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提交 b7a1d552 编写于 作者: X xiaohaichun
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merge metal branch to pointer

上级 13c9388c 1c72c0ef
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Showing with 4360 addition and 2493 deletion
.gitignore
浏览文件 @ b7a1d552
......@@ -24,6 +24,7 @@
*.lai
*.la
*.lib
*.a
# Executables
*.exe
......
metal/PreluKernel/PreluKernel.xcodeproj/project.pbxproj 已删除 100644 → 0
浏览文件 @ 13c9388c
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浏览文件 @ 13c9388c
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浏览文件 @ 13c9388c
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浏览文件 @ 13c9388c
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metal/PreluKernel/PreluKernel/PreluKernel.metal 已删除 100644 → 0
浏览文件 @ 13c9388c
//
// PreluKernel.metal
// PreluKernel
//
// Created by liuRuiLong on 2018/8/23.
// Copyright © 2018年 orange. All rights reserved.
//
#include <metal_stdlib>
using namespace metal;
metal/paddle-mobile-demo/paddle-mobile-demo.xcodeproj/project.pbxproj
浏览文件 @ b7a1d552
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C2C08EB02142748D00C69DBF /* batch_norm_5.w_2 in Resources */,
C23717882148E5A50092444E /* ar_model in Resources */,
C2C08E512142748D00C69DBF /* batch_norm_29.w_2 in Resources */,
C2C08E6E2142748D00C69DBF /* conv2d_10.w_0 in Resources */,
C2C08E8D2142748D00C69DBF /* batch_norm_34.w_0 in Resources */,
C2C08E642142748D00C69DBF /* batch_norm_6.w_1 in Resources */,
C2C08EAB2142748D00C69DBF /* batch_norm_19.w_1 in Resources */,
C2C08F002142748D00C69DBF /* conv2d_24.w_0 in Resources */,
C2C08EAD2142748D00C69DBF /* batch_norm_31.w_1 in Resources */,
C2C08EFA2142748D00C69DBF /* batch_norm_26.w_2 in Resources */,
C2C08F0D2142748D00C69DBF /* batch_norm_30.w_2 in Resources */,
C2C08E922142748D00C69DBF /* batch_norm_5.b_0 in Resources */,
C2C08E822142748D00C69DBF /* batch_norm_21.w_1 in Resources */,
C2C08ECE2142748D00C69DBF /* conv2d_2.w_0 in Resources */,
C2C08E7D2142748D00C69DBF /* batch_norm_0.w_1 in Resources */,
C2C08F0F2142748D00C69DBF /* batch_norm_18.w_2 in Resources */,
C2C08E682142748D00C69DBF /* conv2d_28.w_0 in Resources */,
C2C08E802142748D00C69DBF /* conv2d_12.w_0 in Resources */,
C2C08E752142748D00C69DBF /* batch_norm_21.b_0 in Resources */,
C2C08ED82142748D00C69DBF /* conv2d_22.b_0 in Resources */,
C2C08E552142748D00C69DBF /* batch_norm_14.w_2 in Resources */,
C2C08E6D2142748D00C69DBF /* batch_norm_1.w_0 in Resources */,
C2C08EF52142748D00C69DBF /* batch_norm_29.b_0 in Resources */,
C2C08E912142748D00C69DBF /* batch_norm_33.w_1 in Resources */,
C2C08F052142748D00C69DBF /* conv2d_32.w_0 in Resources */,
C2C08E732142748D00C69DBF /* batch_norm_3.w_1 in Resources */,
C2C08ED32142748D00C69DBF /* depthwise_conv2d_12.w_0 in Resources */,
C2C08F092142748D00C69DBF /* batch_norm_25.w_2 in Resources */,
C2C08E5F2142748D00C69DBF /* conv2d_15.w_0 in Resources */,
C2C08E5A2142748D00C69DBF /* batch_norm_4.w_0 in Resources */,
C2C08EDD2142748D00C69DBF /* conv2d_23.b_0 in Resources */,
C2C08E712142748D00C69DBF /* batch_norm_13.w_2 in Resources */,
C2C08EDE2142748D00C69DBF /* batch_norm_1.w_2 in Resources */,
C2C08EDA2142748D00C69DBF /* batch_norm_12.w_0 in Resources */,
C2C08E812142748D00C69DBF /* batch_norm_3.w_0 in Resources */,
C2C08EA42142748D00C69DBF /* batch_norm_27.w_0 in Resources */,
C2C08ECC2142748D00C69DBF /* batch_norm_12.w_1 in Resources */,
C2C08E592142748D00C69DBF /* batch_norm_6.w_0 in Resources */,
C2C08E3D2142748D00C69DBF /* hand.jpg in Resources */,
C2C08E422142748D00C69DBF /* genet_params in Resources */,
C2C08F122142748D00C69DBF /* conv2d_33.w_0 in Resources */,
C2C08F132142748D00C69DBF /* depthwise_conv2d_7.w_0 in Resources */,
FCBCCC522122EEDC00D94F7E /* ssd_hand_params in Resources */,
FCEEE7D4210627A000444BEC /* banana.jpeg in Resources */,
FC918193211DC70500B6F354 /* iphone.JPG in Resources */,
FCDFD41B211D91C7005AB38B /* synset.txt in Resources */,
FC039B8720E11C550081E9F8 /* Main.storyboard in Resources */,
C2C08EFF2142748D00C69DBF /* conv2d_30.w_0 in Resources */,
C2C08F062142748D00C69DBF /* conv2d_26.w_0 in Resources */,
FCA3A16121313E1F00084FE5 /* hand.jpg in Resources */,
FC8CFEE62135452C0094D569 /* genet_params in Resources */,
FCBCCC532122EEDC00D94F7E /* ssd_hand_model in Resources */,
FC9A19E72148C38400CD9CBF /* ar_model in Resources */,
FC9A19E82148C38400CD9CBF /* ar_params in Resources */,
);
runOnlyForDeploymentPostprocessing = 0;
};
......@@ -1188,8 +359,11 @@
buildActionMask = 2147483647;
files = (
FC039B8420E11C550081E9F8 /* ViewController.swift in Sources */,
FC803BCE214D27930094B8E5 /* VideoCapture.swift in Sources */,
FC013928210204A3008100E3 /* PreProcessKernel.metal in Sources */,
FCF437E8214B6DDB00943429 /* MultiPredictViewController.swift in Sources */,
FCBCCC552122EF5500D94F7E /* MetalHelper.swift in Sources */,
FC803BCD214D27930094B8E5 /* FPSCounter.swift in Sources */,
FC039B8220E11C550081E9F8 /* AppDelegate.swift in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
......@@ -1338,10 +512,10 @@
CLANG_ENABLE_MODULES = YES;
CODE_SIGN_IDENTITY = "iPhone Developer";
CODE_SIGN_STYLE = Automatic;
DEVELOPMENT_TEAM = 6K8JTBT3MF;
DEVELOPMENT_TEAM = A798K58VVL;
ENABLE_BITCODE = NO;
INFOPLIST_FILE = "paddle-mobile-demo/Info.plist";
IPHONEOS_DEPLOYMENT_TARGET = 9.0;
IPHONEOS_DEPLOYMENT_TARGET = 10.0;
LD_RUNPATH_SEARCH_PATHS = (
"$(inherited)",
"@executable_path/Frameworks",
......@@ -1365,10 +539,10 @@
CLANG_ENABLE_MODULES = YES;
CODE_SIGN_IDENTITY = "iPhone Developer";
CODE_SIGN_STYLE = Automatic;
DEVELOPMENT_TEAM = 6K8JTBT3MF;
DEVELOPMENT_TEAM = A798K58VVL;
ENABLE_BITCODE = NO;
INFOPLIST_FILE = "paddle-mobile-demo/Info.plist";
IPHONEOS_DEPLOYMENT_TARGET = 9.0;
IPHONEOS_DEPLOYMENT_TARGET = 10.0;
LD_RUNPATH_SEARCH_PATHS = (
"$(inherited)",
"@executable_path/Frameworks",
......
metal/paddle-mobile-demo/paddle-mobile-demo/Base.lproj/Main.storyboard
浏览文件 @ b7a1d552
......@@ -11,6 +11,34 @@
<capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
</dependencies>
<scenes>
<!--Multi Predict View Controller-->
<scene sceneID="ec4-AW-9Vs">
<objects>
<viewController id="Vwd-lt-764" customClass="MultiPredictViewController" customModule="paddle_mobile_demo" customModuleProvider="target" sceneMemberID="viewController">
<view key="view" contentMode="scaleToFill" id="55D-rz-Ex6">
<rect key="frame" x="0.0" y="0.0" width="375" height="667"/>
<autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
<subviews>
<button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="roundedRect" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="TQt-X9-PdF">
<rect key="frame" x="164" y="318" width="46" height="30"/>
<state key="normal" title="Button"/>
<connections>
<action selector="predictAct:" destination="Vwd-lt-764" eventType="touchUpInside" id="d4z-Cv-6jY"/>
</connections>
</button>
</subviews>
<color key="backgroundColor" white="1" alpha="1" colorSpace="custom" customColorSpace="genericGamma22GrayColorSpace"/>
<constraints>
<constraint firstItem="TQt-X9-PdF" firstAttribute="centerY" secondItem="55D-rz-Ex6" secondAttribute="centerY" id="bL3-wr-TcH"/>
<constraint firstItem="TQt-X9-PdF" firstAttribute="centerX" secondItem="55D-rz-Ex6" secondAttribute="centerX" id="sBi-RQ-sJn"/>
</constraints>
<viewLayoutGuide key="safeArea" id="bsd-h4-RYZ"/>
</view>
</viewController>
<placeholder placeholderIdentifier="IBFirstResponder" id="68E-SG-96s" userLabel="First Responder" sceneMemberID="firstResponder"/>
</objects>
<point key="canvasLocation" x="-559" y="686"/>
</scene>
<!--View Controller-->
<scene sceneID="tne-QT-ifu">
<objects>
......@@ -20,9 +48,9 @@
<autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
<subviews>
<imageView userInteractionEnabled="NO" contentMode="scaleAspectFit" horizontalHuggingPriority="251" verticalHuggingPriority="251" translatesAutoresizingMaskIntoConstraints="NO" id="ZZh-fw-LwK">
<rect key="frame" x="0.0" y="20" width="375" height="247"/>
<rect key="frame" x="0.0" y="20" width="225" height="247"/>
</imageView>
<label opaque="NO" userInteractionEnabled="NO" contentMode="left" horizontalHuggingPriority="251" verticalHuggingPriority="251" text="Thread:" textAlignment="natural" lineBreakMode="tailTruncation" baselineAdjustment="alignBaselines" adjustsFontSizeToFit="NO" translatesAutoresizingMaskIntoConstraints="NO" id="2EB-m2-a3L">
<label opaque="NO" userInteractionEnabled="NO" contentMode="left" horizontalHuggingPriority="251" verticalHuggingPriority="251" text="Platform:" textAlignment="natural" lineBreakMode="tailTruncation" baselineAdjustment="alignBaselines" adjustsFontSizeToFit="NO" translatesAutoresizingMaskIntoConstraints="NO" id="2EB-m2-a3L">
<rect key="frame" x="10" y="538" width="68" height="24"/>
<constraints>
<constraint firstAttribute="width" constant="68" id="Q5J-tq-JSX"/>
......@@ -142,9 +170,14 @@
<fontDescription key="fontDescription" type="system" pointSize="15"/>
<textInputTraits key="textInputTraits" autocapitalizationType="sentences"/>
</textView>
<view contentMode="scaleToFill" translatesAutoresizingMaskIntoConstraints="NO" id="Cil-py-NiA">
<rect key="frame" x="225" y="20" width="150" height="247"/>
<color key="backgroundColor" white="1" alpha="1" colorSpace="custom" customColorSpace="genericGamma22GrayColorSpace"/>
</view>
</subviews>
<color key="backgroundColor" red="1" green="1" blue="1" alpha="1" colorSpace="custom" customColorSpace="sRGB"/>
<constraints>
<constraint firstItem="m5L-O7-P31" firstAttribute="top" secondItem="Cil-py-NiA" secondAttribute="bottom" constant="10" id="16p-IK-b5X"/>
<constraint firstItem="6Tk-OE-BBY" firstAttribute="trailing" secondItem="VQn-bS-fWp" secondAttribute="trailing" constant="10" id="1Xg-0h-9SE"/>
<constraint firstItem="avL-VK-Kha" firstAttribute="leading" secondItem="6Tk-OE-BBY" secondAttribute="leading" constant="10" id="2t9-hS-VXa"/>
<constraint firstItem="R90-Yf-S6g" firstAttribute="centerY" secondItem="wUL-9N-u1V" secondAttribute="centerY" id="76b-Ny-1Og"/>
......@@ -159,11 +192,12 @@
<constraint firstItem="XpL-9M-UOp" firstAttribute="centerY" secondItem="wUL-9N-u1V" secondAttribute="centerY" id="KWW-qT-Rzf"/>
<constraint firstItem="6MG-gv-hD5" firstAttribute="centerY" secondItem="avL-VK-Kha" secondAttribute="centerY" id="KZa-YZ-DEs"/>
<constraint firstItem="2EB-m2-a3L" firstAttribute="leading" secondItem="6Tk-OE-BBY" secondAttribute="leading" constant="10" id="Le3-TN-zOL"/>
<constraint firstItem="ZZh-fw-LwK" firstAttribute="trailing" secondItem="6Tk-OE-BBY" secondAttribute="trailing" id="MeS-HQ-voE"/>
<constraint firstItem="ZZh-fw-LwK" firstAttribute="trailing" secondItem="6Tk-OE-BBY" secondAttribute="trailing" constant="-150" id="MeS-HQ-voE"/>
<constraint firstItem="m5L-O7-P31" firstAttribute="top" secondItem="ZZh-fw-LwK" secondAttribute="bottom" constant="10" id="NUL-Ta-VI8"/>
<constraint firstItem="m5L-O7-P31" firstAttribute="leading" secondItem="6Tk-OE-BBY" secondAttribute="leading" constant="15" id="RFA-z1-9aB"/>
<constraint firstItem="wUL-9N-u1V" firstAttribute="width" secondItem="a3K-ri-NVs" secondAttribute="width" id="Rp6-Bh-BN3"/>
<constraint firstItem="6MG-gv-hD5" firstAttribute="trailing" secondItem="6Tk-OE-BBY" secondAttribute="trailing" id="S0W-0G-75m"/>
<constraint firstItem="Cil-py-NiA" firstAttribute="top" secondItem="6Tk-OE-BBY" secondAttribute="top" id="UNc-Et-9Yv"/>
<constraint firstItem="w7H-Sk-Rai" firstAttribute="leading" secondItem="wUL-9N-u1V" secondAttribute="trailing" id="VBM-8b-jP0"/>
<constraint firstItem="VQn-bS-fWp" firstAttribute="top" secondItem="m5L-O7-P31" secondAttribute="bottom" constant="8" id="VpS-4N-mOo"/>
<constraint firstItem="wUL-9N-u1V" firstAttribute="top" secondItem="2EB-m2-a3L" secondAttribute="bottom" constant="35" id="VpU-j2-gaE"/>
......@@ -175,10 +209,12 @@
<constraint firstItem="ZZh-fw-LwK" firstAttribute="top" secondItem="6Tk-OE-BBY" secondAttribute="top" id="eIC-fZ-OEE"/>
<constraint firstItem="976-fk-Kx2" firstAttribute="centerY" secondItem="wUL-9N-u1V" secondAttribute="centerY" id="fFg-pB-eyU"/>
<constraint firstItem="6Tk-OE-BBY" firstAttribute="bottom" secondItem="wUL-9N-u1V" secondAttribute="bottom" constant="40" id="fG6-0p-I0P"/>
<constraint firstItem="Cil-py-NiA" firstAttribute="trailing" secondItem="6Tk-OE-BBY" secondAttribute="trailing" id="gGK-DB-ibv"/>
<constraint firstItem="XpL-9M-UOp" firstAttribute="leading" secondItem="w7H-Sk-Rai" secondAttribute="trailing" id="guC-Db-cA9"/>
<constraint firstItem="6MG-gv-hD5" firstAttribute="leading" secondItem="avL-VK-Kha" secondAttribute="trailing" constant="10" id="jNW-iC-u7V"/>
<constraint firstItem="4ey-Xr-U4e" firstAttribute="bottom" secondItem="6Tk-OE-BBY" secondAttribute="bottom" id="o1X-q5-P7j"/>
<constraint firstItem="6MG-gv-hD5" firstAttribute="top" secondItem="VQn-bS-fWp" secondAttribute="bottom" constant="8" id="tAE-ss-jlA"/>
<constraint firstItem="Cil-py-NiA" firstAttribute="leading" secondItem="ZZh-fw-LwK" secondAttribute="trailing" id="teJ-PP-h2R"/>
<constraint firstItem="4ey-Xr-U4e" firstAttribute="top" secondItem="wUL-9N-u1V" secondAttribute="bottom" constant="10" id="udc-wT-jqd"/>
<constraint firstItem="ZZh-fw-LwK" firstAttribute="leading" secondItem="6Tk-OE-BBY" secondAttribute="leading" id="vXI-l2-CjL"/>
<constraint firstItem="VQn-bS-fWp" firstAttribute="leading" secondItem="6Tk-OE-BBY" secondAttribute="leading" constant="10" id="wtI-Dl-YPq"/>
......@@ -195,11 +231,12 @@
<outlet property="resultTextView" destination="VQn-bS-fWp" id="306-c7-3vM"/>
<outlet property="selectImageView" destination="ZZh-fw-LwK" id="afR-Bv-6AW"/>
<outlet property="threadPickerView" destination="DlO-dk-RMr" id="Kk4-QV-b5o"/>
<outlet property="videoView" destination="Cil-py-NiA" id="QY2-BP-SNS"/>
</connections>
</viewController>
<placeholder placeholderIdentifier="IBFirstResponder" id="dkx-z0-nzr" sceneMemberID="firstResponder"/>
</objects>
<point key="canvasLocation" x="-724" y="98.50074962518741"/>
<point key="canvasLocation" x="-1127" y="-3"/>
</scene>
</scenes>
<resources>
......
metal/paddle-mobile-demo/paddle-mobile-demo/MultiPredictViewController.swift 0 → 100644
浏览文件 @ b7a1d552
//
// Multi-Predict-ViewController.swift
// paddle-mobile-demo
//
// Created by liuRuiLong on 2018/9/14.
// Copyright © 2018年 orange. All rights reserved.
//
import UIKit
import paddle_mobile
class MultiPredictViewController: UIViewController {
var runner1: Runner!
var runner2: Runner!
override func viewDidLoad() {
super.viewDidLoad()
let mobileNet = MobileNet_ssd_hand.init(device: MetalHelper.shared.device)
let genet = Genet.init(device: MetalHelper.shared.device)
runner1 = Runner.init(inNet: mobileNet, commandQueue: MetalHelper.shared.queue, inPlatform: .GPU)
let queue2 = MetalHelper.shared.device.makeCommandQueue()
runner2 = Runner.init(inNet: genet, commandQueue: MetalHelper.shared.queue, inPlatform: .GPU)
}
@IBAction func predictAct(_ sender: Any) {
let success = self.runner2.load()
// DispatchQueue.global().async {
let image1 = UIImage.init(named: "hand.jpg")
// let success = self.runner2.load()
// if success {
// for i in 0..<10000 {
// print(i)
// self.runner2.predict(cgImage: image1!.cgImage!, completion: { (success, res) in
// print("result1: ")
//// print(res)
// })
// }
// } else {
// print("load failed")
// }
// self.runner1.clear()
// }
// return
// DispatchQueue.global().async {
//// sleep(1)
// let image1 = UIImage.init(named: "banana.jpeg")
//// if success {
// for _ in 0..<10 {
// self.runner2.predict(cgImage: image1!.cgImage!, completion: { (success, res) in
// print("result2: ")
// print(res)
// })
// }
//// } else {
//// print("load failed")
//// }
//// self.runner2.clear()
// }
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/VideoCapture/FPSCounter.swift 0 → 100644
浏览文件 @ b7a1d552
import Foundation
import QuartzCore
public class FPSCounter {
private(set) public var fps: Double = 0
var frames = 0
var startTime: CFTimeInterval = 0
public func start() {
frames = 0
startTime = CACurrentMediaTime()
}
public func frameCompleted() {
frames += 1
let now = CACurrentMediaTime()
let elapsed = now - startTime
if elapsed > 0.1 {
let current = Double(frames) / elapsed
let smoothing = 0.75
fps = smoothing*fps + (1 - smoothing)*current
if elapsed > 1 {
frames = 0
startTime = CACurrentMediaTime()
}
}
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/VideoCapture/VideoCapture.swift 0 → 100644
浏览文件 @ b7a1d552
import UIKit
import Metal
import CoreVideo
import AVFoundation
@available(iOS 10.0, *)
@objc public protocol VideoCaptureDelegate: NSObjectProtocol {
@objc optional func videoCapture(_ capture: VideoCapture, didCaptureSampleBuffer sampleBuffer: CMSampleBuffer, timestamp: CMTime)
@objc optional func videoCapture(_ capture: VideoCapture, didCaptureVideoTexture texture: MTLTexture?, timestamp: CMTime)
@objc optional func videoCapture(_ capture: VideoCapture, didCapturePhoto previewImage: UIImage?)
@objc optional func videoCapture(_ capture: VideoCapture, didCapturePhotoTexture texture: MTLTexture?)
}
/**
Simple interface to the iPhone's camera.
*/
@available(iOS 10.0, *)
public class VideoCapture: NSObject {
public var previewLayer: AVCaptureVideoPreviewLayer?
public weak var delegate: VideoCaptureDelegate?
public var fps = -1
private let device: MTLDevice?
private let videoOrientation: AVCaptureVideoOrientation
private var textureCache: CVMetalTextureCache?
private let captureSession = AVCaptureSession()
private let videoOutput = AVCaptureVideoDataOutput()
private let photoOutput = AVCapturePhotoOutput()
private let queue = DispatchQueue(label: "net.machinethink.camera-queue")
private var lastTimestamp = CMTime()
private let cameraPosition: AVCaptureDevice.Position
public init(device: MTLDevice? = nil, orientation: AVCaptureVideoOrientation = .portrait, position: AVCaptureDevice.Position = .back) {
self.device = device
self.videoOrientation = orientation
self.cameraPosition = position
super.init()
}
public func setUp(sessionPreset: AVCaptureSession.Preset = .medium,
completion: @escaping (Bool) -> Void) {
queue.async {
let success = self.setUpCamera(sessionPreset: sessionPreset)
DispatchQueue.main.async {
completion(success)
}
}
}
func fontCamera() -> AVCaptureDevice? {
let deveices = AVCaptureDevice.DiscoverySession.init(deviceTypes: [.builtInWideAngleCamera], mediaType: AVMediaType.video, position: .front).devices
return deveices.first
}
func setUpCamera(sessionPreset: AVCaptureSession.Preset) -> Bool {
if let inDevice = device{
guard CVMetalTextureCacheCreate(kCFAllocatorDefault, nil, inDevice, nil, &textureCache) == kCVReturnSuccess else {
print("Error: could not create a texture cache")
return false
}
}
captureSession.beginConfiguration()
captureSession.sessionPreset = sessionPreset
var oCaptureDevice: AVCaptureDevice?
switch cameraPosition {
case .back:
oCaptureDevice = AVCaptureDevice.default(for: AVMediaType.video)
break
case .front:
oCaptureDevice = fontCamera()
break
default:
break
}
guard let captureDevice = oCaptureDevice else {
print("Error: no video devices available")
return false
}
guard let videoInput = try? AVCaptureDeviceInput(device: captureDevice) else {
print("Error: could not create AVCaptureDeviceInput")
return false
}
if captureSession.canAddInput(videoInput) {
captureSession.addInput(videoInput)
}
let previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)
previewLayer.videoGravity = AVLayerVideoGravity.resizeAspect
previewLayer.connection?.videoOrientation = self.videoOrientation
self.previewLayer = previewLayer
let settings: [String : Any] = [
kCVPixelBufferPixelFormatTypeKey as String: NSNumber(value: kCVPixelFormatType_32BGRA)
]
videoOutput.videoSettings = settings
videoOutput.alwaysDiscardsLateVideoFrames = true
videoOutput.setSampleBufferDelegate(self, queue: queue)
if captureSession.canAddOutput(videoOutput) {
captureSession.addOutput(videoOutput)
}
// We want the buffers to be in portrait orientation otherwise they are
// rotated by 90 degrees. Need to set this _after_ addOutput()!
videoOutput.connection(with: AVMediaType.video)?.videoOrientation = self.videoOrientation
if captureSession.canAddOutput(photoOutput) {
captureSession.addOutput(photoOutput)
}
captureSession.commitConfiguration()
return true
}
public func start() {
if !captureSession.isRunning {
captureSession.startRunning()
}
}
public func stop() {
if captureSession.isRunning {
captureSession.stopRunning()
}
}
/* Captures a single frame of the camera input. */
public func capturePhoto() {
let settings = AVCapturePhotoSettings(format: [kCVPixelBufferPixelFormatTypeKey as String: NSNumber(value: kCVPixelFormatType_32BGRA)])
settings.previewPhotoFormat = [
kCVPixelBufferPixelFormatTypeKey as String: settings.__availablePreviewPhotoPixelFormatTypes[0],
kCVPixelBufferWidthKey as String: 480,
kCVPixelBufferHeightKey as String: 360,
]
photoOutput.capturePhoto(with: settings, delegate: self)
}
func convertToMTLTexture(sampleBuffer: CMSampleBuffer?) -> MTLTexture? {
if let textureCache = textureCache, let sampleBuffer = sampleBuffer, let imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) {
let width = CVPixelBufferGetWidth(imageBuffer)
let height = CVPixelBufferGetHeight(imageBuffer)
var texture: CVMetalTexture?
