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提交 e273b9dd 编写于 作者: D dolphin8
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metal/paddle-mobile-demo/paddle-mobile-demo.xcodeproj/project.pbxproj
浏览文件 @ e273b9dd
......@@ -14,6 +14,9 @@
FC039B8720E11C550081E9F8 /* Main.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = FC039B8520E11C550081E9F8 /* Main.storyboard */; };
FC039B8920E11C560081E9F8 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = FC039B8820E11C560081E9F8 /* Assets.xcassets */; };
FC039B8C20E11C560081E9F8 /* LaunchScreen.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = FC039B8A20E11C560081E9F8 /* LaunchScreen.storyboard */; };
FC27991021341CE5000B6BAD /* Net.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC27990F21341CE5000B6BAD /* Net.swift */; };
FC3C800F2133F46600D1295E /* MobileNetSSD.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC3C800E2133F46600D1295E /* MobileNetSSD.swift */; };
FC3C80112133F4AB00D1295E /* MobileNet.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC3C80102133F4AB00D1295E /* MobileNet.swift */; };
FC918191211DBC3500B6F354 /* paddle-mobile.png in Resources */ = {isa = PBXBuildFile; fileRef = FC918190211DBC3500B6F354 /* paddle-mobile.png */; };
FC918193211DC70500B6F354 /* iphone.JPG in Resources */ = {isa = PBXBuildFile; fileRef = FC918192211DC70500B6F354 /* iphone.JPG */; };
FCA3A16121313E1F00084FE5 /* hand.jpg in Resources */ = {isa = PBXBuildFile; fileRef = FCA3A16021313E1F00084FE5 /* hand.jpg */; };
......@@ -22,7 +25,6 @@
FCBCCC552122EF5500D94F7E /* MetalHelper.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC542122EF5400D94F7E /* MetalHelper.swift */; };
FCD04E6320F3146B0007374F /* params in Resources */ = {isa = PBXBuildFile; fileRef = FCD04E6120F3146A0007374F /* params */; };
FCD04E6420F3146B0007374F /* model in Resources */ = {isa = PBXBuildFile; fileRef = FCD04E6220F3146A0007374F /* model */; };
FCDFD3FB211D72C3005AB38B /* ModelHelper.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCDFD3FA211D72C3005AB38B /* ModelHelper.swift */; };
FCDFD41B211D91C7005AB38B /* synset.txt in Resources */ = {isa = PBXBuildFile; fileRef = FCDFD41A211D91C7005AB38B /* synset.txt */; };
FCEBEC2C20E1391F00C0B14D /* paddle_mobile.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = FCEBEC2B20E1391F00C0B14D /* paddle_mobile.framework */; };
FCEBEC2D20E1391F00C0B14D /* paddle_mobile.framework in Embed Frameworks */ = {isa = PBXBuildFile; fileRef = FCEBEC2B20E1391F00C0B14D /* paddle_mobile.framework */; settings = {ATTRIBUTES = (CodeSignOnCopy, RemoveHeadersOnCopy, ); }; };
......@@ -55,6 +57,9 @@
FC039B8820E11C560081E9F8 /* Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = Assets.xcassets; sourceTree = "<group>"; };
FC039B8B20E11C560081E9F8 /* Base */ = {isa = PBXFileReference; lastKnownFileType = file.storyboard; name = Base; path = Base.lproj/LaunchScreen.storyboard; sourceTree = "<group>"; };
FC039B8D20E11C560081E9F8 /* Info.plist */ = {isa = PBXFileReference; lastKnownFileType = text.plist.xml; path = Info.plist; sourceTree = "<group>"; };
FC27990F21341CE5000B6BAD /* Net.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Net.swift; sourceTree = "<group>"; };
FC3C800E2133F46600D1295E /* MobileNetSSD.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = MobileNetSSD.swift; sourceTree = "<group>"; };
FC3C80102133F4AB00D1295E /* MobileNet.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = MobileNet.swift; sourceTree = "<group>"; };
FC918190211DBC3500B6F354 /* paddle-mobile.png */ = {isa = PBXFileReference; lastKnownFileType = image.png; path = "paddle-mobile.png"; sourceTree = "<group>"; };
FC918192211DC70500B6F354 /* iphone.JPG */ = {isa = PBXFileReference; lastKnownFileType = image.jpeg; path = iphone.JPG; sourceTree = "<group>"; };
FCA3A16021313E1F00084FE5 /* hand.jpg */ = {isa = PBXFileReference; lastKnownFileType = image.jpeg; path = hand.jpg; sourceTree = "<group>"; };
......@@ -63,7 +68,6 @@
FCBCCC542122EF5400D94F7E /* MetalHelper.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = MetalHelper.swift; sourceTree = "<group>"; };
FCD04E6120F3146A0007374F /* params */ = {isa = PBXFileReference; lastKnownFileType = file; path = params; sourceTree = "<group>"; };
