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提交 dcef687f 编写于 作者: L liuruilong
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add pribox nms convTranspose prelu op

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metal/PreluKernel/PreluKernel.xcodeproj/project.pbxproj 0 → 100644
浏览文件 @ dcef687f
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metal/PreluKernel/PreluKernel.xcodeproj/project.xcworkspace/contents.xcworkspacedata 0 → 100644
浏览文件 @ dcef687f
<?xml version="1.0" encoding="UTF-8"?>
<Workspace
version = "1.0">
<FileRef
location = "self:PreluKernel.xcodeproj">
</FileRef>
</Workspace>
metal/PreluKernel/PreluKernel.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist 0 → 100644
浏览文件 @ dcef687f
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<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
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<true/>
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metal/PreluKernel/PreluKernel.xcodeproj/xcuserdata/liuruilong.xcuserdatad/xcschemes/xcschememanagement.plist 0 → 100644
浏览文件 @ dcef687f
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metal/PreluKernel/PreluKernel/PreluKernel.metal 0 → 100644
浏览文件 @ dcef687f
//
// 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/ModelHelper.swift
浏览文件 @ dcef687f
......@@ -15,104 +15,265 @@ import MetalPerformanceShaders
let modelHelperMap: [SupportModel : Net] = [.mobilenet : MobileNet.init(), .mobilenet_ssd : MobileNet_ssd_hand.init()]
enum SupportModel: String{
case mobilenet = "mobilenet"
case mobilenet_ssd = "mobilenetssd"
static func supportedModels() -> [SupportModel] {
return [.mobilenet, .mobilenet_ssd]
}
case mobilenet = "mobilenet"
case mobilenet_ssd = "mobilenetssd"
static func supportedModels() -> [SupportModel] {
return [.mobilenet, .mobilenet_ssd]
}
}
protocol Net {
var dim: [Int] { get }
var modelPath: String { get }
var paramPath: String { get }
var modelDir: String { get }
var preprocessKernel: CusomKernel { get }
func getTexture(image: CGImage, getTexture: @escaping (MTLTexture) -> Void)
func resultStr(res: [Float]) -> String
var dim: [Int] { get }
var modelPath: String { get }
var paramPath: String { get }
var modelDir: String { get }
var preprocessKernel: CusomKernel { get }
func getTexture(image: CGImage, getTexture: @escaping (MTLTexture) -> Void)
func resultStr(res: [Float]) -> String
}
extension Net {
func getTexture(image: CGImage, getTexture: @escaping (MTLTexture) -> Void) {
let texture = try? MetalHelper.shared.textureLoader.newTexture(cgImage: image, options: [:]) ?! " texture loader error"
MetalHelper.scaleTexture(queue: MetalHelper.shared.queue, input: texture!, size: (224, 224)) { (resTexture) in
getTexture(resTexture)
}
func getTexture(image: CGImage, getTexture: @escaping (MTLTexture) -> Void) {
let texture = try? MetalHelper.shared.textureLoader.newTexture(cgImage: image, options: [:]) ?! " texture loader error"
MetalHelper.scaleTexture(queue: MetalHelper.shared.queue, input: texture!, size: (224, 224)) { (resTexture) in
getTexture(resTexture)
}
}
}
struct MobileNet: Net{
class MobilenetPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = CusomKernel.Shape.init(inWidth: 224, inHeight: 224, inChannel: 3)
super.init(device: device, inFunctionName: "preprocess", outputDim: s, usePaddleMobileLib: false)
class MobilenetPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = CusomKernel.Shape.init(inWidth: 224, inHeight: 224, inChannel: 3)
super.init(device: device, inFunctionName: "preprocess", outputDim: s, usePaddleMobileLib: false)
}
}
class PreWords {
var contents: [String] = []
init(fileName: String, type: String = "txt", inBundle: Bundle = Bundle.main) {
if let filePath = inBundle.path(forResource: fileName, ofType: type) {
let string = try! String.init(contentsOfFile: filePath)
contents = string.components(separatedBy: CharacterSet.newlines).filter{$0.count > 10}.map{
String($0[$0.index($0.startIndex, offsetBy: 10)...])
}
}else{
fatalError("no file call \(fileName)")
}
}
subscript(index: Int) -> String {
return contents[index]
}
}
let labels = PreWords.init(fileName: "synset")
func resultStr(res: [Float]) -> String {
var s: [String] = []
res.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")
}
var preprocessKernel: CusomKernel
let dim = [1, 224, 224, 3]
let modelPath: String
let paramPath: String
let modelDir: String
init() {
modelPath = Bundle.main.path(forResource: "model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "params", ofType: nil) ?! "para null"
modelDir = ""
preprocessKernel = MobilenetPreProccess.init(device: MetalHelper.shared.device)
}
}
struct MobileNet_ssd_hand: Net{
class MobilenetssdPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = CusomKernel.Shape.init(inWidth: 300, inHeight: 300, inChannel: 3)
super.init(device: device, inFunctionName: "mobilenet_ssd_preprocess", outputDim: s, usePaddleMobileLib: false)
}
}
func resultStr(res: [Float]) -> String {
fatalError()
}
func bboxArea(box: [Float32], normalized: Bool) -> Float32 {
if box[2] < box[0] || box[3] < box[1] {
return 0.0
} else {
let w = box[2] - box[0]
let h = box[3] - box[1]
if normalized {
return w * h
} else {
return (w + 1) * (h + 1)
}
}
}
func jaccardOverLap(box1: [Float32], box2: [Float32], normalized: Bool) -> Float32 {
if box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
box2[3] < box1[1] {
return 0.0
} else {
let interXmin = max(box1[0], box2[0])
let interYmin = max(box1[1], box2[1])
let interXmax = min(box1[2], box2[2])
let interYmax = min(box1[3], box2[3])
let interW = interXmax - interXmin
let interH = interYmax - interYmin
let interArea = interW * interH
let bbox1Area = bboxArea(box: box1, normalized: normalized)
let bbox2Area = bboxArea(box: box2, normalized: normalized)
return interArea / (bbox1Area + bbox2Area - interArea)
}
}
func fetchResult(paddleMobileRes: [String : Texture<Float32>]) -> [Float32]{
let bbox = paddleMobileRes["box_coder_0.tmp_0"] ?! " no bbox "
let scores = paddleMobileRes["transpose_12.tmp_0"] ?! " no scores "
let score_thredshold: Float32 = 0.01
let nms_top_k = 400
let keep_top_k = 200
let nms_eta: Float32 = 1.0
var nms_threshold: Float32 = 0.45
class PreWords {
var contents: [String] = []
init(fileName: String, type: String = "txt", inBundle: Bundle = Bundle.main) {
if let filePath = inBundle.path(forResource: fileName, ofType: type) {
let string = try! String.init(contentsOfFile: filePath)
contents = string.components(separatedBy: CharacterSet.newlines).filter{$0.count > 10}.map{
String($0[$0.index($0.startIndex, offsetBy: 10)...])