CVMetalTextureCacheCreateTextureFromImage(kCFAllocatorDefault, textureCache, imageBuffer, nil, .bgra8Unorm, width, height, 0, &texture)
if let texture = texture {
return CVMetalTextureGetTexture(texture)
}
}
return nil
}
func convertToUIImage(sampleBuffer: CMSampleBuffer?) -> UIImage? {
if let sampleBuffer = sampleBuffer,
let imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) {
let width = CVPixelBufferGetWidth(imageBuffer)
let height = CVPixelBufferGetHeight(imageBuffer)
let rect = CGRect(x: 0, y: 0, width: CGFloat(width), height: CGFloat(height))
let ciImage = CIImage(cvPixelBuffer: imageBuffer)
let ciContext = CIContext(options: nil)
if let cgImage = ciContext.createCGImage(ciImage, from: rect) {
return UIImage(cgImage: cgImage)
}
}
return nil
}
}
extension VideoCapture: AVCaptureVideoDataOutputSampleBufferDelegate {
public func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
// Because lowering the capture device's FPS looks ugly in the preview,
// we capture at full speed but only call the delegate at its desired
// framerate. If `fps` is -1, we run at the full framerate.
let timestamp = CMSampleBufferGetPresentationTimeStamp(sampleBuffer)
let deltaTime = timestamp - lastTimestamp
if fps == -1 || deltaTime >= CMTimeMake(1, Int32(fps)) {
lastTimestamp = timestamp
self.delegate?.videoCapture?(self, didCaptureSampleBuffer: sampleBuffer, timestamp: timestamp)
if self.delegate?.responds(to: #selector(VideoCaptureDelegate.videoCapture(_:didCaptureVideoTexture:timestamp:))) ?? false{
let texture = convertToMTLTexture(sampleBuffer: sampleBuffer)
delegate?.videoCapture?(self, didCaptureVideoTexture: texture, timestamp: timestamp)
}
}
}
public func captureOutput(_ output: AVCaptureOutput, didDrop sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
print("dropped frame")
}
}
extension VideoCapture: AVCapturePhotoCaptureDelegate {
public func photoOutput(_ captureOutput: AVCapturePhotoOutput,
didFinishProcessingPhoto photoSampleBuffer: CMSampleBuffer?,
previewPhoto previewPhotoSampleBuffer: CMSampleBuffer?,
resolvedSettings: AVCaptureResolvedPhotoSettings,
bracketSettings: AVCaptureBracketedStillImageSettings?,
error: Error?) {
var imageTexture: MTLTexture?
var previewImage: UIImage?
if error == nil {
if self.delegate?.responds(to: #selector(VideoCaptureDelegate.videoCapture(_:didCapturePhotoTexture:))) ?? false{
imageTexture = convertToMTLTexture(sampleBuffer: photoSampleBuffer)
self.delegate?.videoCapture?(self, didCapturePhotoTexture: imageTexture)
}
if self.delegate?.responds(to: #selector(VideoCaptureDelegate.videoCapture(_:didCapturePhoto:))) ?? false{
previewImage = convertToUIImage(sampleBuffer: previewPhotoSampleBuffer)
self.delegate?.videoCapture?(self, didCapturePhoto: previewImage)
}
}
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/ViewController.swift
浏览文件 @ b7a1d552
......@@ -14,27 +14,32 @@
import UIKit
import MetalKit
import CoreMedia
import paddle_mobile
import MetalPerformanceShaders
let platform: Platform = .GPU
let threadSupport = [1]
var platform: Platform = .GPU
let threadSupport: [(Platform, String)] = [(.GPU, "GPU"), (.CPU, "CPU")]
let modelHelperMap: [SupportModel : Runner] = [.mobilenet_ssd : Runner.init(inNet: MobileNet_ssd_hand.init(device: MetalHelper.shared.device), commandQueue: MetalHelper.shared.queue, inPlatform: platform),
//.mobilenet_ssd : Runner.init(inNet: MobileNet_ssd_hand.init(device: MetalHelper.shared.device), commandQueue: MetalHelper.shared.queue, inPlatform: platform),
let modelHelperMap: [SupportModel : Runner] = [
.genet : Runner.init(inNet: Genet.init(device: MetalHelper.shared.device), commandQueue: MetalHelper.shared.queue, inPlatform: platform),
.mobilenet_ssd_ar : Runner.init(inNet: MobileNet_ssd_AR.init(device: MetalHelper.shared.device), commandQueue: MetalHelper.shared.queue, inPlatform: platform)]
//, .genet : Genet.init()
//let modelHelperMap: [SupportModel : Net] = [.mobilenet : MobileNet.init(), .mobilenet_ssd : MobileNet_ssd_hand.init()]
let netSupport: [SupportModel : Net] = [.genet : Genet.init(device: MetalHelper.shared.device), .mobilenet_ssd_ar : MobileNet_ssd_AR.init(device: MetalHelper.shared.device)]
enum SupportModel: String{
// case mobilenet = "mobilenet"
case mobilenet_ssd = "mobilenetssd"
// case mobilenet_ssd = "mobilenetssd"
case genet = "genet"
case mobilenet_ssd_ar = "mobilenetssd_ar"
static func supportedModels() -> [SupportModel] {
//.mobilenet,
return [.mobilenet_ssd, .genet, .mobilenet_ssd_ar]
// .mobilenet,
// .mobilenet_ssd,
return [.genet, .mobilenet_ssd_ar]
}
}
......@@ -44,24 +49,36 @@ class ViewController: UIViewController {
@IBOutlet weak var elapsedTimeLabel: UILabel!
@IBOutlet weak var modelPickerView: UIPickerView!
@IBOutlet weak var threadPickerView: UIPickerView!
@IBOutlet weak var videoView: UIView!
var videoCapture: VideoCapture!
var selectImage: UIImage?
var inputPointer: UnsafeMutablePointer<Float32>?
var modelType: SupportModel = SupportModel.supportedModels()[0]
var toPredictTexture: MTLTexture?
var runner: Runner {
var runner: Runner!
var threadNum = 1
@IBAction func loadAct(_ sender: Any) {
runner = Runner.init(inNet: netSupport[modelType]!, commandQueue: MetalHelper.shared.queue, inPlatform: platform)
if platform == .CPU {
if inputPointer == nil {
inputPointer = runner.preproccess(image: selectImage!.cgImage!)
get {
return modelHelperMap[modelType] ?! " has no this type "
}
set {
} else if platform == .GPU {
if self.toPredictTexture == nil {
runner.getTexture(image: selectImage!.cgImage!) {[weak self] (texture) in
self?.toPredictTexture = texture
}
}
} else {
fatalError( " unsupport " )
}
var threadNum = 1
@IBAction func loadAct(_ sender: Any) {
if runner.load() {
print(" load success ! ")
} else {
......@@ -81,7 +98,7 @@ class ViewController: UIViewController {
}
@IBAction func predictAct(_ sender: Any) {
let max = 1
let max = 50
switch platform {
case .GPU:
guard let inTexture = toPredictTexture else {
......@@ -91,7 +108,7 @@ class ViewController: UIViewController {
let startDate = Date.init()
for i in 0..<max {
runner.predict(texture: inTexture) { [weak self] (success, res) in
runner.predict(texture: inTexture) { [weak self] (success, resultHolder) in
guard let sSelf = self else {
fatalError()
}
......@@ -99,11 +116,19 @@ class ViewController: UIViewController {
if i == max - 1 {
let time = Date.init().timeIntervalSince(startDate)
DispatchQueue.main.async {
sSelf.resultTextView.text = sSelf.runner.net.resultStr(res: res)
// print(resultHolder!.result![0])
sSelf.resultTextView.text = sSelf.runner.net.resultStr(res: resultHolder!)
sSelf.elapsedTimeLabel.text = "平均耗时: \(time/Double(max) * 1000.0) ms"
}
}
}
DispatchQueue.main.async {
resultHolder?.releasePointer()
}
// print("释放")
}
// print("sleep before ")
// usleep(33000)
......@@ -116,6 +141,7 @@ class ViewController: UIViewController {
for _ in 0..<10 {
runner.predict(inputPointer: inInputPointer) { (success, res) in
res?.releaseOutput()
}
}
......@@ -129,11 +155,12 @@ class ViewController: UIViewController {
if i == max - 1 {
let time = Date.init().timeIntervalSince(startDate)
DispatchQueue.main.async {
sSelf.resultTextView.text = sSelf.runner.net.resultStr(res: res)
// sSelf.resultTextView.text = sSelf.runner.net.resultStr(res: res)
sSelf.elapsedTimeLabel.text = "平均耗时: \(time/Double(max) * 1000.0) ms"
}
}
}
res?.releaseOutput()
}
}
}
......@@ -141,6 +168,13 @@ class ViewController: UIViewController {
override func viewDidLoad() {
super.viewDidLoad()
// if runner.load() {
// print(" load success ! ")
// } else {
// print(" load error ! ")
// }
//
modelPickerView.delegate = self
modelPickerView.dataSource = self
threadPickerView.delegate = self
......@@ -149,15 +183,29 @@ class ViewController: UIViewController {
selectImage = UIImage.init(named: "hand.jpg")
selectImageView.image = selectImage
if platform == .CPU {
inputPointer = runner.preproccess(image: selectImage!.cgImage!)
} else if platform == .GPU {
runner.getTexture(image: selectImage!.cgImage!) {[weak self] (texture) in
self?.toPredictTexture = texture
}
} else {
fatalError( " unsupport " )
}
// if platform == .CPU {
// inputPointer = runner.preproccess(image: selectImage!.cgImage!)
// } else if platform == .GPU {
// runner.getTexture(image: selectImage!.cgImage!) {[weak self] (texture) in
// self?.toPredictTexture = texture
// }
// } else {
// fatalError( " unsupport " )
// }
// videoCapture = VideoCapture.init(device: MetalHelper.shared.device, orientation: .portrait, position: .back)
// videoCapture.fps = 30
// videoCapture.delegate = self
// videoCapture.setUp { (success) in
// DispatchQueue.main.async {
// if let preViewLayer = self.videoCapture.previewLayer {
// self.videoView.layer.addSublayer(preViewLayer)
// self.videoCapture.previewLayer?.frame = self.videoView.bounds
// }
// self.videoCapture.start()
// }
// }
}
}
......@@ -186,7 +234,7 @@ extension ViewController: UIPickerViewDataSource, UIPickerViewDelegate{
if pickerView == modelPickerView {
return SupportModel.supportedModels()[row].rawValue
} else if pickerView == threadPickerView {
return "\(threadSupport[row])"
return threadSupport[row].1
} else {
fatalError()
}
......@@ -196,7 +244,8 @@ extension ViewController: UIPickerViewDataSource, UIPickerViewDelegate{
if pickerView == modelPickerView {
self.modelType = SupportModel.supportedModels()[row]
} else if pickerView == threadPickerView {
self.threadNum = threadSupport[row]
platform = threadSupport[row].0
} else {
fatalError()
}
......@@ -218,4 +267,32 @@ extension ViewController: UIImagePickerControllerDelegate, UINavigationControll
}
}
var bool1 = false
extension ViewController: VideoCaptureDelegate{
func predictTexture(texture: MTLTexture){
runner.scaleTexture(input: texture) { (scaledTexture) in
self.runner.predict(texture: scaledTexture, completion: { (success, resultHolder) in
// print(resultHolder!.result![0])
resultHolder?.releasePointer()
})
}
}
func videoCapture(_ capture: VideoCapture, didCaptureVideoTexture texture: MTLTexture?, timestamp: CMTime) {
// if !bool1 {
// DispatchQueue.main.asyncAfter(deadline: DispatchTime.init(uptimeNanoseconds: 500000000)) {
self.predictTexture(texture: texture!)
// }
// bool1 = true
// }
}
}
metal/paddle-mobile/paddle-mobile.xcodeproj/project.pbxproj
浏览文件 @ b7a1d552
......@@ -16,9 +16,18 @@
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4AA1EA942146661500D0F791 /* ShapeKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EA932146661500D0F791 /* ShapeKernel.swift */; };
4AA1EA982146666500D0F791 /* FlattenOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EA972146666500D0F791 /* FlattenOp.swift */; };
4AA1EA9E2148D6F900D0F791 /* ConcatKernel.inc.metal in Headers */ = {isa = PBXBuildFile; fileRef = 4AA1EA9D2148D6F900D0F791 /* ConcatKernel.inc.metal */; };
4AA1EAA02148DEEE00D0F791 /* ReshapeKernel.inc.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EA9F2148DEEE00D0F791 /* ReshapeKernel.inc.metal */; };
4AA1EAA2214912CD00D0F791 /* FlattenKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EAA1214912CC00D0F791 /* FlattenKernel.swift */; };
4AA1EAA4214A295C00D0F791 /* Split.inc.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EAA3214A295C00D0F791 /* Split.inc.metal */; };
4AA1EAA6214B5F6800D0F791 /* Shape.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EAA5214B5F6800D0F791 /* Shape.metal */; };
4AA1EAA8214B7AFB00D0F791 /* BilinearInterp.inc.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EAA7214B7AFB00D0F791 /* BilinearInterp.inc.metal */; };
4AA1EAAA214F53D800D0F791 /* BoxCoder.inc.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EAA9214F53D800D0F791 /* BoxCoder.inc.metal */; };
4AA1EAAC214F55C800D0F791 /* Softmax.inc.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EAAB214F55C800D0F791 /* Softmax.inc.metal */; };
4AA1EAAE214F5FD900D0F791 /* TransposeKernel.inc.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AA1EAAD214F5FD900D0F791 /* TransposeKernel.inc.metal */; };
4AF928772133F1DB005B6C3A /* BoxCoder.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF928762133F1DB005B6C3A /* BoxCoder.metal */; };
4AF9287921341661005B6C3A /* Softmax.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF9287821341661005B6C3A /* Softmax.metal */; };
4AF928822135673D005B6C3A /* Concat.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF928812135673D005B6C3A /* Concat.metal */; };
4AF928822135673D005B6C3A /* ConcatKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF928812135673D005B6C3A /* ConcatKernel.metal */; };
4AF9288421357BE3005B6C3A /* Elementwise.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF9288321357BE3005B6C3A /* Elementwise.metal */; };
D3831F70E7E0B565B9AC22DA /* Pods_paddle_mobile.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = DD2E06330A1E7129C918DB46 /* Pods_paddle_mobile.framework */; };
FC0226562138F33800F395E2 /* TransposeKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC0226552138F33800F395E2 /* TransposeKernel.metal */; };
......@@ -66,6 +75,12 @@
FC4FD97E2140F2C30073E130 /* libstdc++.tbd in Frameworks */ = {isa = PBXBuildFile; fileRef = FC4FD97D2140F2C30073E130 /* libstdc++.tbd */; };
FC5163F620EF556E00636C28 /* Texture2DTo2DArrayKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC5163F520EF556E00636C28 /* Texture2DTo2DArrayKernel.swift */; };
FC60DB8920E9AAA500FF203F /* MetalExtension.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC60DB8820E9AAA500FF203F /* MetalExtension.swift */; };
FC803BBF214CB65A0094B8E5 /* ConvAddPreluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC803BBE214CB65A0094B8E5 /* ConvAddPreluOp.swift */; };
FC803BC1214CB77A0094B8E5 /* ConvAddPreluKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC803BC0214CB77A0094B8E5 /* ConvAddPreluKernel.swift */; };
FC803BC3214CB79C0094B8E5 /* ConvAddPreluKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC803BC2214CB79C0094B8E5 /* ConvAddPreluKernel.metal */; };
FC803BC5214CB8F00094B8E5 /* ConvAddPrelu.inc.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC803BC4214CB8F00094B8E5 /* ConvAddPrelu.inc.metal */; };
FC803BC7214CBA820094B8E5 /* Macro.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC803BC6214CBA820094B8E5 /* Macro.metal */; };
FC803BC9214CFC8D0094B8E5 /* FetchKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC803BC8214CFC8D0094B8E5 /* FetchKernel.metal */; };
FC82735920E3C04200BE430A /* OpCreator.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC82735820E3C04200BE430A /* OpCreator.swift */; };
FC9A19E32148C31300CD9CBF /* MobilenetSSD_AR.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC9A19E22148C31300CD9CBF /* MobilenetSSD_AR.swift */; };
FC9D037920E229E4000F735A /* OpParam.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC9D037820E229E4000F735A /* OpParam.swift */; };
......@@ -107,6 +122,8 @@
FCDDC6CC212FDFDB00E5EF74 /* ReluKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCDDC6CB212FDFDB00E5EF74 /* ReluKernel.metal */; };
FCDDC6CF212FE14700E5EF74 /* PriorBoxKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCDDC6CE212FE14700E5EF74 /* PriorBoxKernel.metal */; };
FCDE8A33212A917900F4A8F6 /* ConvTransposeOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCDE8A32212A917900F4A8F6 /* ConvTransposeOp.swift */; };
FCE9D7B7214F869000B520C3 /* Net.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCE9D7B6214F869000B520C3 /* Net.swift */; };
FCE9D7B9214FAA4800B520C3 /* NMSFetchResultKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCE9D7B8214FAA4800B520C3 /* NMSFetchResultKernel.metal */; };
FCEB684A212F00DB00D2448E /* PreluKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCEB6849212F00DB00D2448E /* PreluKernel.metal */; };
FCEB684C212F093800D2448E /* PreluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCEB684B212F093800D2448E /* PreluOp.swift */; };
FCEBC0F420F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCEBC0F320F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift */; };
......@@ -124,9 +141,18 @@
4AA1EA91214665D700D0F791 /* ShapeOp.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = ShapeOp.swift; sourceTree = "<group>"; };
4AA1EA932146661500D0F791 /* ShapeKernel.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = ShapeKernel.swift; sourceTree = "<group>"; };
4AA1EA972146666500D0F791 /* FlattenOp.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = FlattenOp.swift; sourceTree = "<group>"; };
4AA1EA9D2148D6F900D0F791 /* ConcatKernel.inc.metal */ = {isa = PBXFileReference; explicitFileType = sourcecode.metal; fileEncoding = 4; path = ConcatKernel.inc.metal; sourceTree = "<group>"; };
4AA1EA9F2148DEEE00D0F791 /* ReshapeKernel.inc.metal */ = {isa = PBXFileReference; explicitFileType = sourcecode.metal; fileEncoding = 4; path = ReshapeKernel.inc.metal; sourceTree = "<group>"; };
4AA1EAA1214912CC00D0F791 /* FlattenKernel.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = FlattenKernel.swift; sourceTree = "<group>"; };
4AA1EAA3214A295C00D0F791 /* Split.inc.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = Split.inc.metal; sourceTree = "<group>"; };
4AA1EAA5214B5F6800D0F791 /* Shape.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = Shape.metal; sourceTree = "<group>"; };
4AA1EAA7214B7AFB00D0F791 /* BilinearInterp.inc.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = BilinearInterp.inc.metal; sourceTree = "<group>"; };
4AA1EAA9214F53D800D0F791 /* BoxCoder.inc.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = BoxCoder.inc.metal; sourceTree = "<group>"; };
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4AF9288321357BE3005B6C3A /* Elementwise.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = Elementwise.metal; sourceTree = "<group>"; };
CDF58151D902A1CBAE56A0C2 /* Pods-paddle-mobile.debug.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-paddle-mobile.debug.xcconfig"; path = "../Pods/Target Support Files/Pods-paddle-mobile/Pods-paddle-mobile.debug.xcconfig"; sourceTree = "<group>"; };
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......@@ -162,7 +188,6 @@
FC0E2DBD20EE460D009C1FAC /* BatchNormKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = BatchNormKernel.swift; sourceTree = "<group>"; };
FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ElementwiseAddKernel.swift; sourceTree = "<group>"; };
FC1B16B220EC9A4F00678B91 /* Kernels.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = Kernels.metal; sourceTree = "<group>"; };
FC27990D21341016000B6BAD /* BoxCoder.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = BoxCoder.metal; sourceTree = "<group>"; };
FC292C5321421B2E00CF622F /* PaddleMobileGPU.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = PaddleMobileGPU.h; sourceTree = "<group>"; };
FC292C5521421B4600CF622F /* PaddleMobileGPU.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = PaddleMobileGPU.m; sourceTree = "<group>"; };
FC292C7C214255BC00CF622F /* CPUCompute.mm */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.cpp.objcpp; path = CPUCompute.mm; sourceTree = "<group>"; };
......@@ -179,6 +204,12 @@
FC4FD97D2140F2C30073E130 /* libstdc++.tbd */ = {isa = PBXFileReference; lastKnownFileType = "sourcecode.text-based-dylib-definition"; name = "libstdc++.tbd"; path = "usr/lib/libstdc++.tbd"; sourceTree = SDKROOT; };
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FC803BC6214CBA820094B8E5 /* Macro.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = Macro.metal; sourceTree = "<group>"; };
FC803BC8214CFC8D0094B8E5 /* FetchKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = FetchKernel.metal; sourceTree = "<group>"; };
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FC9D037820E229E4000F735A /* OpParam.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = OpParam.swift; sourceTree = "<group>"; };
......@@ -220,6 +251,8 @@
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FCE9D7B8214FAA4800B520C3 /* NMSFetchResultKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = NMSFetchResultKernel.metal; sourceTree = "<group>"; };
FCEB6849212F00DB00D2448E /* PreluKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = PreluKernel.metal; sourceTree = "<group>"; };
FCEB684B212F093800D2448E /* PreluOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = PreluOp.swift; sourceTree = "<group>"; };
FCEBC0F320F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; name = ConvAddBatchNormReluOp.swift; path = "paddle-mobile/Operators/ConvAddBatchNormReluOp.swift"; sourceTree = SOURCE_ROOT; };
......@@ -280,6 +313,7 @@
FC039B6C20E11C3C0081E9F8 /* paddle-mobile */ = {
isa = PBXGroup;
children = (
FCE9D7B6214F869000B520C3 /* Net.swift */,
FC9A19E22148C31300CD9CBF /* MobilenetSSD_AR.swift */,
FC33B0EF2147659000714A93 /* MobileNet.swift */,
FC292C862142624800CF622F /* Genet.swift */,
......@@ -355,6 +389,7 @@
FCBCCC6E2123097100D94F7E /* MulticlassNMSOp.swift */,
FCDE8A32212A917900F4A8F6 /* ConvTransposeOp.swift */,
FCEB684B212F093800D2448E /* PreluOp.swift */,
FC803BBE214CB65A0094B8E5 /* ConvAddPreluOp.swift */,
);
path = Operators;
sourceTree = "<group>";
......@@ -391,6 +426,7 @@
FCD04E6720F315020007374F /* PoolKernel.swift */,
FCD04E6B20F31A280007374F /* SoftmaxKernel.swift */,
FCD04E6F20F31B720007374F /* ReshapeKernel.swift */,
4AA1EAA1214912CC00D0F791 /* FlattenKernel.swift */,
FCD04E7320F3437E0007374F /* ConvAddKernel.swift */,
FCBCCC5A2122F66F00D94F7E /* ConvBNReluKernel.swift */,
FCBCCC602122FBDF00D94F7E /* PriorBoxKernel.swift */,
......@@ -402,6 +438,7 @@
4AA1EA87214662BD00D0F791 /* BilinearInterpKernel.swift */,
FCBCCC70212309A700D94F7E /* MulticlassNMSKernel.swift */,
FCDDC6C5212F9FB800E5EF74 /* PreluKernel.swift */,
FC803BC0214CB77A0094B8E5 /* ConvAddPreluKernel.swift */,
);
path = Kernels;
sourceTree = "<group>";
......@@ -436,27 +473,39 @@
FCEB6837212F00B100D2448E /* metal */ = {
isa = PBXGroup;
children = (
FC27990D21341016000B6BAD /* BoxCoder.metal */,
4AF928812135673D005B6C3A /* Concat.metal */,
4AF928812135673D005B6C3A /* ConcatKernel.metal */,
4AA1EA9D2148D6F900D0F791 /* ConcatKernel.inc.metal */,
4AF9288321357BE3005B6C3A /* Elementwise.metal */,
FC1B16B220EC9A4F00678B91 /* Kernels.metal */,
FC4CB74820F0B954007C0C6D /* ConvKernel.metal */,
4AF928762133F1DB005B6C3A /* BoxCoder.metal */,
4AA1EAA9214F53D800D0F791 /* BoxCoder.inc.metal */,
4AA1EAA5214B5F6800D0F791 /* Shape.metal */,
4AA1EA8F214664CD00D0F791 /* Split.metal */,
4AA1EAA3214A295C00D0F791 /* Split.inc.metal */,
4AA1EA892146631C00D0F791 /* BilinearInterp.metal */,
4AA1EAA7214B7AFB00D0F791 /* BilinearInterp.inc.metal */,
4AF9287821341661005B6C3A /* Softmax.metal */,
4AA1EAAB214F55C800D0F791 /* Softmax.inc.metal */,
FCEB6849212F00DB00D2448E /* PreluKernel.metal */,
FCDDC6C9212FDF6800E5EF74 /* BatchNormKernel.metal */,
FCDDC6CB212FDFDB00E5EF74 /* ReluKernel.metal */,
FCDDC6CE212FE14700E5EF74 /* PriorBoxKernel.metal */,
FCA3A1622132A4AC00084FE5 /* ReshapeKernel.metal */,
4AA1EA9F2148DEEE00D0F791 /* ReshapeKernel.inc.metal */,
FCA3A1642132A5EB00084FE5 /* Common.metal */,
FCA67B1621364EF000BD58AA /* ConvTransposeKernel.metal */,
FCA67CD42138272900BD58AA /* ConvAddMetal.metal */,
FCA67CD6213827AC00BD58AA /* ConvAddBNReluKernel.metal */,
FCA67CD82138287B00BD58AA /* ConvBNReluKernel.metal */,
FC0226552138F33800F395E2 /* TransposeKernel.metal */,
4AA1EAAD214F5FD900D0F791 /* TransposeKernel.inc.metal */,
FC0226572138F38D00F395E2 /* PoolKernel.metal */,
FC803BC2214CB79C0094B8E5 /* ConvAddPreluKernel.metal */,
FC803BC4214CB8F00094B8E5 /* ConvAddPrelu.inc.metal */,
FC803BC6214CBA820094B8E5 /* Macro.metal */,
FC803BC8214CFC8D0094B8E5 /* FetchKernel.metal */,
FCE9D7B8214FAA4800B520C3 /* NMSFetchResultKernel.metal */,
);
path = metal;
sourceTree = "<group>";
......@@ -471,6 +520,7 @@
FC4FD9792140E4980073E130 /* PaddleMobile.h in Headers */,
FC292C85214257CB00CF622F /* CPUCompute.h in Headers */,
FC292C5421421B2F00CF622F /* PaddleMobileGPU.h in Headers */,
4AA1EA9E2148D6F900D0F791 /* ConcatKernel.inc.metal in Headers */,
FC039B6F20E11C3C0081E9F8 /* paddle_mobile.h in Headers */,
);
runOnlyForDeploymentPostprocessing = 0;
......@@ -566,7 +616,9 @@
buildActionMask = 2147483647;
files = (
FC9D038020E22FBB000F735A /* FeedOp.swift in Sources */,
4AA1EAAA214F53D800D0F791 /* BoxCoder.inc.metal in Sources */,
FC039B9F20E11CB20081E9F8 /* Tensor.swift in Sources */,
FC803BC9214CFC8D0094B8E5 /* FetchKernel.metal in Sources */,
FCA67CD7213827AC00BD58AA /* ConvAddBNReluKernel.metal in Sources */,
4AF9287921341661005B6C3A /* Softmax.metal in Sources */,
4AA1EA942146661500D0F791 /* ShapeKernel.swift in Sources */,
......@@ -574,6 +626,9 @@
FC039BAA20E11CBC0081E9F8 /* ElementwiseAddOp.swift in Sources */,
FCDE8A33212A917900F4A8F6 /* ConvTransposeOp.swift in Sources */,
FCBCCC6B2123071700D94F7E /* BoxcoderOp.swift in Sources */,
4AA1EAAE214F5FD900D0F791 /* TransposeKernel.inc.metal in Sources */,
4AA1EAA4214A295C00D0F791 /* Split.inc.metal in Sources */,
FC803BC7214CBA820094B8E5 /* Macro.metal in Sources */,
FC039B9B20E11CA00081E9F8 /* Executor.swift in Sources */,
4AF9288421357BE3005B6C3A /* Elementwise.metal in Sources */,
FCD04E7020F31B720007374F /* ReshapeKernel.swift in Sources */,
......@@ -590,10 +645,13 @@
4AA1EA8C2146640900D0F791 /* SplitOp.swift in Sources */,
FC292C81214255BD00CF622F /* CPUCompute.mm in Sources */,
FCEBC0F420F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift in Sources */,
4AA1EAAC214F55C800D0F791 /* Softmax.inc.metal in Sources */,
FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */,
4AF928772133F1DB005B6C3A /* BoxCoder.metal in Sources */,
FC803BBF214CB65A0094B8E5 /* ConvAddPreluOp.swift in Sources */,
FC33B0F02147659000714A93 /* MobileNet.swift in Sources */,
FCEB684C212F093800D2448E /* PreluOp.swift in Sources */,
4AA1EAA8214B7AFB00D0F791 /* BilinearInterp.inc.metal in Sources */,
FCA67CD92138287B00BD58AA /* ConvBNReluKernel.metal in Sources */,
FC60DB8920E9AAA500FF203F /* MetalExtension.swift in Sources */,
FCEBC0F620F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift in Sources */,
......@@ -610,8 +668,10 @@
FCBCCC592122F42700D94F7E /* ConvBNReluOp.swift in Sources */,
FC039BA920E11CBC0081E9F8 /* ConvOp.swift in Sources */,
FC9D038420E23B01000F735A /* Texture.swift in Sources */,
4AA1EAA2214912CD00D0F791 /* FlattenKernel.swift in Sources */,
4AA1EA982146666500D0F791 /* FlattenOp.swift in Sources */,
FCBCCC652122FCD700D94F7E /* TransposeOp.swift in Sources */,
4AA1EAA6214B5F6800D0F791 /* Shape.metal in Sources */,
FCD04E6E20F31B4B0007374F /* ReshapeOp.swift in Sources */,
FC039B9820E11C9A0081E9F8 /* Errors.swift in Sources */,
FC039BBF20E11CC20081E9F8 /* Attribute.swift in Sources */,
......@@ -620,11 +680,13 @@
FC039BB920E11CC20081E9F8 /* Scope.swift in Sources */,
FC292C5621421B4600CF622F /* PaddleMobileGPU.m in Sources */,
FCD04E6620F314C50007374F /* PoolOp.swift in Sources */,
FCE9D7B9214FAA4800B520C3 /* NMSFetchResultKernel.metal in Sources */,
FC039BAC20E11CBC0081E9F8 /* BatchNormOp.swift in Sources */,
FCBCCC6F2123097100D94F7E /* MulticlassNMSOp.swift in Sources */,
FC039BBC20E11CC20081E9F8 /* VarDesc.swift in Sources */,
FC292C872142624800CF622F /* Genet.swift in Sources */,
4AF928822135673D005B6C3A /* Concat.metal in Sources */,
FC803BC5214CB8F00094B8E5 /* ConvAddPrelu.inc.metal in Sources */,
4AF928822135673D005B6C3A /* ConcatKernel.metal in Sources */,
FCBCCC632122FCC000D94F7E /* TransposeKernel.swift in Sources */,
FCBCCC71212309A700D94F7E /* MulticlassNMSKernel.swift in Sources */,
FCDC0FEB21099A1D00DC9EFB /* Tools.swift in Sources */,
......@@ -636,7 +698,9 @@
FC82735920E3C04200BE430A /* OpCreator.swift in Sources */,
FCA3A1652132A5EB00084FE5 /* Common.metal in Sources */,
4AA1EA92214665D700D0F791 /* ShapeOp.swift in Sources */,
FC803BC1214CB77A0094B8E5 /* ConvAddPreluKernel.swift in Sources */,
FCBCCC5D2122F8A100D94F7E /* DepthwiseConvOp.swift in Sources */,
FCE9D7B7214F869000B520C3 /* Net.swift in Sources */,
FC0E2DBE20EE460D009C1FAC /* BatchNormKernel.swift in Sources */,
FC039BAB20E11CBC0081E9F8 /* Operator.swift in Sources */,
FCD04E6A20F319EC0007374F /* SoftmaxOp.swift in Sources */,
......@@ -650,11 +714,13 @@
FCBCCC67212306B000D94F7E /* ConcatOp.swift in Sources */,
FCD04E6C20F31A280007374F /* SoftmaxKernel.swift in Sources */,
FCEB684A212F00DB00D2448E /* PreluKernel.metal in Sources */,
4AA1EAA02148DEEE00D0F791 /* ReshapeKernel.inc.metal in Sources */,
FC9A19E32148C31300CD9CBF /* MobilenetSSD_AR.swift in Sources */,
FCDDC6CF212FE14700E5EF74 /* PriorBoxKernel.metal in Sources */,
FC4CB74B20F12C30007C0C6D /* ProgramOptimize.swift in Sources */,
FC5163F620EF556E00636C28 /* Texture2DTo2DArrayKernel.swift in Sources */,
FC039BC020E11CC20081E9F8 /* BlockDesc.swift in Sources */,
FC803BC3214CB79C0094B8E5 /* ConvAddPreluKernel.metal in Sources */,
4AA1EA90214664CD00D0F791 /* Split.metal in Sources */,
FCD04E6820F315020007374F /* PoolKernel.swift in Sources */,
FC0226582138F38D00F395E2 /* PoolKernel.metal in Sources */,
......@@ -815,7 +881,7 @@
"$(PROJECT_DIR)/paddle-mobile/CPU",
);
MACH_O_TYPE = mh_dylib;
MTL_LANGUAGE_REVISION = UseDeploymentTarget;
MTL_LANGUAGE_REVISION = Metal12;
PRODUCT_BUNDLE_IDENTIFIER = "orange.paddle-mobile";
PRODUCT_NAME = "$(TARGET_NAME:c99extidentifier)";
SKIP_INSTALL = YES;
......@@ -851,7 +917,7 @@
"$(PROJECT_DIR)/paddle-mobile/CPU",
);
MACH_O_TYPE = mh_dylib;
MTL_LANGUAGE_REVISION = UseDeploymentTarget;
MTL_LANGUAGE_REVISION = Metal12;
PRODUCT_BUNDLE_IDENTIFIER = "orange.paddle-mobile";
PRODUCT_NAME = "$(TARGET_NAME:c99extidentifier)";
SKIP_INSTALL = YES;
......