FCD04E6220F3146A0007374F /* model */ = {isa = PBXFileReference; lastKnownFileType = file; path = model; sourceTree = "<group>"; };
FCDFD3FA211D72C3005AB38B /* ModelHelper.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ModelHelper.swift; sourceTree = "<group>"; };
FCDFD41A211D91C7005AB38B /* synset.txt */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text; path = synset.txt; sourceTree = "<group>"; };
FCEBEC2B20E1391F00C0B14D /* paddle_mobile.framework */ = {isa = PBXFileReference; explicitFileType = wrapper.framework; path = paddle_mobile.framework; sourceTree = BUILT_PRODUCTS_DIR; };
FCEEE7D3210627A000444BEC /* banana.jpeg */ = {isa = PBXFileReference; lastKnownFileType = image.jpeg; path = banana.jpeg; sourceTree = "<group>"; };
......@@ -131,7 +135,9 @@
FC039B8A20E11C560081E9F8 /* LaunchScreen.storyboard */,
FC039B8D20E11C560081E9F8 /* Info.plist */,
FCBCCC542122EF5400D94F7E /* MetalHelper.swift */,
FCDFD3FA211D72C3005AB38B /* ModelHelper.swift */,
FC3C800E2133F46600D1295E /* MobileNetSSD.swift */,
FC3C80102133F4AB00D1295E /* MobileNet.swift */,
FC27990F21341CE5000B6BAD /* Net.swift */,
);
path = "paddle-mobile-demo";
sourceTree = "<group>";
......@@ -300,9 +306,11 @@
buildActionMask = 2147483647;
files = (
FC039B8420E11C550081E9F8 /* ViewController.swift in Sources */,
FCDFD3FB211D72C3005AB38B /* ModelHelper.swift in Sources */,
FC013928210204A3008100E3 /* PreProcessKernel.metal in Sources */,
FC27991021341CE5000B6BAD /* Net.swift in Sources */,
FCBCCC552122EF5500D94F7E /* MetalHelper.swift in Sources */,
FC3C80112133F4AB00D1295E /* MobileNet.swift in Sources */,
FC3C800F2133F46600D1295E /* MobileNetSSD.swift in Sources */,
FC039B8220E11C550081E9F8 /* AppDelegate.swift in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
......
metal/paddle-mobile-demo/paddle-mobile-demo/MobileNet.swift 0 → 100644
浏览文件 @ e273b9dd
/* 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
import paddle_mobile
class MobileNet: Net{
var program: Program?
var executor: Executor<Float32>?
let except: Int = 0
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)
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/ModelHelper.swift → metal/paddle-mobile-demo/paddle-mobile-demo/MobileNetSSD.swift
浏览文件 @ e273b9dd
//
// ModelHelper.swift
// paddle-mobile-demo
//
// 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 MetalKit
import Foundation
import paddle_mobile
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]
}
}
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
func fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32]
}
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)
}
}
class MobileNet_ssd_hand: Net{
func fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32] {
return paddleMobileRes.resultArr
}
}
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)
}
}
var program: Program?
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
var executor: Executor<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)
}
}
struct MobileNet_ssd_hand: Net{
let except: Int = 2
class MobilenetssdPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = CusomKernel.Shape.init(inWidth: 300, inHeight: 300, inChannel: 3)
......@@ -105,7 +31,6 @@ struct MobileNet_ssd_hand: Net{
func resultStr(res: [Float]) -> String {
return "哈哈哈, 还没好"
// fatalError()
}
func bboxArea(box: [Float32], normalized: Bool) -> Float32 {
......@@ -141,10 +66,18 @@ struct MobileNet_ssd_hand: Net{
}
func fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32]{
let scores = paddleMobileRes.intermediateResults![0] as! Texture<Float32>
let bbox = paddleMobileRes.intermediateResults![1] as! Texture<Float32>
// let bbox = paddleMobileRes["box_coder_0.tmp_0"] ?! " no bbox "
// let scores = paddleMobileRes["transpose_12.tmp_0"] ?! " no scores "
guard let interRes = paddleMobileRes.intermediateResults else {
fatalError(" need have inter result ")
}
guard let scores = interRes["Scores"], scores.count > 0, let score = scores[0] as? Texture<Float32> else {
fatalError(" need score ")
}
guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? Texture<Float32> else {
fatalError()
}
let score_thredshold: Float32 = 0.01
let nms_top_k = 400
let keep_top_k = 200
......@@ -155,43 +88,14 @@ struct MobileNet_ssd_hand: Net{