}
}else{
fatalError("no file call \(fileName)")
}
}
subscript(index: Int) -> String{
return contents[index]
}
let bboxArr = bbox.metalTexture.floatArray { (f) -> Float32 in
return f
}
let labels = PreWords.init(fileName: "synset")
func resultStr(res: [Float]) -> String {
var s: [String] = []
res.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")
let scoresArr = scores.metalTexture.floatArray { (f) -> Float32 in
return f
}
var preprocessKernel: CusomKernel
let dim = [1, 224, 224, 3]
let modelPath: String
let paramPath: String
let modelDir: String
var scoreFormatArr: [Float32] = []
var outputArr: [Float32] = []
init() {
modelPath = Bundle.main.path(forResource: "model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "params", ofType: nil) ?! "para null"
modelDir = ""
preprocessKernel = MobilenetPreProccess.init(device: MetalHelper.shared.device)
let numOfOneC = (scores.originDim[2] + 3) / 4 // 480
let cNumOfOneClass = numOfOneC * 4 // 1920
let boxSize = bbox.originDim[2] // 4
let classNum = scores.originDim[1] // 7
let classNumOneTexture = classNum * 4 // 28
for c in 0..<classNum {
for n in 0..<numOfOneC {
let to = n * classNumOneTexture + c * 4
scoreFormatArr.append(scoresArr[to])
scoreFormatArr.append(scoresArr[to + 1])
scoreFormatArr.append(scoresArr[to + 2])
scoreFormatArr.append(scoresArr[to + 3])
}
}
}
struct MobileNet_ssd_hand: Net{
class MobilenetssdPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = CusomKernel.Shape.init(inWidth: 300, inHeight: 300, inChannel: 3)
super.init(device: device, inFunctionName: "mobilenet_ssd_preprocess", outputDim: s, usePaddleMobileLib: false)
var selectedIndexs: [Int : [(Int, Float32)]] = [:]
var numDet: Int = 0
for i in 0..<classNum {
var sliceScore = scoreFormatArr[(i * cNumOfOneClass)..<((i + 1) * cNumOfOneClass)]
var scoreThresholdArr: [(Float32, Int)] = []
for i in 0..<cNumOfOneClass {
if sliceScore[i] > score_thredshold {
scoreThresholdArr.append((sliceScore[i], i))
}
}
scoreThresholdArr.sort { $0 > $1 }
if scoreThresholdArr.count > nms_top_k {
scoreThresholdArr.removeLast(scoreThresholdArr.count - nms_top_k)
}
var selectedIndex: [(Int, Float32)] = []
while scoreThresholdArr.count > 0 {
let idx = scoreThresholdArr[0].1
let score = scoreThresholdArr[0].0
var keep = true
for j in 0..<selectedIndex.count {
if keep {
let keptIdx = selectedIndex[j].0
let box1 = Array<Float32>(bboxArr[(idx * boxSize)..<(idx * boxSize + 4)])
let box2 = Array<Float32>(bboxArr[(idx * boxSize)..<(keptIdx * boxSize + 4)])
let overlap = jaccardOverLap(box1: box1, box2: box2, normalized: true)
keep = (overlap <= nms_threshold)
} else {
break
}
}
if keep {
selectedIndex.append((idx, score))
}
scoreThresholdArr.removeFirst()
if keep && nms_eta < 1.0 && nms_threshold > 0.5 {
nms_threshold *= nms_eta
}
}
selectedIndexs[i] = selectedIndex
numDet += selectedIndex.count
}
func resultStr(res: [Float]) -> String {
fatalError()
var scoreIndexPairs: [(Float32, (Int, Int))] = []
for selected in selectedIndexs {
for scoreIndex in selected.value {
scoreIndexPairs.append((scoreIndex.1, (selected.key, scoreIndex.0)))
}
}
var preprocessKernel: CusomKernel
let dim = [1, 300, 300, 3]
let modelPath: String
let paramPath: String
let modelDir: String
scoreIndexPairs.sort { $0.0 > $1.0 }
if scoreIndexPairs.count > keep_top_k {
scoreIndexPairs.removeLast(scoreIndexPairs.count - keep_top_k)
}
init() {
modelPath = Bundle.main.path(forResource: "ssd_hand_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "ssd_hand_params", ofType: nil) ?! "para null"
modelDir = ""
preprocessKernel = MobilenetssdPreProccess.init(device: MetalHelper.shared.device)
var newIndices: [Int : [(Int, Float32)]] = [:]
for scoreIndexPair in scoreIndexPairs {
// label: scoreIndexPair.1.0
let label = scoreIndexPair.1.0
if newIndices[label] != nil {
newIndices[label]?.append((scoreIndexPair.1.0, scoreIndexPair.0))
} else {
newIndices[label] = [(scoreIndexPair.1.0, scoreIndexPair.0)]
}
}
for indice in newIndices {
let selectedIndexAndScore = indice.value
for indexAndScore in selectedIndexAndScore {
outputArr.append(Float32(indice.key)) // label
outputArr.append(indexAndScore.1) // score
let subBox = bboxArr[(indexAndScore.0 * boxSize)..<(indexAndScore.0 * boxSize + 4)]
outputArr.append(contentsOf: subBox)
}
}
return outputArr
}
var preprocessKernel: CusomKernel
let dim = [1, 300, 300, 3]
let modelPath: String
let paramPath: String
let modelDir: String
init() {
modelPath = Bundle.main.path(forResource: "ssd_hand_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "ssd_hand_params", ofType: nil) ?! "para null"
modelDir = ""
preprocessKernel = MobilenetssdPreProccess.init(device: MetalHelper.shared.device)
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/ViewController.swift
浏览文件 @ dcef687f
......@@ -20,158 +20,157 @@ import MetalPerformanceShaders
let threadSupport = [1]
class ViewController: UIViewController {
@IBOutlet weak var resultTextView: UITextView!
@IBOutlet weak var selectImageView: UIImageView!
@IBOutlet weak var elapsedTimeLabel: UILabel!
@IBOutlet weak var modelPickerView: UIPickerView!
@IBOutlet weak var threadPickerView: UIPickerView!
var selectImage: UIImage?
var program: Program?
var executor: Executor<Float32>?
var modelType: SupportModel = SupportModel.supportedModels()[0]
var toPredictTexture: MTLTexture?
var modelHelper: Net {
return modelHelperMap[modelType] ?! " has no this type "
@IBOutlet weak var resultTextView: UITextView!
@IBOutlet weak var selectImageView: UIImageView!
@IBOutlet weak var elapsedTimeLabel: UILabel!
@IBOutlet weak var modelPickerView: UIPickerView!
@IBOutlet weak var threadPickerView: UIPickerView!
var selectImage: UIImage?
var program: Program?
var executor: Executor<Float32>?
var modelType: SupportModel = SupportModel.supportedModels()[0]
var toPredictTexture: MTLTexture?
var modelHelper: Net {
return modelHelperMap[modelType] ?! " has no this type "
}
var threadNum = 1
@IBAction func loadAct(_ sender: Any) {
let inModelHelper = modelHelper
let queue = MetalHelper.shared.queue
let loader = Loader<Float32>.init()
do {
let modelPath = inModelHelper.modelPath
let paraPath = inModelHelper.paramPath
program = try loader.load(device: MetalHelper.shared.device, modelPath: modelPath, paraPath: paraPath)
executor = try Executor<Float32>.init(inDevice: MetalHelper.shared.device, inQueue: queue, inProgram: program!)
} catch let error {
print(error)
}
var threadNum = 1
@IBAction func loadAct(_ sender: Any) {
let inModelHelper = modelHelper
let queue = MetalHelper.shared.queue
let loader = Loader<Float32>.init()
do {
let modelPath = inModelHelper.modelPath
let paraPath = inModelHelper.paramPath
program = try loader.load(device: MetalHelper.shared.device, modelPath: modelPath, paraPath: paraPath)
executor = try Executor<Float32>.init(inDevice: MetalHelper.shared.device, inQueue: queue, inProgram: program!)
