbableDigit = i + } + } + + return UInt(mostProbableDigit) + } + +} + + + + diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/MPSCNNHelloWorld-Bridging-Header.h b/MPSCNNHelloWorld/MPSCNNHelloWorld/MPSCNNHelloWorld-Bridging-Header.h new file mode 100755 index 00000000..f6988377 --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/MPSCNNHelloWorld-Bridging-Header.h @@ -0,0 +1,14 @@ +/* + Copyright (C) 2016 Apple Inc. All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + A bridging header so our swift code can see our atomics in objC + */ + +#ifndef MPSCNNHelloWorld_Bridging_Header_h +#define MPSCNNHelloWorld_Bridging_Header_h + +#import "atomics.h" + +#endif /* MPSCNNHelloWorld_Bridging_Header_h */ diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/SlimMPSCNN.swift b/MPSCNNHelloWorld/MPSCNNHelloWorld/SlimMPSCNN.swift new file mode 100755 index 00000000..643e689f --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/SlimMPSCNN.swift @@ -0,0 +1,215 @@ +/* + Copyright (C) 2016 Apple Inc. All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + This file describes slimmer routines to create some common MPSCNNFunctions, it is useful especially to fetch network parameters from .dat files + */ + +import Foundation +import MetalPerformanceShaders + +/** + This depends on MetalPerformanceShaders.framework + + The SlimMPSCNNConvolution is a wrapper class around MPSCNNConvolution used to encapsulate: + - making an MPSCNNConvolutionDescriptor, + - adding network parameters (weights and bias binaries by memory mapping the binaries) + - getting our convolution layer + */ +class SlimMPSCNNConvolution: MPSCNNConvolution{ + /** + A property to keep info from init time whether we will pad input image or not for use during encode call + */ + private var padding = true + + /** + Initializes a fully connected kernel. + + - Parameters: + - kernelWidth: Kernel Width + - kernelHeight: Kernel Height + - inputFeatureChannels: Number feature channels in input of this layer + - outputFeatureChannels: Number feature channels from output of this layer + - neuronFilter: A neuronFilter to add at the end as activation, default is nil + - device: The MTLDevice on which this SlimMPSCNNConvolution filter will be used + - kernelParamsBinaryName: name of the layer to fetch kernelParameters by adding a prefix "weights_" or "bias_" + - padding: Bool value whether to use padding or not + - strideXY: Stride of the filter + - destinationFeatureChannelOffset: FeatureChannel no. in the destination MPSImage to start writing from, helps with concat operations + - groupNum: if grouping is used, default value is 1 meaning no groups + + - Returns: + A valid SlimMPSCNNConvolution object or nil, if failure. + */ + + + init(kernelWidth: UInt, kernelHeight: UInt, inputFeatureChannels: UInt, outputFeatureChannels: UInt, neuronFilter: MPSCNNNeuron? = nil, device: MTLDevice, kernelParamsBinaryName: String, padding willPad: Bool = true, strideXY: (UInt, UInt) = (1, 1), destinationFeatureChannelOffset: UInt = 0, groupNum: UInt = 1){ + + // calculate the size of weights and bias required to be memory mapped into memory + let sizeBias = outputFeatureChannels * UInt(MemoryLayout.size) + let sizeWeights = inputFeatureChannels * kernelHeight * kernelWidth * outputFeatureChannels * UInt(MemoryLayout.size) + + // get the url to this layer's weights and bias + let wtPath = Bundle.main.path(forResource: "weights_" + kernelParamsBinaryName, ofType: "dat") + let bsPath = Bundle.main.path(forResource: "bias_" + kernelParamsBinaryName, ofType: "dat") + + // open file descriptors in read-only mode to parameter files + let fd_w = open( wtPath!, O_RDONLY, S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH) + let fd_b = open( bsPath!, O_RDONLY, S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH) + + assert(fd_w != -1, "Error: failed to open output file at \""+wtPath!