metal/paddle-mobile/paddle-mobile.xcodeproj/xcuserdata/liuruilong.xcuserdatad/xcschemes/paddle-mobile.xcscheme
浏览文件 @ b7a1d552
......@@ -33,7 +33,7 @@
</AdditionalOptions>
</TestAction>
<LaunchAction
buildConfiguration = "Release"
buildConfiguration = "Debug"
selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
launchStyle = "0"
......
metal/paddle-mobile/paddle-mobile/CPU/PaddleMobile.h
浏览文件 @ b7a1d552
......@@ -17,7 +17,17 @@
#import <CoreImage/CoreImage.h>
#import <Foundation/Foundation.h>
@interface PaddleMobile : NSObject
@interface PaddleMobileCPUResult: NSObject
@property (assign, nonatomic, readonly) float *output;
@property (assign, nonatomic, readonly) int outputSize;
-(void)releaseOutput;
@end
@interface PaddleMobileCPU : NSObject
/*
创建对象
......@@ -42,25 +52,8 @@
andModelParamsLen:(size_t)combinedParamsLen
andCombinedParamsBuf:(const uint8_t *)combinedParamsBuf;
/*
* 进行预测, means 和 scale 为训练模型时的预处理参数, 如训练时没有做这些预处理则直接使用 predict
*/
- (NSArray *)predict:(CGImageRef)image
dim:(NSArray<NSNumber *> *)dim
means:(NSArray<NSNumber *> *)means
scale:(float)scale;
/*
* 预测输入
* */
- (NSArray *)predictInput:(float *)input
dim:(NSArray<NSNumber *> *)dim
means:(NSArray<NSNumber *> *)means
scale:(float)scale;
/*
* 对图像进行预处理
* 对图像进行预处理, 需要外部开辟 output 内存, 外部释放 output 内存
* */
-(void)preprocess:(CGImageRef)image
output:(float *)output
......@@ -68,6 +61,22 @@
scale:(float)scale
dim:(NSArray<NSNumber *> *)dim;
/*
* 预测预处理后的数据, 返回结果使用结束需要调用其 realseOutput 函数进行释放
* */
- (PaddleMobileCPUResult *)predictInput:(float *)input
dim:(NSArray<NSNumber *> *)dim;
/*
进行预测, means 和 scale 为训练模型时的预处理参数, 如训练时没有做这些预处理则直接使用 predict
*/
- (NSArray *)predict:(CGImageRef)image dim:(NSArray<NSNumber *> *)dim means:(NSArray<NSNumber *> *)means scale:(float)scale;
/*
进行预测, 默认 means 为 0, scale 为 1.0
*/
- (NSArray *)predict:(CGImageRef)image dim:(NSArray<NSNumber *> *)dim;
/*
清理内存
*/
......
metal/paddle-mobile/paddle-mobile/CPUCompute.h
浏览文件 @ b7a1d552
......@@ -16,6 +16,12 @@
#import <Foundation/Foundation.h>
@interface CPUResult: NSObject
@property (assign, nonatomic) float *output;
@property (assign, nonatomic) int outputSize;
@end
@interface NMSCompute: NSObject
@property (assign, nonatomic) float scoreThredshold;
......@@ -34,6 +40,6 @@
@property (strong, nonatomic) NSArray<NSNumber *> *bboxDim;
-(NSArray<NSNumber *> *)computeWithScore:(float *)score andBBoxs:(float *)bbox;
-(CPUResult *)computeWithScore:(float *)score andBBoxs:(float *)bbox;
@end
metal/paddle-mobile/paddle-mobile/CPUCompute.mm
浏览文件 @ b7a1d552
......@@ -21,6 +21,8 @@
#import <algorithm>
struct NMSParam {
float *score_data;
......@@ -282,9 +284,12 @@ void MultiClassNMSCompute(NMSParam *param) {
param->output_size = output_size;
}
@implementation CPUResult
@end
@implementation NMSCompute
-(NSArray<NSNumber *> *)computeWithScore:(float *)score andBBoxs:(float *)bbox {
-(CPUResult *)computeWithScore:(float *)score andBBoxs:(float *)bbox {
NMSParam param;
param.box_data = bbox;
param.score_data = score;
......@@ -306,12 +311,10 @@ void MultiClassNMSCompute(NMSParam *param) {
}
param.box_dim = box_dim;
MultiClassNMSCompute(&param);
NSMutableArray<NSNumber *> *output = [NSMutableArray arrayWithCapacity:param.output_size];
for (int i = 0; i < param.output_size; ++i) {
[output addObject:[NSNumber numberWithFloat:param.output[i]]];
}
delete param.output;
return output;
CPUResult *cr = [[CPUResult alloc] init];
cr.output = param.output;
cr.outputSize = param.output_size;
return cr;
}
@end
......
metal/paddle-mobile/paddle-mobile/Common/MetalExtension.swift
浏览文件 @ b7a1d552
......@@ -71,7 +71,128 @@ extension MTLDevice {
return buffer!
}
func texture2tensor_loop<P>(texture: MTLTexture, cb: ([Int], P)->Void) -> Void {
let bpR = texture.width * 4 * MemoryLayout<P>.size
let bpI = texture.height * bpR
let region = MTLRegion.init(origin: MTLOrigin.init(x: 0, y: 0, z: 0), size: MTLSize.init(width: texture.width, height: texture.height, depth: 1))
for i in 0..<texture.arrayLength {
let pointer: UnsafeMutablePointer<P> = UnsafeMutablePointer<P>.allocate(capacity: bpI)
texture.getBytes(pointer, bytesPerRow: bpR, bytesPerImage: bpI, from: region, mipmapLevel: 0, slice: i)
for tx in 0..<texture.width * texture.height * 4 {
var k = tx
var xyzn: [Int] = [0, 0, 0, 0]
xyzn[1] = k / (texture.width * 4)
k %= (texture.width * 4)
xyzn[3] = k % 4
xyzn[0] = k / 4
xyzn[2] = i
cb(xyzn, pointer[tx])
}
}
}
func texture2tensor_3<P>(texture: MTLTexture, dim: [Int], transpose: [Int] = [0, 1, 2, 3]) -> [P] {
var tdim: [Int] = [1, 1, 1, 1]
for i in 0..<dim.count {
tdim[4 - dim.count + i] = dim[i]
}
let count = dim.reduce(1) { $0 * $1 }
var tensor: [P] = .init(repeating: Float32(0.0) as! P, count: count)
let ndim: [Int] = transpose.map { tdim[$0] }
assert(dim.count == 3)
assert(texture.width == ndim[3])
assert(texture.height == ndim[2])
assert(ndim[0] == 1)
assert(texture.arrayLength == (ndim[1] + 3) / 4)
texture2tensor_loop(texture: texture) { (xyzn: [Int], v: P) in
var tg: [Int] = [0, 0, 0, 0]
tg[1] = xyzn[2] * 4 + xyzn[3]
tg[2] = xyzn[1]
tg[3] = xyzn[0]
var ig: [Int] = [0, 0, 0, 0]
for k in 0..<4 {
ig[transpose[k]] = tg[k]
}
let ix = ig[0] * tdim[1] * tdim[2] * tdim[3] + ig[1] * tdim[2] * tdim[3] + ig[2] * tdim[3] + ig[3]
if ix < count {
tensor[ix] = v
}
}
return tensor
}
func texture2tensor_2<P>(texture: MTLTexture, dim: [Int], transpose: [Int] = [0, 1, 2, 3]) -> [P] {
var tdim: [Int] = [1, 1, 1, 1]
for i in 0..<dim.count {
tdim[4 - dim.count + i] = dim[i]
}
let count = dim.reduce(1) { $0 * $1 }
var tensor: [P] = .init(repeating: Float32(0.0) as! P, count: count)
let ndim: [Int] = transpose.map { tdim[$0] }
assert(dim.count == 2)
let w = (ndim[3] + 3) / 4
assert(texture.width == w)
assert(texture.height == ndim[2])
assert(ndim[0] == 1)
assert(ndim[1] == 1)
assert(texture.arrayLength == 1)
texture2tensor_loop(texture: texture) { (xyzn: [Int], v: P) in
var tg: [Int] = [0, 0, 0, 0]
tg[2] = xyzn[1]
tg[3] = xyzn[0] * 4 + xyzn[3]
var ig: [Int] = [0, 0, 0, 0]
for k in 0..<4 {
ig[transpose[k]] = tg[k]
}
let ix = ig[0] * tdim[1] * tdim[2] * tdim[3] + ig[1] * tdim[2] * tdim[3] + ig[2] * tdim[3] + ig[3]
if ix < count {
tensor[ix] = v
}
}
return tensor
}
func texture2tensor_1<P>(texture: MTLTexture, dim: [Int], transpose: [Int] = [0, 1, 2, 3]) -> [P] {
var tdim: [Int] = [1, 1, 1, 1]
for i in 0..<dim.count {
tdim[4 - dim.count + i] = dim[i]
}
let count = dim.reduce(1) { $0 * $1 }
var tensor: [P] = .init(repeating: Float32(0.0) as! P, count: count)
let ndim: [Int] = transpose.map { tdim[$0] }
assert(dim.count == 1)
let w = (ndim[3] + 3) / 4
assert(texture.width == w)
assert(texture.height == 1)
assert(ndim[0] == 1)
assert(ndim[1] == 1)
assert(ndim[2] == 1)
assert(texture.arrayLength == 1)
texture2tensor_loop(texture: texture) { (xyzn: [Int], v: P) in
var tg: [Int] = [0, 0, 0, 0]
tg[3] = xyzn[0] * 4 + xyzn[3]
var ig: [Int] = [0, 0, 0, 0]
for k in 0..<4 {
ig[transpose[k]] = tg[k]
}
let ix = ig[0] * tdim[1] * tdim[2] * tdim[3] + ig[1] * tdim[2] * tdim[3] + ig[2] * tdim[3] + ig[3]
if ix < count {
tensor[ix] = v
}
}
return tensor
}
func texture2tensor<P>(texture: MTLTexture, dim: [Int], transpose: [Int] = [0, 1, 2, 3]) -> [P] {
if dim.count == 3 {
return texture2tensor_3(texture: texture, dim: dim, transpose: transpose)
} else if dim.count == 2 {
return texture2tensor_2(texture: texture, dim: dim, transpose: transpose)
} else if dim.count == 1 {
return texture2tensor_1(texture: texture, dim: dim, transpose: transpose)
}
var tdim: [Int] = [1, 1, 1, 1]
for i in 0..<dim.count {
tdim[4 - dim.count + i] = dim[i]
......@@ -84,30 +205,19 @@ extension MTLDevice {
assert(texture.height == ndim[1])
assert(texture.arrayLength == (ndim[0] * ndim[3] + 3) / 4)
let bpR = ndim[2] * 4 * MemoryLayout<P>.size
let bpI = ndim[1] * bpR
let region = MTLRegion.init(origin: MTLOrigin.init(x: 0, y: 0, z: 0), size: MTLSize.init(width: ndim[2], height: ndim[1], depth: 1))
for i in 0..<texture.arrayLength {
let pointer: UnsafeMutablePointer<P> = UnsafeMutablePointer<P>.allocate(capacity: ndim[1] * ndim[2] * 4 * MemoryLayout<P>.size)
texture.getBytes(pointer, bytesPerRow: bpR, bytesPerImage: bpI, from: region, mipmapLevel: 0, slice: i)
for h in 0..<ndim[1] {
for w in 0..<ndim[2] {
texture2tensor_loop(texture: texture) { (xyzn: [Int], v: P) in
var tg: [Int] = [0, 0, 0, 0]
tg[1] = xyzn[1]
tg[2] = xyzn[0]
tg[0] = (xyzn[2] * 4 + xyzn[3]) / ndim[3]
tg[3] = (xyzn[2] * 4 + xyzn[3]) % ndim[3]
var ig: [Int] = [0, 0, 0, 0]
for k in 0..<4 {
let tx = (h * ndim[2] + w) * 4 + k
let n = (i * 4 + k) / ndim[3]
let c = (i * 4 + k) % ndim[3]
let jg = [n, h, w, c]
var ig = [0, 0, 0, 0]
for d in 0..<4 {
ig[transpose[d]] = jg[d]
ig[transpose[k]] = tg[k]
}
let ix = ig[0] * tdim[1] * tdim[2] * tdim[3] + ig[1] * tdim[2] * tdim[3] + ig[2] * tdim[3] + ig[3]
if ix < count {
tensor[ix] = pointer[tx]
}
}
}
tensor[ix] = v
}
}
return tensor
......
metal/paddle-mobile/paddle-mobile/Common/PaddleMobileUnitTest.swift
浏览文件 @ b7a1d552
......@@ -83,38 +83,38 @@ public class PaddleMobileUnitTest {
}
public func testConcat() {
let buffer = queue.makeCommandBuffer() ?! "buffer is nil"
var it: [[Float32]] = []
for _ in 0..<7 {
it.append((0..<12).map { Float32($0) })
}
let input = it.map { device.tensor2texture(value: $0, dim: [3, 4]) }
let output = device.tensor2texture(value: [Float32](), dim: [3, 28])
let param = ConcatTestParam.init(
input: input,
output: output,
dims: [[3, 4], [3, 4], [3, 4], [3, 4], [3, 4], [3, 4], [3, 4]],
axis: 1,
odim: [3, 28]
)
let concatKernel = ConcatKernel<Float32>.init(device: device, testParam: param)
concatKernel.test(cmdBuffer: buffer, param: param)
buffer.addCompletedHandler { (buffer) in
for i in 0..<it.count {
let _: Float32? = input[i].logDesc()
self.tensorPrint(tensor: it[i], dim: [3, 4])
}
let _: Float32? = output.logDesc()
let tx: [Float32] = self.device.texture2tensor(texture: output, dim: [3, 28])
self.tensorPrint(tensor: tx, dim: [3, 28])
}
buffer.commit()
// let buffer = queue.makeCommandBuffer() ?! "buffer is nil"
// var it: [[Float32]] = []
// for _ in 0..<7 {
// it.append((0..<12).map { Float32($0) })
// }
// let input = it.map { device.tensor2texture(value: $0, dim: [3, 4]) }
// let output = device.tensor2texture(value: [Float32](), dim: [3, 28])
//
// let param = ConcatTestParam.init(
// input: input,
// output: output,
// dims: [[3, 4], [3, 4], [3, 4], [3, 4], [3, 4], [3, 4], [3, 4]],
// axis: 1,
// odim: [3, 28]
// )
// let concatKernel = ConcatKernel<Float32>.init(device: device, testParam: param)
// concatKernel.test(cmdBuffer: buffer, param: param)
// buffer.addCompletedHandler { (buffer) in
// for i in 0..<it.count {
// let _: Float32? = input[i].logDesc()
// self.tensorPrint(tensor: it[i], dim: [3, 4])
// }
// let _: Float32? = output.logDesc()
// let tx: [Float32] = self.device.texture2tensor(texture: output, dim: [3, 28])
// self.tensorPrint(tensor: tx, dim: [3, 28])
// }
//
// buffer.commit()
}
public func testReshape() {
let buffer = queue.makeCommandBuffer() ?! "buffer is nil"
// let buffer = queue.makeCommandBuffer() ?! "buffer is nil"
// let input: [Float32] = (0..<24).map { Float32($0) }
// let inTexture = device.tensor2texture(value: input, dim: [2, 3, 4])
// let outTexture = device.tensor2texture(value: [Float32](), dim: [4, 6])
......@@ -139,32 +139,32 @@ public class PaddleMobileUnitTest {
// self.tensorPrint(tensor: tx, dim: [4, 6])
// }
let input: [Float32] = (0..<24).map { Float32($0) }
let inTexture = device.tensor2texture(value: input, dim: [2, 3, 4])
let outTexture = device.tensor2texture(value: [Float32](), dim: [24])
let mp = ReshapeMetalParam.init(
idim: (1, 2, 3, 4),
itrans: (0, 1, 2, 3),
odim: (1, 1, 1, 24),
otrans: (0, 1, 2, 3)
)
let param = ReshapeTestParam.init(
inputTexture: inTexture,
outputTexture: outTexture,
param: mp
)
let reshapeKernel = ReshapeKernel<Float32>.init(device: device, testParam: param)
reshapeKernel.test(commandBuffer: buffer, testParam: param)
buffer.addCompletedHandler { (buffer) in
let _: Float32? = inTexture.logDesc()
let _: Float32? = outTexture.logDesc()
self.tensorPrint(tensor: input, dim: [2, 3, 4])
let tx: [Float32] = self.device.texture2tensor(texture: outTexture, dim: [24])
self.tensorPrint(tensor: tx, dim: [24])
}
buffer.commit()
// let input: [Float32] = (0..<24).map { Float32($0) }
// let inTexture = device.tensor2texture(value: input, dim: [2, 3, 4])
// let outTexture = device.tensor2texture(value: [Float32](), dim: [24])
// let mp = ReshapeMetalParam.init(
// idim: (1, 2, 3, 4),
// itrans: (0, 1, 2, 3),
// odim: (1, 1, 1, 24),
// otrans: (0, 1, 2, 3)
// )
// let param = ReshapeTestParam.init(
// inputTexture: inTexture,
// outputTexture: outTexture,
// param: mp
// )
// let reshapeKernel = ReshapeKernel<Float32>.init(device: device, testParam: param)
// reshapeKernel.test(commandBuffer: buffer, testParam: param)
// buffer.addCompletedHandler { (buffer) in
// let _: Float32? = inTexture.logDesc()
// let _: Float32? = outTexture.logDesc()
// self.tensorPrint(tensor: input, dim: [2, 3, 4])
// let tx: [Float32] = self.device.texture2tensor(texture: outTexture, dim: [24])
// self.tensorPrint(tensor: tx, dim: [24])
// }
//
//
// buffer.commit()
}
public func testTranspose() {
......@@ -195,23 +195,23 @@ public class PaddleMobileUnitTest {
// let tx: [Float32] = self.device.texture2tensor(texture: outputTexture, dim: [3, 3, 2, 4])
// self.tensorPrint(tensor: tx, dim: [3, 3, 2, 4])
// }
let input: [Float32] = (0..<24).map { Float32($0) }
let inputTexture = device.tensor2texture(value: input, dim: [2, 3, 4])
let outputTexture = device.tensor2texture(value: [Float](), dim: [3, 4, 2])
let param = TransposeTestParam.init(inputTexture: inputTexture, outputTexture: outputTexture, iC: 4, oC: 2, axis: [0, 2, 3, 1])
let transposeKernel = TransposeKernel<Float32>.init(device: device, testParam: param)
transposeKernel.test(commandBuffer: buffer, param: param)
buffer.addCompletedHandler { (buffer) in
let _: Float32? = inputTexture.logDesc(header: "input texture", stridable: false)
let _: Float32? = outputTexture.logDesc(header: "output texture", stridable: false)
self.tensorPrint(tensor: input, dim: [2, 3, 4])
let tx: [Float32] = self.device.texture2tensor(texture: outputTexture, dim: [3, 4, 2])
self.tensorPrint(tensor: tx, dim: [3, 4, 2])
}
//
// let input: [Float32] = (0..<24).map { Float32($0) }
// let inputTexture = device.tensor2texture(value: input, dim: [2, 3, 4])
// let outputTexture = device.tensor2texture(value: [Float](), dim: [3, 4, 2])
// let param = TransposeTestParam.init(inputTexture: inputTexture, outputTexture: outputTexture, iC: 4, oC: 2, axis: [0, 2, 3, 1])
// let transposeKernel = TransposeKernel<Float32>.init(device: device, testParam: param)
//
// transposeKernel.test(commandBuffer: buffer, param: param)
//
// buffer.addCompletedHandler { (buffer) in
// let _: Float32? = inputTexture.logDesc(header: "input texture", stridable: false)
// let _: Float32? = outputTexture.logDesc(header: "output texture", stridable: false)
// self.tensorPrint(tensor: input, dim: [2, 3, 4])
// let tx: [Float32] = self.device.texture2tensor(texture: outputTexture, dim: [3, 4, 2])
// self.tensorPrint(tensor: tx, dim: [3, 4, 2])
// }
//
buffer.commit()
}
......
metal/paddle-mobile/paddle-mobile/Common/Types.swift
浏览文件 @ b7a1d552
......@@ -243,7 +243,7 @@ extension Tensor: Variant {
extension Texture: Variant {
}
extension ResultHolder: Variant {
extension GPUResultHolder: Variant {
}
extension InputTexture: Variant {
......@@ -252,3 +252,43 @@ extension InputTexture: Variant {
extension MTLTexture where Self: Variant {
}
class FetchHolder: Variant {
var resultBuffer: MTLBuffer?
var dim: [Int]
var capacity: Int
init(inCapacity: Int, inDim: [Int]) {
capacity = inCapacity
dim = inDim
}
func initBuffer(device: MTLDevice) {
resultBuffer = device.makeBuffer(length: capacity * 4, options: [])
}
var result: UnsafeMutablePointer<Float32> {
guard let inResultBuffer = resultBuffer else {
fatalError()
}
return inResultBuffer.contents().bindMemory(to: Float32.self, capacity: capacity)
}
}
extension FetchHolder: CustomStringConvertible, CustomDebugStringConvertible {
var description: String {
fatalError()
// return "\(result)"
}
var debugDescription: String {
fatalError()
// return "\(result)"
}
}
metal/paddle-mobile/paddle-mobile/Genet.swift
浏览文件 @ b7a1d552
......@@ -46,8 +46,9 @@ public class Genet: Net {
}
}
override public func resultStr(res: [Float]) -> String {
return " \(Array<Float>(res.suffix(10))) ... "
override public func resultStr(res: ResultHolder) -> String {
// fatalError()
return " \(res.result![0]) ... "
}
}
metal/paddle-mobile/paddle-mobile/MobileNet.swift
浏览文件 @ b7a1d552
......@@ -42,9 +42,12 @@ class MobileNet: Net{
let labels = PreWords.init(fileName: "synset")
override public func resultStr(res: [Float]) -> String {
override public func resultStr(res: ResultHolder) -> String {
guard let resPointer = res.result else {
fatalError()
}
var s: [String] = []
res.top(r: 5).enumerated().forEach{
(0..<res.capacity).map { resPointer[$0] }.top(r: 5).enumerated().forEach{
s.append(String(format: "%d: %@ (%3.2f%%)", $0 + 1, labels[$1.0], $1.1 * 100))
}
return s.joined(separator: "\n")
......