return f
}
let scoresArr = scores.metalTexture.floatArray { (f) -> Float32 in
return f
}
var scoreFormatArr: [Float32] = []
let scoreFormatArr: [Float32] = score.metalTexture.realNHWC(dim: (n: score.originDim[0], h: score.originDim[1], w: score.originDim[2], c: score.originDim[3]))
var outputArr: [Float32] = []
let numOfOneC = (scores.tensorDim[2] + 3) / 4 // 480
let cNumOfOneClass = scores.tensorDim[2] // 1917
let cPaddedNumOfOneClass = numOfOneC * 4 // 1920
let cNumOfOneClass = score.tensorDim[2] // 1917
let boxSize = bbox.tensorDim[2] // 4
let classNum = scores.tensorDim[1] // 7
let classNumOneTexture = classNum * 4 // 28
for c in 0..<classNum {
for n in 0..<numOfOneC {
let to = n * classNumOneTexture + c * 4
if n == numOfOneC - 1 {
for i in 0..<(4 - (cPaddedNumOfOneClass - cNumOfOneClass)) {
scoreFormatArr.append(scoresArr[to + i])
}
} else {
scoreFormatArr.append(scoresArr[to])
scoreFormatArr.append(scoresArr[to + 1])
scoreFormatArr.append(scoresArr[to + 2])
scoreFormatArr.append(scoresArr[to + 3])
}
}
}
let classNum = score.tensorDim[1] // 7
var selectedIndexs: [Int : [(Int, Float32)]] = [:]
var numDet: Int = 0
for i in 0..<classNum {
var sliceScore = Array<Float32>(scoreFormatArr[(i * cNumOfOneClass)..<((i + 1) * cNumOfOneClass)])
......@@ -249,7 +153,7 @@ struct MobileNet_ssd_hand: Net{
}
scoreIndexPairs.sort { $0.0 > $1.0 }
if scoreIndexPairs.count > keep_top_k {
scoreIndexPairs.removeLast(scoreIndexPairs.count - keep_top_k)
}
......@@ -259,9 +163,9 @@ struct MobileNet_ssd_hand: Net{
// label: scoreIndexPair.1.0
let label = scoreIndexPair.1.0
if newIndices[label] != nil {
newIndices[label]?.append((scoreIndexPair.1.0, scoreIndexPair.0))
newIndices[label]?.append((scoreIndexPair.1.1, scoreIndexPair.0))
} else {
newIndices[label] = [(scoreIndexPair.1.0, scoreIndexPair.0)]
newIndices[label] = [(scoreIndexPair.1.1, scoreIndexPair.0)]
}
}
......@@ -275,7 +179,6 @@ struct MobileNet_ssd_hand: Net{
}
}
print(" fuck success !")
print(outputArr)
return outputArr
}
......@@ -292,4 +195,3 @@ struct MobileNet_ssd_hand: Net{
preprocessKernel = MobilenetssdPreProccess.init(device: MetalHelper.shared.device)
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/Net.swift 0 → 100644
浏览文件 @ e273b9dd
//
// Net.swift
// paddle-mobile-demo
//
// Created by liuRuiLong on 2018/8/27.
// Copyright © 2018年 orange. All rights reserved.
//
import Foundation
import UIKit
import MetalKit
import Foundation
import paddle_mobile
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]
}
}
protocol Net {
var program: Program? { get set }
var executor: Executor<Float32>? { get set }
var except: Int { get }
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
func fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32]
mutating func load() throws
func predict(inTexture: MTLTexture, completion: @escaping ([Float32]) -> Void) throws
mutating func clear()
}
extension Net {
mutating func load() throws {
let queue = MetalHelper.shared.queue
let loader = Loader<Float32>.init()
do {
program = try loader.load(device: MetalHelper.shared.device, modelPath: modelPath, paraPath: paramPath)
executor = try Executor<Float32>.init(inDevice: MetalHelper.shared.device, inQueue: queue, inProgram: program!)
} catch let error {
throw error
}
}
func predict(inTexture: MTLTexture, completion: @escaping ([Float32]) -> Void) throws {
guard let inExecutor = executor else {
fatalError(" 请先 load ")
}
try inExecutor.predict(input: inTexture, dim: dim, completionHandle: { (result) in
let resultArr = self.fetchResult(paddleMobileRes: result)
completion(resultArr)
}, preProcessKernle: preprocessKernel, except: except)
}
mutating func clear() {
executor?.clear()
program = nil
executor = nil
}
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 fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32] {
return paddleMobileRes.resultArr
}
// func predict()
}
metal/paddle-mobile-demo/paddle-mobile-demo/ViewController.swift
浏览文件 @ e273b9dd
......@@ -26,25 +26,21 @@ class ViewController: UIViewController {
@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 net: Net {
get {
return modelHelperMap[modelType] ?! " has no this type "
}
set {
}
}
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!)