+"\" errno = \(errno)\n") + assert(fd_b != -1, "Error: failed to open output file at \""+bsPath!+"\" errno = \(errno)\n") + + // memory map the parameters + let hdrW = mmap(nil, Int(sizeWeights), PROT_READ, MAP_FILE | MAP_SHARED, fd_w, 0) + let hdrB = mmap(nil, Int(sizeBias), PROT_READ, MAP_FILE | MAP_SHARED, fd_b, 0) + + // cast Void pointers to Float + let w = UnsafePointer(hdrW!.bindMemory(to: Float.self, capacity: Int(sizeWeights))) + let b = UnsafePointer(hdrB!.bindMemory(to: Float.self, capacity: Int(sizeBias))) + + assert(w != UnsafePointer(bitPattern: -1), "mmap failed with errno = \(errno)") + assert(b != UnsafePointer(bitPattern: -1), "mmap failed with errno = \(errno)") + + // create appropriate convolution descriptor with appropriate stride + let convDesc = MPSCNNConvolutionDescriptor(kernelWidth: Int(kernelWidth), + kernelHeight: Int(kernelHeight), + inputFeatureChannels: Int(inputFeatureChannels), + outputFeatureChannels: Int(outputFeatureChannels), + neuronFilter: neuronFilter) + convDesc.strideInPixelsX = Int(strideXY.0) + convDesc.strideInPixelsY = Int(strideXY.1) + + assert(groupNum > 0, "Group size can't be less than 1") + convDesc.groups = Int(groupNum) + + // initialize the convolution layer by calling the parent's (MPSCNNConvlution's) initializer + super.init(device: device, + convolutionDescriptor: convDesc, + kernelWeights: w, + biasTerms: b, + flags: MPSCNNConvolutionFlags.none) + self.destinationFeatureChannelOffset = Int(destinationFeatureChannelOffset) + + // set padding for calculation of offset during encode call + padding = willPad + + // unmap files at initialization of MPSCNNConvolution, the weights are copied and packed internally we no longer require these + assert(munmap(hdrW, Int(sizeWeights)) == 0, "munmap failed with errno = \(errno)") + assert(munmap(hdrB, Int(sizeBias)) == 0, "munmap failed with errno = \(errno)") + + // close file descriptors + close(fd_w) + close(fd_b) + } + + /** + Encode a MPSCNNKernel into a command Buffer. The operation shall proceed out-of-place. + + We calculate the appropriate offset as per how TensorFlow calculates its padding using input image size and stride here. + + This [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py) has an explanation in header comments how tensorFlow pads its convolution input images. + + - Parameters: + - commandBuffer: A valid MTLCommandBuffer to receive the encoded filter + - sourceImage: A valid MPSImage object containing the source image. + - destinationImage: A valid MPSImage to be overwritten by result image. destinationImage may not alias sourceImage + */ + override func encode(commandBuffer: MTLCommandBuffer, sourceImage: MPSImage, destinationImage: MPSImage) { + // select offset according to padding being used or not + if padding { + let pad_along_height = ((destinationImage.height - 1) * strideInPixelsY + kernelHeight - sourceImage.height) + let pad_along_width = ((destinationImage.width - 1) * strideInPixelsX + kernelWidth - sourceImage.width) + let pad_top = Int(pad_along_height / 2) + let pad_left = Int(pad_along_width / 2) + + self.offset = MPSOffset(x: ((Int(kernelWidth)/2) - pad_left), y: (Int(kernelHeight/2) - pad_top), z: 0) + } + else{ + self.offset = MPSOffset(x: Int(kernelWidth)/2, y: Int(kernelHeight)/2, z: 0) + } + + super.encode(commandBuffer: commandBuffer, sourceImage: sourceImage, destinationImage: destinationImage) + } +} + +/** + This depends on MetalPerformanceShaders.framework + + The SlimMPSCNNFullyConnected is a wrapper class around MPSCNNFullyConnected used to encapsulate: + - making an MPSCNNConvolutionDescriptor, + - adding network parameters (weights and bias binaries by memory mapping the binaries) + - getting our fullyConnected layer + */ +class SlimMPSCNNFullyConnected: MPSCNNFullyConnected{ + /** + Initializes a fully connected kernel. + + - Parameters: + - kernelWidth: Kernel Width + - kernelHeight: Kernel Height + - inputFeatureChannels: Number feature channels in input of this layer + - outputFeatureChannels: Number feature channels from output of this layer + - neuronFilter: A neuronFilter to add at the end as activation, default is nil + - device: The MTLDevice on which this SlimMPSCNNConvolution filter will be used + - kernelParamsBinaryName: name of the layer to fetch kernelParameters by adding a prefix "weights_" or "bias_" + - destinationFeatureChannelOffset: FeatureChannel no. in the destination MPSImage to start writing from, helps with concat operations + + - Returns: + A valid SlimMPSCNNFullyConnected object or nil, if failure. + */ + + init(kernelWidth: UInt, kernelHeight: UInt, inputFeatureChannels: UInt, outputFeatureChannels: UInt, neuronFilter: MPSCNNNeuron? = nil, device: MTLDevice, kernelParamsBinaryName: String, destinationFeatureChannelOffset: UInt = 0){ + + // calculate the size of weights and bias required to be memory mapped into memory + let sizeBias = outputFeatureChannels * UInt(MemoryLayout.size) + let sizeWeights = inputFeatureChannels * kernelHeight * kernelWidth * outputFeatureChannels * UInt(MemoryLayout.size) + + // get the url to this layer's weights and bias + let wtPath = Bundle.main.path(forResource: "weights_" + kernelParamsBinaryName, ofType: "dat") + let bsPath = Bundle.main.path(forResource: "bias_" + kernelParamsBinaryName, ofType: "dat") + + // open file descriptors in read-only mode to parameter files + let fd_w = open(wtPath!, O_RDONLY, S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH) + let fd_b = open(bsPath!, O_RDONLY, S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH) + + assert(fd_w != -1, "Error: failed to open output file at \""+wtPath!+"\" errno = \(errno)\n") + assert(fd_b != -1, "Error: failed to open output file at \""+bsPath!+"\" errno = \(errno)\n") + + // memory map the parameters + let hdrW = mmap(nil, Int(sizeWeights), PROT_READ, MAP_FILE | MAP_SHARED, fd_w, 0) + let hdrB = mmap(nil, Int(sizeBias), PROT_READ, MAP_FILE | MAP_SHARED, fd_b, 0) + + // cast Void pointers to Float + let w = UnsafePointer(hdrW!.bindMemory(to: Float.self, capacity: Int(sizeWeights))) + let b = UnsafePointer(hdrB!.bindMemory(to: Float.self, capacity: Int(sizeBias))) + + assert(w != UnsafePointer(bitPattern: -1), "mmap failed with errno = \(errno)") + assert(b != UnsafePointer(bitPattern: -1), "mmap failed with errno = \(errno)") + + // create appropriate convolution descriptor (in fully connected, stride is always 1) + let convDesc = MPSCNNConvolutionDescriptor(kernelWidth: Int(kernelWidth), + kernelHeight: Int(kernelHeight), + inputFeatureChannels: Int(inputFeatureChannels), + outputFeatureChannels: Int(outputFeatureChannels), + neuronFilter: neuronFilter) + + // initialize the convolution layer by calling the parent's (MPSCNNFullyConnected's) initializer + super.init(device: device, + convolutionDescriptor: convDesc, + kernelWeights: w, + biasTerms: b, + flags: MPSCNNConvolutionFlags.none) + + self.destinationFeatureChannelOffset = Int(destinationFeatureChannelOffset) + + // unmap files at initialization of MPSCNNFullyConnected, the weights are copied and packed internally we no longer require these + assert(munmap(hdrW, Int(sizeWeights)) == 0, "munmap failed with errno = \(errno)") + assert(munmap(hdrB, Int(sizeBias)) == 0, "munmap failed with errno = \(errno)") + + // close file descriptors + close(fd_w) + close(fd_b) + } +} diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/ViewController.swift b/MPSCNNHelloWorld/MPSCNNHelloWorld/ViewController.swift new file mode 100755 index 00000000..7c5e9e3c --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/ViewController.swift @@ -0,0 +1,165 @@ +/* + Copyright (C) 2016 Apple Inc. All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + View Controller for Metal Performance Shaders Sample Code. +*/ + +import UIKit +import MetalPerformanceShaders + +class ViewController: UIViewController{ + + // some properties used to control the app and store appropriate values + // we will start with the simple 1 layer + var deep = false + var commandQueue: MTLCommandQueue! + var device: MTLDevice! + + // Networks we have + var neuralNetwork: MNIST_Full_LayerNN? = nil + var neuralNetworkDeep: MNIST_Deep_ConvNN? = nil + var runningNet: MNIST_Full_LayerNN? = nil + + // loading MNIST Test Set here + let MNISTdata = GetMNISTData() + + // MNIST dataset image parameters + let mnistInputWidth = 28 + let mnistInputHeight = 28 + let mnistInputNumPixels = 784 + + // Outlets to labels and view + @IBOutlet weak var digitView: DrawView! + @IBOutlet weak var predictionLabel: UILabel! + @IBOutlet weak var accuracyLabel: UILabel! + + + override func viewDidLoad() { + super.viewDidLoad() + + // Load default device. + device = MTLCreateSystemDefaultDevice() + + // Make sure the current device supports MetalPerformanceShaders. + guard MPSSupportsMTLDevice(device) else { + print("Metal Performance Shaders not Supported on current Device") + return + } + + // Create new command queue. + commandQueue = device!.makeCommandQueue() + + // initialize the networks we shall use to detect digits + neuralNetwork = MNIST_Full_LayerNN(withCommandQueue: commandQueue) + neuralNetworkDeep = MNIST_Deep_ConvNN(withCommandQueue: commandQueue) + runningNet = neuralNetwork + } + + @IBAction func tappedDeepButton(_ sender: UIButton) { + // switch network to be used between the deep and the single layered + if deep { + sender.setTitle("Use Deep Net", for: UIControlState.normal) + runningNet = neuralNetwork + } + else{ + sender.setTitle("Use Single Layer", for: UIControlState.normal) + runningNet = neuralNetworkDeep + } + + deep = !deep + } + + @IBAction func tappedClear(_ sender: UIButton) { + // clear the digitview + digitView.lines = [] + digitView.setNeedsDisplay() + predictionLabel.isHidden = true + + } + + @IBAction func tappedTestSet(_ sender: UIButton) { + // placeholder to count number of correct detections on the test set + var correctDetections = Int32(0) + let total = Float(10000) + accuracyLabel.isHidden = false + __atomic_reset() + + // validate NeuralNetwork was initialized properly + assert(runningNet != nil) + + for i in 0.. + +static atomic_int cnt = ATOMIC_VAR_INIT(0); +void __atomic_increment(); +void __atomic_reset(); +int __get_atomic_count(); + +#endif /* atomics_h */ diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/atomics.m b/MPSCNNHelloWorld/MPSCNNHelloWorld/atomics.m new file mode 100755 index 00000000..793f3ce9 --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/atomics.m @@ -0,0 +1,19 @@ +/* + Copyright (C) 2016 Apple Inc. All Rights Reserved. + See LICENSE.txt for this sample’s licensing information + + Abstract: + We define some custom atomics to be used the network so seperate threads at end of commandBuffers can safely increment. +*/ + +#import "atomics.h" + +void __atomic_increment(){ + atomic_fetch_add(&cnt, 1); +} +void __atomic_reset(){ + cnt = ATOMIC_VAR_INIT(0); +} +int __get_atomic_count(){ + return atomic_load(&cnt); +} diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/deep_weights/binaries/bias_conv1.dat b/MPSCNNHelloWorld/MPSCNNHelloWorld/deep_weights/binaries/bias_conv1.dat new file mode 100755 index 00000000..7add15e3 --- /dev/null +++ b/MPSCNNHelloWorld/MPSCNNHelloWorld/deep_weights/binaries/bias_conv1.dat @@ -0,0 +1,2 @@ +Cs==|=£=\====޽==_=w=]=5==a=p==| +=o==/=Ħ=[=7Í=/i=!=.=s==-=oӟ=i= \ No newline at end of file diff --git a/MPSCNNHelloWorld/MPSCNNHelloWorld/deep_weights/binaries/bias_conv2.dat b/MPSCNNHelloWorld/MPSCNNHelloWorld/deep_weights/binaries/bias_conv2.dat new file mode 100755 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