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. The sample demonstrates how to encode different layers to the GPU and perform image recognition using trained parameters(weights and bias) that have been fetched from, pre-trained and saved network on TensorFlow.
This commit is contained in:
Liu Lantao
parent
95df29821e
commit
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Sample code project: MPSCNNHelloWorld: Simple Digit Detection Convolution Neural Networks (CNN)
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Version: 1.1
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IMPORTANT: This Apple software is supplied to you by Apple
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Inc. ("Apple") in consideration of your agreement to the following
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terms, and your use, installation, modification or redistribution of
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this Apple software constitutes acceptance of these terms. If you do
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not agree with these terms, please do not use, install, modify or
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redistribute this Apple software.
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In consideration of your agreement to abide by the following terms, and
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subject to these terms, Apple grants you a personal, non-exclusive
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license, under Apple's copyrights in this original Apple software (the
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"Apple Software"), to use, reproduce, modify and redistribute the Apple
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Software, with or without modifications, in source and/or binary forms;
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provided that if you redistribute the Apple Software in its entirety and
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without modifications, you must retain this notice and the following
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text and disclaimers in all such redistributions of the Apple Software.
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Neither the name, trademarks, service marks or logos of Apple Inc. may
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be used to endorse or promote products derived from the Apple Software
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without specific prior written permission from Apple. Except as
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expressly stated in this notice, no other rights or licenses, express or
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implied, are granted by Apple herein, including but not limited to any
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patent rights that may be infringed by your derivative works or by other
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works in which the Apple Software may be incorporated.
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The Apple Software is provided by Apple on an "AS IS" basis. APPLE
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MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION
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THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS
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FOR A PARTICULAR PURPOSE, REGARDING THE APPLE SOFTWARE OR ITS USE AND
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OPERATION ALONE OR IN COMBINATION WITH YOUR PRODUCTS.
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IN NO EVENT SHALL APPLE BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL
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OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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INTERRUPTION) ARISING IN ANY WAY OUT OF THE USE, REPRODUCTION,
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AND WHETHER UNDER THEORY OF CONTRACT, TORT (INCLUDING NEGLIGENCE),
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STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS BEEN ADVISED OF THE
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POSSIBILITY OF SUCH DAMAGE.
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Copyright (C) 2016 Apple Inc. All Rights Reserved.
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|
||||
<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="11185.2" systemVersion="16A254d" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" useTraitCollections="YES" colorMatched="YES" initialViewController="BYZ-38-t0r">
|
||||
<dependencies>
|
||||
<deployment identifier="iOS"/>
|
||||
<plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="11151.3"/>
|
||||
<capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
|
||||
</dependencies>
|
||||
<scenes>
|
||||
<!--View Controller-->
|
||||
<scene sceneID="tne-QT-ifu">
|
||||
<objects>
|
||||
<viewController id="BYZ-38-t0r" customClass="ViewController" customModule="Digit_Detector" customModuleProvider="target" sceneMemberID="viewController">
|
||||
<layoutGuides>
|
||||
<viewControllerLayoutGuide type="top" id="y3c-jy-aDJ"/>
|
||||
<viewControllerLayoutGuide type="bottom" id="wfy-db-euE"/>
|
||||
</layoutGuides>
|
||||