try self.net.load()
} catch let error {
print(error)
}
......@@ -58,9 +54,7 @@ class ViewController: UIViewController {
}
@IBAction func clearAct(_ sender: Any) {
executor?.clear()
program = nil
executor = nil
net.clear()
}
@IBAction func predictAct(_ sender: Any) {
......@@ -68,39 +62,54 @@ class ViewController: UIViewController {
resultTextView.text = "请选择图片 ! "
return
}
guard let inExecutor = executor else {
resultTextView.text = "请先 load ! "
return
}
do {
let max = 1
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()
}
let resultArr = sSelf.modelHelper.fetchResult(paddleMobileRes: result)
if i == max - 1 {
let time = Date.init().timeIntervalSince(startDate)
DispatchQueue.main.async {
sSelf.resultTextView.text = sSelf.modelHelper.resultStr(res: resultArr)
sSelf.elapsedTimeLabel.text = "平均耗时: \(time/Double(max/2) * 1000.0) ms"
}
}
}, preProcessKernle: self.modelHelper.preprocessKernel, except: 2)
try net.predict(inTexture: inTexture) { [weak self] (result) in
guard let sSelf = self else {
fatalError()
}
print(result)
let resultStr = sSelf.net.resultStr(res: result)
DispatchQueue.main.async {
sSelf.resultTextView.text = resultStr
}
}
} catch let error {
print(error)
}
// guard let inExecutor = executor else {
// resultTextView.text = "请先 load ! "
// return
// }
//
// do {
// let max = 1
// var startDate = Date.init()
// for i in 0..<max {
// try inExecutor.predict(input: inTexture, dim: modelHelper.dim, completionHandle: { [weak self] (result) in
// guard let sSelf = self else {
// fatalError()
// }
//
// if i == (max / 2 - 1) {
// startDate = Date.init()
// }
//
// let resultArr = sSelf.modelHelper.fetchResult(paddleMobileRes: result)
//
// if i == max - 1 {
// let time = Date.init().timeIntervalSince(startDate)
// DispatchQueue.main.async {
// sSelf.resultTextView.text = sSelf.modelHelper.resultStr(res: resultArr)
// sSelf.elapsedTimeLabel.text = "平均耗时: \(time/Double(max/2) * 1000.0) ms"
// }
// }
// }, preProcessKernle: self.modelHelper.preprocessKernel, except: modelHelper.except)
// }
// } catch let error {
// print(error)
// }
}
override func viewDidLoad() {
......@@ -112,7 +121,7 @@ class ViewController: UIViewController {
selectImage = UIImage.init(named: "hand.jpg")
selectImageView.image = selectImage
modelHelper.getTexture(image: selectImage!.cgImage!) {[weak self] (texture) in
net.getTexture(image: selectImage!.cgImage!) {[weak self] (texture) in
self?.toPredictTexture = texture
}
}
......@@ -168,7 +177,7 @@ extension ViewController: UIImagePickerControllerDelegate, UINavigationControll
}
sSelf.selectImage = image
sSelf.selectImageView.image = image
sSelf.modelHelper.getTexture(image: image.cgImage!, getTexture: { (texture) in
sSelf.net.getTexture(image: image.cgImage!, getTexture: { (texture) in
sSelf.toPredictTexture = texture
})
}
......
metal/paddle-mobile/paddle-mobile.xcodeproj/project.pbxproj
浏览文件 @ e273b9dd
......@@ -38,6 +38,7 @@
FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */; };
FC1B16B320EC9A4F00678B91 /* Kernels.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC1B16B220EC9A4F00678B91 /* Kernels.metal */; };
FC1B186620ECF1C600678B91 /* ResizeKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC1B186520ECF1C600678B91 /* ResizeKernel.swift */; };
FC27990E21341016000B6BAD /* BoxCoder.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC27990D21341016000B6BAD /* BoxCoder.metal */; };
FC3602CC2108819F00FACB58 /* PaddleMobileUnitTest.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC3602CB2108819F00FACB58 /* PaddleMobileUnitTest.swift */; };
FC4CB74920F0B954007C0C6D /* ConvKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC4CB74820F0B954007C0C6D /* ConvKernel.metal */; };
FC4CB74B20F12C30007C0C6D /* ProgramOptimize.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC4CB74A20F12C30007C0C6D /* ProgramOptimize.swift */; };
......@@ -122,6 +123,7 @@
FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ElementwiseAddKernel.swift; sourceTree = "<group>"; };
FC1B16B220EC9A4F00678B91 /* Kernels.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = Kernels.metal; sourceTree = "<group>"; };
FC1B186520ECF1C600678B91 /* ResizeKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ResizeKernel.swift; sourceTree = "<group>"; };
FC27990D21341016000B6BAD /* BoxCoder.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = BoxCoder.metal; sourceTree = "<group>"; };
FC3602CB2108819F00FACB58 /* PaddleMobileUnitTest.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = PaddleMobileUnitTest.swift; sourceTree = "<group>"; };
FC4CB74820F0B954007C0C6D /* ConvKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ConvKernel.metal; sourceTree = "<group>"; };
FC4CB74A20F12C30007C0C6D /* ProgramOptimize.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ProgramOptimize.swift; sourceTree = "<group>"; };
......@@ -351,6 +353,7 @@
FCEB6837212F00B100D2448E /* metal */ = {
isa = PBXGroup;
children = (
FC27990D21341016000B6BAD /* BoxCoder.metal */,
FC1B16B220EC9A4F00678B91 /* Kernels.metal */,
FC4CB74820F0B954007C0C6D /* ConvKernel.metal */,
4AF928762133F1DB005B6C3A /* BoxCoder.metal */,
......