} catch let error {
print(error)
}
}
@IBAction func selectImageAct(_ sender: Any) {
let imagePicker = UIImagePickerController()
imagePicker.sourceType = .camera
imagePicker.delegate = self
self.present(imagePicker, animated: true, completion: nil)
}
@IBAction func selectImageAct(_ sender: Any) {
let imagePicker = UIImagePickerController()
imagePicker.sourceType = .camera
imagePicker.delegate = self
self.present(imagePicker, animated: true, completion: nil)
}
@IBAction func clearAct(_ sender: Any) {
executor?.clear()
program = nil
executor = nil
}
@IBAction func predictAct(_ sender: Any) {
guard let inTexture = toPredictTexture else {
resultTextView.text = "请选择图片 ! "
return
}
@IBAction func clearAct(_ sender: Any) {
executor?.clear()
program = nil
executor = nil
guard let inExecutor = executor else {
resultTextView.text = "请先 load ! "
return
}
@IBAction func predictAct(_ sender: Any) {
guard let inTexture = toPredictTexture else {
resultTextView.text = "请选择图片 ! "
return
}
guard let inExecutor = executor else {
resultTextView.text = "请先 load ! "
return
}
do {
let max = 10
var startDate = Date.init()
for i in 0..<max {
try inExecutor.predict(input: inTexture, expect: modelHelper.dim, completionHandle: { [weak self] (result) in
guard let sSelf = self else {
fatalError()
}
if i == (max / 2 - 1) {
startDate = Date.init()
}
if i == max - 1 {
let time = Date.init().timeIntervalSince(startDate)
DispatchQueue.main.async {
sSelf.resultTextView.text = sSelf.modelHelper.resultStr(res: result.resultArr)
sSelf.elapsedTimeLabel.text = "平均耗时: \(time/Double(max/2) * 1000.0) ms"
}
}
}, preProcessKernle: self.modelHelper.preprocessKernel)
do {
let max = 10
var startDate = Date.init()
for i in 0..<max {
try inExecutor.predict(input: inTexture, expect: modelHelper.dim, completionHandle: { [weak self] (result) in
guard let sSelf = self else {
fatalError()
}
if i == (max / 2 - 1) {
startDate = Date.init()
}
if i == max - 1 {
let time = Date.init().timeIntervalSince(startDate)
DispatchQueue.main.async {
sSelf.resultTextView.text = sSelf.modelHelper.resultStr(res: result.resultArr)
sSelf.elapsedTimeLabel.text = "平均耗时: \(time/Double(max/2) * 1000.0) ms"
}
} catch let error {
print(error)
}
}
}, preProcessKernle: self.modelHelper.preprocessKernel)
}
} catch let error {
print(error)
}
override func viewDidLoad() {
super.viewDidLoad()
modelPickerView.delegate = self
modelPickerView.dataSource = self
threadPickerView.delegate = self
threadPickerView.dataSource = self
selectImage = UIImage.init(named: "banana.jpeg")
selectImageView.image = selectImage
modelHelper.getTexture(image: selectImage!.cgImage!) {[weak self] (texture) in
self?.toPredictTexture = texture
}
}
override func viewDidLoad() {
super.viewDidLoad()
modelPickerView.delegate = self
modelPickerView.dataSource = self
threadPickerView.delegate = self
threadPickerView.dataSource = self
selectImage = UIImage.init(named: "banana.jpeg")
selectImageView.image = selectImage
modelHelper.getTexture(image: selectImage!.cgImage!) {[weak self] (texture) in
self?.toPredictTexture = texture
}
}
}
extension ViewController: UIPickerViewDataSource, UIPickerViewDelegate{
func numberOfComponents(in pickerView: UIPickerView) -> Int {
if pickerView == modelPickerView {
return 1
} else if pickerView == threadPickerView {
return 1
} else {
fatalError()
}
func numberOfComponents(in pickerView: UIPickerView) -> Int {
if pickerView == modelPickerView {
return 1
} else if pickerView == threadPickerView {
return 1
} else {
fatalError()
}
func pickerView(_ pickerView: UIPickerView, numberOfRowsInComponent component: Int) -> Int {
if pickerView == modelPickerView {
return SupportModel.supportedModels().count
} else if pickerView == threadPickerView {
return threadSupport.count
} else {
fatalError()
}
}
func pickerView(_ pickerView: UIPickerView, numberOfRowsInComponent component: Int) -> Int {
if pickerView == modelPickerView {
return SupportModel.supportedModels().count
} else if pickerView == threadPickerView {
return threadSupport.count
} else {
fatalError()
}
public func pickerView(_ pickerView: UIPickerView, titleForRow row: Int, forComponent component: Int) -> String? {
if pickerView == modelPickerView {
return SupportModel.supportedModels()[row].rawValue
} else if pickerView == threadPickerView {
return "\(threadSupport[row])"
} else {
fatalError()
}
}
public func pickerView(_ pickerView: UIPickerView, titleForRow row: Int, forComponent component: Int) -> String? {
if pickerView == modelPickerView {
return SupportModel.supportedModels()[row].rawValue
} else if pickerView == threadPickerView {
return "\(threadSupport[row])"
} else {
fatalError()
}
public func pickerView(_ pickerView: UIPickerView, didSelectRow row: Int, inComponent component: Int) {
if pickerView == modelPickerView {
self.modelType = SupportModel.supportedModels()[row]
} else if pickerView == threadPickerView {
self.threadNum = threadSupport[row]
} else {
fatalError()
}
}
public func pickerView(_ pickerView: UIPickerView, didSelectRow row: Int, inComponent component: Int) {
if pickerView == modelPickerView {
self.modelType = SupportModel.supportedModels()[row]
} else if pickerView == threadPickerView {
self.threadNum = threadSupport[row]
} else {
fatalError()
}
}
}
extension ViewController: UIImagePickerControllerDelegate, UINavigationControllerDelegate {
func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [String : Any]) {
picker.dismiss(animated: true){[weak self] in
guard let sSelf = self, let image = info["UIImagePickerControllerOriginalImage"] as? UIImage else{
fatalError("no image")
}
sSelf.selectImage = image
sSelf.selectImageView.image = image
sSelf.modelHelper.getTexture(image: image.cgImage!, getTexture: { (texture) in
sSelf.toPredictTexture = texture
})
}
func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [String : Any]) {
picker.dismiss(animated: true){[weak self] in
guard let sSelf = self, let image = info["UIImagePickerControllerOriginalImage"] as? UIImage else{
fatalError("no image")
}
sSelf.selectImage = image
sSelf.selectImageView.image = image
sSelf.modelHelper.getTexture(image: image.cgImage!, getTexture: { (texture) in
sSelf.toPredictTexture = texture
})
}
}
}
metal/paddle-mobile-unit-test/paddle-mobile-unit-test/AppDelegate.swift
浏览文件 @ dcef687f
//
// AppDelegate.swift
// paddle-mobile-unit-test
//
// Created by liuRuiLong on 2018/8/10.
// Copyright © 2018年 orange. All rights reserved.
//
/* 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 UIKit
......
metal/paddle-mobile-unit-test/paddle-mobile-unit-test/ViewController.swift
浏览文件 @ dcef687f
//
// ViewController.swift
// paddle-mobile-unit-test
//
// Created by liuRuiLong on 2018/8/10.
// Copyright © 2018年 orange. All rights reserved.
//
/* 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 UIKit
import Metal
......
metal/paddle-mobile/paddle-mobile.xcodeproj/project.pbxproj
浏览文件 @ dcef687f
......@@ -69,6 +69,14 @@
FCD04E7220F343420007374F /* ConvAddOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCD04E7120F343420007374F /* ConvAddOp.swift */; };
FCD04E7420F3437E0007374F /* ConvAddKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCD04E7320F3437E0007374F /* ConvAddKernel.swift */; };
FCDC0FEB21099A1D00DC9EFB /* Tools.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCDC0FEA21099A1D00DC9EFB /* Tools.swift */; };
FCDDC6C6212F9FB800E5EF74 /* PreluKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCDDC6C5212F9FB800E5EF74 /* PreluKernel.swift */; };
FCDDC6C8212FA3CA00E5EF74 /* ConvTransposeKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCDDC6C7212FA3CA00E5EF74 /* ConvTransposeKernel.swift */; };
FCDDC6CA212FDF6800E5EF74 /* BatchNormKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCDDC6C9212FDF6800E5EF74 /* BatchNormKernel.metal */; };
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 */; };