metal/paddle-mobile/paddle-mobile/MobileNetSSD.swift
浏览文件 @ b7a1d552
......@@ -46,51 +46,52 @@ public class MobileNet_ssd_hand: Net{
}
}
override public func resultStr(res: [Float]) -> String {
override public func resultStr(res: ResultHolder) -> String {
return " \(res)"
}
override func fetchResult(paddleMobileRes: ResultHolder) -> [Float32] {
override func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
guard let interRes = paddleMobileRes.intermediateResults else {
fatalError(" need have inter result ")
}
guard let scores = interRes["Scores"], scores.count > 0, let score = scores[0] as? Texture<Float32> else {
fatalError(" need score ")
}
guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? Texture<Float32> else {
fatalError()
}
var scoreFormatArr: [Float32] = score.metalTexture.realNHWC(dim: (n: score.padToFourDim[0], h: score.padToFourDim[1], w: score.padToFourDim[2], c: score.padToFourDim[3]))
// print("score: ")
// print(scoreFormatArr.strideArray())
// guard let interRes = paddleMobileRes.intermediateResults else {
// fatalError(" need have inter result ")
// }
//
var bboxArr = bbox.metalTexture.float32Array()
// print("bbox: ")
// print(bboxArr.strideArray())
let nmsCompute = NMSCompute.init()
nmsCompute.scoreThredshold = 0.01
nmsCompute.nmsTopK = 400
nmsCompute.keepTopK = 200
nmsCompute.nmsEta = 1.0
nmsCompute.nmsThreshold = 0.45
nmsCompute.background_label = 0;
nmsCompute.scoreDim = [NSNumber.init(value: score.tensorDim[0]), NSNumber.init(value: score.tensorDim[1]), NSNumber.init(value: score.tensorDim[2])]
nmsCompute.bboxDim = [NSNumber.init(value: bbox.tensorDim[0]), NSNumber.init(value: bbox.tensorDim[1]), NSNumber.init(value: bbox.tensorDim[2])]
guard let result = nmsCompute.compute(withScore: &scoreFormatArr, andBBoxs: &bboxArr) else {
fatalError( " result error " )
}
let output: [Float32] = result.map { $0.floatValue }
return output
// guard let scores = interRes["Scores"], scores.count > 0, let score = scores[0] as? Texture<Float32> else {
// fatalError(" need score ")
// }
//
// guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? Texture<Float32> else {
// fatalError()
// }
//
// var scoreFormatArr: [Float32] = score.metalTexture.realNHWC(dim: (n: score.padToFourDim[0], h: score.padToFourDim[1], w: score.padToFourDim[2], c: score.padToFourDim[3]))
//// print("score: ")
//// print(scoreFormatArr.strideArray())
////
// var bboxArr = bbox.metalTexture.float32Array()
//// print("bbox: ")
//// print(bboxArr.strideArray())
//
// let nmsCompute = NMSCompute.init()
// nmsCompute.scoreThredshold = 0.01
// nmsCompute.nmsTopK = 400
// nmsCompute.keepTopK = 200
// nmsCompute.nmsEta = 1.0
// nmsCompute.nmsThreshold = 0.45
// nmsCompute.background_label = 0;
//
// nmsCompute.scoreDim = [NSNumber.init(value: score.tensorDim[0]), NSNumber.init(value: score.tensorDim[1]), NSNumber.init(value: score.tensorDim[2])]
//
// nmsCompute.bboxDim = [NSNumber.init(value: bbox.tensorDim[0]), NSNumber.init(value: bbox.tensorDim[1]), NSNumber.init(value: bbox.tensorDim[2])]
// guard let result = nmsCompute.compute(withScore: &scoreFormatArr, andBBoxs: &bboxArr) else {
// fatalError( " result error " )
// }
//
// let output: [Float32] = result.map { $0.floatValue }
//
//
// return output
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/MobilenetSSD_AR.swift
浏览文件 @ b7a1d552
......@@ -30,50 +30,112 @@ public class MobileNet_ssd_AR: Net{
class MobilenetssdPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = CusomKernel.Shape.init(inWidth: 160, inHeight: 160, inChannel: 3)
super.init(device: device, inFunctionName: "mobilent_ar_preprocess_half", outputDim: s, usePaddleMobileLib: false)
super.init(device: device, inFunctionName: "mobilent_ar_preprocess", outputDim: s, usePaddleMobileLib: false)
}
}
override public func resultStr(res: [Float]) -> String {
return " \(res)"
override public func resultStr(res: ResultHolder) -> String {
return " \(res.result![0])"
}
override func fetchResult(paddleMobileRes: ResultHolder) -> [Float32] {
override func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
guard let interRes = paddleMobileRes.intermediateResults else {
fatalError(" need have inter result ")
}
guard let scores = interRes["Scores"], scores.count > 0, let score = scores[0] as? Texture<Float32> else {
guard let scores = interRes["Scores"], scores.count > 0, let score = scores[0] as? FetchHolder else {
fatalError(" need score ")
}
guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? Texture<Float32> else {
guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? FetchHolder else {
fatalError()
}
var scoreFormatArr: [Float32] = score.metalTexture.realNHWC(dim: (n: score.padToFourDim[0], h: score.padToFourDim[1], w: score.padToFourDim[2], c: score.padToFourDim[3]))
// print("score: ")
// print(scoreFormatArr.strideArray())
//
var bboxArr = bbox.metalTexture.float32Array()
// print("bbox: ")
// print(bboxArr.strideArray())
// let startDate = Date.init()
// print("scoreFormatArr: ")
//print((0..<score.capacity).map{ score.result[$0] }.strideArray())
//
// print("bbox arr: ")
//
// print((0..<bbox.capacity).map{ bbox.result[$0] }.strideArray())
let nmsCompute = NMSCompute.init()
nmsCompute.scoreThredshold = 0.01
nmsCompute.nmsTopK = 400
nmsCompute.keepTopK = 200
nmsCompute.scoreThredshold = 0.25
nmsCompute.nmsTopK = 100
nmsCompute.keepTopK = 100
nmsCompute.nmsEta = 1.0
nmsCompute.nmsThreshold = 0.45
nmsCompute.nmsThreshold = 0.449999988
nmsCompute.background_label = 0;
nmsCompute.scoreDim = [NSNumber.init(value: score.tensorDim[0]), NSNumber.init(value: score.tensorDim[1]), NSNumber.init(value: score.tensorDim[2])]
nmsCompute.bboxDim = [NSNumber.init(value: bbox.tensorDim[0]), NSNumber.init(value: bbox.tensorDim[1]), NSNumber.init(value: bbox.tensorDim[2])]
guard let result = nmsCompute.compute(withScore: &scoreFormatArr, andBBoxs: &bboxArr) else {
nmsCompute.scoreDim = [NSNumber.init(value: score.dim[0]), NSNumber.init(value: score.dim[1]), NSNumber.init(value: score.dim[2])]
nmsCompute.bboxDim = [NSNumber.init(value: bbox.dim[0]), NSNumber.init(value: bbox.dim[1]), NSNumber.init(value: bbox.dim[2])]
guard let result = nmsCompute.compute(withScore: score.result, andBBoxs: bbox.result) else {
fatalError( " result error " )
}
let resultHolder = ResultHolder.init(inResult: result.output, inCapacity: Int(result.outputSize))
// for i in 0..<Int(result.outputSize) {
//
// print("i \(i) : \(result.output[i])")
// }
// print(Date.init().timeIntervalSince(startDate))
let output: [Float32] = result.map { $0.floatValue }
return output
// print(resultHolder.result![0])
return resultHolder
}
override func updateProgram(program: Program) {
for i in [56, 66, 76, 86, 93, 99] {
let opDesc = program.programDesc.blocks[0].ops[i]
let output = opDesc.outputs["Out"]!.first!
let v = program.scope[output]!
let originTexture = v as! Texture<Float32>
originTexture.tensorDim = Dim.init(inDim: [originTexture.tensorDim[1] / 7, originTexture.tensorDim[0] * 7])
originTexture.dim = Dim.init(inDim: [1, 1, originTexture.dim[3] / 7, originTexture.dim[2] * 7])
originTexture.padToFourDim = Dim.init(inDim: [1, 1, originTexture.padToFourDim[3] / 7, originTexture.padToFourDim[2] * 7])
program.scope[output] = originTexture
if i == 99 {
opDesc.attrs["axis"] = 0
} else {
opDesc.attrs["shape"] = originTexture.tensorDim.dims.map { Int32($0) }
}
}
for i in [58, 59, 88, 89, 95, 96, 68, 69, 78, 79] {
let opDesc = program.programDesc.blocks[0].ops[i]
let output = opDesc.outputs["Out"]!.first!
let v = program.scope[output]!
let originTexture = v as! Texture<Float32>
originTexture.tensorDim = Dim.init(inDim: [originTexture.tensorDim[1], originTexture.tensorDim[2]])
opDesc.attrs["shape"] = originTexture.tensorDim.dims.map { Int32($0) }
}
for i in [60, 101, 90, 97, 70, 80] {
let opDesc = program.programDesc.blocks[0].ops[i]
let output = opDesc.outputs["Out"]!.first!
let v = program.scope[output]!
let originTexture = v as! Texture<Float32>
originTexture.tensorDim = Dim.init(inDim: [originTexture.tensorDim[1], originTexture.tensorDim[2]])
opDesc.attrs["axis"] = (opDesc.attrs["axis"]! as! Int) - 1
}
for i in [102] {
let opDesc = program.programDesc.blocks[0].ops[i]
for output in opDesc.outputs["Out"]! {
let v = program.scope[output]!
let originTexture = v as! Texture<Float32>
originTexture.tensorDim = Dim.init(inDim: [originTexture.tensorDim[1], originTexture.tensorDim[2]])
}
opDesc.attrs["axis"] = (opDesc.attrs["axis"]! as! Int) - 1
print(" split axis \(opDesc.attrs["axis"])")
}
// 99
}
}
metal/paddle-mobile/paddle-mobile/Net.swift 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
import Foundation
public class ResultHolder: NSObject {
@objc public let result: UnsafeMutablePointer<Float32>?
@objc public let capacity: Int
init(inResult: UnsafeMutablePointer<Float32>?, inCapacity: Int) {
result = inResult
capacity = inCapacity
}
public func releasePointer() {
result?.deinitialize(count: capacity)
result?.deallocate()
}
}
public class Net: NSObject {
var except: Int = 0
var means: [Float] = []
var scale: Float = 0.0
var dim: (n: Int, h: Int, w: Int, c: Int) = (n: 0, h: 0, w: 0, c: 0)
var preprocessKernel: CusomKernel? = nil
var paramPointer: UnsafeMutableRawPointer? = nil
var paramSize: Int = 0
var modelPointer: UnsafeMutableRawPointer? = nil
var modelSize: Int = 0
var modelPath: String = ""
var paramPath: String = ""
var modelDir: String = ""
@objc public init(device: MTLDevice,paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
self.paramPointer = paramPointer
self.paramSize = paramSize
self.modelPointer = modePointer
self.modelSize = modelSize
super.init()
}
public func resultStr(res: ResultHolder) -> String {
fatalError()
}
func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
return ResultHolder.init(inResult: paddleMobileRes.resultPointer, inCapacity: paddleMobileRes.capacity)
}
@objc public init(device: MTLDevice) {
super.init()
}
func updateProgram(program: Program) {
}
}
metal/paddle-mobile/paddle-mobile/Operators/Base/OpCreator.swift
浏览文件 @ b7a1d552
......@@ -64,7 +64,8 @@ class OpCreator<P: PrecisionType> {
gBilinearInterpType : BilinearInterpOp<P>.creat,
gSplit : SplitOp<P>.creat,
gShape : ShapeOp<P>.creat,
gFlatten : FlattenOp<P>.creat]
gFlatten : FlattenOp<P>.creat,
gConvAddPreluType : ConvAddPreluOp<P>.creat]
private init(){}
}
metal/paddle-mobile/paddle-mobile/Operators/Base/Operator.swift
浏览文件 @ b7a1d552
......@@ -19,6 +19,12 @@ protocol Fusion {
static func fusionNode() -> Node
static func change() -> [String : [(from: String, to: String)]]
static func fusionType() -> String
static func needCheck() -> [(Int, String)]
}
extension Fusion {
static func needCheck() -> [(Int, String)] {
return []
}
}
protocol Runable {
......@@ -26,6 +32,7 @@ protocol Runable {
func runImpl(device: MTLDevice,buffer: MTLCommandBuffer) throws
func delogOutput()
func inputVariant() -> [String : [Variant]]
func computeMiddleResult(device: MTLDevice, buffer: MTLCommandBuffer)
}
extension Runable where Self: OperatorProtocol{
......@@ -38,11 +45,16 @@ extension Runable where Self: OperatorProtocol{
}
func inputVariant() -> [String : [Variant]] {
return [:]
// fatalError(" op \(type) need implement inputVariant")
// return [:]
fatalError(" op \(type) need implement inputVariant")
}
func computeMiddleResult(device: MTLDevice, buffer: MTLCommandBuffer) {
fatalError(" need implement ")
}
func delogOutput() {
print(type + ": has no implementation" )
}
}
......@@ -144,6 +156,7 @@ let gBilinearInterpType = "bilinear_interp"
let gSplit = "split"
let gShape = "shape"
let gFlatten = "flatten"
let gConvAddPreluType = "conv_add_prelu"
let opInfos = [gConvType : (inputs: ["Input"], outputs: ["Output"]),
gBatchNormType : (inputs: ["X"], outputs: ["Y"]),
......@@ -169,5 +182,7 @@ let opInfos = [gConvType : (inputs: ["Input"], outputs: ["Out
gBilinearInterpType : (inputs: ["X"], outputs: ["Out"]),
gSplit : (inputs: ["X"], outputs: ["Out"]),
gShape : (inputs: ["Input"], outputs: ["Out"]),
gFlatten : (inputs: ["X"], outputs: ["Out"])
gFlatten : (inputs: ["X"], outputs: ["Out"]),
gConvAddPreluType : (inputs: ["Input"], outputs: ["Out"])
]
metal/paddle-mobile/paddle-mobile/Operators/BatchNormOp.swift
浏览文件 @ b7a1d552
......@@ -19,11 +19,14 @@ class BatchNormParam<P: PrecisionType>: OpParam {
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
input = try BatchNormParam.inputX(inputs: opDesc.inputs, from: inScope)
if input.transpose != [0, 2, 3, 1] {
fatalError("batch norm only accepts NHWC")
}
output = try BatchNormParam.outputY(outputs: opDesc.outputs, from: inScope)
inputBias = try BatchNormParam.inputBiase(inputs: opDesc.paraInputs, from: inScope)
inputMean = try BatchNormParam.inputMean(inputs: opDesc.paraInputs, from: inScope)
inputScale = try BatchNormParam.inputScale(inputs: opDesc.paraInputs, from: inScope)
inputVariance = try BatchNormParam.inputVariance(inputs: opDesc.paraInputs, from: inScope)
bias = try BatchNormParam.getFirstTensor(key: "Bias", map: opDesc.paraInputs, from: inScope)
mean = try BatchNormParam.getFirstTensor(key: "Mean", map: opDesc.paraInputs, from: inScope)
scale = try BatchNormParam.getFirstTensor(key: "Scale", map: opDesc.paraInputs, from: inScope)
variance = try BatchNormParam.getFirstTensor(key: "Variance", map: opDesc.paraInputs, from: inScope)
epsilon = try BatchNormParam.getAttr(key: "epsilon", attrs: opDesc.attrs)
momentum = try BatchNormParam.getAttr(key: "momentum", attrs: opDesc.attrs)
} catch let error {
......@@ -32,10 +35,10 @@ class BatchNormParam<P: PrecisionType>: OpParam {
}
let input: Texture<P>
var output: Texture<P>
let inputBias: Tensor<ParamPrecisionType>
let inputMean: Tensor<ParamPrecisionType>
let inputScale: Tensor<ParamPrecisionType>
let inputVariance: Tensor<ParamPrecisionType>
let bias: Tensor<P>
let mean: Tensor<P>
let scale: Tensor<P>
let variance: Tensor<P>
let epsilon: Float
let momentum: Float
}
......@@ -53,9 +56,11 @@ class BatchNormOp<P: PrecisionType>: Operator<BatchNormKernel<P>, BatchNormParam
throw error
}
}
}
func delogOutput() {
print(" \(type) output: ")
let device = para.output.metalTexture!.device
let outputArray: [Float32] = device.texture2tensor(texture: para.output.metalTexture, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
print(outputArray.strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/BilinearInterpOp.swift
浏览文件 @ b7a1d552
......@@ -19,15 +19,15 @@ class BilinearInterpParam<P: PrecisionType>: OpParam {
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
input = try BilinearInterpParam.inputX(inputs: opDesc.inputs, from: inScope)
// if (input.transpose != [0, 2, 3, 1]) || (input.tensorDim.cout() != 4) {
// fatalError()
// }
output = try BilinearInterpParam.outputOut(outputs: opDesc.outputs, from: inScope)
out_h = try BilinearInterpParam.getAttr(key: "out_h", attrs: opDesc.attrs)
out_w = try BilinearInterpParam.getAttr(key: "out_w", attrs: opDesc.attrs)
} catch let error {
throw error
}
if (input.transpose != [0, 2, 3, 1]) || (input.tensorDim.cout() != 4) {
fatalError()
}
}
let input: Texture<P>
var output: Texture<P>
......@@ -53,6 +53,10 @@ class BilinearInterpOp<P: PrecisionType>: Operator<BilinearInterpKernel<P>, Bili
func delogOutput() {
print(" \(type) output: ")
let device = para.output.metalTexture!.device
let outputArray: [Float32] = device.texture2tensor(texture: para.output.metalTexture, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
// print(outputArray)
print(outputArray.strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/BoxcoderOp.swift
浏览文件 @ b7a1d552
......@@ -27,6 +27,10 @@ class BoxcoderParam<P: PrecisionType>: OpParam {
} catch let error {
throw error
}
assert(priorBox.tensorDim.cout() == 2)
assert(priorBoxVar.tensorDim.cout() == 2)
assert(targetBox.tensorDim.cout() == 3)
assert(output.tensorDim.cout() == 3)
assert(priorBox.transpose == [0, 1, 2, 3])
assert(priorBoxVar.transpose == [0, 1, 2, 3])
assert(targetBox.transpose == [0, 1, 2, 3])
......@@ -59,30 +63,19 @@ class BoxcoderOp<P: PrecisionType>: Operator<BoxcoderKernel<P>, BoxcoderParam<P>
func delogOutput() {
print(" \(type) output: ")
// let priorBoxpadToFourDim = para.priorBox.padToFourDim
// let priorBoxArray: [Float32] = para.priorBox.metalTexture.realNHWC(dim: (n: priorBoxpadToFourDim[0], h: priorBoxpadToFourDim[1], w: priorBoxpadToFourDim[2], c: priorBoxpadToFourDim[3]))
// print(" prior box ")
// print(priorBoxArray.strideArray())
//
// let priorBoxVarpadToFourDim = para.priorBoxVar.padToFourDim
// let priorBoxVarArray: [Float32] = para.priorBoxVar.metalTexture.realNHWC(dim: (n: priorBoxVarpadToFourDim[0], h: priorBoxVarpadToFourDim[1], w: priorBoxVarpadToFourDim[2], c: priorBoxVarpadToFourDim[3]))
// print(" prior box var ")
// print(priorBoxVarArray.strideArray())
//
// let targetBoxpadToFourDim = para.targetBox.padToFourDim
// let targetBoxArray: [Float32] = para.targetBox.metalTexture.realNHWC(dim: (n: targetBoxpadToFourDim[0], h: targetBoxpadToFourDim[1], w: targetBoxpadToFourDim[2], c: targetBoxpadToFourDim[3]))
// print(" target box ")
// print(targetBoxArray.strideArray())
let targetBoxpadToFourDim = para.targetBox.padToFourDim
let targetBoxArray = para.targetBox.metalTexture.realNHWC(dim: (n: targetBoxpadToFourDim[0], h: targetBoxpadToFourDim[1], w: targetBoxpadToFourDim[2], c: targetBoxpadToFourDim[3]))
let device = para.output.metalTexture!.device
let pbv : [Float32] = device.texture2tensor(texture: para.priorBoxVar.metalTexture!, dim: para.priorBoxVar.tensorDim.dims, transpose: para.priorBoxVar.transpose)
let pb : [Float32] = device.texture2tensor(texture: para.priorBox.metalTexture!, dim: para.priorBox.tensorDim.dims, transpose: para.priorBox.transpose)
let tb : [Float32] = device.texture2tensor(texture: para.targetBox.metalTexture!, dim: para.targetBox.tensorDim.dims, transpose: para.targetBox.transpose)
let out : [Float32] = device.texture2tensor(texture: para.output.metalTexture!, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
print(" prior box var ")
print(pbv.strideArray())
print(" target box ")
print(targetBoxArray.strideArray())
let padToFourDim = para.output.padToFourDim
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: padToFourDim[0], h: padToFourDim[1], w: padToFourDim[2], c: padToFourDim[3]))
print(tb.strideArray())
print(" prior box ")
print(pb.strideArray())
print(" output ")
print(outputArray.strideArray())
print(out.strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ConcatOp.swift
浏览文件 @ b7a1d552
......@@ -65,15 +65,10 @@ class ConcatOp<P: PrecisionType>: Operator<ConcatKernel<P>, ConcatParam<P>>, Run
func delogOutput() {
print(" \(type) output: ")
let padToFourDim = para.output.padToFourDim
if para.output.transpose == [0, 1, 2, 3] {
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: padToFourDim[0], h: padToFourDim[1], w: padToFourDim[2], c: padToFourDim[3]))
let device = para.output.metalTexture!.device
let outputArray: [Float32] = device.texture2tensor(texture: para.output.metalTexture, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
print(outputArray.strideArray())
} else if para.output.transpose == [0, 2, 3, 1] {
print(para.output.metalTexture.toTensor(dim: (n: padToFourDim[0], c: padToFourDim[1], h: padToFourDim[2], w: padToFourDim[3])).strideArray())
} else {
fatalError(" not implemet")
}
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ConvAddPreluOp.swift 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
import Foundation
class ConvAddPreluParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
filter = try ConvAddPreluParam.inputFilter(paraInputs: opDesc.paraInputs, from: inScope)
input = try ConvAddPreluParam.input(inputs: opDesc.inputs, from: inScope)
output = try ConvAddPreluParam.outputOut(outputs: opDesc.outputs, from: inScope)
stride = try ConvAddPreluParam.getAttr(key: "strides", attrs: opDesc.attrs)
paddings = try ConvAddPreluParam.getAttr(key: "paddings", attrs: opDesc.attrs)
dilations = try ConvAddPreluParam.getAttr(key: "dilations", attrs: opDesc.attrs)
groups = try ConvAddPreluParam.getAttr(key: "groups", attrs: opDesc.attrs)
alpha = try ConvAddPreluParam.paramInputAlpha(inputs: opDesc.paraInputs, from: inScope)
mode = try ConvAddPreluParam.getAttr(key: "mode", attrs: opDesc.attrs)
y = try ConvAddPreluParam.inputY(inputs: opDesc.paraInputs, from: inScope)
} catch let error {
throw error
}
}
let input: Texture<P>
let y: Tensor<ParamPrecisionType>
let filter: Tensor<ParamPrecisionType>
let mode: String
let alpha: Tensor<P>
var output: Texture<P>
let stride: [Int32]
let paddings: [Int32]
let dilations: [Int32]
let groups: Int
}
class ConvAddPreluOp<P: PrecisionType>: Operator<ConvAddPreluKernel<P>, ConvAddPreluParam<P>>, Runable, Creator, InferShaperable, Fusion{
typealias OpType = ConvAddPreluOp<P>
static func fusionNode() -> Node {
let beginNode = Node.init(inType: gConvType)
_ = beginNode
--> Node.init(inType: gElementwiseAddType) --> Node.init(inType: gPreluType)
return beginNode
}
static func change() -> [String : [(from: String, to: String)]] {
return [:]
}
static func fusionType() -> String {
return gConvAddPreluType
}
func inferShape() {
let inDims = para.input.dim
let filterDim = para.filter.dim
let strides = para.stride
let paddings = para.paddings
let dilations = para.dilations
var outDim = [inDims[0]]
for i in 0..<strides.count {
let dilation: Int = Int(dilations[i])
let filterSize: Int = filterDim[i + 1]
let inputSize: Int = inDims[i + 1]
let padding: Int = Int(paddings[i])
let stride: Int = Int(strides[i])
let dKernel = dilation * (filterSize - 1) + 1
let outputSize = (inputSize + 2 * padding - dKernel) / stride + 1
outDim.append(outputSize)
}
outDim.append(filterDim[0])
para.output.dim = Dim.init(inDim: outDim)
}
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
}
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/FetchOp.swift
浏览文件 @ b7a1d552
......@@ -15,14 +15,15 @@
import Foundation
class FetchParam<P: PrecisionType>: OpParam{
var output: Texture<P>
var output: FetchHolder
let input: Texture<P>
let scope: Scope
required init(opDesc: OpDesc, inScope: Scope) throws {
scope = inScope
do {
input = try FetchParam.inputX(inputs: opDesc.inputs, from: inScope)
output = input
output = FetchHolder.init(inCapacity: input.numel(), inDim: input.tensorDim.dims)
scope.setOutput(output: output)
} catch let error {
throw error
}
......@@ -34,14 +35,40 @@ class FetchParam<P: PrecisionType>: OpParam{
class FetchKernel<P: PrecisionType>: Kernel, Computable {
func compute(commandBuffer: MTLCommandBuffer, param: FetchParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setBuffer(param.output.resultBuffer!, offset: 0, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.input.metalTexture)
encoder.endEncoding()
}
required init(device: MTLDevice, param: FetchParam<P>) {
super.init(device: device, inFunctionName: "place_holder")
param.output.initBuffer(device: device)
if computePrecision == .Float16 {
if param.input.transpose == [0, 2, 3, 1] {
super.init(device: device, inFunctionName: "fetch_half")
} else {
// fatalError(" not support ")
super.init(device: device, inFunctionName: "fetch_placeholder_half")
print(" not support ")
}
} else if computePrecision == .Float32 {
if param.input.transpose == [0, 2, 3, 1] {
super.init(device: device, inFunctionName: "fetch")
} else {
print(" not support ")
super.init(device: device, inFunctionName: "fetch_placeholder")
// fatalError(" not support ")
}
} else {
fatalError(" not support ")
}
}
}
class FetchOp<P: PrecisionType>: Operator< FetchKernel<P>, FetchParam<P>>, Runable, Creator, InferShaperable{
class FetchOp<P: PrecisionType>: Operator< FetchKernel<P>, FetchParam<P>>, Runable, Creator, InferShaperable {
typealias OpType = FetchOp<P>
......@@ -50,7 +77,11 @@ class FetchOp<P: PrecisionType>: Operator< FetchKernel<P>, FetchParam<P>>, Runab
}
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
scope.setOutput(output: para.output)
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
}
}
metal/paddle-mobile/paddle-mobile/Operators/FlattenOp.swift
浏览文件 @ b7a1d552
......@@ -14,7 +14,24 @@
import Foundation
class FlattenOp<P: PrecisionType>: Operator<ReshapeKernel<P>, ReshapeParam<P>>, Runable, Creator, InferShaperable{
class FlattenParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
input = try FlattenParam.inputX(inputs: opDesc.inputs, from: inScope)
output = try FlattenParam.outputOut(outputs: opDesc.outputs, from: inScope)
axis = try FlattenParam.getAttr(key: "axis", attrs: opDesc.attrs)
} catch let error {
throw error
}
}
let input: Texture<P>
var output: Texture<P>
let axis: Int
}
class FlattenOp<P: PrecisionType>: Operator<FlattenKernel<P>, FlattenParam<P>>, Runable, Creator, InferShaperable{
typealias OpType = FlattenOp<P>
......@@ -32,6 +49,9 @@ class FlattenOp<P: PrecisionType>: Operator<ReshapeKernel<P>, ReshapeParam<P>>,
func delogOutput() {
print(" \(type) output: ")
let device = para.output.metalTexture!.device
let outputArray: [Float32] = device.texture2tensor(texture: para.output.metalTexture, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
print(outputArray.strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BatchNormKernel.swift
浏览文件 @ b7a1d552
......@@ -15,20 +15,21 @@
import Foundation
class BatchNormKernel<P: PrecisionType>: Kernel, Computable {
// var newScale: MTLBuffer
// var newBias: MTLBuffer
//
required init(device: MTLDevice, param: BatchNormParam<P>) {
// guard let newScale = device.makeBuffer(length: param.inputScale.buffer.length) else {
// fatalError()
// }
//
// guard let newBias = device.makeBuffer(length: param.inputBias.buffer.length) else {
// fatalError()
// }
// self.newScale = newScale
// self.newBias = newBias
//
let count = param.variance.dim.numel()
let varianceP = param.variance.data.pointer
let meanP = param.mean.data.pointer
let scaleP = param.scale.data.pointer
let biasP = param.bias.data.pointer
for i in 0..<count {
let invStd = P(1 / (Float32(varianceP[i]) + param.epsilon).squareRoot())
biasP[i] = biasP[i] - meanP[i] * invStd * scaleP[i]
scaleP[i] = invStd * scaleP[i]
}
param.bias.initBuffer(device: device, precision: computePrecision)
param.scale.initBuffer(device: device, precision: computePrecision)
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "batchnorm")
} else if computePrecision == .Float16 {
......@@ -36,37 +37,16 @@ class BatchNormKernel<P: PrecisionType>: Kernel, Computable {
} else {
fatalError()
}
//
// let varianceBuffer : MTLBuffer = param.inputVariance.buffer
//
// var invStd: [Float32] = Array(repeating: 0, count: varianceBuffer.length)
// let varianceContents = varianceBuffer.contents().assumingMemoryBound(to: P.self)
// for i in 0..<(varianceBuffer.length / MemoryLayout<P>.stride) {
// invStd[i] = 1 / (Float32(varianceContents[i]) + param.epsilon).squareRoot()
// }
//
// let newScaleContents = newScale.contents().assumingMemoryBound(to: P.self)
// let newBiasContents = newBias.contents().assumingMemoryBound(to: P.self)
// let scale : MTLBuffer = param.inputScale.buffer
// let scaleContents = scale.contents().assumingMemoryBound(to: P.self)
// let bias : MTLBuffer = param.inputBias.buffer
// let biasContents = bias.contents().assumingMemoryBound(to: P.self)
// let meanContents = param.inputMean.buffer.contents().assumingMemoryBound(to: P.self)
//
// for i in 0..<(newScale.length / MemoryLayout<P>.stride) {
// newScaleContents[i] = P(invStd[i] * Float32(scaleContents[i]))
// newBiasContents[i] = P(Float32(biasContents[i]) - Float32(meanContents[i]) * invStd[i] * Float32(scaleContents[i]))
// }
}
func compute(commandBuffer: MTLCommandBuffer, param: BatchNormParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encoder is nil")
}
// encoder.setTexture(param.input.metalTexture, index: 0)
// encoder.setTexture(param.output.metalTexture, index: 1)
// encoder.setBuffer(newScale, offset: 0, index: 0)
// encoder.setBuffer(newBias, offset: 0, index: 1)
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.setBuffer(param.scale.buffer, offset: 0, index: 0)
encoder.setBuffer(param.bias.buffer, offset: 0, index: 1)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BilinearInterpKernel.swift
浏览文件 @ b7a1d552
......@@ -27,10 +27,16 @@ class BilinearInterpKernel<P: PrecisionType>: Kernel, Computable{
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
let ratio_h: Float32 = Float32(param.input.tensorDim.dims[2]) / Float32(param.output.tensorDim.dims[2])
let ratio_w: Float32 = Float32(param.input.tensorDim.dims[3]) / Float32(param.output.tensorDim.dims[3])
var ratio_h: Float32 = 0
var ratio_w: Float32 = 0
if param.output.tensorDim.dims[2] > 1 {
ratio_h = Float32(param.input.tensorDim.dims[2]-1) / Float32(param.output.tensorDim.dims[2]-1)
}
if param.output.tensorDim.dims[3] > 1 {
ratio_w = Float32(param.input.tensorDim.dims[3]-1) / Float32(param.output.tensorDim.dims[3]-1)
}
var p = BilinearInterpMetalParam.init(ratio_h: ratio_h, ratio_w: ratio_w)
encoder.setBytes(&p, length: MemoryLayout<ConcatMetalParam>.size, index: 0)
encoder.setBytes(&p, length: MemoryLayout<BilinearInterpMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
......@@ -38,7 +44,7 @@ class BilinearInterpKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: BilinearInterpParam<P>) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "bilinear_interp")
super.init(device: device, inFunctionName: "bilinear_interp_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "bilinear_interp_half")
} else {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BoxcoderKernel.swift
浏览文件 @ b7a1d552
......@@ -33,9 +33,9 @@ class BoxcoderKernel<P: PrecisionType>: Kernel, Computable{
}
required init(device: MTLDevice, param: BoxcoderParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
param.output.initTexture(device: device, inTranspose: [0, 3, 1, 2], computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "boxcoder")
super.init(device: device, inFunctionName: "boxcoder_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "boxcoder_half")
} else {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConcatKernel.swift
浏览文件 @ b7a1d552
......@@ -31,101 +31,111 @@ struct ConcatMetalParam {
}
class ConcatKernel<P: PrecisionType>: Kernel, Computable{
var v = "normal"
var pm = ConcatMetalParam.init()
func compute(commandBuffer: MTLCommandBuffer, param: ConcatParam<P>) throws {
func encodeTest(_ cmdBuffer: MTLCommandBuffer, _ param: ConcatTestParam, _ istart: Int, _ iend: Int) {
let encoder = cmdBuffer.makeComputeCommandEncoder()!