metal/paddle-mobile/paddle-mobile/Common/MetalExtension.swift
浏览文件 @ e273b9dd
......@@ -442,10 +442,5 @@ public extension MTLBuffer {
}
return array;
}
}
metal/paddle-mobile/paddle-mobile/Common/Types.swift
浏览文件 @ e273b9dd
......@@ -52,9 +52,6 @@ extension Float16: PrecisionType {
public init(inFloat: Float32) {
self = Int16(inFloat)
}
}
extension Float32: PrecisionType {
......@@ -214,10 +211,6 @@ extension DataLayout: Equatable {
}
}
public protocol Variant: CustomStringConvertible, CustomDebugStringConvertible {
}
......
metal/paddle-mobile/paddle-mobile/Executor.swift
浏览文件 @ e273b9dd
......@@ -14,14 +14,14 @@
import Foundation
let testTo = 94
let testTo = 99
public class ResultHolder<P: PrecisionType> {
public let dim: [Int]
public let resultArr: [P]
public var intermediateResults: [Variant]?
public var intermediateResults: [String : [Variant]]?
public let elapsedTime: Double
public init(inDim: [Int], inResult: [P], inElapsedTime: Double, inIntermediateResults: [Variant]? = nil) {
public init(inDim: [Int], inResult: [P], inElapsedTime: Double, inIntermediateResults: [String : [Variant]]? = nil) {
dim = inDim
resultArr = inResult
elapsedTime = inElapsedTime
......@@ -62,7 +62,7 @@ public class Executor<P: PrecisionType> {
queue = inQueue
for block in inProgram.programDesc.blocks {
//block.ops.count
for i in 0..<(testTo + 1) {
for i in 0..<block.ops.count {
let op = block.ops[i]
do {
let op = try OpCreator<P>.shared.creat(device: inDevice, opDesc: op, scope: inProgram.scope)
......@@ -75,7 +75,7 @@ public class Executor<P: PrecisionType> {
}
}
public func predict(input: MTLTexture, expect: [Int], completionHandle: @escaping (ResultHolder<P>) -> Void, preProcessKernle: CusomKernel? = nil, except: Int = 0) throws {
public func predict(input: MTLTexture, dim: [Int], completionHandle: @escaping (ResultHolder<P>) -> Void, preProcessKernle: CusomKernel? = nil, except: Int = 0) throws {
guard let buffer = queue.makeCommandBuffer() else {
throw PaddleMobileError.predictError(message: "CommandBuffer is nil")
}
......@@ -92,7 +92,7 @@ public class Executor<P: PrecisionType> {
}
let beforeDate = Date.init()
let inputTexture = InputTexture.init(inMTLTexture: resInput, inExpectDim: Dim.init(inDim: expect))
let inputTexture = InputTexture.init(inMTLTexture: resInput, inExpectDim: Dim.init(inDim: dim))
program.scope.setInput(input: inputTexture)
//(ops.count - except)
for i in 0..<ops.count {
......@@ -104,9 +104,9 @@ public class Executor<P: PrecisionType> {
}
}
var outputTextures: [Variant]?
var outputTextures: [String : [Variant]]?
if except > 0 {
outputTextures = ops[ops.count - except].inputs()
outputTextures = ops[ops.count - except].inputVariant()
}
buffer.addCompletedHandler { (commandbuffer) in
......@@ -130,13 +130,11 @@ public class Executor<P: PrecisionType> {
}
// return
self.ops[testTo].delogOutput()
// self.ops[testTo].delogOutput()
// self.ops[91].delogOutput()
// self.ops[92].delogOutput()
// self.ops[93].delogOutput()
return;
let afterDate = Date.init()
var resultHolder: ResultHolder<P>
......
metal/paddle-mobile/paddle-mobile/Operators/Base/Operator.swift
浏览文件 @ e273b9dd
......@@ -25,7 +25,7 @@ protocol Runable {
func run(device: MTLDevice, buffer: MTLCommandBuffer) throws
func runImpl(device: MTLDevice,buffer: MTLCommandBuffer) throws
func delogOutput()
func inputs() -> [Variant]
func inputVariant() -> [String : [Variant]]
}
extension Runable where Self: OperatorProtocol{
......@@ -35,7 +35,10 @@ extension Runable where Self: OperatorProtocol{
} catch let error {
throw error
}
// print(type + ": " + para.outputDesc())
}
func inputVariant() -> [String : [Variant]] {
fatalError(" op \(type) need implement inputVariant")
}
func delogOutput() {
......@@ -98,6 +101,7 @@ class Operator <KernelType: Computable , ParameterType>: OperatorProtocol where
let scope: Scope
var kernel: KerType
required init(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws {
print("create op: \(opDesc.type)")
type = opDesc.type
scope = inScope
inputs = opDesc.inputs
......