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 */; };
FCEBC0F620F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCEBC0F520F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift */; };
FCF2D73820E64E70007AC5F5 /* Kernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCF2D73720E64E70007AC5F5 /* Kernel.swift */; };
......@@ -141,9 +149,17 @@
FCD04E7120F343420007374F /* ConvAddOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvAddOp.swift; sourceTree = "<group>"; };
FCD04E7320F3437E0007374F /* ConvAddKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvAddKernel.swift; sourceTree = "<group>"; };
FCDC0FEA21099A1D00DC9EFB /* Tools.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Tools.swift; sourceTree = "<group>"; };
FCDDC6C5212F9FB800E5EF74 /* PreluKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = PreluKernel.swift; sourceTree = "<group>"; };
FCDDC6C7212FA3CA00E5EF74 /* ConvTransposeKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvTransposeKernel.swift; sourceTree = "<group>"; };
FCDDC6C9212FDF6800E5EF74 /* BatchNormKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = BatchNormKernel.metal; sourceTree = "<group>"; };
FCDDC6CB212FDFDB00E5EF74 /* ReluKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ReluKernel.metal; sourceTree = "<group>"; };
FCDDC6CE212FE14700E5EF74 /* PriorBoxKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = PriorBoxKernel.metal; sourceTree = "<group>"; };
FCDE8A32212A917900F4A8F6 /* ConvTransposeOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvTransposeOp.swift; 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; };
FCEBC0F520F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvAddBatchNormReluKernel.swift; sourceTree = "<group>"; };
FCF2D73720E64E70007AC5F5 /* Kernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; name = Kernel.swift; path = "paddle-mobile/Operators/Kernels/Kernel.swift"; sourceTree = SOURCE_ROOT; };
FCF2D73720E64E70007AC5F5 /* Kernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; name = Kernel.swift; path = "paddle-mobile/Operators/Kernels/Base/Kernel.swift"; sourceTree = SOURCE_ROOT; };
/* End PBXFileReference section */
/* Begin PBXFrameworksBuildPhase section */
......@@ -255,6 +271,8 @@
FCBCCC66212306B000D94F7E /* ConcatOp.swift */,
FCBCCC6A2123071700D94F7E /* BoxcoderOp.swift */,
FCBCCC6E2123097100D94F7E /* MulticlassNMSOp.swift */,
FCDE8A32212A917900F4A8F6 /* ConvTransposeOp.swift */,
FCEB684B212F093800D2448E /* PreluOp.swift */,
);
path = Operators;
sourceTree = "<group>";
......@@ -279,15 +297,15 @@
FC086BA520E67E8500D85EF7 /* Kernels */ = {
isa = PBXGroup;
children = (
FCDDC6CD212FE02100E5EF74 /* Base */,
FCEB6837212F00B100D2448E /* metal */,
FCDDC6C7212FA3CA00E5EF74 /* ConvTransposeKernel.swift */,
FC0E2DBB20EE45FE009C1FAC /* ConvKernel.swift */,
FCF2D73720E64E70007AC5F5 /* Kernel.swift */,
FC1B16B220EC9A4F00678B91 /* Kernels.metal */,
FC1B186520ECF1C600678B91 /* ResizeKernel.swift */,
FC0E2DB920EE3B8D009C1FAC /* ReluKernel.swift */,
FC0E2DBD20EE460D009C1FAC /* BatchNormKernel.swift */,
FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */,
FC5163F520EF556E00636C28 /* Texture2DTo2DArrayKernel.swift */,
FC4CB74820F0B954007C0C6D /* ConvKernel.metal */,
FCEBC0F520F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift */,
FCD04E6720F315020007374F /* PoolKernel.swift */,
FCD04E6B20F31A280007374F /* SoftmaxKernel.swift */,
......@@ -299,6 +317,7 @@
FCBCCC68212306D300D94F7E /* ConcatKernel.swift */,
FCBCCC6C2123073A00D94F7E /* BoxcoderKernel.swift */,
FCBCCC70212309A700D94F7E /* MulticlassNMSKernel.swift */,
FCDDC6C5212F9FB800E5EF74 /* PreluKernel.swift */,
);
path = Kernels;
sourceTree = "<group>";
......@@ -313,6 +332,27 @@
path = Base;
sourceTree = "<group>";
};
FCDDC6CD212FE02100E5EF74 /* Base */ = {
isa = PBXGroup;
children = (
FCF2D73720E64E70007AC5F5 /* Kernel.swift */,
);
path = Base;
sourceTree = "<group>";
};
FCEB6837212F00B100D2448E /* metal */ = {
isa = PBXGroup;
children = (
FC1B16B220EC9A4F00678B91 /* Kernels.metal */,
FC4CB74820F0B954007C0C6D /* ConvKernel.metal */,
FCEB6849212F00DB00D2448E /* PreluKernel.metal */,
FCDDC6C9212FDF6800E5EF74 /* BatchNormKernel.metal */,
FCDDC6CB212FDFDB00E5EF74 /* ReluKernel.metal */,
FCDDC6CE212FE14700E5EF74 /* PriorBoxKernel.metal */,
);
path = metal;
sourceTree = "<group>";
};
/* End PBXGroup section */
/* Begin PBXHeadersBuildPhase section */
......@@ -417,6 +457,7 @@
FC039B9F20E11CB20081E9F8 /* Tensor.swift in Sources */,
FC0E2DBC20EE45FE009C1FAC /* ConvKernel.swift in Sources */,
FC039BAA20E11CBC0081E9F8 /* ElementwiseAddOp.swift in Sources */,
FCDE8A33212A917900F4A8F6 /* ConvTransposeOp.swift in Sources */,
FCBCCC6B2123071700D94F7E /* BoxcoderOp.swift in Sources */,
FC039B9B20E11CA00081E9F8 /* Executor.swift in Sources */,
FCD04E7020F31B720007374F /* ReshapeKernel.swift in Sources */,
......@@ -426,11 +467,15 @@
FC3602CC2108819F00FACB58 /* PaddleMobileUnitTest.swift in Sources */,
FC1B186620ECF1C600678B91 /* ResizeKernel.swift in Sources */,
FCF2D73820E64E70007AC5F5 /* Kernel.swift in Sources */,
FCDDC6CC212FDFDB00E5EF74 /* ReluKernel.metal in Sources */,
FCDDC6C6212F9FB800E5EF74 /* PreluKernel.swift in Sources */,
FCBCCC5B2122F66F00D94F7E /* ConvBNReluKernel.swift in Sources */,
FCEBC0F420F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift in Sources */,
FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */,
FCEB684C212F093800D2448E /* PreluOp.swift in Sources */,
FC60DB8920E9AAA500FF203F /* MetalExtension.swift in Sources */,
FCEBC0F620F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift in Sources */,
FCDDC6CA212FDF6800E5EF74 /* BatchNormKernel.metal in Sources */,
FC1B16B320EC9A4F00678B91 /* Kernels.metal in Sources */,
FC039BBA20E11CC20081E9F8 /* TensorDesc.swift in Sources */,
FC039BA020E11CB20081E9F8 /* Dim.swift in Sources */,
......@@ -456,6 +501,7 @@
FC0E2DBA20EE3B8D009C1FAC /* ReluKernel.swift in Sources */,
FCBCCC6D2123073A00D94F7E /* BoxcoderKernel.swift in Sources */,
FCBCCC69212306D300D94F7E /* ConcatKernel.swift in Sources */,
FCDDC6C8212FA3CA00E5EF74 /* ConvTransposeKernel.swift in Sources */,
FC82735920E3C04200BE430A /* OpCreator.swift in Sources */,
FCBCCC5D2122F8A100D94F7E /* DepthwiseConvOp.swift in Sources */,
FC0E2DBE20EE460D009C1FAC /* BatchNormKernel.swift in Sources */,
......@@ -468,6 +514,8 @@
FC039BA220E11CB70081E9F8 /* Loader.swift in Sources */,
FCBCCC67212306B000D94F7E /* ConcatOp.swift in Sources */,
FCD04E6C20F31A280007374F /* SoftmaxKernel.swift in Sources */,
FCEB684A212F00DB00D2448E /* PreluKernel.metal in Sources */,
FCDDC6CF212FE14700E5EF74 /* PriorBoxKernel.metal in Sources */,
FC4CB74B20F12C30007C0C6D /* ProgramOptimize.swift in Sources */,
FC5163F620EF556E00636C28 /* Texture2DTo2DArrayKernel.swift in Sources */,
FC039BC020E11CC20081E9F8 /* BlockDesc.swift in Sources */,
......
metal/paddle-mobile/paddle-mobile/Executor.swift
浏览文件 @ dcef687f
......@@ -17,11 +17,13 @@ import Foundation
public class ResultHolder<P: PrecisionType> {
public let dim: [Int]
public let resultArr: [P]
public var intermediateResults: [Texture<P>]?
public let elapsedTime: Double
public init(inDim: [Int], inResult: [P], inElapsedTime: Double) {
public init(inDim: [Int], inResult: [P], inElapsedTime: Double, inIntermediateResults: [Texture<P>]? = nil) {
dim = inDim
resultArr = inResult
elapsedTime = inElapsedTime
intermediateResults = inIntermediateResults
}
}
......@@ -69,7 +71,7 @@ public class Executor<P: PrecisionType> {
}
}
}
}
}
public func predict(input: MTLTexture, expect: [Int], completionHandle: @escaping (ResultHolder<P>) -> Void, preProcessKernle: CusomKernel? = nil) throws {
guard let buffer = queue.makeCommandBuffer() else {
......@@ -116,7 +118,6 @@ public class Executor<P: PrecisionType> {
// self.ops[2].delogOutput()
let afterDate = Date.init()
guard let outputVar = self.program.scope.output() else {
......