var p = ConcatMetalParam.init()
var odim: [Int32] = [1, 1, 1, 1]
for i in 0..<param.odim.count {
odim[4-param.odim.count+i] = Int32(param.odim[i])
}
p.odim = (odim[0], odim[1], odim[2], odim[3])
p.axis = Int32(4 - param.odim.count + param.axis)
for i in 0..<istart {
p.offset += Int32(param.dims[i][param.axis])
}
var vdim: [Int32] = []
for i in 0..<(iend - istart) {
encoder.setTexture(param.input[i+istart], index: i)
vdim.append(Int32(param.dims[i+istart][Int(param.axis)]))
}
for i in (iend-istart)..<6 {
encoder.setTexture(param.input[0], index: i)
vdim.append(0)
}
p.vdim = (vdim[0], vdim[1], vdim[2], vdim[3], vdim[4], vdim[5])
encoder.setTexture(param.output, index: 6)
encoder.setTexture(param.output, index: 7)
encoder.setBytes(&p, length: MemoryLayout<ConcatMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output)
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
let num = param.input.count
for i in 0..<num {
encoder.setTexture(param.input[i].metalTexture, index: i)
}
encoder.setTexture(param.output.metalTexture, index: num)
if v == "normal" {
encoder.setTexture(param.output.metalTexture, index: num + 1)
}
encoder.setBytes(&pm, length: MemoryLayout<ConcatMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
func encode(_ cmdBuffer: MTLCommandBuffer, _ param: ConcatParam<P>, _ istart: Int, _ iend: Int) throws {
guard let encoder = cmdBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
var p = ConcatMetalParam.init()
let odim = (0..<4).map { Int32(param.output.dim[$0]) }
p.odim = (odim[0], odim[1], odim[2], odim[3])
p.axis = Int32(4 - param.output.tensorDim.cout() + param.axis)
required init(device: MTLDevice, param: ConcatParam<P>) {
param.output.initTexture(device: device, inTranspose: param.transpose, computePrecision: computePrecision)
let orank = param.output.tensorDim.cout()
let num = param.input.count
assert(num <= 6)
var axis = 4 - param.output.tensorDim.cout() + param.axis
for i in 0..<4 {
if Int32(param.transpose[i]) == p.axis {
p.axis = Int32(i)
if param.transpose[i] == axis {
axis = i
break
}
}
for i in 0..<istart {
p.offset += Int32(param.input[i+istart].dim[Int(p.axis)])
pm.axis = Int32(axis)
pm.odim = (Int32(param.output.dim[0]), Int32(param.output.dim[1]), Int32(param.output.dim[2]), Int32(param.output.dim[3]))
pm.trans = (Int32(param.output.transpose[0]), Int32(param.output.transpose[1]), Int32(param.output.transpose[2]), Int32(param.output.transpose[3]))
var vdim: [Int] = [0, 0, 0, 0, 0, 0]
for i in 0..<num {
vdim[i] = param.input[i].dim[axis]
}
var vdim: [Int32] = []
for i in 0..<(iend - istart) {
encoder.setTexture(param.input[i+istart].metalTexture, index: i)
vdim.append(Int32(param.input[i+istart].dim[Int(p.axis)]))
if orank == 4 {
if axis == 1 {
v = "y"
} else if axis == 2 {
v = "x"
} else {
if (param.output.dim[0] == 1) && axis == 3 {
var vz = true
for i in 0..<num {
if vdim[i] % 4 != 0 {
vz = false
break
}
for i in (iend-istart)..<6 {
encoder.setTexture(param.input[0].metalTexture, index: i)
vdim.append(0)
}
p.trans = (Int32(param.transpose[0]), Int32(param.transpose[1]), Int32(param.transpose[2]), Int32(param.transpose[3]))
p.vdim = (vdim[0], vdim[1], vdim[2], vdim[3], vdim[4], vdim[5])
encoder.setTexture(param.output.metalTexture, index: 6)
encoder.setTexture(param.output.metalTexture, index: 7)
encoder.setBytes(&p, length: MemoryLayout<ConcatMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
if vz {
v = "z"
for i in 0..<num {
vdim[i] = vdim[i] / 4
}
func compute(commandBuffer: MTLCommandBuffer, param: ConcatParam<P>) throws {
let group = param.input.count / 6
let remain = param.input.count % 6
for i in 0..<group {
try self.encode(commandBuffer, param, 6 * i, 6 * (i + 1))
}
if remain > 0 {
try self.encode(commandBuffer, param, 6 * group, param.input.count)
}
}
func test(cmdBuffer: MTLCommandBuffer, param: ConcatTestParam) {
let group = param.input.count / 6
let remain = param.input.count % 6
for i in 0..<group {
self.encodeTest(cmdBuffer, param, 6 * i, 6 * (i + 1))
} else if orank == 3 {
if axis == 2 {
v = "y"
} else if axis == 3 {
v = "x"
} else if axis == 1 {
var vz = true
for i in 0..<num {
if vdim[i] % 4 != 0 {
vz = false
break
}
if remain > 0 {
self.encodeTest(cmdBuffer, param, 6 * group, param.input.count)
}
if vz {
v = "z"
for i in 0..<num {
vdim[i] = vdim[i] / 4
}
required init(device: MTLDevice, param: ConcatParam<P>) {
param.output.initTexture(device: device, inTranspose: param.transpose, computePrecision: computePrecision)
}
}
} else {
if axis == 2 {
v = "y"
} else if axis == 3 {
var vx = true
for i in 0..<num {
if vdim[i] % 4 != 0 {
vx = false
break
}
}
if vx {
v = "x"
for i in 0..<num {
vdim[i] = vdim[i] / 4
}
}
}
}
pm.vdim = (Int32(vdim[0]), Int32(vdim[1]), Int32(vdim[2]), Int32(vdim[3]), Int32(vdim[4]), Int32(vdim[5]))
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "concat")
super.init(device: device, inFunctionName: "concat_\(orank)_\(num)_\(v)_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "concat_half")
super.init(device: device, inFunctionName: "concat_\(orank)_\(num)_\(v)_half")
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddPreluKernel.swift 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
import Foundation
class ConvAddPreluKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddPreluParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.y.initBuffer(device: device, precision: computePrecision)
param.alpha.initBuffer(device: device, precision: computePrecision)
if computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_half")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_half")
} else {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_half")
}
} else if param.filter.channel == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_half")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_half")
} else {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_half")
}
} else if param.filter.width == 3 && param.filter.height == 3 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_half")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_half")
} else {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_half")
}
} else if param.filter.width == 1 && param.filter.height == 5 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_half")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_half")
} else {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_half")
}
} else if param.filter.width == 5 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_half")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_half")
} else {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_half")
}
} else {
fatalError(" unsupport yet ")
}
} else if computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_float")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_float")
} else {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_float")
}
} else if param.filter.channel == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_float")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_float")
} else {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_float")
}
} else if param.filter.width == 3 && param.filter.height == 3 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_float")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_float")
} else {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_float")
}
} else if param.filter.width == 1 && param.filter.height == 5 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_float")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_float")
} else {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_float")
}
} else if param.filter.width == 5 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_float")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_float")
} else {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_float")
}
} else {
fatalError(" unsupport yet ")
}
} else {
fatalError()
}
let offsetY = (Int(param.dilations[1]) * (param.filter.height - 1) + 1)/2 - Int(param.paddings[1])
let offsetX = (Int(param.dilations[0]) * (param.filter.width - 1) + 1)/2 - Int(param.paddings[0])
// print(" function: \(functionName)")
// print("offset x: \(offsetX)")
// print("offset y: \(offsetY)")
let offsetZ = 0.0
let inMetalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), dilationX: UInt16(param.dilations[0]), dilationY: UInt16(param.dilations[1]))
// print("metal param: ")
// print(inMetalParam)
metalParam = inMetalParam
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvAddPreluParam<P>) throws {
// guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
// throw PaddleMobileError.predictError(message: " encode is nil")
// }
//
// encoder.setTexture(param.input.metalTexture, index: 0)
// encoder.setTexture(param.output.metalTexture, index: 1)
// encoder.setBytes(&metalParam, length: MemoryLayout<MetalConvParam>.size, index: 0)
// encoder.setBuffer(param.filter.buffer, offset: 0, index: 1)
// encoder.setBuffer(param.y.buffer, offset: 0, index: 2)
// encoder.setBuffer(param.alpha.buffer, offset: 0, index: 3)
// encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
// encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/FlattenKernel.swift 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
import Foundation
struct FlattenMetalParam {
var idim: (Int32, Int32, Int32, Int32)
var itrans: (Int32, Int32, Int32, Int32)
var odim: (Int32, Int32, Int32, Int32)
var otrans: (Int32, Int32, Int32, Int32)
}
class FlattenKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: FlattenMetalParam
required init(device: MTLDevice, param: FlattenParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
var id: [Int32] = [1, 1, 1, 1]
for i in 0..<param.input.tensorDim.cout() {
id[4-param.input.tensorDim.cout()+i] = Int32(param.input.tensorDim[i])
}
let it: [Int32] = param.input.transpose.map { Int32($0) }
var od: [Int32] = [1, 1, 1, 1]
for i in 0..<param.output.tensorDim.cout() {
od[4-param.output.tensorDim.cout()+i] = Int32(param.output.tensorDim[i])
}
let ot: [Int32] = param.output.transpose.map { Int32($0) }
metalParam = FlattenMetalParam.init(
idim: (id[0], id[1], id[2], id[3]),
itrans: (it[0], it[1], it[2], it[3]),
odim: (od[0], od[1], od[2], od[3]),
otrans: (ot[0], ot[1], ot[2], ot[3])
)
let irank = param.input.tensorDim.cout()
let orank = param.output.tensorDim.cout()
assert(orank == 2)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "reshape_\(irank)_2_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "reshape_\(irank)_2_half")
} else {
fatalError()
}
}
func compute(commandBuffer: MTLCommandBuffer, param: FlattenParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encoder is nil")
}
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.setBytes(&metalParam, length: MemoryLayout<ReshapeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/MulticlassNMSKernel.swift
浏览文件 @ b7a1d552
......@@ -15,11 +15,41 @@
import Foundation
class MulticlassNMSKernel<P: PrecisionType>: Kernel, Computable{
let pipline1: MTLComputePipelineState
required init(device: MTLDevice, param: MulticlassNMSParam<P>) {
super.init(device: device, inFunctionName: "place_holder")
param.middleOutput.initBuffer(device: device)
param.bboxOutput.initBuffer(device: device)
if computePrecision == .Float32 {
pipline1 = device.pipeLine(funcName: "nms_fetch_bbox", inPaddleMobileLib: true)
super.init(device: device, inFunctionName: "nms_fetch_result")
} else if computePrecision == .Float16 {
pipline1 = device.pipeLine(funcName: "nms_fetch_bbox_half", inPaddleMobileLib: true)
super.init(device: device, inFunctionName: "nms_fetch_result_half")
} else {
fatalError( " unsupport precision " )
}
}
func compute(commandBuffer: MTLCommandBuffer, param: MulticlassNMSParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoder.setTexture(param.scores.metalTexture, index: 0)
encoder.setBuffer(param.middleOutput.resultBuffer!, offset: 0, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.scores.metalTexture)
encoder.endEncoding()
guard let encoderBox = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoderBox.setTexture(param.bboxes.metalTexture, index: 0)
encoderBox.setBuffer(param.bboxOutput.resultBuffer!, offset: 0, index: 0)
encoderBox.dispatch(computePipline: pipline1, outTexture: param.bboxes.metalTexture)
encoderBox.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift
浏览文件 @ b7a1d552
......@@ -34,24 +34,44 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: PriorBoxParam<P>) {
param.output.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
let originDim = param.output.tensorDim;
param.output.tensorDim = Dim.init(inDim: [1, originDim[0], originDim[1], originDim[2] * originDim[3]])
param.output.padToFourDim = Dim.init(inDim: [1, originDim[0], originDim[1], originDim[2] * originDim[3]])
param.output.initTexture(device: device, inTranspose: [0, 1, 2, 3], computePrecision: computePrecision)
param.outputVariances.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
if computePrecision == .Float32 {
if param.min_max_aspect_ratios_order {
super.init(device: device, inFunctionName: "prior_box_MinMaxAspectRatiosOrder")
} else {
super.init(device: device, inFunctionName: "prior_box")
}
} else if computePrecision == .Float16 {
if param.min_max_aspect_ratios_order {
super.init(device: device, inFunctionName: "prior_box_MinMaxAspectRatiosOrder_half")
} else {
super.init(device: device, inFunctionName: "prior_box_half")
}
} else {
fatalError()
}
let n = 1
let h = param.output.dim[1]
let w = param.output.dim[2]
let c = param.output.dim[3] * param.output.dim[0]
param.output.dim = Dim.init(inDim: [n, h, w, c])
param.output.transpose = [0, 1, 2, 3]
guard param.minSizes.count == 1 else {
fatalError(" need implement ")
}
// let n = 1
// let h = param.output.dim[1]
// let w = param.output.dim[2]
// let c = param.output.dim[3] * param.output.dim[0]
//
// param.output.dim = Dim.init(inDim: [n, h, w, c])
// param.output.transpose = [0, 1, 2, 3]
let imageWidth = Float32(param.inputImage.padToFourDim[3])
let imageHeight = Float32(param.inputImage.padToFourDim[2])
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ReshapeKernel.swift
浏览文件 @ b7a1d552
......@@ -49,10 +49,12 @@ class ReshapeKernel<P: PrecisionType>: Kernel, Computable{
odim: (od[0], od[1], od[2], od[3]),
otrans: (ot[0], ot[1], ot[2], ot[3])
)
let irank = param.input.tensorDim.cout()
let orank = param.output.tensorDim.cout()
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "reshape")
super.init(device: device, inFunctionName: "reshape_\(irank)_\(orank)_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "reshape_half")
super.init(device: device, inFunctionName: "reshape_\(irank)_\(orank)_half")
} else {
fatalError()
}
......@@ -81,15 +83,15 @@ class ReshapeKernel<P: PrecisionType>: Kernel, Computable{
encoder.endEncoding()
}
func test(commandBuffer: MTLCommandBuffer, testParam: ReshapeTestParam) {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
fatalError()
}
encoder.setTexture(testParam.inputTexture, index: 0)
encoder.setTexture(testParam.outputTexture, index: 1)
var pm: ReshapeMetalParam = testParam.param
encoder.setBytes(&pm, length: MemoryLayout<ReshapeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: testParam.outputTexture)
encoder.endEncoding()
}
// func test(commandBuffer: MTLCommandBuffer, testParam: ReshapeTestParam) {
// guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
// fatalError()
// }
// encoder.setTexture(testParam.inputTexture, index: 0)
// encoder.setTexture(testParam.outputTexture, index: 1)
// var pm: ReshapeMetalParam = testParam.param
// encoder.setBytes(&pm, length: MemoryLayout<ReshapeMetalParam>.size, index: 0)
// encoder.dispatch(computePipline: pipline, outTexture: testParam.outputTexture)
// encoder.endEncoding()
// }
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ShapeKernel.swift
浏览文件 @ b7a1d552
......@@ -19,19 +19,20 @@ struct ShapeMetalParam {
class ShapeKernel<P: PrecisionType>: Kernel, Computable{
func compute(commandBuffer: MTLCommandBuffer, param: ShapeParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoder.setTexture(param.output.metalTexture, index: 0)
encoder.endEncoding()
// print("shape compute")
// guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
// throw PaddleMobileError.predictError(message: " encode is nil")
// }
// encoder.setTexture(param.output.metalTexture, index: 0)
// encoder.endEncoding()
}
required init(device: MTLDevice, param: ShapeParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "split")
super.init(device: device, inFunctionName: "shape")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "split_half")
super.init(device: device, inFunctionName: "shape_half")
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/SoftmaxKernel.swift
浏览文件 @ b7a1d552
......@@ -29,7 +29,7 @@ class SoftmaxKernel<P: PrecisionType>: Kernel, Computable{
K: Int32(param.input.tensorDim[1])
)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "softmax")
super.init(device: device, inFunctionName: "softmax_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "softmax_half")
} else {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/SplitKernel.swift
浏览文件 @ b7a1d552
......@@ -15,23 +15,76 @@
import Foundation
struct SplitMetalParam {
var idim: (Int32, Int32, Int32, Int32) = (1, 1, 1, 1)
var axis: Int32 = 0
var offset: Int32 = 0
var trans: (Int32, Int32, Int32, Int32) = (0, 1, 2, 3)
var vdim: (Int32, Int32, Int32, Int32) = (0, 0, 0, 0)
}
class SplitKernel<P: PrecisionType>: Kernel, Computable{
var smp: SplitMetalParam
func compute(commandBuffer: MTLCommandBuffer, param: SplitParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoder.setTexture(param.output.metalTexture, index: 0)
encoder.setTexture(param.input.metalTexture, index: 0)
for i in 0..<param.outputList.count {
encoder.setTexture(param.outputList[i].metalTexture, index: i + 1)
}
encoder.setBytes(&smp, length: MemoryLayout<SplitMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.input.metalTexture)
encoder.endEncoding()
}
required init(device: MTLDevice, param: SplitParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
// param.output.initTexture(device: device, computePrecision: computePrecision)
let num = param.outputList.count
let rank = param.input.tensorDim.cout()
assert(num >= 2 && num <= 4)
for output in param.outputList {
output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
}
smp = SplitMetalParam.init()
smp.idim = (Int32(param.input.dim[0]), Int32(param.input.dim[1]), Int32(param.input.dim[2]), Int32(param.input.dim[3]))
smp.axis = Int32(param.axis + param.input.dim.cout() - param.input.tensorDim.cout())
for i in 0..<4 {
if param.input.transpose[i] == smp.axis {
smp.axis = Int32(i)
break
}
}
smp.trans = (Int32(param.input.transpose[0]), Int32(param.input.transpose[1]), Int32(param.input.transpose[2]), Int32(param.input.transpose[3]))
var vdim: [Int32] = [0, 0, 0, 0]
for i in 0..<num {
vdim[i] = Int32(param.outputList[i].tensorDim[param.axis])
}
smp.vdim = (vdim[0], vdim[1], vdim[2], vdim[3])
var v = "normal"
if rank == 4 {
if smp.axis == 1 {
v = "y"
} else if smp.axis == 2 {
v = "x"
}
} else if rank == 3 {
if smp.axis == 2 {
v = "y"
} else if smp.axis == 3 {
v = "x"
}
} else if rank == 2 {
if smp.axis == 2 {
v = "y"
}
}
if v == "normal" {
fatalError("split unsupported")
}
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "split")
super.init(device: device, inFunctionName: "split_\(rank)_\(num)_\(v)_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "split_half")
super.init(device: device, inFunctionName: "split_\(rank)_\(num)_\(v)_half")
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/TransposeKernel.swift
浏览文件 @ b7a1d552
......@@ -17,73 +17,52 @@ import Foundation
struct TransposeMetalParam {
var iC: Int32 = 0
var oC: Int32 = 0
var i0: Int32
var i1: Int32
var i2: Int32
var i3: Int32
init(_ i0: Int32, _ i1: Int32, _ i2: Int32, _ i3: Int32) {
self.i0 = i0
self.i1 = i1
self.i2 = i2
self.i3 = i3
}
init(_ axis: [Int]) {
self.init(Int32(axis[0]), Int32(axis[1]), Int32(axis[2]), Int32(axis[3]))
}
}
struct TransposeTestParam: TestParam {
let inputTexture: MTLTexture
let outputTexture: MTLTexture
let iC: Int
let oC: Int
let axis: [Int]
var axis: (Int32, Int32, Int32, Int32) = (0, 1, 2, 3)
}
class TransposeKernel<P: PrecisionType>: Kernel, Computable, Testable {
class TransposeKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: TransposeMetalParam = TransposeMetalParam.init()
required init(device: MTLDevice, param: TransposeParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 1, 2, 3], computePrecision: computePrecision)
if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "transpose_half")
} else if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "transpose")
} else {
fatalError()
}
var invT: [Int] = [0, 1, 2, 3]
for (i, v) in param.input.transpose.enumerated() {
invT[v] = i
}
param.output.initTexture(device: device, computePrecision: computePrecision)
let rank = param.input.tensorDim.cout()
var axis: [Int] = [0, 1, 2, 3]
for i in 0..<param.axis.count {
axis[4-param.axis.count+i] = 4 - param.axis.count + Int(param.axis[i])
axis[4-rank+i] = 4 - rank + Int(param.axis[i])
}
let realAxis = axis.map {invT[$0]}
var tmp = TransposeMetalParam.init(realAxis)
tmp.iC = Int32(param.input.dim[param.input.transpose[3]])
tmp.oC = Int32(param.output.dim[3])
if realAxis == [0, 1, 2, 3] {
// print("====> transpose! FAST :)")
} else {
// print("====> transpose! SLOW :(")
var naxis: [Int] = [0, 0, 0, 0]
for i in 0..<4 {
for j in 0..<4 {
if param.input.transpose[j] == axis[i] {
naxis[i] = j
break
}
metalParam = tmp
}
required init(device: MTLDevice, testParam: TransposeTestParam) {
}
metalParam.iC = Int32(param.input.dim[param.input.transpose[3]])
metalParam.oC = Int32(param.output.dim[3])
metalParam.axis = (Int32(naxis[0]), Int32(naxis[1]), Int32(naxis[2]), Int32(naxis[3]))
var kernelFunc = "transpose_undefined"
if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "transpose_half")
if param.input.transpose == axis {
kernelFunc = "transpose_copy_half"
} else {
kernelFunc = "transpose_\(rank)_half"
}
} else if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "transpose")
if param.input.transpose == axis {
kernelFunc = "transpose_copy_float"
} else {
kernelFunc = "transpose_\(rank)_float"
}
} else {
fatalError()
}
print("===========>", kernelFunc)
print(metalParam)
super.init(device: device, inFunctionName: kernelFunc)
}
var metalParam: TransposeMetalParam!