metal/paddle-mobile/paddle-mobile/Operators/BatchNormOp.swift
浏览文件 @ e273b9dd
......@@ -43,15 +43,11 @@ class BatchNormParam<P: PrecisionType>: OpParam {
}
class BatchNormOp<P: PrecisionType>: Operator<BatchNormKernel<P>, BatchNormParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input, para.inputBias, para.inputMean, para.inputScale, para.inputVariance]
}
typealias OpType = BatchNormOp<P>
func inferShape() {
para.output.dim = para.input.dim
}
typealias OpType = BatchNormOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......
metal/paddle-mobile/paddle-mobile/Operators/BoxcoderOp.swift
浏览文件 @ e273b9dd
///* 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. */
///* 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 BoxcoderParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
priorBox = try BoxcoderParam.getFirstTensor(key: "PriorBox", map: opDesc.inputs, from: inScope)
priorBoxVar = try BoxcoderParam.getFirstTensor(key: "PriorBoxVar", map: opDesc.inputs, from: inScope)
targetBox = try BoxcoderParam.getFirstTensor(key: "TargetBox", map: opDesc.inputs, from: inScope)
output = try BoxcoderParam.getFirstTensor(key: "OutputBox", map: opDesc.outputs, from: inScope)
codeType = try BoxcoderParam.getAttr(key: "code_type", attrs: opDesc.attrs)
boxNormalized = try BoxcoderParam.getAttr(key: "box_normalized", attrs: opDesc.attrs)
} catch let error {
throw error
}
assert(priorBox.transpose == [0, 1, 2, 3])
assert(priorBoxVar.transpose == [0, 1, 2, 3])
assert(targetBox.transpose == [0, 1, 2, 3])
assert(codeType == "decode_center_size") // encode_center_size is not implemented
assert((targetBox.tensorDim.cout() == 3) && (targetBox.tensorDim[0] == 1)) // N must be 1 (only handle batch size = 1)
}
let priorBox: Texture<P>
let priorBoxVar: Texture<P>
let targetBox: Texture<P>
var output: Texture<P>
let codeType: String
let boxNormalized: Bool
}
class BoxcoderOp<P: PrecisionType>: Operator<BoxcoderKernel<P>, BoxcoderParam<P>>, Runable, Creator, InferShaperable{
import Foundation
typealias OpType = BoxcoderOp<P>
func inferShape() {
// para.output.dim = para.input.dim
}
class BoxcoderParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
priorBox = try BoxcoderParam.getFirstTensor(key: "PriorBox", map: opDesc.inputs, from: inScope)
priorBoxVar = try BoxcoderParam.getFirstTensor(key: "PriorBoxVar", map: opDesc.inputs, from: inScope)
targetBox = try BoxcoderParam.getFirstTensor(key: "TargetBox", map: opDesc.inputs, from: inScope)
output = try BoxcoderParam.getFirstTensor(key: "OutputBox", map: opDesc.outputs, from: inScope)
codeType = try BoxcoderParam.getAttr(key: "code_type", attrs: opDesc.attrs)
boxNormalized = try BoxcoderParam.getAttr(key: "box_normalized", attrs: opDesc.attrs)
} catch let error {
throw error
}
assert(priorBox.transpose == [0, 1, 2, 3])
assert(priorBoxVar.transpose == [0, 1, 2, 3])
assert(targetBox.transpose == [0, 1, 2, 3])
assert(codeType == "decode_center_size") // encode_center_size is not implemented
assert((targetBox.tensorDim.cout() == 3) && (targetBox.tensorDim[0] == 1)) // N must be 1 (only handle batch size = 1)
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
let priorBox: Texture<P>
let priorBoxVar: Texture<P>
let targetBox: Texture<P>
var output: Texture<P>
let codeType: String
let boxNormalized: Bool
}
class BoxcoderOp<P: PrecisionType>: Operator<BoxcoderKernel<P>, BoxcoderParam<P>>, Runable, Creator, InferShaperable{
func delogOutput() {
func inputs() -> [Variant] {
return [para.priorBox, para.priorBoxVar, para.targetBox]
}
print(" \(type) output: ")
let priorBoxOriginDim = para.priorBox.originDim
let priorBoxArray = para.priorBox.metalTexture.realNHWC(dim: (n: priorBoxOriginDim[0], h: priorBoxOriginDim[1], w: priorBoxOriginDim[2], c: priorBoxOriginDim[3]))
print(" prior box ")
print(priorBoxArray.strideArray())
func inferShape() {
// para.output.dim = para.input.dim
}
let priorBoxVarOriginDim = para.priorBoxVar.originDim
let priorBoxVarArray = para.priorBoxVar.metalTexture.realNHWC(dim: (n: priorBoxVarOriginDim[0], h: priorBoxVarOriginDim[1], w: priorBoxVarOriginDim[2], c: priorBoxVarOriginDim[3]))