metal/paddle-mobile/paddle-mobile/Operators/Base/OpParam.swift
浏览文件 @ dcef687f
......@@ -22,147 +22,194 @@ import Foundation
*/
protocol OpParam {
associatedtype OutputType: Variant
var output: OutputType { get set }
func outputDesc() -> String
associatedtype ParamPrecisionType: PrecisionType
init(opDesc: OpDesc, inScope: Scope) throws
static func getFirstTensor<VarType: Variant>(key: String, map: [String : [String]], from: Scope) throws -> VarType
static func inputX<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputBiase<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputMean<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputScale<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputVariance<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputFilter<VarType: Variant>(paraInputs: [String : [String]], from: Scope) throws -> VarType
static func input<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func output<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType
static func outputY<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType
static func inputY<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func outputOut<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType
static func getAttr<T>(key: String, attrs: [String : Attr]) throws -> T
associatedtype OutputType: Variant
var output: OutputType { get set }
func outputDesc() -> String
associatedtype ParamPrecisionType: PrecisionType
init(opDesc: OpDesc, inScope: Scope) throws
static func getFirstTensor<VarType: Variant>(key: String, map: [String : [String]], from: Scope) throws -> VarType
static func inputX<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputBiase<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputMean<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputScale<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputVariance<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputFilter<VarType: Variant>(paraInputs: [String : [String]], from: Scope) throws -> VarType
static func input<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func output<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType
static func outputY<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType
static func inputY<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func inputImage<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
static func outputBoxes<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType
static func outputOut<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType
static func outputVariances<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType
static func getAttr<T>(key: String, attrs: [String : Attr]) throws -> T
static func inputAlpha<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType
}
extension OpParam {
func outputDesc() -> String {
return output.debugDescription
func outputDesc() -> String {
return output.debugDescription
}
static func getFirstTensor<VarType: Variant>(key: String, map: [String : [String]], from: Scope) throws -> VarType {
guard let mapKeys = map[key], mapKeys.count > 0 else {
throw PaddleMobileError.paramError(message: key + " not found in \(map) or maped values is empty")
}
static func getFirstTensor<VarType: Variant>(key: String, map: [String : [String]], from: Scope) throws -> VarType {
guard let mapKeys = map[key], mapKeys.count > 0 else {
throw PaddleMobileError.paramError(message: key + " not found in \(map) or maped values is empty")
}
guard let variant = from[mapKeys[0]], let v = variant as? VarType else {
throw PaddleMobileError.paramError(message: mapKeys[0] + " not found in scope")
}
return v
guard let variant = from[mapKeys[0]], let v = variant as? VarType else {
throw PaddleMobileError.paramError(message: mapKeys[0] + " not found in scope")
}
static func inputX<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorX: VarType = try getFirstTensor(key: "X", map: inputs, from: from)
return tensorX
} catch let error {
throw error
}
return v
}
static func outputVariances<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorVariances: VarType = try getFirstTensor(key: "Variances", map: outputs, from: from)
return tensorVariances
} catch let error {
throw error
}
static func input<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorInput: VarType = try getFirstTensor(key: "Input", map: inputs, from: from)
return tensorInput
} catch let error {
throw error
}
}
static func inputAlpha<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let alphaTensor: VarType = try getFirstTensor(key: "Alpha", map: inputs, from: from)
return alphaTensor
} catch let error {
throw error
}
static func output<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorOutput: VarType = try getFirstTensor(key: "Output", map: outputs, from: from)
return tensorOutput
} catch let error {
throw error
}
}
static func outputY<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorOutputY: VarType = try getFirstTensor(key: "Y", map: outputs, from: from)
return tensorOutputY
} catch let error {
throw error
}
}
static func inputY<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorY: VarType = try getFirstTensor(key: "Y", map: inputs, from: from)
return tensorY
} catch let error {
throw error
}
}
static func inputImage<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorImage: VarType = try getFirstTensor(key: "Image", map: inputs, from: from)
return tensorImage
} catch let error {
throw error
}
static func outputOut<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType {
do {
let out: VarType = try getFirstTensor(key: "Out", map: outputs, from: from)
return out
} catch let error {
throw error
}
}
static func inputFilter<VarType: Variant>(paraInputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorFilter: VarType = try getFirstTensor(key: "Filter", map: paraInputs, from: from)
return tensorFilter
} catch let error {
throw error
}
}
static func inputX<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorX: VarType = try getFirstTensor(key: "X", map: inputs, from: from)
return tensorX
} catch let error {
throw error
}
static func inputBiase<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorBias: VarType = try getFirstTensor(key: "Bias", map: inputs, from: from)
return tensorBias
} catch let error {
throw error
}
}
static func outputBoxes<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorBox: VarType = try getFirstTensor(key: "Boxes", map: outputs, from: from)
return tensorBox
} catch let error {
throw error
}
static func inputMean<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorMean: VarType = try getFirstTensor(key: "Mean", map: inputs, from: from)
return tensorMean
} catch let error {
throw error
}
}
static func input<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorInput: VarType = try getFirstTensor(key: "Input", map: inputs, from: from)
return tensorInput
} catch let error {
throw error
}
static func inputScale<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorScale: VarType = try getFirstTensor(key: "Scale", map: inputs, from: from)
return tensorScale
} catch let error {
throw error
}
}
static func output<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorOutput: VarType = try getFirstTensor(key: "Output", map: outputs, from: from)
return tensorOutput
} catch let error {
throw error
}
static func inputVariance<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorVariance: VarType = try getFirstTensor(key: "Variance", map: inputs, from: from)
return tensorVariance
} catch let error {
throw error
}
}
static func outputY<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorOutputY: VarType = try getFirstTensor(key: "Y", map: outputs, from: from)
return tensorOutputY
} catch let error {
throw error
}
}
static func inputY<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorY: VarType = try getFirstTensor(key: "Y", map: inputs, from: from)
return tensorY
} catch let error {
throw error
}
}
static func outputOut<VarType: Variant>(outputs: [String : [String]], from: Scope) throws -> VarType {
do {
let out: VarType = try getFirstTensor(key: "Out", map: outputs, from: from)
return out
} catch let error {
throw error
}
}
static func inputFilter<VarType: Variant>(paraInputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorFilter: VarType = try getFirstTensor(key: "Filter", map: paraInputs, from: from)
return tensorFilter
} catch let error {
throw error
}
}
static func inputBiase<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorBias: VarType = try getFirstTensor(key: "Bias", map: inputs, from: from)
return tensorBias
} catch let error {
throw error
}
}
static func inputMean<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorMean: VarType = try getFirstTensor(key: "Mean", map: inputs, from: from)
return tensorMean
} catch let error {
throw error
}
}
static func inputScale<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorScale: VarType = try getFirstTensor(key: "Scale", map: inputs, from: from)
return tensorScale
} catch let error {
throw error
}
}
static func inputVariance<VarType: Variant>(inputs: [String : [String]], from: Scope) throws -> VarType {
do {
let tensorVariance: VarType = try getFirstTensor(key: "Variance", map: inputs, from: from)
return tensorVariance
} catch let error {
throw error
}
}
static func getAttr<T>(key: String, attrs: [String : Attr]) throws -> T{
guard let attr = attrs[key] else {
throw PaddleMobileError.paramError(message: "attr \(key) can't found in: \(attrs)" )
}
static func getAttr<T>(key: String, attrs: [String : Attr]) throws -> T{
guard let attr = attrs[key] else {
throw PaddleMobileError.paramError(message: "attr \(key) can't found in: \(attrs)" )
}
guard let tAttr = attr as? T else {
throw PaddleMobileError.paramError(message: "key: \(key) attr: \(attr) type error" )
}
return tAttr
guard let tAttr = attr as? T else {
throw PaddleMobileError.paramError(message: "key: \(key) attr: \(attr) type error" )
}
return tAttr
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvAddBatchNormReluOp.swift
浏览文件 @ dcef687f
......@@ -14,10 +14,12 @@
import Foundation
class ConvAddBatchNormReluParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
filter = try ConvAddBatchNormReluParam.inputFilter(paraInputs: opDesc.paraInputs, from: inScope)
input = try ConvAddBatchNormReluParam.input(inputs: opDesc.inputs, from: inScope)
output = try ConvAddBatchNormReluParam.outputOut(outputs: opDesc.outputs, from: inScope)
......@@ -29,6 +31,7 @@ class ConvAddBatchNormReluParam<P: PrecisionType>: OpParam {
groups = try ConvAddBatchNormReluParam.getAttr(key: "groups", attrs: opDesc.attrs)
variance = try ConvAddBatchNormReluParam.inputVariance(inputs: opDesc.paraInputs, from: inScope)
bias = try ConvAddBatchNormReluParam.inputBiase(inputs: opDesc.paraInputs, from: inScope)
scale = try ConvAddBatchNormReluParam.inputScale(inputs: opDesc.paraInputs, from: inScope)
mean = try ConvAddBatchNormReluParam.inputMean(inputs: opDesc.paraInputs, from: inScope)
y = try ConvAddBatchNormReluParam.inputY(inputs: opDesc.paraInputs, from: inScope)
......