<view key="view" contentMode="scaleToFill" id="8bC-Xf-vdC">
|
||||
<rect key="frame" x="0.0" y="0.0" width="375" height="667"/>
|
||||
<autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
|
||||
<subviews>
|
||||
<label hidden="YES" opaque="NO" userInteractionEnabled="NO" contentMode="left" horizontalHuggingPriority="251" verticalHuggingPriority="251" text="Accuracy = " textAlignment="center" lineBreakMode="tailTruncation" baselineAdjustment="alignBaselines" adjustsFontSizeToFit="NO" translatesAutoresizingMaskIntoConstraints="NO" id="Itt-4A-FVR">
|
||||
<fontDescription key="fontDescription" type="system" pointSize="17"/>
|
||||
<nil key="textColor"/>
|
||||
<nil key="highlightedColor"/>
|
||||
</label>
|
||||
<button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="roundedRect" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="fMB-WM-8YO">
|
||||
<fontDescription key="fontDescription" type="system" pointSize="26"/>
|
||||
<state key="normal" title="Use MNIST Test Set"/>
|
||||
<connections>
|
||||
<action selector="tappedTestSet:" destination="BYZ-38-t0r" eventType="touchUpInside" id="iwN-td-1sk"/>
|
||||
</connections>
|
||||
</button>
|
||||
<button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="roundedRect" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="HEd-C6-X5l">
|
||||
<fontDescription key="fontDescription" type="system" pointSize="26"/>
|
||||
<state key="normal" title="Detect Digit"/>
|
||||
<connections>
|
||||
<action selector="tappedDetectDigit:" destination="BYZ-38-t0r" eventType="touchUpInside" id="Rtq-vr-h56"/>
|
||||
</connections>
|
||||
</button>
|
||||
<button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="roundedRect" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="NsO-ek-NfA">
|
||||
<fontDescription key="fontDescription" type="system" pointSize="25"/>
|
||||
<state key="normal" title="Clear"/>
|
||||
<connections>
|
||||
<action selector="tappedClear:" destination="BYZ-38-t0r" eventType="touchUpInside" id="aCb-Qa-Y9q"/>
|
||||
</connections>
|
||||
</button>
|
||||
<label hidden="YES" opaque="NO" userInteractionEnabled="NO" contentMode="left" horizontalHuggingPriority="251" verticalHuggingPriority="251" text="0" textAlignment="center" lineBreakMode="tailTruncation" baselineAdjustment="alignBaselines" adjustsFontSizeToFit="NO" translatesAutoresizingMaskIntoConstraints="NO" id="Aa3-61-EIv">
|
||||
<fontDescription key="fontDescription" type="system" pointSize="23"/>
|
||||
<nil key="textColor"/>
|
||||
<nil key="highlightedColor"/>
|
||||
</label>
|
||||
<view contentMode="scaleToFill" translatesAutoresizingMaskIntoConstraints="NO" id="PzX-rP-SKt" customClass="DrawView" customModule="Digit_Detector" customModuleProvider="target">
|
||||
<color key="backgroundColor" white="0.0" alpha="1" colorSpace="custom" customColorSpace="genericGamma22GrayColorSpace"/>
|
||||
<constraints>
|
||||
<constraint firstAttribute="height" constant="240" id="cpN-BA-HfR"/>
|
||||
<constraint firstAttribute="width" constant="240" id="zZB-ag-7Eh"/>
|
||||
</constraints>
|
||||
</view>
|
||||
<button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="roundedRect" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="lv9-QR-RxB">
|
||||
<fontDescription key="fontDescription" type="system" pointSize="26"/>
|
||||
<state key="normal" title="Use Deep Net"/>
|
||||
<connections>
|
||||
<action selector="tappedDeepButton:" destination="BYZ-38-t0r" eventType="touchUpInside" id="wAp-88-Gdp"/>
|
||||
</connections>
|
||||
</button>
|
||||
</subviews>
|
||||
<color key="backgroundColor" white="1" alpha="1" colorSpace="custom" customColorSpace="genericGamma22GrayColorSpace"/>
|
||||
<constraints>
|
||||
<constraint firstItem="PzX-rP-SKt" firstAttribute="top" secondItem="NsO-ek-NfA" secondAttribute="bottom" constant="6" id="5Br-ID-zhu"/>
|
||||
<constraint firstItem="lv9-QR-RxB" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="9CN-JK-3QO"/>
|
||||
<constraint firstItem="Itt-4A-FVR" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="BPl-aC-Jiq"/>
|
||||
<constraint firstItem="PzX-rP-SKt" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="LZI-YF-JxH"/>
|
||||
<constraint firstItem="HEd-C6-X5l" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="P7g-P9-3IL"/>
|
||||
<constraint firstItem="Aa3-61-EIv" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="Q2L-ju-FT5"/>
|
||||
<constraint firstItem="NsO-ek-NfA" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="Q6a-Xz-gCB"/>
|
||||
<constraint firstItem="wfy-db-euE" firstAttribute="top" secondItem="lv9-QR-RxB" secondAttribute="bottom" constant="8" id="RDs-tb-ujw"/>
|
||||
<constraint firstItem="fMB-WM-8YO" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="T3b-5e-9x9"/>
|
||||
<constraint firstItem="HEd-C6-X5l" firstAttribute="top" secondItem="PzX-rP-SKt" secondAttribute="bottom" constant="23" id="XZb-1B-Gug"/>
|
||||
<constraint firstItem="PzX-rP-SKt" firstAttribute="centerY" secondItem="8bC-Xf-vdC" secondAttribute="centerY" id="YHc-FZ-FDx"/>
|
||||
<constraint firstItem="Aa3-61-EIv" firstAttribute="top" secondItem="HEd-C6-X5l" secondAttribute="bottom" constant="24" id="kKx-jg-CxZ"/>
|
||||
<constraint firstItem="fMB-WM-8YO" firstAttribute="top" secondItem="y3c-jy-aDJ" secondAttribute="bottom" id="pRd-8b-Hib"/>
|
||||
<constraint firstItem="Itt-4A-FVR" firstAttribute="top" secondItem="fMB-WM-8YO" secondAttribute="bottom" constant="24" id="zhT-JQ-n6p"/>