print(" prior box var ")
print(priorBoxVarArray.strideArray())
typealias OpType = BoxcoderOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
}
let targetBoxOriginDim = para.targetBox.originDim
let targetBoxArray = para.targetBox.metalTexture.realNHWC(dim: (n: targetBoxOriginDim[0], h: targetBoxOriginDim[1], w: targetBoxOriginDim[2], c: targetBoxOriginDim[3]))
print(" target box ")
print(targetBoxArray.strideArray())
func delogOutput() {
// let outputArray = para.output.metalTexture.floatArray { (o: Float32) -> Float32 in
// return o
// }
let priorBoxOriginDim = para.priorBox.originDim
let priorBoxArray = para.priorBox.metalTexture.realNHWC(dim: (n: priorBoxOriginDim[0], h: priorBoxOriginDim[1], w: priorBoxOriginDim[2], c: priorBoxOriginDim[3]))
print(" prior box ")
print(priorBoxArray.strideArray())
let priorBoxVarOriginDim = para.priorBoxVar.originDim
let priorBoxVarArray = para.priorBoxVar.metalTexture.realNHWC(dim: (n: priorBoxVarOriginDim[0], h: priorBoxVarOriginDim[1], w: priorBoxVarOriginDim[2], c: priorBoxVarOriginDim[3]))
print(" prior box var ")
print(priorBoxVarArray.strideArray())
let targetBoxOriginDim = para.targetBox.originDim
let targetBoxArray = para.targetBox.metalTexture.realNHWC(dim: (n: targetBoxOriginDim[0], h: targetBoxOriginDim[1], w: targetBoxOriginDim[2], c: targetBoxOriginDim[3]))
print(" target box ")
print(targetBoxArray.strideArray())
let originDim = para.output.originDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
// print(outputArray.strideArray())
//box_coder_0.tmp_0
// writeToLibrary(fileName: "boxcoder_output", array: outputArray)
// print(para.output.metalTexture)
// print(" write done ")
}
let originDim = para.output.originDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConcatOp.swift
浏览文件 @ e273b9dd
......@@ -40,16 +40,13 @@ class ConcatParam<P: PrecisionType>: OpParam {
class ConcatOp<P: PrecisionType>: Operator<ConcatKernel<P>, ConcatParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return para.input
}
typealias OpType = ConcatOp<P>
func inferShape() {
// let dim = para.input.reduce([0, 0]) {[$0[0] + $1.dim[0], $1.dim[1]]}
// para.output.dim = Dim.init(inDim: dim)
}
typealias OpType = ConcatOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......@@ -59,34 +56,10 @@ class ConcatOp<P: PrecisionType>: Operator<ConcatKernel<P>, ConcatParam<P>>, Run
}
func delogOutput() {
print(" \(type) output: ")
let originDim = para.output.originDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
// let outputArray = para.output.metalTexture.floatArray { (o: Float32) -> Float32 in
// return o
// }
//
//// print(outputArray.strideArray())
//
// writeToLibrary(fileName: "concat_out", array: outputArray)
//
// let device: MTLDevice = MTLCreateSystemDefaultDevice()!
// let tensorArray: [P] = device.texture2tensor(texture: para.output.metalTexture, dim: [1917, 4])
// print(tensorArray.strideArray())
// print(para.output.metalTexture)
// writeToLibrary(fileName: "concat_out", array: outputArray)
// print(" write done ")
// print(outputArray.strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ConvAddBatchNormReluOp.swift
浏览文件 @ e273b9dd
......@@ -61,10 +61,6 @@ class ConvAddBatchNormReluParam<P: PrecisionType>: OpParam {
class ConvAddBatchNormReluOp<P: PrecisionType>: Operator<ConvAddBatchNormReluKernel<P>, ConvAddBatchNormReluParam<P>>, Runable, Creator, InferShaperable, Fusion{
func inputs() -> [Variant] {
return [para.variance, para.bias, para.mean, para.scale, para.y, para.filter, para.input]
}
typealias OpType = ConvAddBatchNormReluOp<P>
func inferShape() {
......@@ -115,7 +111,8 @@ class ConvAddBatchNormReluOp<P: PrecisionType>: Operator<ConvAddBatchNormReluKer
}
func delogOutput() {
print(" conv add batchnorm relu output ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
// let _: P? = para.input.metalTexture.logDesc(header: "conv add batchnorm relu input: ", stridable: false)
// para.filter.logDataPointer(header: "filter data pointer: ")
// print("filter: \(para.filter)")
......