metal/paddle-mobile/paddle-mobile/Operators/ConvAddOp.swift
浏览文件 @ dcef687f
......@@ -61,6 +61,7 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
typealias OpType = ConvAddOp<P>
func inferShape() {
let inDims = para.input.dim
let filterDim = para.filter.dim
let strides = para.stride
......
metal/paddle-mobile/paddle-mobile/Operators/ConvTransposeOp.swift 0 → 100644
浏览文件 @ dcef687f
///* 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 ConvTransposeParam<P: PrecisionType>: ConvParam<P> {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
try super.init(opDesc: opDesc, inScope: inScope)
} catch let error {
throw error
}
}
}
class ConvTransposeOp<P: PrecisionType>: Operator<ConvTransposeKernel<P>, ConvTransposeParam<P>>, Runable, Creator, InferShaperable{
func inferShape() {
// para.output.dim = para.input.dim
}
typealias OpType = ConvTransposeOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
}
func delogOutput() {
print("conv transpose delog")
let _: P? = para.input.metalTexture.logDesc(header: "conv transpose input: ", stridable: true)
let _: P? = para.output.metalTexture.logDesc(header: "conv transpose output: ", stridable: true)
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddBatchNormReluKernel.swift
浏览文件 @ dcef687f
......@@ -109,6 +109,7 @@ class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable
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)
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddKernel.swift
浏览文件 @ dcef687f
......@@ -13,38 +13,44 @@
limitations under the License. */
import Foundation
import MetalPerformanceShaders
class ConvAddKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddParam<P>) {
super.init(device: device, inFunctionName: "conv_add_1x1")
let offsetX = param.filter.width/2 - Int(param.paddings[0])
let offsetY = param.filter.height/2 - Int(param.paddings[1])
param.filter.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
param.y.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
print("offset x: \(offsetX)")
print("offset y: \(offsetY)")
let offsetZ = 0.0
metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), paddedZ: UInt16(param.input.metalTexture.arrayLength * 4 - param.input.dim[3]))
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddParam<P>) {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x1")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3")
} else {
super.init(device: device, inFunctionName: "conv_add_3x3")
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvAddParam<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.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
param.output.initTexture(device: device, transpose: [0, 3, 1, 2])
let offsetX = param.filter.width/2 - Int(param.paddings[0])
let offsetY = param.filter.height/2 - Int(param.paddings[1])
param.filter.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
param.y.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
print("offset x: \(offsetX)")
print("offset y: \(offsetY)")
let offsetZ = 0.0
metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), paddedZ: UInt16(param.input.metalTexture.arrayLength * 4 - param.input.dim[3]))
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvAddParam<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.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvBNReluKernel.swift
浏览文件 @ dcef687f
......@@ -13,6 +13,7 @@
limitations under the License. */
import Foundation
import MetalPerformanceShaders
struct ConvBNReluTestParam: TestParam {
let inputTexture: MTLTexture
......@@ -24,6 +25,7 @@ struct ConvBNReluTestParam: TestParam {
let newBiaseBuffer: MTLBuffer
let filterSize: (width: Int, height: Int, channel: Int)
init(inInputTexture: MTLTexture, inOutputTexture: MTLTexture, inMetalParam: MetalConvParam, inFilterBuffer: MTLBuffer, inBiaseBuffer: MTLBuffer, inNewScaleBuffer: MTLBuffer, inNewBiaseBuffer: MTLBuffer, inFilterSize: (width: Int, height: Int, channel: Int)) {
inputTexture = inInputTexture
outputTexture = inOutputTexture
metalParam = inMetalParam
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvKernel.swift
浏览文件 @ dcef687f
......@@ -14,38 +14,46 @@
import Foundation
public struct MetalConvParam {
let offsetX: Int16
let offsetY: Int16
let offsetZ: Int16
let strideX: UInt16
let strideY: UInt16
let paddedZ: UInt16
let offsetX: Int16
let offsetY: Int16
let offsetZ: Int16
let strideX: UInt16
let strideY: UInt16
let paddedZ: UInt16
}
class ConvKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvParam<P>) {
super.init(device: device, inFunctionName: "conv_add_1x1")
let offsetX = param.filter.dim[2]/2 - Int(param.paddings[0])
let offsetY = param.filter.dim[1]/2 - Int(param.paddings[1])
let offsetZ = 0.0
param.filter.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), paddedZ: UInt16(param.input.metalTexture.arrayLength * 4 - param.input.dim[3]))
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvParam<P>) {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_1x1")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_3x3")
} else {
super.init(device: device, inFunctionName: "conv_3x3")
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvParam<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.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
let offsetX = param.filter.dim[2]/2 - Int(param.paddings[0])
let offsetY = param.filter.dim[1]/2 - Int(param.paddings[1])
let offsetZ = 0.0
param.filter.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), paddedZ: UInt16(param.input.metalTexture.arrayLength * 4 - param.input.dim[3]))
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvParam<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.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvTransposeKernel.swift 0 → 100644
浏览文件 @ dcef687f
/* 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 MetalConvTransposeParam {
let kernelW: UInt16;
let kernelH: UInt16;
let strideX: UInt16;
let strideY: UInt16;
let paddingX: UInt16;
let paddingY: UInt16;
let dilationX: UInt16;
let dilationY: UInt16;
}
class ConvTransposeKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: MetalConvTransposeParam!
required init(device: MTLDevice, param: ConvTransposeParam<P>) {
super.init(device: device, inFunctionName: "conv_transpose")
let kernelWidth = UInt16(param.filter.width)
let kernelHeight = UInt16(param.filter.height)
let strideX = UInt16(param.stride[0])
let strideY = UInt16(param.stride[1])
let paddingX = UInt16(param.paddings[0])
let paddingY = UInt16(param.paddings[1])
let dilationX = UInt16(param.dilations[0])
let dilationY = UInt16(param.dilations[1])
metalParam = MetalConvTransposeParam.init(kernelW: kernelWidth, kernelH: kernelHeight, strideX: strideX, strideY: strideY, paddingX: paddingX, paddingY: paddingY, dilationX: dilationX, dilationY: dilationY)
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvTransposeParam<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<MetalConvTransposeParam>.size, index: 0)
encoder.setBuffer(param.filter.buffer, offset: 0, index: 1)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PreluKernel.swift 0 → 100644
浏览文件 @ dcef687f
//
// PreluKernel.swift
// paddle-mobile
//
// Created by liuRuiLong on 2018/8/24.
// Copyright © 2018年 orange. All rights reserved.