|
||||
</constraints>
|
||||
</view>
|
||||
<connections>
|
||||
<outlet property="DigitView" destination="PzX-rP-SKt" id="bZU-8T-7ZI"/>
|
||||
<outlet property="accuracyLabel" destination="Itt-4A-FVR" id="REI-58-rzY"/>
|
||||
<outlet property="predictionLabel" destination="Aa3-61-EIv" id="OkV-W4-Clr"/>
|
||||
</connections>
|
||||
</viewController>
|
||||
<placeholder placeholderIdentifier="IBFirstResponder" id="dkx-z0-nzr" sceneMemberID="firstResponder"/>
|
||||
</objects>
|
||||
<point key="canvasLocation" x="-3427.734375" y="430.89311859443626"/>
|
||||
</scene>
|
||||
</scenes>
|
||||
</document>
|
||||
|
|
@ -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<UITouch>, with event: UIEvent?) {
|
||||
lastPoint = touches.first!.location(in: self)
|
||||
}
|
||||
|
||||
override func touchesMoved(_ touches: Set<UITouch>, 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
|
||||
}
|
||||
}
|
||||
|
|
@ -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<UInt8>(bitPattern: -1), "mmap failed with errno = \(errno)")
|
||||
assert(l != UnsafePointer<UInt8>(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)
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,47 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>CFBundleDevelopmentRegion</key>
|
||||
<string>en</string>
|
||||
<key>CFBundleExecutable</key>
|
||||
<string>$(EXECUTABLE_NAME)</string>
|
||||
<key>CFBundleIdentifier</key>
|
||||
<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
|
||||
<key>CFBundleInfoDictionaryVersion</key>
|
||||
<string>6.0</string>
|
||||
<key>CFBundleName</key>
|
||||
<string>$(PRODUCT_NAME)</string>
|
||||
<key>CFBundlePackageType</key>
|
||||
<string>APPL</string>
|
||||
<key>CFBundleShortVersionString</key>
|
||||
<string>1.0</string>
|
||||
<key>CFBundleSignature</key>
|
||||
<string>????</string>
|
||||
<key>CFBundleVersion</key>
|
||||
<string>1</string>
|
||||
<key>LSRequiresIPhoneOS</key>
|
||||
<true/>
|
||||
<key>UILaunchStoryboardName</key>
|
||||
<string>LaunchScreen</string>
|
||||
<key>UIMainStoryboardFile</key>
|
||||
<string>Main</string>
|
||||
<key>UIRequiredDeviceCapabilities</key>
|
||||
<array>
|
||||
<string>armv7</string>
|
||||
</array>
|
||||
<key>UISupportedInterfaceOrientations</key>
|
||||
<array>
|
||||
<string>UIInterfaceOrientationPortrait</string>
|
||||
<string>UIInterfaceOrientationLandscapeLeft</string>
|
||||
<string>UIInterfaceOrientationLandscapeRight</string>
|
||||
</array>
|
||||
<key>UISupportedInterfaceOrientations~ipad</key>
|
||||
<array>
|
||||
<string>UIInterfaceOrientationPortrait</string>
|
||||
<string>UIInterfaceOrientationPortraitUpsideDown</string>
|
||||
<string>UIInterfaceOrientationLandscapeLeft</string>
|
||||
<string>UIInterfaceOrientationLandscapeRight</string>
|
||||
</array>
|
||||
</dict>
|
||||
</plist>
|
||||
|
|
@ -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
|
||||
}
|
||||
}
|
||||
|
|
@ -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<UInt16>.size*1*4,
|
||||
bytesPerImage: MemoryLayout<UInt16>.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)
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -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 */
|
||||
|
|
@ -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<Float>.size)
|
||||
let sizeWeights = inputFeatureChannels * kernelHeight * kernelWidth * outputFeatureChannels * UInt(MemoryLayout<Float>.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<Float>(bitPattern: -1), "mmap failed with errno = \(errno)")
|
||||
assert(b != UnsafePointer<Float>(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<Float>.size)
|
||||
let sizeWeights = inputFeatureChannels * kernelHeight * kernelWidth * outputFeatureChannels * UInt(MemoryLayout<Float>.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<Float>(bitPattern: -1), "mmap failed with errno = \(errno)")
|
||||
assert(b != UnsafePointer<Float>(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)
|
||||
}
|
||||
}
|
||||
|
|
@ -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..<Int(total){
|
||||
inference(imageNum: i, correctLabel: UInt(MNISTdata.labels[i]))
|
||||
if i % 100 == 0 {
|
||||
accuracyLabel.text = "\(i/100)% Done"
|
||||
// this command helps update the UI in the loop regularly
|
||||
RunLoop.current.run(mode: RunLoopMode.defaultRunLoopMode, before: Date.distantPast)
|
||||
}
|
||||
}
|
||||
// display accuracy of the network on the MNIST test set
|
||||
correctDetections = __get_atomic_count()
|
||||
|
||||
accuracyLabel.isHidden = false
|
||||
accuracyLabel.text = "Accuracy = \(Float(correctDetections * 100)/total)%"
|
||||
}
|
||||
|
||||
@IBAction func tappedDetectDigit(_ sender: UIButton) {
|
||||
// get the digitView context so we can get the pixel values from it to intput to network
|
||||
let context = digitView.getViewContext()
|
||||
|
||||
// validate NeuralNetwork was initialized properly
|
||||
assert(runningNet != nil)
|
||||
|
||||
// putting input into MTLTexture in the MPSImage
|
||||
runningNet?.srcImage.texture.replace(region: MTLRegion( origin: MTLOrigin(x: 0, y: 0, z: 0),
|
||||
size: MTLSize(width: mnistInputWidth, height: mnistInputHeight, depth: 1)),
|
||||
mipmapLevel: 0,
|
||||
slice: 0,
|
||||
withBytes: context!.data!,
|
||||
bytesPerRow: mnistInputWidth,
|
||||
bytesPerImage: 0)
|
||||
// run the network forward pass
|
||||
let label = (runningNet?.forward())!