func compute(commandBuffer: MTLCommandBuffer, param: TransposeParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
......@@ -97,18 +76,4 @@ class TransposeKernel<P: PrecisionType>: Kernel, Computable, Testable {
}
public func test(commandBuffer: MTLCommandBuffer, param: TransposeTestParam) {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
fatalError()
}
encoder.setTexture(param.inputTexture, index: 0)
encoder.setTexture(param.outputTexture, index: 1)
var tmp = TransposeMetalParam.init(param.axis)
tmp.iC = Int32(param.iC)
tmp.oC = Int32(param.oC)
encoder.setBytes(&tmp, length: MemoryLayout<TransposeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.outputTexture)
encoder.endEncoding()
}}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/BatchNormKernel.metal
浏览文件 @ b7a1d552
......@@ -15,28 +15,28 @@
#include <metal_stdlib>
using namespace metal;
kernel void batchnorm_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half4 * newScale [[buffer(0)]],
const device half4 * newBias [[buffer(1)]],
kernel void batchnorm(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 * nscale [[buffer(0)]],
const device float4 * nbias [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
const half4 input = inTexture.read(gid.xy, gid.z);
half4 output = input * newScale[gid.z] + newBias[gid.z];
const float4 input = inTexture.read(gid.xy, gid.z);
float4 output = input * nscale[gid.z] + nbias[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void batchnorm(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 * newScale [[buffer(0)]],
const device float4 * newBias [[buffer(1)]],
kernel void batchnorm_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half4 * newScale [[buffer(0)]],
const device half4 * newBias [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
const float4 input = inTexture.read(gid.xy, gid.z);
float4 output = input * newScale[gid.z] + newBias[gid.z];
const half4 input = inTexture.read(gid.xy, gid.z);
half4 output = input * newScale[gid.z] + newBias[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/BilinearInterp.inc.metal 0 → 100644
浏览文件 @ b7a1d552
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define FUNC(f, p) CONCAT2_(f, p)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC(bilinear_interp, P)(texture2d_array<P, access::read> input [[texture(0)]],
texture2d_array<P, access::write> output [[texture(1)]],
constant bilinear_interp_param & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) r;
if ((input.get_width() == output.get_width()) && (input.get_height() == output.get_height())) {
r = input.read(gid.xy, gid.z);
} else {
P w = gid.x * pm.ratio_w;
P h = gid.y * pm.ratio_h;
uint w0 = w, h0 = h;
uint w1 = w0 + 1, h1 = h0 + 1;
P w1lambda = w - w0, h1lambda = h - h0;
P w2lambda = 1.0 - w1lambda, h2lambda = 1.0 - h1lambda;
if (w1 >= input.get_width()) w1 = w0;
if (h1 >= input.get_height()) h1 = h0;
VECTOR(P, 4) r0 = input.read(uint2(w0, h0), gid.z);
VECTOR(P, 4) r1 = input.read(uint2(w1, h0), gid.z);
VECTOR(P, 4) r2 = input.read(uint2(w0, h1), gid.z);
VECTOR(P, 4) r3 = input.read(uint2(w1, h1), gid.z);
r = h2lambda * (w2lambda * r0 + w1lambda * r1)
+ h1lambda * (w2lambda * r2 + w1lambda * r3);
}
output.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/BilinearInterp.metal
浏览文件 @ b7a1d552
......@@ -16,60 +16,14 @@
using namespace metal;
struct bilinear_interp_param {
// int32_t out_h;
// int32_t out_w;
float ratio_h;
float ratio_w;
};
kernel void bilinear_interp(texture2d_array<float, access::read> input [[texture(0)]],
texture2d_array<float, access::write> output [[texture(2)]],
constant bilinear_interp_param & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
float4 r;
if ((input.get_width() == output.get_width()) && (input.get_height() == output.get_height())) {
r = input.read(gid.xy, gid.z);
} else {
float w = gid.x * pm.ratio_w;
float h = gid.y * pm.ratio_h;
uint w0 = w, h0 = h;
uint w1 = w0 + 1, h1 = h0 + 1;
float w1lambda = w - w0, h1lambda = h - h0;
float w2lambda = 1.0 - w1lambda, h2lambda = 1.0 - h1lambda;
if (w1 >= input.get_width()) w1 = w0;
if (h1 >= input.get_height()) h1 = h0;
float4 r0 = input.read(uint2(w0, h0), gid.z);
float4 r1 = input.read(uint2(w1, h0), gid.z);
float4 r2 = input.read(uint2(w0, h1), gid.z);
float4 r3 = input.read(uint2(w1, h1), gid.z);
r = h2lambda * (w2lambda * r0 + w1lambda * r1) + h1lambda * (w2lambda * r2 + w1lambda * r3);
}
output.write(r, gid.xy, gid.z);
}
#define P float
#include "BilinearInterp.inc.metal"
#undef P
kernel void bilinear_interp_half(texture2d_array<half, access::read> input [[texture(0)]],
texture2d_array<half, access::write> output [[texture(2)]],
constant bilinear_interp_param & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
half4 r;
if ((input.get_width() == output.get_width()) && (input.get_height() == output.get_height())) {
r = input.read(gid.xy, gid.z);
} else {
half w = gid.x * pm.ratio_w;
half h = gid.y * pm.ratio_h;
uint w0 = w, h0 = h;
uint w1 = w0 + 1, h1 = h0 + 1;
half w1lambda = w - w0, h1lambda = h - h0;
half w2lambda = 1.0 - w1lambda, h2lambda = 1.0 - h1lambda;
if (w1 >= input.get_width()) w1 = w0;
if (h1 >= input.get_height()) h1 = h0;
half4 r0 = input.read(uint2(w0, h0), gid.z);
half4 r1 = input.read(uint2(w1, h0), gid.z);
half4 r2 = input.read(uint2(w0, h1), gid.z);
half4 r3 = input.read(uint2(w1, h1), gid.z);
r = h2lambda * (w2lambda * r0 + w1lambda * r1) + h1lambda * (w2lambda * r2 + w1lambda * r3);
}
output.write(r, gid.xy, gid.z);
output.write(r, gid.xy, gid.z);
}
#define P half
#include "BilinearInterp.inc.metal"
#undef P
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/BoxCoder.inc.metal 0 → 100644
浏览文件 @ b7a1d552
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define FUNC(f, p) CONCAT2_(f, p)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC(boxcoder, P)(texture2d_array<P, access::read> priorBox [[texture(0)]],
texture2d_array<P, access::read> priorBoxVar [[texture(1)]],
texture2d_array<P, access::read> targetBox [[texture(2)]],
texture2d_array<P, access::write> output[[texture(3)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) p = priorBox.read(uint2(0, gid.x), gid.z);
VECTOR(P, 4) pv = priorBoxVar.read(uint2(0, gid.x), gid.z);
VECTOR(P, 4) t;
t[0] = targetBox.read(uint2(0, gid.x), gid.z)[0];
t[1] = targetBox.read(uint2(1, gid.x), gid.z)[0];
t[2] = targetBox.read(uint2(2, gid.x), gid.z)[0];
t[3] = targetBox.read(uint2(3, gid.x), gid.z)[0];
P px = (p.x + p.z) / 2;
P py = (p.y + p.w) / 2;
P pw = p.z - p.x;
P ph = p.w - p.y;
P tx = pv.x * t.x * pw + px;
P ty = pv.y * t.y * ph + py;
P tw = exp(pv.z * t.z) * pw;
P th = exp(pv.w * t.w) * ph;
VECTOR(P, 4) r;
r.x = tx - tw / 2;
r.y = ty - th / 2;
r.z = tx + tw / 2;
r.w = ty + th / 2;
output.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/BoxCoder.metal
浏览文件 @ b7a1d552
......@@ -15,58 +15,9 @@
#include <metal_stdlib>
using namespace metal;
kernel void boxcoder(texture2d_array<float, access::read> priorBox [[texture(0)]],
texture2d_array<float, access::read> priorBoxVar [[texture(1)]],
texture2d_array<float, access::read> targetBox [[texture(2)]],
texture2d_array<float, access::write> output[[texture(3)]],
uint3 gid [[thread_position_in_grid]]) {
float4 t = targetBox.read(gid.xy, gid.z);
float4 p = priorBox.read(gid.xy, gid.z);
float4 pv = priorBoxVar.read(gid.xy, gid.z);
float px = (p.x + p.z) / 2;
float py = (p.y + p.w) / 2;
float pw = p.z - p.x;
float ph = p.w - p.y;
float tx = pv.x * t.x * pw + px;
float ty = pv.y * t.y * ph + py;
float tw = exp(pv.z * t.z) * pw;
float th = exp(pv.w * t.w) * ph;
float4 r;
r.x = tx - tw / 2;
r.y = ty - th / 2;
r.z = tx + tw / 2;
r.w = ty + th / 2;
output.write(r, gid.xy, gid.z);
}
kernel void boxcoder_half(texture2d_array<half, access::read> priorBox [[texture(0)]],
texture2d_array<half, access::read> priorBoxVar [[texture(1)]],
texture2d_array<half, access::read> targetBox [[texture(2)]],
texture2d_array<half, access::write> output[[texture(3)]],
uint3 gid [[thread_position_in_grid]]) {
half4 t = targetBox.read(gid.xy, gid.z);
half4 p = priorBox.read(gid.xy, gid.z);
half4 pv = priorBoxVar.read(gid.xy, gid.z);
float px = (float(p.x) + float(p.z)) / 2;
float py = (float(p.y) + float(p.w)) / 2;
float pw = float(p.z) - float(p.x);
float ph = float(p.w) - float(p.y);
float tx = float(pv.x) * float(t.x) * pw + px;
float ty = float(pv.y) * float(t.y) * ph + py;
float tw = exp(float(pv.z) * float(t.z)) * pw;
float th = exp(float(pv.w) * float(t.w)) * ph;
float4 r;
r.x = tx - tw / 2;
r.y = ty - th / 2;
r.z = tx + tw / 2;
r.w = ty + th / 2;
output.write(half4(r), gid.xy, gid.z);
}
#define P float
#include "BoxCoder.inc.metal"
#undef P
#define P half
#include "BoxCoder.inc.metal"
#undef P
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Common.metal
浏览文件 @ b7a1d552
......@@ -15,6 +15,55 @@
#include <metal_stdlib>
using namespace metal;
inline void xyzn2abcd_1(int xyzn[4], int abcd[4]) {
abcd[0] = abcd[1] = abcd[2] = 0;
abcd[3] = xyzn[0] * 4 + xyzn[3];
}
inline void xyzn2abcd_2(int xyzn[4], int abcd[4]) {
abcd[0] = abcd[1] = 0;
abcd[2] = xyzn[1];
abcd[3] = xyzn[0] * 4 + xyzn[3];
}
inline void xyzn2abcd_3(int xyzn[4], int abcd[4]) {
abcd[0] = 0;
abcd[3] = xyzn[0];
abcd[2] = xyzn[1];
abcd[1] = xyzn[2] * 4 + xyzn[3];
}
inline void xyzn2abcd_4(int C, int xyzn[4], int abcd[4]) {
abcd[2] = xyzn[0];
abcd[1] = xyzn[1];
uint t = xyzn[2] * 4 + xyzn[3];
abcd[0] = t / C;
abcd[3] = t % C;
}
inline void abcd2xyzn_1(int abcd[4], int xyzn[4]) {
xyzn[1] = xyzn[2] = 0;
xyzn[0] = abcd[3] / 4;
xyzn[1] = abcd[3] % 4;
}
inline void abcd2xyzn_2(int abcd[4], int xyzn[4]) {
xyzn[2] = 0;
xyzn[1] = abcd[2];
xyzn[0] = abcd[3] / 4;
xyzn[3] = abcd[3] % 4;
}
inline void abcd2xyzn_3(int abcd[4], int xyzn[4]) {
xyzn[0] = abcd[3];
xyzn[1] = abcd[2];
xyzn[2] = abcd[1] / 4;
xyzn[3] = abcd[1] % 4;
}
inline void abcd2xyzn_4(int C, int abcd[4], int xyzn[4]) {
xyzn[0] = abcd[2];
xyzn[1] = abcd[1];
uint t = abcd[0] * C + abcd[3];
xyzn[2] = t / 4;
xyzn[3] = t % 4;
}
inline void xyzn2abcd(int C, int xyzn[4], int abcd[4]) {
abcd[2] = xyzn[0];
abcd[1] = xyzn[1];
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Concat.metal 已删除 100644 → 0
浏览文件 @ 13c9388c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct ConcatParam {
int32_t odim[4];
int32_t axis;
int32_t offset;
int32_t trans[4];
int32_t vdim[6];
};
kernel void concat(texture2d_array<float, access::read> in0 [[texture(0)]],
texture2d_array<float, access::read> in1 [[texture(1)]],
texture2d_array<float, access::read> in2 [[texture(2)]],
texture2d_array<float, access::read> in3 [[texture(3)]],
texture2d_array<float, access::read> in4 [[texture(4)]],
texture2d_array<float, access::read> in5 [[texture(5)]],
texture2d_array<float, access::read> inx [[texture(6)]],
texture2d_array<float, access::write> out [[texture(7)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
ConcatParam cp = pm;
int xyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, abcd[4], oxyzn[4];
float4 r;
for (int i = 0; i < 4; i++) {
xyzn[3] = i;
xyzn2abcd(cp.odim[3], xyzn, abcd);
int k = abcd[cp.axis] - cp.offset;
int j = 0;
if (k < 0) {
r[i] = inx.read(gid.xy, gid.z)[i];
} else {
for (; j < 6; j++) {
if (k < cp.vdim[j]) {
break;
}
k -= cp.vdim[j];
}
int ta = cp.odim[cp.axis];
abcd[cp.axis] = k;
cp.odim[cp.axis] = cp.vdim[j];
abcd2xyzn(cp.odim[3], abcd, oxyzn);
cp.odim[cp.axis] = ta;
switch (j) {
case 0: r[i] = in0.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 1: r[i] = in1.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 2: r[i] = in2.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 3: r[i] = in3.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 4: r[i] = in4.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 5: r[i] = in5.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
}
}
}
out.write(r, gid.xy, gid.z);
}
kernel void concat_half(texture2d_array<half, access::read> in0 [[texture(0)]],
texture2d_array<half, access::read> in1 [[texture(1)]],
texture2d_array<half, access::read> in2 [[texture(2)]],
texture2d_array<half, access::read> in3 [[texture(3)]],
texture2d_array<half, access::read> in4 [[texture(4)]],
texture2d_array<half, access::read> in5 [[texture(5)]],
texture2d_array<half, access::read> inx [[texture(6)]],
texture2d_array<half, access::write> out [[texture(7)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
ConcatParam cp = pm;
int xyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, abcd[4], oxyzn[4];
half4 r;
for (int i = 0; i < 4; i++) {
xyzn[3] = i;
xyzn2abcd(cp.odim[3], xyzn, abcd);
int k = abcd[cp.axis] - cp.offset;
int j = 0;
if (k < 0) {
r[i] = inx.read(gid.xy, gid.z)[i];
} else {
for (; j < 6; j++) {
if (k < cp.vdim[j]) {
break;
}
k -= cp.vdim[j];
}
int ta = cp.odim[cp.axis];
abcd[cp.axis] = k;
cp.odim[cp.axis] = cp.vdim[j];
abcd2xyzn(cp.odim[3], abcd, oxyzn);
cp.odim[cp.axis] = ta;
switch (j) {
case 0: r[i] = in0.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 1: r[i] = in1.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 2: r[i] = in2.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 3: r[i] = in3.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 4: r[i] = in4.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 5: r[i] = in5.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
}
}
}
out.write(r, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConcatKernel.inc.metal 0 → 100644
浏览文件 @ b7a1d552
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT4_(a, b, c, d) a ## _ ## b ## _ ## c ## _ ## d
#define CONCAT5_(a, b, c, d, e) a ## _ ## b ## _ ## c ## _ ## d ## _ ## e
#define FUNC(f, r, n, v, p) CONCAT5_(f, r, n, v, p)
#define VECTOR(p, n) CONCAT2(p, n)
#define FUNC_R(f, r) CONCAT2_(f, r)
#if V == VX
#define VV x
#elif V == VY
#define VV y
#elif V == VZ
#define VV z
#else
#define VV normal
#endif
#if V == VNORMAL
//kernel void FUNC(concat, R, N, normal, P)(array<texture2d_array<P, access::read>, N> in [[texture(0)]],
// texture2d_array<P, access::read> out_x [[texture(N)]],
// texture2d_array<P, access::write> out [[texture(N+1)]],
// constant ConcatParam & pm [[buffer(0)]],
// uint3 gid [[thread_position_in_grid]]) {
//}
kernel void FUNC(concat, R, N, VV, P)(texture2d_array<P, access::read> in0 [[texture(0)]],
texture2d_array<P, access::read> in1 [[texture(1)]],
#if N >= 3
texture2d_array<P, access::read> in2 [[texture(2)]],
#endif
#if N >= 4
texture2d_array<P, access::read> in3 [[texture(3)]],
#endif
#if N >= 5
texture2d_array<P, access::read> in4 [[texture(4)]],
#endif
#if N >= 6
texture2d_array<P, access::read> in5 [[texture(5)]],
#endif
texture2d_array<P, access::read> inx [[texture(N)]],
texture2d_array<P, access::write> out [[texture(N+1)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
ConcatParam cp = pm;
int xyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, abcd[4], oxyzn[4];
VECTOR(P, 4) r = inx.read(gid.xy, gid.z);
for (int i = 0; i < 4; i++) {
xyzn[3] = i;
#if R == 4
xyzn2abcd_4(cp.odim[3], xyzn, abcd);
#else
FUNC_R(xyzn2abcd, R)(xyzn, abcd);
#endif
int k = abcd[cp.axis] - cp.offset;
if (k < 0) continue;
int j = 0;
for (; j < N; j++) {
if (k < cp.vdim[j]) {
break;
}
k -= cp.vdim[j];
}
if (k > cp.vdim[N-1]) {
continue;
}
int ta = cp.odim[cp.axis];
abcd[cp.axis] = k;
cp.odim[cp.axis] = cp.vdim[j];
#if R == 4
abcd2xyzn_4(cp.odim[3], abcd, oxyzn);
#else
FUNC_R(abcd2xyzn, R)(abcd, oxyzn);
#endif
cp.odim[cp.axis] = ta;
switch (j) {
case 0: r[i] = in0.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 1: r[i] = in1.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#if N >= 3
case 2: r[i] = in2.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#endif
#if N >= 4
case 3: r[i] = in3.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#endif
#if N >= 5
case 4: r[i] = in4.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#endif
#if N >= 6
case 5: r[i] = in5.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#endif
}
}
out.write(r, gid.xy, gid.z);
}
#endif // V == NORMAL
#if V == VX
kernel void FUNC(concat, R, N, VV, P)(texture2d_array<P, access::read> in0 [[texture(0)]],
texture2d_array<P, access::read> in1 [[texture(1)]],
#if N >= 3
texture2d_array<P, access::read> in2 [[texture(2)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::read> in3 [[texture(3)]],
#endif // N >= 4
#if N >= 5
texture2d_array<P, access::read> in4 [[texture(4)]],
#endif // N >= 5
#if N >= 6
texture2d_array<P, access::read> in5 [[texture(5)]],
#endif // N >= 6
texture2d_array<P, access::write> out [[texture(N)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
int x = gid.x - pm.offset;
if (x < 0) return;
if (x < pm.vdim[0]) {
VECTOR(P, 4) r = in0.read(gid.xy, gid.z);
out.write(r, gid.xy, gid.z);
return;
}
x -= pm.vdim[0];
if (x < pm.vdim[1]) {
VECTOR(P, 4) r = in1.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#if N >= 3
x -= pm.vdim[1];
if (x < pm.vdim[2]) {
VECTOR(P, 4) r = in2.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 3
#if N >= 4
x -= pm.vdim[2];
if (x < pm.vdim[3]) {
VECTOR(P, 4) r = in3.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 4
#if N >= 5
x -= pm.vdim[3];
if (x < pm.vdim[4]) {
VECTOR(P, 4) r = in4.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 5
#if N >= 6
x -= pm.vdim[4];
if (x < pm.vdim[5]) {
VECTOR(P, 4) r = in5.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 6
}
#endif // V == VX
#if V == VY
kernel void FUNC(concat, R, N, VV, P)(texture2d_array<P, access::read> in0 [[texture(0)]],
texture2d_array<P, access::read> in1 [[texture(1)]],
#if N >= 3
texture2d_array<P, access::read> in2 [[texture(2)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::read> in3 [[texture(3)]],
#endif // N >= 4
#if N >= 5
texture2d_array<P, access::read> in4 [[texture(4)]],
#endif // N >= 5
#if N >= 6
texture2d_array<P, access::read> in5 [[texture(5)]],
#endif // N >= 6
texture2d_array<P, access::write> out [[texture(N)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
int y = gid.y - pm.offset;
if (y < 0) return;
if (y < pm.vdim[0]) {
VECTOR(P, 4) r = in0.read(gid.xy, gid.z);
out.write(r, gid.xy, gid.z);
return;
}
y -= pm.vdim[0];
if (y < pm.vdim[1]) {
VECTOR(P, 4) r = in1.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#if N >= 3
y -= pm.vdim[1];
if (y < pm.vdim[2]) {
VECTOR(P, 4) r = in2.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 3
#if N >= 4
y -= pm.vdim[2];
if (y < pm.vdim[3]) {
VECTOR(P, 4) r = in3.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 4
#if N >= 5
y -= pm.vdim[3];
if (y < pm.vdim[4]) {
VECTOR(P, 4) r = in4.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 5
#if N >= 6
y -= pm.vdim[4];
if (y < pm.vdim[5]) {
VECTOR(P, 4) r = in5.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 6
}
#endif // V == VY
#if V == VZ
kernel void FUNC(concat, R, N, VV, P)(texture2d_array<P, access::read> in0 [[texture(0)]],
texture2d_array<P, access::read> in1 [[texture(1)]],
#if N >= 3
texture2d_array<P, access::read> in2 [[texture(2)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::read> in3 [[texture(3)]],
#endif // N >= 4
#if N >= 5
texture2d_array<P, access::read> in4 [[texture(4)]],
#endif // N >= 5
#if N >= 6
texture2d_array<P, access::read> in5 [[texture(5)]],
#endif // N >= 6
texture2d_array<P, access::write> out [[texture(N)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
int z = gid.z - pm.offset;
if (z < 0) return;
if (z < pm.vdim[0]) {
VECTOR(P, 4) r = in0.read(gid.xy, gid.z);
out.write(r, gid.xy, gid.z);
return;
}
z -= pm.vdim[0];
if (z < pm.vdim[1]) {
VECTOR(P, 4) r = in1.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#if N >= 3
z -= pm.vdim[1];
if (z < pm.vdim[2]) {
VECTOR(P, 4) r = in2.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 3
#if N >= 4
z -= pm.vdim[2];
if (z < pm.vdim[3]) {
VECTOR(P, 4) r = in3.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 4
#if N >= 5
z -= pm.vdim[3];
if (z < pm.vdim[4]) {
VECTOR(P, 4) r = in4.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 5
#if N >= 6
z -= pm.vdim[4];
if (z < pm.vdim[5]) {
VECTOR(P, 4) r = in5.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 6
}
#endif // V == VZ
#undef VV
#endif // #ifdef P
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConcatKernel.metal 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct ConcatParam {
int32_t odim[4];
int32_t axis;
int32_t offset;
int32_t trans[4];
int32_t vdim[6];
};
#define VNORMAL 1
#define VX 2
#define VY 3
#define VZ 4
// >> fast mode
// only support concat_{2,3,4}_{2,3,4,5,6}_y_{float,half}
// only support concat_{3,4}_{2,3,4,5,6}_x_{float,half}
// only support concat_{1,2,3,4}_{2,3,4,5,6}_z_{float,half}
// >> normal mode (loop mode)
// ssd-ar: (R=4, N=3, V=z), (R=3, N=2, V=y), (R=2, N=5, V=x), (R=3, N=5, V=x)
// ssd: (R=2, N=6, V=y), (R=3, N=6, V=y)
// genet: (R=4, N=2, V=normal)
// ssd-ar: (R=3, N=5, V=x)
#define V VX
#define R 3
#define N 5
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd-ar: (R=2, N=5, V=x)
#define V VX
#define R 2
#define N 5
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd-ar: (R=3, N=2, V=y)
#define V VY
#define R 3
#define N 2
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd-ar: (R=4, N=3, V=z)
#define V VZ
#define R 4
#define N 3
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd: (R=2, N=6, V=y)
#define V VY
#define R 2
#define N 6
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd: (R=3, N=6, V=y)
#define V VY
#define R 3
#define N 6
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
#define V VNORMAL
#define R 4
#define N 2
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
#define V VY
#define R 2
#define N 2
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
#define V VY
#define R 2
#define N 5
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvAddBNReluKernel.metal
浏览文件 @ b7a1d552
......@@ -17,13 +17,14 @@
using namespace metal;
kernel void conv_add_batch_norm_relu_1x1_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
kernel void conv_add_batch_norm_relu_1x1_half(
texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
const device half4 *new_scale [[buffer(3)]],
const device half4 *new_biase [[buffer(4)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
......@@ -41,7 +42,7 @@ kernel void conv_add_batch_norm_relu_1x1_half(texture2d_array<half, access::samp
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
half4 output = half4(0.0);
float4 output = float4(0.0);
half4 input;
for (uint i = 0; i < input_arr_size; ++i) {
......@@ -58,18 +59,18 @@ kernel void conv_add_batch_norm_relu_1x1_half(texture2d_array<half, access::samp
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = half4(fmax((float4(output) + float4(biase[gid.z])) * new_scale[gid.z] + new_biase[gid.z], 0.0));
outTexture.write(output, gid.xy, gid.z);
output = fmax((output + float4(biase[gid.z])) * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void conv_add_batch_norm_relu_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
kernel void conv_add_batch_norm_relu_3x3_half(
texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
const device half4 *new_scale [[buffer(3)]],
const device half4 *new_biase [[buffer(4)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
......@@ -86,7 +87,7 @@ kernel void conv_add_batch_norm_relu_3x3_half(texture2d_array<half, access::samp
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
half4 output = half4(0.0);
float4 output = float4(0.0);
half4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
......@@ -113,18 +114,18 @@ kernel void conv_add_batch_norm_relu_3x3_half(texture2d_array<half, access::samp
output.w += dot(input[j], weight_w);
}
}
output = half4(fmax((float4(output) + float4(biase[gid.z])) * new_scale[gid.z] + new_biase[gid.z], 0.0));
outTexture.write(output, gid.xy, gid.z);
output = fmax((output + float4(biase[gid.z])) * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void depthwise_conv_add_batch_norm_relu_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
kernel void depthwise_conv_add_batch_norm_relu_3x3_half(
texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
const device half4 *new_scale [[buffer(3)]],
const device half4 *new_biase [[buffer(4)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
......@@ -138,7 +139,7 @@ kernel void depthwise_conv_add_batch_norm_relu_3x3_half(texture2d_array<half, ac
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 9;
uint weithTo = gid.z * kernelHXW * 4;
half4 output = half4(0.0);
float4 output = float4(0.0);
half4 inputs[9];
inputs[0] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y - 1), output_slice);
inputs[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 1), output_slice);
......@@ -156,11 +157,12 @@ kernel void depthwise_conv_add_batch_norm_relu_3x3_half(texture2d_array<half, ac
output.z += input.z * weights[weithTo + 2 * kernelHXW + j];
output.w += input.w * weights[weithTo + 3 * kernelHXW + j];
}
output = half4(fmax((float4(output) + float4(biase[gid.z])) * new_scale[gid.z] + new_biase[gid.z], 0.0));
outTexture.write(output, gid.xy, gid.z);
output = fmax((output + float4(biase[gid.z])) * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
/*---------------------------------------------*/
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvAddPrelu.inc.metal 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#include "Macro.metal"
#pragma mark - convAdd
kernel void FUNC3_(conv_add_1x1, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device VECTOR(P, 4) *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 1;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(0.0);
VECTOR(P, 4) input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample,float2(posInInput.x, posInInput.y), i);
VECTOR(P, 4) weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
VECTOR(P, 4) weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
VECTOR(P, 4) weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
VECTOR(P, 4) weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = output + float4(biase[gid.z]);
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
kernel void FUNC3_(conv_add_3x3, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device VECTOR(P, 4) *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
const ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 9;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(0.0);
ushort dilation_x = param.dilationX;
ushort dilation_y = param.dilationY;
VECTOR(P, 4) input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y - dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y - dilation_y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y + dilation_y), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y + dilation_y), i);
for (int j = 0; j < 9; ++j) {
VECTOR(P, 4) weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
VECTOR(P, 4) weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
VECTOR(P, 4) weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
VECTOR(P, 4) weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
output = output + float4(biase[gid.z]);
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
kernel void FUNC3_(conv_add_5x1, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device VECTOR(P, 4) *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
const ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(biase[gid.z]);;
ushort dilation_y = param.dilationY;
VECTOR(P, 4) input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 2 * dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 2 * dilation_y), i);
for (int j = 0; j < 5; ++j) {
VECTOR(P, 4) weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
VECTOR(P, 4) weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
VECTOR(P, 4) weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
VECTOR(P, 4) weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
kernel void FUNC3_(conv_add_1x5, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device VECTOR(P, 4) *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
const ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(biase[gid.z]);
ushort dilation_x = param.dilationX;
VECTOR(P, 4) input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 2 * dilation_x, posInInput.y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x + 2 * dilation_x, posInInput.y), i);
for (int j = 0; j < 5; ++j) {
VECTOR(P, 4) weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
VECTOR(P, 4) weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
VECTOR(P, 4) weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
VECTOR(P, 4) weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
kernel void FUNC3_(depthwise_conv_add_3x3, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device P *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
uint output_slice = gid.z;
ushort2 stride = ushort2(param.strideX, param.strideY);
ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 9;
uint weithTo = gid.z * kernelHXW * 4;
float4 output = float4(biase[gid.z]);
VECTOR(P, 4) inputs[9];
inputs[0] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y - 1), output_slice);
inputs[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 1), output_slice);
inputs[2] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y - 1), output_slice);
inputs[3] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y), output_slice);
inputs[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), output_slice);
inputs[5] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y), output_slice);
inputs[6] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y + 1), output_slice);
inputs[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 1), output_slice);
inputs[8] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y + 1), output_slice);
for (int j = 0; j < 9; ++j) {
VECTOR(P, 4) input = inputs[j];
output.x += input.x * weights[weithTo + 0 * kernelHXW + j];
output.y += input.y * weights[weithTo + 1 * kernelHXW + j];
output.z += input.z * weights[weithTo + 2 * kernelHXW + j];
output.w += input.w * weights[weithTo + 3 * kernelHXW + j];
}
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvAddPreluKernel.metal 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
#define P float
#define PRELU_CHANNEL prelu_channel
#define PRELU_TYPE prelu_channel
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_CHANNEL
#define PRELU_ELEMENT prelu_element
#define PRELU_TYPE prelu_element
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_ELEMENT
#define PRELU_OTHER prelu_other
#define PRELU_TYPE prelu_other
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_OTHER
#undef P
#define P half
#define PRELU_CHANNEL prelu_channel
#define PRELU_TYPE prelu_channel
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_CHANNEL
#define PRELU_ELEMENT prelu_element
#define PRELU_TYPE prelu_element
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_ELEMENT
#define PRELU_OTHER prelu_other
#define PRELU_TYPE prelu_other
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_OTHER
#undef P
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/FetchKernel.metal 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
kernel void fetch(texture2d_array<float, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
int input_height = inTexture.get_height();
const float4 input = inTexture.read(gid.xy, gid.z);
int output_to = 4 * input_width * input_height;
output[gid.z * output_to + 0 * input_width * input_height + gid.y * input_width + gid.x] = input.x;
output[gid.z * output_to + 1 * input_width * input_height + gid.y * input_width + gid.x] = input.y;
output[gid.z * output_to + 2 * input_width * input_height + gid.y * input_width + gid.x] = input.z;
output[gid.z * output_to + 3 * input_width * input_height + gid.y * input_width + gid.x] = input.w;
}
kernel void fetch_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
device float * output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
int input_height = inTexture.get_height();
const half4 input = inTexture.read(gid.xy, gid.z);
int output_to = 4 * input_width * input_height;
output[gid.z * output_to + 0 * input_width * input_height + gid.y * input_width + gid.x] = input.x;
output[gid.z * output_to + 1 * input_width * input_height + gid.y * input_width + gid.x] = input.y;
output[gid.z * output_to + 2 * input_width * input_height + gid.y * input_width + gid.x] = input.z;
output[gid.z * output_to + 3 * input_width * input_height + gid.y * input_width + gid.x] = input.w;
}
kernel void fetch_placeholder(texture2d_array<float, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
}
kernel void fetch_placeholder_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Macro.metal 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT4_(a, b, c, d) a ## _ ## b ## _ ## c ## _ ## d
#define CONCAT5_(a, b, c, d, e) a ## _ ## b ## _ ## c ## _ ## d ## _ ## e
#define FUNC(f, r, n, v, p) CONCAT5_(f, r, n, v, p)
#define VECTOR(p, n) CONCAT2(p, n)
#define FUNC3_(a, b, c) CONCAT3_(a, b, c)
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/NMSFetchResultKernel.metal 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
kernel void nms_fetch_result(texture2d_array<float, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
const float4 input = inTexture.read(gid.xy, gid.z);
output[gid.y * input_width + gid.x] = input.x;
}
kernel void nms_fetch_result_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
const half4 input = inTexture.read(gid.xy, gid.z);
output[gid.y * input_width + gid.x] = input.x;
}
kernel void nms_fetch_bbox(texture2d_array<float, access::read> inTexture [[texture(0)]],
device float4 *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
// int input_height = inTexture.get_height();
const float4 input = inTexture.read(gid.xy, gid.z);
output[gid.y * input_width + gid.x] = input;
}
kernel void nms_fetch_bbox_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
device float4 *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
// int input_height = inTexture.get_height();
const half4 input = inTexture.read(gid.xy, gid.z);
output[gid.y * input_width + gid.x] = float4(input);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/PriorBoxKernel.metal
浏览文件 @ b7a1d552
......@@ -161,3 +161,207 @@ kernel void prior_box_half(texture2d_array<half, access::read> inTexture [[textu
}
}
kernel void prior_box_MinMaxAspectRatiosOrder(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outBoxTexture [[texture(1)]],
texture2d_array<float, access::write> varianceTexture [[texture(2)]],
const device float *aspect_ratios [[buffer(0)]],
constant PriorBoxMetalParam &param [[buffer(1)]],
const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() ||
gid.y >= outBoxTexture.get_height() ||
gid.z >= outBoxTexture.get_array_size()) return;
float center_x = (gid.x + param.offset) * param.stepWidth;
float center_y = (gid.y + param.offset) * param.stepHeight;
float box_width, box_height;
if (gid.z == 0) {
box_width = box_height = param.minSize / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(res, gid.xy, gid.z);
}
if (gid.z == 1 && param.maxSizeSize > 0) {
box_width = box_height = sqrt(param.minSize * param.maxSize) / 2;
float4 max_box;
max_box.x = (center_x - box_width) / param.imageWidth;
max_box.y = (center_y - box_height) / param.imageHeight;
max_box.z = (center_x + box_width) / param.imageWidth;
max_box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = min(max(max_box, 0.0), 1.0);
} else {
res = max_box;
}
outBoxTexture.write(res, gid.xy, gid.z);
}
int aspect_to = 0;
if (param.maxSizeSize > 0) {
aspect_to = gid.z - 2;
} else {
aspect_to = gid.z - 1;
}
if (aspect_to >= 0 && aspect_to < int(param.aspecRatiosSize)) {
int skip = 0;
for (int i = 0; i < aspect_to + 1; ++i) {
if (fabs(aspect_ratios[i] - 1.) < 1e-6) {
skip += 1;
}
}
aspect_to += skip;
float ar = aspect_ratios[aspect_to];
box_width = param.minSize * sqrt(ar) / 2;
box_height = param.minSize / sqrt(ar) / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(res, gid.xy, gid.z);
}
float4 variance = variances[0];
if (gid.z < param.numPriors) {
float4 variances_output;
variances_output.x = variance.x;
variances_output.y = variance.y;
variances_output.z = variance.z;
variances_output.w = variance.w;
varianceTexture.write(variances_output, gid.xy, gid.z);
}
}
kernel void prior_box_MinMaxAspectRatiosOrder_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outBoxTexture [[texture(1)]],
texture2d_array<half, access::write> varianceTexture [[texture(2)]],
const device half *aspect_ratios [[buffer(0)]],
constant PriorBoxMetalParam &param [[buffer(1)]],
const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() ||
gid.y >= outBoxTexture.get_height() ||
gid.z >= outBoxTexture.get_array_size()) return;
float center_x = (gid.x + param.offset) * param.stepWidth;
float center_y = (gid.y + param.offset) * param.stepHeight;
float box_width, box_height;
if (gid.z == 0) {
box_width = box_height = param.minSize / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(half4(res), gid.xy, gid.z);
}
if (gid.z == 1 && param.maxSizeSize > 0) {
box_width = box_height = sqrt(param.minSize * param.maxSize) / 2;
float4 max_box;
max_box.x = (center_x - box_width) / param.imageWidth;
max_box.y = (center_y - box_height) / param.imageHeight;
max_box.z = (center_x + box_width) / param.imageWidth;
max_box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = min(max(max_box, 0.0), 1.0);
} else {
res = max_box;
}
outBoxTexture.write(half4(res), gid.xy, gid.z);
}
int aspect_to = 0;
if (param.maxSizeSize > 0) {
aspect_to = gid.z - 2;
} else {
aspect_to = gid.z - 1;
}
if (aspect_to > 0 && aspect_to < int(param.aspecRatiosSize) && fabs(aspect_ratios[aspect_to] - 1.) > 1e-6) {
float ar = aspect_ratios[aspect_to];
box_width = param.minSize * sqrt(ar) / 2;
box_height = param.minSize / sqrt(ar) / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(half4(res), gid.xy, gid.z);
}
float4 variance = variances[0];
if (gid.z < param.numPriors) {
float4 variances_output;
variances_output.x = variance.x;
variances_output.y = variance.y;
variances_output.z = variance.z;
variances_output.w = variance.w;
varianceTexture.write(half4(variances_output), gid.xy, gid.z);
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ReshapeKernel.inc.metal 0 → 100644
浏览文件 @ b7a1d552
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT4_(a, b, c, d) a ## _ ## b ## _ ## c ## _ ## d
#define FUNC(f, r1, r2, p) CONCAT4_(f, r1, r2, p)
#define VECTOR(p, n) CONCAT2(p, n)
#define FUNC_R(f, r) CONCAT2_(f, r)
kernel void FUNC(reshape, RIN, ROUT, P)(texture2d_array<P, access::read> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant ReshapeParam &rp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
int oxyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, oabcd[4], ixyzn[4], iabcd[4];
ReshapeParam lrp = rp;
int oC = lrp.odim[lrp.otrans[3]];
int iC = lrp.idim[lrp.itrans[3]];
int count = lrp.odim[0] * lrp.odim[1] * lrp.odim[2] * lrp.odim[3];
VECTOR(P, 4) r;
for (int n = 0; n < 4; n++) {
oxyzn[3] = n;
#if ROUT == 4
xyzn2abcd_4(oC, oxyzn, oabcd);
#else
FUNC_R(xyzn2abcd, ROUT)(oxyzn, oabcd);
#endif
int tabcd[4];
invtrans(lrp.otrans, oabcd, tabcd);
int index = abcd2index(lrp.odim, tabcd);
if (index < count) {
index2abcd(lrp.idim, index, tabcd);
trans(lrp.itrans, tabcd, iabcd);
#if RIN == 4
abcd2xyzn_4(iC, iabcd, ixyzn);
#else
FUNC_R(abcd2xyzn, RIN)(iabcd, ixyzn);
#endif
r[n] = inTexture.read(uint2(ixyzn[0], ixyzn[1]), ixyzn[2])[ixyzn[3]];
} else {
r[n] = 0;
}
}
outTexture.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ReshapeKernel.metal
浏览文件 @ b7a1d552
......@@ -8,7 +8,7 @@
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
WITHOUT WARRANTIES OR CONRITIONS OF ANY KINR, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
......@@ -24,114 +24,127 @@ struct ReshapeParam {
int32_t otrans[4];
};
//kernel void reshape(texture2d_array<float, access::read> inTexture [[texture(0)]],
// texture2d_array<float, access::write> outTexture [[texture(1)]],
// constant ReshapeParam &rp [[buffer(0)]],
// uint3 gid [[thread_position_in_grid]]) {
// if (gid.x >= outTexture.get_width() ||
// gid.y >= outTexture.get_height() ||
// gid.z >= outTexture.get_array_size()) return;
//
// int oxyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, oabcd[4], ixyzn[4];
// ReshapeParam lrp = rp;
// int oC = lrp.odim[lrp.otrans[3]];
// int iC = lrp.idim[lrp.itrans[3]];
// int count = lrp.odim[0] * lrp.odim[1] * lrp.odim[2] * lrp.odim[3];
// float4 r;
// for (int n = 0; n < 4; n++) {
// oxyzn[3] = n;
//
// //4 (gid.x gid.y, gid.z, 0~4)
// xyzn2abcd(oC, oxyzn, oabcd);
// int tabcd[4];
// invtrans(lrp.otrans, oabcd, tabcd);
// int index = abcd2index(lrp.odim, tabcd);
// if (index < count) {
// int c = index % 4;
//
// int temp0 = index % (inTexture.get_array_size() * 4);
// int slice = temp0 / 4;
//
// int temp1 = index % (inTexture.get_array_size() * 4 * lrp.idim[2]);
// int w = temp1 / (inTexture.get_array_size() * 4);
//
// int h = index / (inTexture.get_array_size() * 4 * lrp.idim[2]);
//
//// index2abcd(lrp.idim, index, tabcd);
//// abcd2xyzn(iC, tabcd, ixyzn);
// r[n] = inTexture.read(uint2(w, h), slice)[c];
// } else {
// r[n] = 0;
// }
// }
// outTexture.write(r, gid.xy, gid.z);
//}
kernel void reshape(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant ReshapeParam &rp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
int oxyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, oabcd[4], ixyzn[4], iabcd[4];
ReshapeParam lrp = rp;
int oC = lrp.odim[lrp.otrans[3]];
int iC = lrp.idim[lrp.itrans[3]];
int count = lrp.odim[0] * lrp.odim[1] * lrp.odim[2] * lrp.odim[3];
float4 r;
for (int n = 0; n < 4; n++) {
oxyzn[3] = n;
xyzn2abcd(oC, oxyzn, oabcd);
int tabcd[4];
invtrans(lrp.otrans, oabcd, tabcd);
int index = abcd2index(lrp.odim, tabcd);
if (index < count) {
index2abcd(lrp.idim, index, tabcd);
trans(lrp.itrans, tabcd, iabcd);
abcd2xyzn(iC, iabcd, ixyzn);
r[n] = inTexture.read(uint2(ixyzn[0], ixyzn[1]), ixyzn[2])[ixyzn[3]];
} else {
r[n] = 0;
}
}
outTexture.write(r, gid.xy, gid.z);
}
kernel void reshape_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant ReshapeParam &rp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
int oxyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, oabcd[4], ixyzn[4], iabcd[4];
ReshapeParam lrp = rp;
int oC = lrp.odim[lrp.otrans[3]];
int iC = lrp.idim[lrp.itrans[3]];
int count = lrp.odim[0] * lrp.odim[1] * lrp.odim[2] * lrp.odim[3];
half4 r;
for (int n = 0; n < 4; n++) {
oxyzn[3] = n;
xyzn2abcd(oC, oxyzn, oabcd);
int tabcd[4];
invtrans(lrp.otrans, oabcd, tabcd);
int index = abcd2index(lrp.odim, tabcd);
if (index < count) {
index2abcd(lrp.idim, index, tabcd);
trans(lrp.itrans, tabcd, iabcd);
abcd2xyzn(iC, iabcd, ixyzn);
r[n] = inTexture.read(uint2(ixyzn[0], ixyzn[1]), ixyzn[2])[ixyzn[3]];
} else {
r[n] = 0;
}
}
outTexture.write(r, gid.xy, gid.z);
}
#define P float
#define RIN 4
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 3
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 2
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 1
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#undef P
#define P half
#define RIN 4
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 3
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 2
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 1
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#undef P
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Shape.metal 0 → 100644
浏览文件 @ b7a1d552
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
kernel void shape() {
}
kernel void shape_half() {
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Softmax.inc.metal 0 → 100644
浏览文件 @ b7a1d552
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define FUNC(f, p) CONCAT2_(f, p)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC(softmax, P)(texture2d_array<P, access::read> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant SoftmaxParam &sp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
// int zsize = inTexture.get_array_size();
P maxv = inTexture.read(uint2(0, gid.y), 0)[0];
int group = sp.K / 4;
int remain = sp.K % 4;
for (int x = 0; x < group; x++) {
VECTOR(P, 4) r = inTexture.read(uint2(x, gid.y), 0);
maxv = max(maxv, max(r[0], max(r[1], max(r[2], r[3]))));
}
if (remain > 0) {
VECTOR(P, 4) r = inTexture.read(uint2(group, gid.y), 0);
for (int i = 0; i < remain; i++) {
maxv = max(maxv, r[i]);
}
}
VECTOR(P, 4) rsum = {0, 0, 0, 0};
for (int x = 0; x < group; x++) {
VECTOR(P, 4) r = inTexture.read(uint2(x, gid.y), 0);
rsum += exp(r - maxv);
}
P sum = rsum[0] + rsum[1] + rsum[2] + rsum[3];
if (remain > 0) {
VECTOR(P, 4) r = inTexture.read(uint2(group, gid.y), 0);
for (int i = 0; i < remain; i++) {
sum += exp(r[i] - maxv);
}
}
VECTOR(P, 4) rr = inTexture.read(gid.xy, gid.z);
rr = exp(rr - maxv) / sum;
outTexture.write(rr, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Softmax.metal
浏览文件 @ b7a1d552
......@@ -20,81 +20,10 @@ struct SoftmaxParam {
int K;
};
kernel void softmax(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant SoftmaxParam &sp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
// int zsize = inTexture.get_array_size();
float maxv = inTexture.read(gid.xy, 0)[0];
int group = sp.K / 4;
int remain = sp.K % 4;
for (int z = 0; z < group; z++) {
float4 r = inTexture.read(gid.xy, z);
maxv = max(maxv, max(r[0], max(r[1], max(r[2], r[3]))));
}
if (remain > 0) {
float4 r = inTexture.read(gid.xy, group);
for (int i = 0; i < remain; i++) {
maxv = max(maxv, r[i]);
}
}
float4 rsum = {0, 0, 0, 0};
for (int z = 0; z < group; z++) {
float4 r = inTexture.read(gid.xy, z);
rsum += exp(r - maxv);
}
float sum = rsum[0] + rsum[1] + rsum[2] + rsum[3];
if (remain > 0) {
float4 r = inTexture.read(gid.xy, group);
for (int i = 0; i < remain; i++) {
sum += exp(r[i] - maxv);
}
}
float4 rr = inTexture.read(gid.xy, gid.z);
rr = exp(rr - maxv) / sum;
outTexture.write(rr, gid.xy, gid.z);
}
kernel void softmax_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant SoftmaxParam &sp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
// int zsize = inTexture.get_array_size();
half maxv = inTexture.read(gid.xy, 0)[0];
int group = sp.K / 4;
int remain = sp.K % 4;
for (int z = 0; z < group; z++) {
half4 r = inTexture.read(gid.xy, z);
maxv = max(maxv, max(r[0], max(r[1], max(r[2], r[3]))));
}
if (remain > 0) {
half4 r = inTexture.read(gid.xy, group);
for (int i = 0; i < remain; i++) {
maxv = max(maxv, r[i]);
}
}
float4 rsum = {0, 0, 0, 0};
for (int z = 0; z < group; z++) {
half4 r = inTexture.read(gid.xy, z);
rsum += exp(float4(r) - float4(maxv));
}
float sum = rsum[0] + rsum[1] + rsum[2] + rsum[3];
if (remain > 0) {
half4 r = inTexture.read(gid.xy, group);
for (int i = 0; i < remain; i++) {
sum += exp(float(r[i]) - float(maxv));
}
}
half4 rr = inTexture.read(gid.xy, gid.z);
rr = half4(exp(float4(rr) - float(maxv)) / sum);
outTexture.write(rr, gid.xy, gid.z);
}
#define P float
#include "Softmax.inc.metal"
#undef P
#define P half
#include "Softmax.inc.metal"
#undef P
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Split.inc.metal 0 → 100644
浏览文件 @ b7a1d552
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT4_(a, b, c, d) a ## _ ## b ## _ ## c ## _ ## d
#define CONCAT5_(a, b, c, d, e) a ## _ ## b ## _ ## c ## _ ## d ## _ ## e
#define FUNC(f, r, n, v, p) CONCAT5_(f, r, n, v, p)
#define VECTOR(p, n) CONCAT2(p, n)
#define FUNC_R(f, r) CONCAT2_(f, r)
#if V == VX
#define VV x
#elif V == VY
#define VV y
#elif V == VZ
#define VV z
#else
#define VV normal
#endif
#if V == VY
kernel void FUNC(split, R, N, VV, P)(texture2d_array<P, access::read> input [[texture(0)]],
texture2d_array<P, access::write> out1 [[texture(1)]],
texture2d_array<P, access::write> out2 [[texture(2)]],
#if N >= 3
texture2d_array<P, access::write> out3 [[texture(3)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::write> out4 [[texture(4)]],
#endif // N >= 4
constant SplitParam &sp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) r = input.read(gid.xy, gid.z);
int y = gid.y - sp.offset;
if (y < sp.vdim[0]) {
out1.write(r, gid.xy, gid.z);
return;
}
y -= sp.vdim[0];
if (y < sp.vdim[1]) {
out2.write(r, uint2(gid.x, y), gid.z);
return;
}
#if N >= 3
y -= sp.vdim[1];
if (y < sp.vdim[2]) {
out3.write(r, uint2(gid.x, y), gid.z);
return;
}
#endif // N >= 3
#if N >= 4
y -= sp.vdim[2];
if (y < sp.vdim[3]) {
out4.write(r, uint2(gid.x, y), gid.z);
return;
}
#endif // N >= 4
}
#endif // V == VY
#if V == VX
kernel void FUNC(split, R, N, VV, P)(texture2d_array<P, access::read> input [[texture(0)]],
texture2d_array<P, access::write> out1 [[texture(1)]],
texture2d_array<P, access::write> out2 [[texture(2)]],
#if N >= 3
texture2d_array<P, access::write> out3 [[texture(3)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::write> out4 [[texture(4)]],
#endif // N >= 4
constant SplitParam &sp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) r = input.read(gid.xy, gid.z);
int x = gid.x;
if (x < sp.vdim[0]) {
out1.write(r, gid.xy, gid.z);
return;
}
x -= sp.vdim[0];
if (x < sp.vdim[1]) {
out2.write(r, uint2(x, gid.y), gid.z);
return;
}
#if N >= 3
x -= sp.vdim[1];
if (x < sp.vdim[2]) {
out3.write(r, uint2(x, gid.y), gid.z);
return;
}
#endif // N >= 3
#if N >= 4
x -= sp.vdim[2];
if (x < sp.vdim[3]) {
out4.write(r, uint2(x, gid.y), gid.z);
return;
}
#endif // N >= 4
}
#endif // V == VX
#undef VV
#endif
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Split.metal
浏览文件 @ b7a1d552
......@@ -13,18 +13,52 @@
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
kernel void split(texture2d_array<float, access::write> output[[texture(0)]],
uint3 gid [[thread_position_in_grid]]) {
float4 r;
struct SplitParam {
int32_t idim[4];
int32_t axis;
int32_t offset;
int32_t trans[4];
int32_t vdim[4];
};
#define VNORMAL 1
#define VX 2
#define VY 3
#define VZ 4
// only support split_{2, 3, 4}_{2, 3, 4}_y_{float, half}
// only support split_{3, 4}_{2, 3, 4}_x_{float, half}
output.write(r, gid.xy, gid.z);
}
//// ssd-ar: (R=3, N=2, V=y)
#define V VY
#define R 3
#define N 2
#define P float
#include "Split.inc.metal"
#undef P
#define P half
#include "Split.inc.metal"
#undef P
#undef N
#undef R
#undef V
kernel void split_half(texture2d_array<half, access::write> output[[texture(0)]],
uint3 gid [[thread_position_in_grid]]) {
float4 r;
output.write(half4(r), gid.xy, gid.z);
}
//// ssd-ar: (R=2, N=2, V=y)
#define V VY
#define R 2
#define N 2
#define P float
#include "Split.inc.metal"
#undef P
#define P half
#include "Split.inc.metal"
#undef P
#undef N
#undef R
#undef V
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/TransposeKernel.inc.metal 0 → 100644
浏览文件 @ b7a1d552
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define FUNC(f, r, p) CONCAT3_(f, r, p)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC(transpose, R, P)(texture2d_array<P, access::read> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) r;
int oxyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0};
int iabcd[4], oabcd[4], ixyzn[4];
for (int n = 0; n < 4; n++) {
oxyzn[3] = n;
#if R == 4
xyzn2abcd_4(pm.oC, oxyzn, iabcd);
#endif // R == 4
#if R == 3
xyzn2abcd_3(oxyzn, oabcd);
#endif // R == 3
#if R == 2
xyzn2abcd_2(oxyzn, oabcd);
#endif // R == 2
iabcd[pm.axis[0]] = oabcd[0];
iabcd[pm.axis[1]] = oabcd[1];
iabcd[pm.axis[2]] = oabcd[2];
iabcd[pm.axis[3]] = oabcd[3];
#if R == 4
abcd2xyzn_4(pm.iC, iabcd, ixyzn);
#endif // R == 4
#if R == 3
abcd2xyzn_3(iabcd, ixyzn);
#endif // R == 3
#if R == 2
abcd2xyzn_2(iabcd, ixyzn);
#endif // R == 2
r[n] = inTexture.read(uint2(ixyzn[0], ixyzn[1]), ixyzn[2])[ixyzn[3]];
}
outTexture.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/TransposeKernel.metal
浏览文件 @ b7a1d552
......@@ -22,59 +22,42 @@ struct TransposeParam {
int axis[4];
};
kernel void transpose(texture2d_array<float, access::read> inTexture [[texture(0)]],
kernel void transpose_copy_float(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if ((pm.axis[0] == 0) && (pm.axis[1] == 1) && (pm.axis[2] == 2) && (pm.axis[3] == 3)) {
// do nothing
float4 r = inTexture.read(gid.xy, gid.z);
outTexture.write(r, gid.xy, gid.z);
} else {
float4 r;
for (int n = 0; n < 4; n++) {
int ixyzn[] = {int(gid.x), int(gid.y), int(gid.z), n};
int iabcd[4], oabcd[4], oxyzn[4];
xyzn2abcd(pm.oC, ixyzn, iabcd);
oabcd[pm.axis[0]] = iabcd[0];
oabcd[pm.axis[1]] = iabcd[1];
oabcd[pm.axis[2]] = iabcd[2];
oabcd[pm.axis[3]] = iabcd[3];
abcd2xyzn(pm.iC, oabcd, oxyzn);
float4 rt = inTexture.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2]);
r[n] = rt[oxyzn[3]];
}
outTexture.write(r, gid.xy, gid.z);
}
outTexture.write(inTexture.read(gid.xy, gid.z), gid.xy, gid.z);
}
kernel void transpose_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
kernel void transpose_copy_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if ((pm.axis[0] == 0) && (pm.axis[1] == 1) && (pm.axis[2] == 2) && (pm.axis[3] == 3)) {
// do nothing
half4 r = inTexture.read(gid.xy, gid.z);
outTexture.write(r, gid.xy, gid.z);
} else {
half4 r;
for (int n = 0; n < 4; n++) {
int ixyzn[] = {int(gid.x), int(gid.y), int(gid.z), n};
int iabcd[4], oabcd[4], oxyzn[4];
xyzn2abcd(pm.oC, ixyzn, iabcd);
oabcd[pm.axis[0]] = iabcd[0];
oabcd[pm.axis[1]] = iabcd[1];
oabcd[pm.axis[2]] = iabcd[2];
oabcd[pm.axis[3]] = iabcd[3];
abcd2xyzn(pm.iC, oabcd, oxyzn);
half4 rt = inTexture.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2]);
r[n] = rt[oxyzn[3]];
}
outTexture.write(r, gid.xy, gid.z);
}
outTexture.write(inTexture.read(gid.xy, gid.z), gid.xy, gid.z);
}
#define R 4
#define P float
#include "TransposeKernel.inc.metal"
#undef P
#define P half
#include "TransposeKernel.inc.metal"
#undef P
#undef R
#define R 3
#define P float
#include "TransposeKernel.inc.metal"
#undef P
#define P half
#include "TransposeKernel.inc.metal"
#undef P
#undef R
#define R 2
#define P float
#include "TransposeKernel.inc.metal"
#undef P
#define P half
#include "TransposeKernel.inc.metal"
#undef P
#undef R
metal/paddle-mobile/paddle-mobile/Operators/MulticlassNMSOp.swift
浏览文件 @ b7a1d552
......@@ -21,10 +21,16 @@ class MulticlassNMSParam<P: PrecisionType>: OpParam {
scores = try MulticlassNMSParam.getFirstTensor(key: "Scores", map: opDesc.inputs, from: inScope)
bboxes = try MulticlassNMSParam.getFirstTensor(key: "BBoxes", map: opDesc.inputs, from: inScope)
output = try MulticlassNMSParam.outputOut(outputs: opDesc.outputs, from: inScope)
middleOutput = FetchHolder.init(inCapacity: scores.tensorDim.numel(), inDim: scores.tensorDim.dims)
bboxOutput = FetchHolder.init(inCapacity: bboxes.tensorDim.numel(), inDim: bboxes.tensorDim.dims)
} catch let error {
throw error
}
}
var bboxOutput: FetchHolder
var middleOutput: FetchHolder
let scores: Texture<P>
let bboxes: Texture<P>
var output: Texture<P>
......@@ -33,7 +39,15 @@ class MulticlassNMSParam<P: PrecisionType>: OpParam {
class MulticlassNMSOp<P: PrecisionType>: Operator<MulticlassNMSKernel<P>, MulticlassNMSParam<P>>, Runable, Creator, InferShaperable{
func inputVariant() -> [String : [Variant]] {
return ["Scores" : [para.scores], "BBoxes" : [para.bboxes]]
return ["Scores" : [para.middleOutput], "BBoxes" : [para.bboxOutput]]
}
func computeMiddleResult(device: MTLDevice, buffer: MTLCommandBuffer) {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let _ {
fatalError()
}
}
func inferShape() {
......@@ -42,11 +56,12 @@ class MulticlassNMSOp<P: PrecisionType>: Operator<MulticlassNMSKernel<P>, Multic
typealias OpType = MulticlassNMSOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
func delogOutput() {
print(" nms - output: ")
print(para.bboxes.metalTexture.float32Array().strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/PriorBoxOp.swift
浏览文件 @ b7a1d552
......@@ -17,6 +17,11 @@ import Foundation
class PriorBoxParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
min_max_aspect_ratios_order = try PriorBoxParam.getAttr(key: "min_max_aspect_ratios_order", attrs: opDesc.attrs)
} catch _ {
}
do {
input = try PriorBoxParam.input(inputs: opDesc.inputs, from: inScope)
output = try PriorBoxParam.outputBoxes(outputs: opDesc.outputs, from: inScope)
......@@ -36,6 +41,7 @@ class PriorBoxParam<P: PrecisionType>: OpParam {
}
}
var min_max_aspect_ratios_order: Bool = false
let minSizes: [Float32]
let maxSizes: [Float32]
let aspectRatios: [Float32]
......@@ -72,10 +78,24 @@ class PriorBoxOp<P: PrecisionType>: Operator<PriorBoxKernel<P>, PriorBoxParam<P>
print(" \(type) output: ")
// output
let outputArray = para.output.metalTexture.float32Array()
print(outputArray)
// let outputArray = para.output.metalTexture.float32Array()
// print(outputArray.strideArray())
// let device = para.input.metalTexture!.device
// let boxes:[Float32] = device.texture2tensor(texture: para.output.metalTexture!, dim: para.output.tensorDim.dims, transpose: [2,0,1,3])
// let variances:[Float32] = device.texture2tensor(texture: para.outputVariances.metalTexture!, dim: para.outputVariances.tensorDim.dims, transpose: [2,0,1,3])
// print("boxes: ")
// print(boxes.strideArray())
// print("variances: ")
// print(variances.strideArray())
// output
// print(" \(type) output: ")
print(" \(type) output: ")
let box = para.output.metalTexture.realNHWC(dim: (para.output.dim[0], para.output.dim[1], para.output.dim[2], para.output.dim[3]))
print(" dim: \(para.output.dim)")
print(box.strideArray())
// print((0..<box.count).map { (index: $0, value: box[$0])})
// print(para.output.realNHWC().strideArray())
// let padToFourDim = para.output.padToFourDim
// if para.output.transpose == [0, 1, 2, 3] {
// let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: padToFourDim[0], h: padToFourDim[1], w: padToFourDim[2], c: padToFourDim[3]), texturePrecision: computePrecision)
......