//
import Foundation
class PreluKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: PreluParam<P>) {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "prelu_channel")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "prelu_element")
} else {
super.init(device: device, inFunctionName: "prelu_other")
}
}
func compute(commandBuffer: MTLCommandBuffer, param: PreluParam<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(param.alpha.buffer, offset: 0, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift
浏览文件 @ dcef687f
......@@ -14,18 +14,89 @@
import Foundation
struct PriorBoxMetalParam {
let offset: Float32
let stepWidth: Float32
let stepHeight: Float32
let minSize: Float32
let maxSize: Float32
let imageWidth: Float32
let imageHeight: Float32
let clip: Bool
let numPriors: uint
let aspecRatiosSize: uint
let minSizeSize: uint
let maxSizeSize: uint
}
class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: PriorBoxMetalParam!
required init(device: MTLDevice, param: PriorBoxParam<P>) {
super.init(device: device, inFunctionName: "priorbox")
param.output.initTexture(device: device, transpose: [2, 0, 1, 3])
param.outputVariances.initTexture(device: device, transpose: [2, 0, 1, 3])
let imageWidth = Float32(param.inputImage.originDim[3])
let imageHeight = Float32(param.inputImage.originDim[2])
let featureWidth = param.inputImage.originDim[3]
let featureHeight = param.inputImage.originDim[2]
if param.stepW == 0 || param.stepH == 0 {
param.stepW = Float32(imageWidth) / Float32(featureWidth)
param.stepH = Float32(imageHeight) / Float32(featureHeight)
}
var outputAspectRatior: [Float32] = []
outputAspectRatior.append(1.0)
let epsilon = 1e-6
for ar in param.aspectRatios {
var alreadyExist = false
for outputAr in outputAspectRatior {
if fabs(Double(ar) - Double(outputAr)) < Double(epsilon) {
alreadyExist = true
break
}
}
if !alreadyExist {
outputAspectRatior.append(ar)
}
if param.flip {
outputAspectRatior.append(1.0 / ar)
}
}
param.newAspectRatios = outputAspectRatior
let aspectRatiosSize = uint(outputAspectRatior.count)
let maxSizeSize: uint = uint(param.maxSizes.count)
let minSizeSize: uint = uint(param.minSizes.count)
let numPriors = aspectRatiosSize * minSizeSize + maxSizeSize
let minSize = param.minSizes.last ?? 0.0
let maxSize = param.maxSizes.last ?? 0.0
metalParam = PriorBoxMetalParam.init(offset: param.offset, stepWidth: param.stepW, stepHeight: param.stepH, minSize: minSize, maxSize: maxSize, imageWidth: imageWidth, imageHeight: imageHeight, clip: param.clip, numPriors: numPriors, aspecRatiosSize: aspectRatiosSize, minSizeSize: minSizeSize, maxSizeSize: maxSizeSize)
}
func compute(commandBuffer: MTLCommandBuffer, param: PriorBoxParam<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.setTexture(param.outputVariances.metalTexture, index: 2)
encoder.setBytes(&metalParam, length: MemoryLayout<PriorBoxMetalParam>.size, index: 0)
encoder.setBytes(param.aspectRatios, length: MemoryLayout<Float32>.size * param.aspectRatios.count, index: 1)
encoder.setBytes(param.variances, length: MemoryLayout<Float32>.size * param.variances.count, index: 2)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
required init(device: MTLDevice, param: PriorBoxParam<P>) {
super.init(device: device, inFunctionName: "priorbox")
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/BatchNormKernel.metal 0 → 100644
浏览文件 @ dcef687f
/* 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 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 half4 input = inTexture.read(gid.xy, gid.z);
half4 output = input * newScale[gid.z] + newBias[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)]],
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];
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvKernel.metal → metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvKernel.metal
浏览文件 @ dcef687f
......@@ -314,49 +314,6 @@ kernel void conv_add_batch_norm_relu_3x3(texture2d_array<float, access::sample>
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
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);
float4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_add_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
......@@ -398,3 +355,347 @@ kernel void depthwise_conv_add_batch_norm_relu_3x3(texture2d_array<float, access
output = fmax((output + biase[gid.z]) * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
struct MetalConvTransposeParam{
ushort kernelW;
ushort kernelH;
ushort strideX;
ushort strideY;
ushort paddingX;
ushort paddingY;
ushort dilationX;
ushort dilationY;
};
kernel void conv_transpose(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvTransposeParam &param [[buffer(0)]],
const device float4 *weights [[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;
}
int input_array_size = inTexture.get_array_size();
uint kernel_one_output_slice = input_array_size * param.kernelW * param.kernelH;
uint kernel_stride_z = gid.z * 4 * (kernel_one_output_slice);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 output;
for (int w = 0; w < param.kernelW; ++w) {
int input_x = (gid.x - w * param.dilationX + param.paddingX) / param.strideX;
if (input_x < 0 || input_x >= int(inTexture.get_width())) {
continue;
}
for (int h = 0; h < param.kernelH; ++h) {
int input_y = (gid.y - h * param.dilationY + param.paddingY) / param.strideY;
if (input_y < 0 || input_y >= int(inTexture.get_height())) {
continue;
}
uint kernel_index = (w * param.kernelH + h) * inTexture.get_array_size();
for (int slice = 0; slice < input_array_size; ++slice) {
float4 input;
float4 kernel_slice = weights[kernel_stride_z + 0 * kernel_one_output_slice + kernel_index + slice];
float4 kernel_slice1 = weights[kernel_stride_z + 1 * kernel_one_output_slice + kernel_index + slice];
float4 kernel_slice2 = weights[kernel_stride_z + 2 * kernel_one_output_slice + kernel_index + slice];
float4 kernel_slice3 = weights[kernel_stride_z + 3 * kernel_one_output_slice + kernel_index + slice];
input = inTexture.sample(sample, float2(input_x, input_x), slice);
output.x += dot(input, kernel_slice);
output.x += dot(input, kernel_slice1);
output.x += dot(input, kernel_slice2);
output.x += dot(input, kernel_slice3);
}
}
}
outTexture.write(output, gid.xy, gid.z);
}
// conv
#pragma mark -- conv
kernel void conv_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[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;
}
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);
float4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y - 1), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 1), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y - 1), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y + 1), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 1), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y + 1), i);
for (int j = 0; j < 9; ++j) {
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float *weights [[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;
}
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(0.0);
float4 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) {
float4 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];
}
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[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;
}
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);
float4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
outTexture.write(output, gid.xy, gid.z);
}
#pragma mark - convAdd
kernel void conv_add_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
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);
float4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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);
float4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y - 1), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 1), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y - 1), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y + 1), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 1), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y + 1), i);
for (int j = 0; j < 9; ++j) {
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
output = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_add_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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(0.0);
float4 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) {
float4 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];
}
output = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/Kernels.metal → metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Kernels.metal
浏览文件 @ dcef687f
......@@ -36,18 +36,6 @@ kernel void resize(texture2d<half, access::read> inTexture [[texture(0)]],
outTexture.write(half4(input.x, input.y, input.z, input.w), gid.xy, gid.z);
}
kernel void relu(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(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;
constexpr sampler s(coord::pixel, filter::nearest, address::clamp_to_zero);
const half4 input = inTexture.read(gid.xy, gid.z);
const float4 relu = fmax((float4)input, 0.0);
outTexture.write(half4(relu), gid.xy, gid.z);
}
kernel void elementwise_add(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half4 *biasTerms [[buffer(0)]],
......@@ -60,18 +48,6 @@ kernel void elementwise_add(texture2d_array<half, access::read> inTexture [[text
outTexture.write(input, gid.xy, gid.z);
}
kernel void batchnorm(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 half4 input = inTexture.read(gid.xy, gid.z);
half4 output = input * newScale[gid.z] + newBias[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