|
||||
|
||||
// show the prediction
|
||||
predictionLabel.text = "\(label)"
|
||||
predictionLabel.isHidden = false
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
This function runs the inference network on the test set
|
||||
|
||||
- Parameters:
|
||||
- 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:
|
||||
Void
|
||||
*/
|
||||
func inference(imageNum: Int, correctLabel: UInt){
|
||||
// get the correct image pixels from the test set
|
||||
var mnist_input_image = [UInt8]()
|
||||
mnist_input_image += MNISTdata.images[(imageNum*mnistInputNumPixels)..<((imageNum+1)*mnistInputNumPixels)]
|
||||
|
||||
// create a source image for the network to forward
|
||||
let inputImage = MPSImage(device: device, imageDescriptor: (runningNet?.sid)!)
|
||||
|
||||
// put image in source texture (input layer)
|
||||
inputImage.texture.replace(region: MTLRegion(origin: MTLOrigin(x: 0, y: 0, z: 0),
|
||||
size: MTLSize(width: mnistInputWidth, height: mnistInputHeight, depth: 1)),
|
||||
mipmapLevel: 0,
|
||||
slice: 0,
|
||||
withBytes: mnist_input_image,
|
||||
bytesPerRow: mnistInputWidth,
|
||||
bytesPerImage: 0)
|
||||
|
||||
// run the network forward pass
|
||||
_ = runningNet!.forward(inputImage: inputImage, imageNum : imageNum, correctLabel: correctLabel)
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -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.
|
||||
*/
|
||||
|
||||
#ifndef atomics_h
|
||||
#define atomics_h
|
||||
|
||||
#import <stdatomic.h>
|
||||
|
||||
static atomic_int cnt = ATOMIC_VAR_INIT(0);
|
||||
void __atomic_increment();
|
||||
void __atomic_reset();
|
||||
int __get_atomic_count();
|
||||
|
||||
#endif /* atomics_h */
|
||||
|
|
@ -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);
|
||||
}
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
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|
||||
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||||
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|||
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|
||||
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||||
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|
|
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|
|||
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|
||||
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|
|
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|
|||
Ї/НОЃjП><3E>kЦ=.І<>О<EFBFBD><D09E>В<\\Є?XНжН<D0B6>3$?\ЖПdзnО
|
||||
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|
|
@ -0,0 +1,30 @@
|
|||
# MPSCNNHelloWorld: Simple Digit Detection Convolution Neural Networks (CNN)
|
||||
|
||||
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.
|
||||
The sample demonstrates how to encode different layers to the GPU and perform image recognition using trained parameters(weights and bias) that have been fetched from, pre-trained and saved network on TensorFlow.
|
||||
|
||||
The Single Network can be found at:
|
||||
https://www.tensorflow.org/versions/r0.8/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners
|
||||
|
||||
The Deep Network can be found at:
|
||||
https://www.tensorflow.org/versions/r0.8/tutorials/mnist/pros/index.html#deep-mnist-for-experts
|
||||
|
||||
The network parameters are stored a binary .dat files that are memory-mapped when needed.
|
||||
|
||||
## Requirements
|
||||
|
||||
### Build
|
||||
|
||||
Xcode 8.0 or later; iOS 10.0 SDK or later
|
||||
|
||||
### Runtime
|
||||
|
||||
iOS 10.0 or later
|
||||
|
||||
### Device Feature Set
|
||||
|
||||
iOS GPU Family 2 v1
|
||||
iOS GPU Family 2 v2
|
||||
iOS GPU Family 3 v1
|
||||
|
||||
Copyright (C) 2016 Apple Inc. All rights reserved.
|
||||
Loading…
Reference in New Issue