metal/paddle-mobile/paddle-mobile/Operators/ReluOp.swift
浏览文件 @ b7a1d552
......@@ -47,6 +47,9 @@ class ReluOp<P: PrecisionType>: Operator<ReluKernel<P>, ReluParam<P>>, Runable,
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
let device = para.output.metalTexture!.device
let outputArray: [Float32] = device.texture2tensor(texture: para.output.metalTexture, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
print(outputArray.strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ReshapeOp.swift
浏览文件 @ b7a1d552
......@@ -43,15 +43,12 @@ class ReshapeParam<P: PrecisionType>: OpParam {
}
output.padToFourDim = Dim.init(inDim: dim)
output.dim = output.padToFourDim
// inplace = try ReshapeParam.getAttr(key: "inplace", attrs: opDesc.attrs)
} catch let error {
throw error
}
}
let input: Texture<P>
let shape: [Int32]
// let inplace: Bool
var output: Texture<P>
}
......@@ -72,15 +69,9 @@ class ReshapeOp<P: PrecisionType>: Operator<ReshapeKernel<P>, ReshapeParam<P>>,
}
func delogOutput() {
print("reshape delog")
// let _: P? = para.input.metalTexture.logDesc(header: "reshape input: ", stridable: false)
//
// let _: P? = para.output.metalTexture.logDesc(header: "reshape output: ", stridable: false)
let padToFourDim = para.output.padToFourDim
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: padToFourDim[0], h: padToFourDim[1], w: padToFourDim[2], c: padToFourDim[3]))
// print(para.output.metalTexture.toTensor(dim: (n: padToFourDim[0], c: padToFourDim[1], h: padToFourDim[2], w: padToFourDim[3])).strideArray())
let device = para.output.metalTexture!.device
let outputArray: [Float32] = device.texture2tensor(texture: para.output.metalTexture, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
print(outputArray.strideArray())
// print(outputArray)
}
}
metal/paddle-mobile/paddle-mobile/Operators/ShapeOp.swift
浏览文件 @ b7a1d552
......@@ -18,17 +18,19 @@ class ShapeParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
output = try ShapeParam.output(outputs: opDesc.outputs, from: inScope)
input = try ShapeParam.input(inputs: opDesc.inputs, from: inScope)
output = try ShapeParam.outputOut(outputs: opDesc.outputs, from: inScope)
} catch let error {
throw error
}
}
var output: Texture<P>
let input: Texture<P>
}
class ShapeOp<P: PrecisionType>: Operator<SplitKernel<P>, SplitParam<P>>, Runable, Creator, InferShaperable{
class ShapeOp<P: PrecisionType>: Operator<ShapeKernel<P>, ShapeParam<P>>, Runable, Creator, InferShaperable{
typealias OpType = SplitOp<P>
typealias OpType = ShapeOp<P>
func inferShape() {
// para.output.dim = para.input.dim
......
metal/paddle-mobile/paddle-mobile/Operators/SplitOp.swift
浏览文件 @ b7a1d552
......@@ -18,13 +18,33 @@ class SplitParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
// output = try SplitParam.output(outputs: opDesc.outputs, from: inScope)
output = try SplitParam.outputOut(outputs: opDesc.outputs, from: inScope)
input = try SplitParam.inputX(inputs: opDesc.inputs, from: inScope)
output = Texture<P>.init(device: input.metalTexture!.device, inDim: input.dim)
axis = try SplitParam.getAttr(key: "axis", attrs: opDesc.attrs)
sections = try SplitParam.getAttr(key: "sections", attrs: opDesc.attrs)
if axis < 0 {
axis = input.tensorDim.cout() + axis
}
guard let outlist = opDesc.outputs["Out"] else {
fatalError()
}
for out in outlist {
guard let variant = inScope[out], let v = variant as? Texture<P> else {
fatalError()
}
outputList.append(v)
sections.append(Int32(v.tensorDim.dims[axis]))
}
} catch let error {
throw error
}
}
var axis: Int
let input: Texture<P>
var output: Texture<P>
var outputList: [Texture<P>] = []
var sections: [Int32] = []
}
class SplitOp<P: PrecisionType>: Operator<SplitKernel<P>, SplitParam<P>>, Runable, Creator, InferShaperable{
......@@ -45,6 +65,11 @@ class SplitOp<P: PrecisionType>: Operator<SplitKernel<P>, SplitParam<P>>, Runabl
func delogOutput() {
print(" \(type) output: ")
let device = para.input.metalTexture!.device
for out in para.outputList {
let arr: [Float32] = device.texture2tensor(texture: out.metalTexture, dim: out.tensorDim.dims, transpose: out.transpose)
print(arr.strideArray())
}
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/TransposeOp.swift
浏览文件 @ b7a1d552
......@@ -48,15 +48,9 @@ class TransposeOp<P: PrecisionType>: Operator<TransposeKernel<P>, TransposeParam
func delogOutput() {
print(" \(type) output: ")
let padToFourDim = para.output.padToFourDim
if para.output.transpose == [0, 1, 2, 3] {
let outputArray = para.output.metalTexture.realNHWC(dim: (n: padToFourDim[0], h: padToFourDim[1], w: padToFourDim[2], c: padToFourDim[3]))
let device = para.output.metalTexture!.device
let outputArray: [Float32] = device.texture2tensor(texture: para.output.metalTexture, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
print(outputArray.strideArray())
} else if para.output.transpose == [0, 2, 3, 1] {
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
} else {
print(" not implement")
}
}
}
......
metal/paddle-mobile/paddle-mobile/PaddleMobile.swift
浏览文件 @ b7a1d552
......@@ -16,40 +16,15 @@ import Foundation
class ScaleKernel: CusomKernel {
init(device: MTLDevice, shape: Shape) {
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "scale", outputDim: shape, usePaddleMobileLib: false)
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "scale_half", outputDim: shape, usePaddleMobileLib: false)
} else {
fatalError(" unsupport ")
}
}
public class Net: NSObject {
var except: Int = 0
var means: [Float] = []
var scale: Float = 0.0
var dim: (n: Int, h: Int, w: Int, c: Int) = (n: 0, h: 0, w: 0, c: 0)
var preprocessKernel: CusomKernel? = nil
var paramPointer: UnsafeMutableRawPointer? = nil
var paramSize: Int = 0
var modelPointer: UnsafeMutableRawPointer? = nil
var modelSize: Int = 0
var modelPath: String = ""
var paramPath: String = ""
var modelDir: String = ""
public func resultStr(res: [Float]) -> String {
fatalError()
}
func fetchResult(paddleMobileRes: ResultHolder) -> [Float32] {
return paddleMobileRes.resultArr
}
@objc public init(device: MTLDevice) {
super.init()
}
@objc public init(device: MTLDevice,paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
self.paramPointer = paramPointer
self.paramSize = paramSize
self.modelPointer = modePointer
self.modelSize = modelSize
super.init()
}
}
public class Runner: NSObject {
......@@ -60,7 +35,7 @@ public class Runner: NSObject {
public let net: Net
let device: MTLDevice?
let platform: Platform
var cpuPaddleMobile: PaddleMobile?
var cpuPaddleMobile: PaddleMobileCPU?
let numel: Int
let meansNumber: [NSNumber]
......@@ -80,7 +55,7 @@ public class Runner: NSObject {
textureLoader = MTKTextureLoader.init(device: inDevice)
}
if platform == .CPU {
cpuPaddleMobile = PaddleMobile.init()
cpuPaddleMobile = PaddleMobileCPU.init()
}
numel = net.dim.n * net.dim.c * net.dim.h * net.dim.w
meansNumber = net.means.map { NSNumber.init(value: $0) }
......@@ -101,8 +76,10 @@ public class Runner: NSObject {
}
let loader = Loader<Float32>.init()
do {
//program = try loader.load(device: inDevice, modelPath: net.modelPath, paraPath: net.paramPath)
program = try loader.load(device: inDevice, paramPointer: net.paramPointer!, paramSize: net.paramSize,modePointer:net.modelPointer!,modelSize:net.modelSize)
// program = try loader.load(device: inDevice, modelPath: net.modelPath, paraPath: net.paramPath)
net.updateProgram(program: program!)
executor = try Executor<Float32>.init(inDevice: inDevice, inQueue: inQueue, inProgram: program!)
} catch let error {
print(error)
......@@ -114,12 +91,13 @@ public class Runner: NSObject {
return true
}
@objc public func predict(inputPointer: UnsafeMutablePointer<Float32>, completion: @escaping ( _ success: Bool, _ resultArray: [Float32]) -> Void) {
guard let res = cpuPaddleMobile?.predictInput(inputPointer, dim: dimsNum, means: meansNumber, scale: net.scale) else {
completion(false, [])
@objc public func predict(inputPointer: UnsafeMutablePointer<Float32>, completion: @escaping ( _ success: Bool, _ result: PaddleMobileCPUResult?) -> Void) {
guard let res = cpuPaddleMobile?.predictInput(inputPointer, dim: dimsNum) else {
completion(false, nil)
return
}
completion(true, res.map { ($0 as! NSNumber).floatValue })
completion(true, res)
}
/**
......@@ -127,18 +105,18 @@ public class Runner: NSObject {
* texture: 需要预测的 texture 需要做过预处理
* ( _ success: Bool, _ time:TimeInterval, _ resultArray: [Float32]) -> Void : 回调闭包, 三个参数分别为: 是否成功, 预测耗时, 结果数组
*/
@objc public func predict(texture: MTLTexture, completion: @escaping ( _ success: Bool, _ resultArray: [Float32]) -> Void) {
@objc public func predict(texture: MTLTexture, completion: @escaping ( _ success: Bool, _ result: ResultHolder?) -> Void) {
do {
try self.executor?.predict(input: texture, dim: [self.net.dim.n, self.net.dim.h, self.net.dim.w, self.net.dim.c], completionHandle: { [weak self] (res) in
guard let SSelf = self else {
fatalError( " self nil " )
}
let resultArray = SSelf.net.fetchResult(paddleMobileRes: res)
completion(true, resultArray)
let result = SSelf.net.fetchResult(paddleMobileRes: res)
completion(true, result)
}, preProcessKernle: self.net.preprocessKernel, except: self.net.except)
} catch let error {
print(error)
completion(false, [])
completion(false, nil)
return
}
}
......@@ -148,21 +126,21 @@ public class Runner: NSObject {
* cgImage: 需要预测的图片
* ( _ success: Bool, _ time:TimeInterval, _ resultArray: [Float32]) -> Void : 回调闭包, 三个参数分别为: 是否成功, 预测耗时, 结果数组
*/
@objc public func predict(cgImage: CGImage, completion: @escaping ( _ success: Bool, _ resultArray: [Float32]) -> Void) {
if platform == .GPU {
getTexture(image: cgImage) { [weak self] (texture) in
guard let SSelf = self else {
fatalError( "" )
}
SSelf.predict(texture: texture, completion: completion)
}
} else if platform == .CPU {
let input = preproccess(image: cgImage)
predict(inputPointer: input, completion: completion)
input.deinitialize(count: numel)
input.deallocate()
}
}
// @objc public func predict(cgImage: CGImage, completion: @escaping ( _ success: Bool, _ resultArray: [Float32]) -> Void) {
// if platform == .GPU {
// getTexture(image: cgImage) { [weak self] (texture) in
// guard let SSelf = self else {
// fatalError( "" )
// }
// SSelf.predict(texture: texture, completion: completion)
// }
// } else if platform == .CPU {
// let input = preproccess(image: cgImage)
// predict(inputPointer: input, completion: completion)
// input.deinitialize(count: numel)
// input.deallocate()
// }
// }
/*
* 清理内存, 调用此函数后, 不能再使用, 需重新 load
......@@ -193,10 +171,10 @@ public class Runner: NSObject {
*/
@objc public func getTexture(image: CGImage, getTexture: @escaping (MTLTexture) -> Void) {
let texture = try? textureLoader?.newTexture(cgImage: image, options: [:]) ?! " texture loader error"
scaleTexture(input: texture!, size: (net.dim.w, net.dim.h), complete: getTexture)
scaleTexture(input: texture!, complete: getTexture)
}
func scaleTexture(input: MTLTexture, size:(width: Int, height: Int), complete: @escaping (MTLTexture) -> Void) {
public func scaleTexture(input: MTLTexture , complete: @escaping (MTLTexture) -> Void) {
guard let inQueue = queue, let inDevice = device else {
fatalError( " queue or devcie nil " )
......@@ -206,7 +184,7 @@ public class Runner: NSObject {
fatalError( " make buffer error" )
}
let scaleKernel = ScaleKernel.init(device: inDevice, shape: CusomKernel.Shape.init(inWidth: size.width, inHeight: size.height, inChannel: 3))
let scaleKernel = ScaleKernel.init(device: inDevice, shape: CusomKernel.Shape.init(inWidth: net.dim.w, inHeight: net.dim.h, inChannel: 3))
do {
try scaleKernel.compute(inputTexuture: input, commandBuffer: buffer)
......
metal/paddle-mobile/paddle-mobile/PaddleMobileGPU.m
浏览文件 @ b7a1d552
......@@ -43,7 +43,15 @@
}
-(void)predict:(id<MTLTexture>)texture withCompletion:(void (^)(BOOL, NSArray<NSNumber *> *))completion {
[runner predictWithTexture:texture completion:completion];
[runner predictWithTexture:texture completion:^(BOOL success, ResultHolder * _Nullable result) {
NSMutableArray<NSNumber *> *resultArray = [NSMutableArray arrayWithCapacity:result.capacity];
for (int i = 0; i < result.capacity; ++i) {
[resultArray addObject:[NSNumber numberWithFloat:result.result[i]]];
}
completion(success, resultArray);
}];
// [runner predictWithTexture:texture completion:completion];
}
-(void)clear {
......
metal/paddle-mobile/paddle-mobile/Program/BlockDesc.swift
浏览文件 @ b7a1d552
......@@ -14,7 +14,7 @@
import Foundation
struct BlockDesc {
class BlockDesc {
let index: Int
let parentIndex: Int
let vars: [VarDesc]
......
metal/paddle-mobile/paddle-mobile/Program/OpDesc.swift
浏览文件 @ b7a1d552
......@@ -14,7 +14,7 @@
import Foundation
struct OpDesc {
class OpDesc {
let inputs: [String : [String]]
var paraInputs: [String : [String]]
var outputs: [String : [String]]
......
metal/paddle-mobile/paddle-mobile/Program/Program.swift
浏览文件 @ b7a1d552
......@@ -14,7 +14,7 @@
import Foundation
public struct Program {
public class Program {
let paramPath: String
let programDesc: ProgramDesc
let scope: Scope
......
metal/paddle-mobile/paddle-mobile/Program/ProgramDesc.swift
浏览文件 @ b7a1d552
......@@ -14,7 +14,7 @@
import Foundation
public struct ProgramDesc {
public class ProgramDesc {
var blocks: [BlockDesc] = []
init(protoProgram: PaddleMobile_Framework_Proto_ProgramDesc) {
for block in protoProgram.blocks {
......
metal/paddle-mobile/paddle-mobile/Program/ProgramOptimize.swift
浏览文件 @ b7a1d552
......@@ -35,6 +35,22 @@ class Node {
type = inType
}
subscript(index: Int) -> [Node] {
var nodes: [Node] = []
getNodesWithLocation(index: index, nowIndex: 0, nodes: &nodes)
return nodes
}
func getNodesWithLocation(index: Int, nowIndex: Int, nodes: inout [Node]) {
if index == nowIndex {
nodes.append(self)
}
for output in outputs {
output.getNodesWithLocation(index: index, nowIndex: nowIndex + 1, nodes: &nodes)
}
}
static func -->(lNode: Node, rNode: Node) -> Node {
lNode.outputs.append(rNode)
rNode.inputs.append(lNode)
......@@ -77,7 +93,7 @@ class Node {
for attr in inOpdesc.attrs {
beginNode.opDesc?.attrs[attr.key] = attr.value
// print(beginNode.opDesc?.attrs)
// print(beginNode.opDesc?.attrs)
}
for paraInput in inOpdesc.paraInputs {
......@@ -119,6 +135,27 @@ class Node {
}
}
func relationship() -> [String : Node]{
var map: [String : Node] = [:]
relationship(map: &map)
return map
}
private func relationship(map: inout [String : Node]) {
guard let inOpDesc = opDesc else {
return
}
for output in inOpDesc.outputs {
for outputKey in output.value {
map[outputKey] = self
}
}
for output in outputs {
output.relationship(map: &map)
}
}
}
......@@ -145,9 +182,11 @@ extension Node: Equatable {
class ProgramOptimize<P: PrecisionType> {
// register fusion
let fusionOps: [Fusion.Type] = [ConvAddBatchNormReluOp<P>.self,
ConvAddPreluOp<P>.self,
ConvAddOp<P>.self,
ConvBNReluOp<P>.self,
DwConvBNReluOp<P>.self]
DwConvBNReluOp<P>.self
]
func optimize(originProgramDesc: ProgramDesc) -> ProgramDesc {
......@@ -157,7 +196,7 @@ class ProgramOptimize<P: PrecisionType> {
var mapForNodeChain: [String : Node] = [:]
var nodes: [Node] = []
var typeMapNodes: [String : [Node]] = [:]
var typeMapNodes: [String : [(node: Node, output: [String : Node])]] = [:]
let block = originProgramDesc.blocks[0]
for opDesc in block.ops {
guard let opInputKeys = opInfos[opDesc.type]?.inputs, let outputKeys = opInfos[opDesc.type]?.outputs else {
......@@ -186,10 +225,10 @@ class ProgramOptimize<P: PrecisionType> {
nodes.append(node)
if var inNodes = typeMapNodes[opDesc.type] {
inNodes.append(node)
inNodes.append((node, mapForNodeChain))
typeMapNodes[opDesc.type] = inNodes
} else {
typeMapNodes[opDesc.type] = [node]
typeMapNodes[opDesc.type] = [(node, mapForNodeChain)]
}
}
......@@ -198,10 +237,34 @@ class ProgramOptimize<P: PrecisionType> {
let depth = fusionNode.depth()
if let toMatchNodes = typeMapNodes[fusionNode.type] {
for node in toMatchNodes {
let toNode = node.to(depth: depth)
let toNode = node.node.to(depth: depth)
if toNode == fusionNode { // match
var canFolder = true
let relationshipMap = toNode.relationship()
for toCheck in fusion.needCheck() {
// let nodes = toCheck
let checkNodes = toNode[toCheck.0]
for checkNode in checkNodes {
let inputToChecks = checkNode.opDesc?.inputs[toCheck.1] ?? []
for inputToCheck in inputToChecks {
if node.output[inputToCheck] == nil {
if relationshipMap[inputToCheck] == nil {
canFolder = false
}
}
}
}
}
if !canFolder {
continue
}
var removeNodes: [Node] = []
node.folderWith(fusion: fusion, removedNodes: &removeNodes)
node.node.folderWith(fusion: fusion, removedNodes: &removeNodes)
for removeNode in removeNodes {
nodes.remove(element: removeNode)
}
......
metal/paddle-mobile/paddle-mobile/Program/TensorDesc.swift
浏览文件 @ b7a1d552
......@@ -14,7 +14,7 @@
import Foundation
struct TensorDesc {
class TensorDesc {
let dims: [Int]
let dataType: VarTypeType
let dataLayout: DataLayout = DataLayout.NCHW()
......
metal/paddle-mobile/paddle-mobile/Program/VarDesc.swift
浏览文件 @ b7a1d552
......@@ -56,7 +56,7 @@ enum VarTypeType: Int {
}
}
struct VarDesc {
class VarDesc {
let name: String
let persistable: Bool
let type: VarTypeType
......
metal/paddle-mobile/paddle-mobile/framework/Executor.swift
浏览文件 @ b7a1d552
......@@ -14,39 +14,50 @@
import Foundation
let testTo = 3
let testTo = 81
var isTest = false
let computePrecision: ComputePrecision = .Float16
public class ResultHolder {
public class GPUResultHolder {
public let dim: [Int]
public let resultArr: [Float32]
public let capacity: Int
public var resultPointer: UnsafeMutablePointer<Float32>?
public var intermediateResults: [String : [Variant]]?
public let elapsedTime: Double
public init(inDim: [Int], inResult: [Float32], inElapsedTime: Double, inIntermediateResults: [String : [Variant]]? = nil) {
public init(inDim: [Int], inPointer: UnsafeMutablePointer<Float32>?, inCapacity: Int, inElapsedTime: Double, inIntermediateResults: [String : [Variant]]? = nil) {
dim = inDim
resultArr = inResult
capacity = inCapacity
if let inInPointer = inPointer {
resultPointer = UnsafeMutablePointer<Float32>.allocate(capacity: inCapacity)
resultPointer?.initialize(from: inInPointer, count: inCapacity)
}
elapsedTime = inElapsedTime
intermediateResults = inIntermediateResults
}
}
extension ResultHolder: CustomDebugStringConvertible, CustomStringConvertible {
extension GPUResultHolder: CustomDebugStringConvertible, CustomStringConvertible {
public var debugDescription: String {
var str = ""
str += "Dim: \(dim) \n value:[ "
if resultArr.count < 20 {
for d in resultArr {
str += " \(d) "
}
} else {
for d in stride(from: 0, to: resultArr.count, by: resultArr.count/20) {
str += " \(resultArr[d]) "
}
}
str += " ]"
return str
// var str = ""
// str += "Dim: \(dim) \n value:[ "
// if resultArr.count < 20 {
// for d in resultArr {
// str += " \(d) "
// }
// } else {
// for d in stride(from: 0, to: resultArr.count, by: resultArr.count/20) {
// str += " \(resultArr[d]) "
// }
// }
// str += " ]"
// return str
fatalError()
}
public var description: String {
......@@ -65,11 +76,23 @@ public class Executor<P: PrecisionType> {
program = inProgram
device = inDevice
queue = inQueue
// print("before for ")
//print(program.scope.vars["fea_pyramid1_mbox_conf_flat.Flatten.output.1.tmp_0"])
for block in inProgram.programDesc.blocks {
//block.ops.count
for i in 0..<block.ops.count {
let op = block.ops[i]
do {
// print("in for i \(i): ")
// print(program.scope.vars["fea_pyramid1_mbox_conf_flat.Flatten.output.1.tmp_0"])
//
// if i == 56 {
// print(program.scope.vars["fea_pyramid1_mbox_conf_flat.Flatten.output.1.tmp_0"])
//
// }
let op = try OpCreator<P>.shared.creat(device: inDevice, opDesc: op, scope: inProgram.scope)
ops.append(op)
} catch let error {
......@@ -79,7 +102,7 @@ public class Executor<P: PrecisionType> {
}
}
public func predict(input: MTLTexture, dim: [Int], completionHandle: @escaping (ResultHolder) -> Void, preProcessKernle: CusomKernel? = nil, except: Int = 0) throws {
public func predict(input: MTLTexture, dim: [Int], completionHandle: @escaping (GPUResultHolder) -> Void, preProcessKernle: CusomKernel? = nil, except: Int = 0) throws {
guard let buffer = queue.makeCommandBuffer() else {
throw PaddleMobileError.predictError(message: "CommandBuffer is nil")
}
......@@ -101,7 +124,7 @@ public class Executor<P: PrecisionType> {
let inputTexture = InputTexture.init(inMTLTexture: resInput, inExpectDim: Dim.init(inDim: dim))
program.scope.setInput(input: inputTexture)
//(ops.count - except)
for i in 0..<ops.count {
for i in 0..<(ops.count - except) {
let op = ops[i]
do {
try op.run(device: device, buffer: buffer)
......@@ -112,51 +135,57 @@ public class Executor<P: PrecisionType> {
var outputTextures: [String : [Variant]]?
if except > 0 {
ops[ops.count - except].computeMiddleResult(device: device, buffer: buffer)
outputTextures = ops[ops.count - except].inputVariant()
}
buffer.addCompletedHandler { [weak self] (commandbuffer) in
// let inputArr = resInput.toTensor(dim: (n: dim[0], c: dim[3], h: dim[1], w: dim[2]))
//// print(inputArr.strideArray())
// print(dim)
// print(inputArr.strideArray())
//
//// print(dim)
// writeToLibrary(fileName: "test_image_ssd_ar", array: inputArr)
// print(" write done ")
// print("write to library done")
// return
// print(inputArr)
// let stridableInput: [(index: Int, value: Float)] = input.stridableFloatArray()
// print(stridableInput)
// let _: Flo? = input.logDesc(header: "input: ", stridable: true)
// for i in 0..<self.ops.count {
// let op = self.ops[i]
// print(inputArr)
//
// let stridableInput: [(index: Int, value: Float)] = input.stridableFloatArray()
// print(stridableInput)
//
// let _: Flo? = input.logDesc(header: "input: ", stridable: true)
// for i in 0..<self!.ops.count {
// let op = self!.ops[i]
// print(" 第 \(i) 个 op: ")
// op.delogOutput()
// }
// return;
// self.ops[testTo - 2].delogOutput()
// self.ops[testTo - 1].delogOutput()
// self.ops[60].delogOutput()
// self!.ops[testTo - 2].delogOutput()
// self!.ops[testTo - 1].delogOutput()
// self!.ops[60].delogOutput()
// return
guard let SSelf = self else {
// return
fatalError()
}
let afterDate = Date.init()
var resultHolder: ResultHolder
var resultHolder: GPUResultHolder
if except > 0 {
resultHolder = ResultHolder.init(inDim: [], inResult: [], inElapsedTime: afterDate.timeIntervalSince(beforeDate), inIntermediateResults: outputTextures)
resultHolder = GPUResultHolder.init(inDim: [], inPointer: nil, inCapacity: 0, inElapsedTime: afterDate.timeIntervalSince(beforeDate), inIntermediateResults: outputTextures)
} else {
let outputVar: Variant = SSelf.program.scope.output()!
let output: Texture<P> = outputVar as! Texture<P>
let output: FetchHolder = outputVar as! FetchHolder
// let beforeToTensorDate = Date.init()
resultHolder = GPUResultHolder.init(inDim: output.dim, inPointer: output.result, inCapacity: output.capacity, inElapsedTime: afterDate.timeIntervalSince(beforeDate))
resultHolder = ResultHolder.init(inDim: output.dim.dims, inResult: output.toTensor(), inElapsedTime: afterDate.timeIntervalSince(beforeDate))
// let timeToTensor = Date.init().timeIntervalSince(beforeToTensorDate)
// print(timeToTensor)
}
completionHandle(resultHolder)
......
metal/paddle-mobile/paddle-mobile/framework/Loader.swift
浏览文件 @ b7a1d552
......@@ -214,7 +214,7 @@ public class Loader<P: PrecisionType> {
}
} else {
if varDesc.name == fetchKey {
scope[varDesc.name] = ResultHolder.init(inDim: [], inResult: [], inElapsedTime: 0.0)
// scope[varDesc.name] = ResultHolder.init(inDim: [], inResult: [], inCapacity: <#Int#>, inElapsedTime: 0.0)
} else if varDesc.name == feedKey {
}
}
......
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