//kernel void texture2d_to_2d_array(texture2d<half, access::read> inTexture [[texture(0)]],
// texture2d_array<half, access::write> outTexture [[texture(1)]],
......@@ -230,76 +206,6 @@ kernel void softmax_half(texture2d_array<half, access::read> inTexture [[texture
outTexture.write(rr, gid.xy, gid.z);
}
kernel void prior_box(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) {
int max_sizes_size;
float max_sizes[2];
bool clip;
float img_width;
float img_height;
float step_width;
float step_height;
float offset;
float aspect_ratios[2];
int aspect_ratios_size;
float center_x = (gid.x + offset) * step_width;
float center_y = (gid.y + offset) * step_width;
float box_width, box_height;
int min_sizes_size;
float min_sizes[2];
float min_size;
float max_size;
if (gid.z < aspect_ratios_size) {
float ar = aspect_ratios[gid.z];
box_width = min_size * sqrt(ar) / 2;
box_height = min_size / sqrt(ar) / 2;
float4 box;
box.x = (center_x - box_width) / img_width;
box.y = (center_y - box_height) / img_height;
box.z = (center_x + box_width) / img_width;
box.w = (center_y + box_height) / img_height;
float4 res;
if (clip) {
res = min(max(box, 0.0), 1.0);
} else {
res = box;
}
outTexture.write(res, gid.xy, gid.z);
} else if (gid.z >= aspect_ratios_size) {
int max_index = gid.z - aspect_ratios_size;
if (max_sizes_size > 0 && min_sizes_size > 0) {
box_width = box_height = sqrt(min_size * max_size) / 2;
float4 max_box;
max_box.x = (center_x - box_width) / img_width;
max_box.y = (center_y - box_height) / img_height;
max_box.z = (center_x + box_width) / img_width;
max_box.w = (center_y + box_height) / img_height;
float4 res;
if (clip) {
res = min(max(max_box, 0.0), 1.0);
} else {
res = max_box;
}
outTexture.write(max_box, gid.xy, gid.z);
}
}
}
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/PreluKernel.metal 0 → 100644
浏览文件 @ dcef687f
/* 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 prelu_channel(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 *alpha [[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;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, gid.x, gid.y, gid.z);
float4 output;
output.x = input.x > 0 ? input.x : alpha[gid.z].x;
output.x = input.y > 0 ? input.y : alpha[gid.z].y;
output.x = input.z > 0 ? input.z : alpha[gid.z].z;
output.x = input.w > 0 ? input.w : alpha[gid.z].w;
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_element(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 *alpha [[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;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, gid.x, gid.y, gid.z);
int alpha_to = (gid.y * inTexture.get_width() + gid.x) * inTexture.get_array_size();
float4 output;
output.x = input.x > 0 ? input.x : alpha[alpha_to + gid.z].x;
output.x = input.y > 0 ? input.y : alpha[alpha_to + gid.z].y;
output.x = input.z > 0 ? input.z : alpha[alpha_to + gid.z].z;
output.x = input.w > 0 ? input.w : alpha[alpha_to + gid.z].w;
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_other(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float *alpha [[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;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, gid.x, gid.y, gid.z);
float4 output;
output.x = input.x > 0 ? input.x : alpha[0];
output.x = input.y > 0 ? input.y : alpha[0];
output.x = input.z > 0 ? input.z : alpha[0];
output.x = input.w > 0 ? input.w : alpha[0];
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/PriorBoxKernel.metal 0 → 100644
浏览文件 @ dcef687f
/* 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;
struct PriorBoxMetalParam {
float offset;
float stepWidth;
float stepHeight;
float minSize;
float maxSize;
float imageWidth;
float imageHeight;
bool clip;
uint numPriors;
uint aspecRatiosSize;
uint minSizeSize;
uint maxSizeSize;
};
kernel void prior_box(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outBoxTexture [[texture(1)]],
texture2d_array<float, access::write> varianceTexture [[texture(2)]],
constant PriorBoxMetalParam &param [[buffer(0)]],
const device float *aspect_ratios [[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 < param.aspecRatiosSize) {
float ar = aspect_ratios[gid.z];
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 = min(max(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(res, gid.xy, gid.z);
} else if (gid.z >= param.aspecRatiosSize) {
if (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(max_box, 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;
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ReluKernel.metal 0 → 100644
浏览文件 @ dcef687f
/* 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 relu_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(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;
constexpr sampler s(coord::pixel, filter::nearest, address::clamp_to_zero);
const half4 input = inTexture.read(gid.xy, gid.z);
const float4 relu = fmax((float4)input, 0.0);
outTexture.write(half4(relu), gid.xy, gid.z);
}
kernel void relu(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(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;
constexpr sampler s(coord::pixel, filter::nearest, address::clamp_to_zero);
const float4 input = inTexture.read(gid.xy, gid.z);
const float4 relu = fmax((float4)input, 0.0);
outTexture.write(float4(relu), gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/PreluOp.swift 0 → 100644
浏览文件 @ dcef687f
///* 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 PreluParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
input = try PreluParam.inputX(inputs: opDesc.inputs, from: inScope)
output = try PreluParam.outputOut(outputs: opDesc.outputs, from: inScope)
alpha = try PreluParam.inputAlpha(inputs: opDesc.inputs, from: inScope)
mode = try PreluParam.getAttr(key: "mode", attrs: opDesc.attrs)
} catch let error {
throw error
}
}
let mode: String
let alpha: Tensor<P>
let input: Texture<P>
var output: Texture<P>
}
class PreluOp<P: PrecisionType>: Operator<PreluKernel<P>, PreluParam<P>>, Runable, Creator, InferShaperable{
func inferShape() {
// para.output.dim = para.input.dim
}
typealias OpType = PreluOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
}
func delogOutput() {
print("softmax delog")
let _: P? = para.input.metalTexture.logDesc(header: "softmax input: ", stridable: false)
let _: P? = para.output.metalTexture.logDesc(header: "softmax output: ", stridable: false)
}
}
metal/paddle-mobile/paddle-mobile/Operators/PriorBoxOp.swift
浏览文件 @ dcef687f
......@@ -19,27 +19,49 @@ class PriorBoxParam<P: PrecisionType>: OpParam {
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
input = try PriorBoxParam.input(inputs: opDesc.inputs, from: inScope)
output = try PriorBoxParam.getFirstTensor(key: "Boxes", map: opDesc.outputs, from: inScope)
variances = try PriorBoxParam.getFirstTensor(key: "Variances", map: opDesc.outputs, from: inScope)
output = try PriorBoxParam.outputBoxes(outputs: opDesc.outputs, from: inScope)
inputImage = try PriorBoxParam.inputImage(inputs: opDesc.inputs, from: inScope)
outputVariances = try PriorBoxParam.outputVariances(outputs: opDesc.outputs, from: inScope)
minSizes = try PriorBoxParam.getAttr(key: "min_sizes", attrs: opDesc.attrs)
maxSizes = try PriorBoxParam.getAttr(key: "max_sizes", attrs: opDesc.attrs)
aspectRatios = try PriorBoxParam.getAttr(key: "aspect_ratios", attrs: opDesc.attrs)
variances = try PriorBoxParam.getAttr(key: "variances", attrs: opDesc.attrs)
flip = try PriorBoxParam.getAttr(key: "flip", attrs: opDesc.attrs)
clip = try PriorBoxParam.getAttr(key: "clop", attrs: opDesc.attrs)
stepW = try PriorBoxParam.getAttr(key: "step_w", attrs: opDesc.attrs)
stepH = try PriorBoxParam.getAttr(key: "step_h", attrs: opDesc.attrs)
offset = try PriorBoxParam.getAttr(key: "offset", attrs: opDesc.attrs)
} catch let error {
throw error
}
}
let minSizes: [Float32]
let maxSizes: [Float32]
let aspectRatios: [Float32]
var newAspectRatios: [Float32]?
let variances: [Float32]
let flip: Bool
let clip: Bool
var stepW: Float32
var stepH: Float32
let offset: Float32
let input: Texture<P>
let inputImage: Texture<P>
var output: Texture<P>
let variances: Texture<P>
let outputVariances: Texture<P>
}
class PriorBoxOp<P: PrecisionType>: Operator<PriorBoxKernel<P>, PriorBoxParam<P>>, Runable, Creator, InferShaperable{
func inferShape() {
para.output.dim = para.input.dim
}
typealias OpType = PriorBoxOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
// try kernel.compute(commandBuffer: buffer, param: para)
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
......
metal/paddle-mobile/paddle-mobile/framework/Dim.swift
浏览文件 @ dcef687f
......@@ -31,11 +31,11 @@ public struct Dim {
return dims.reduce(1) { $0 * $1 }
}
static func ==(left: Dim, right: Dim) -> Bool {
public static func ==(left: Dim, right: Dim) -> Bool {
return left.dims == right.dims;
}
subscript(index: Int) -> Int {
public subscript(index: Int) -> Int {
return dims[index];
}
......
metal/paddle-mobile/paddle-mobile/framework/Texture.swift
浏览文件 @ dcef687f
......@@ -41,15 +41,15 @@ extension InputTexture {
public class Texture<P: PrecisionType>: Tensorial {
var dim: Dim
var tensorDim: Dim
private(set) var originDim: Dim
private(set) public var originDim: Dim
private var textureDesc: MTLTextureDescriptor!
var metalTexture: MTLTexture!
public var metalTexture: MTLTexture!
var transpose: [Int] = [0, 1, 2, 3]
func initTexture(device: MTLDevice, transpose: [Int] = [0, 1, 2, 3]) {
let newDim = transpose.map { originDim[$0] }
let newLayout = transpose.map {layout.layoutWithDim[$0] }
let newLayout = transpose.map { layout.layoutWithDim[$0] }
layout = DataLayout.init(newLayout)
dim = Dim.init(inDim: newDim)
......
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