metal/paddle-mobile/paddle-mobile/Operators/ConvAddOp.swift
浏览文件 @ e273b9dd
......@@ -43,30 +43,9 @@ class ConvAddParam<P: PrecisionType>: OpParam {
}
class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>, Runable, Creator, InferShaperable, Fusion{
func delogOutput() {
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
typealias OpType = ConvAddOp<P>
// print(" conv add: ")
// print(para.input.metalTexture)
// print(" filter array: ")
// let filterArray: [P] = para.filter.buffer.array()
// print(filterArray)
// let input = para.input.metalTexture.floatArray { (p: P) -> P in
// return p
// }
// print(input)
// let output = para.output.metalTexture.floatArray { (p: P) -> P in
// return p
// }
// print(para.output.metalTexture)
// print(output)
}
static func fusionNode() -> Node {
......@@ -80,16 +59,10 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
return [:]
}
func inputs() -> [Variant] {
return [para.input, para.y, para.filter]
}
static func fusionType() -> String {
return gConvAddType
}
typealias OpType = ConvAddOp<P>
func inferShape() {
......@@ -122,4 +95,7 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
}
}
func delogOutput() {
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvBNReluOp.swift
浏览文件 @ e273b9dd
......@@ -109,74 +109,8 @@ class ConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNReluPa
}
func delogOutput() {
// let _: P? = para.input.metalTexture.logDesc(header: "conv add batchnorm relu input: ", stridable: false)
// para.filter.logDataPointer(header: "filter data pointer: ")
// print("filter: \(para.filter)")
// print("biase: \(para.y)")
// print("padding: \(para.paddings)")
// print("stride: \(para.stride)")
// let _: P? = para.y.buffer?.logDesc(header: " biase: ", stridable: false)
// let _: P? = para.newBiase?.logDesc(header: "new biase: ", stridable: false)
// let _: P? = para.newScale?.logDesc(header: "new scale: ", stridable: false)
// print("input: ")
// print(para.input.metalTexture)
//
// let input = para.input.metalTexture.floatArray { (p: P) -> P in
// return p
// }
// for i in 0..<input.count {
// print(" index \(i) : \(input[i])")
// }
// print(input)
// writeToLibrary(fileName: "input35", array: input)
// print(input)
print(para.newBiase?.length)
print(para.newScale?.length)
// let newScale = para.newScale?.contents().bindMemory(to: P.self, capacity: para.newScale!.length)
// let newBiase = para.newBiase?.contents().bindMemory(to: P.self, capacity: para.newBiase!.length)
//
// let filterArray: [Float32] = para.filter.buffer.array();
//// writeToLibrary(fileName: "filter35", array: filterArray)
//
// print(filterArray)
//
// print("new scale: ")
// for i in 0..<(para.newScale!.length / MemoryLayout<P>.size) {
// print("index: \(i) \(newScale![i]) ")
// }
//
// print("new biase: ")
// for i in 0..<(para.newBiase!.length / MemoryLayout<P>.size) {
// print("index: \(i) \(newBiase![i]) ")
// }
// print(para.output.metalTexture)
//
//
//
// let output = para.output.metalTexture.floatArray { (p: P) -> P in
// return p
// }
// print(output)
//
//
// writeToLibrary(fileName: "batch_norm_34.tmp_2", array: output)
// print(" write done")
//
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
// let _: P? = para.output.metalTexture.logDesc(header: "conv add batchnorm relu output: ", stridable: true)
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvOp.swift
浏览文件 @ e273b9dd
......@@ -41,19 +41,8 @@ class ConvParam<P: PrecisionType>: OpParam {
}
class ConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runable, Creator, InferShaperable {
func inputs() -> [Variant] {
return [para.input, para.filter]
}
required init(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws {
do {
try super.init(device: device, opDesc: opDesc, inScope: inScope)
} catch let error {
throw error
}
}
typealias OpType = ConvOp<P>
func inferShape() {
let inDims = para.input.dim
let filterDim = para.filter.dim
......@@ -76,7 +65,6 @@ class ConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runable,
para.output.dim = Dim.init(inDim: outDim)
}
typealias OpType = ConvOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......@@ -87,7 +75,7 @@ class ConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runable,
func delogOutput() {
print("conv output : ")
print(para.output.metalTexture)
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
// let _: Float16? = para.output.metalTexture.logDesc()
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvTransposeOp.swift
浏览文件 @ e273b9dd
......@@ -28,15 +28,12 @@ class ConvTransposeParam<P: PrecisionType>: ConvParam<P> {
class ConvTransposeOp<P: PrecisionType>: Operator<ConvTransposeKernel<P>, ConvTransposeParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input, para.filter]
}
typealias OpType = ConvTransposeOp<P>
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)
......@@ -44,6 +41,7 @@ class ConvTransposeOp<P: PrecisionType>: Operator<ConvTransposeKernel<P>, ConvTr
throw error
}
}
func delogOutput() {
print("conv transpose delog")
let _: P? = para.input.metalTexture.logDesc(header: "conv transpose input: ", stridable: true)
......
metal/paddle-mobile/paddle-mobile/Operators/DepthwiseConvOp.swift
浏览文件 @ e273b9dd
......@@ -15,11 +15,9 @@
import Foundation
class DepthConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runable, Creator, InferShaperable {
func inputs() -> [Variant] {
return [para.input, para.filter]
}
typealias OpType = DepthConvOp<P>
required init(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws {
do {
try super.init(device: device, opDesc: opDesc, inScope: inScope)
......@@ -50,8 +48,6 @@ class DepthConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runa
para.output.dim = Dim.init(inDim: outDim)
}
typealias OpType = DepthConvOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......@@ -61,8 +57,7 @@ class DepthConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runa
}
func delogOutput() {
print("conv output : ")
print(para.output.metalTexture)
// let _: Float16? = para.output.metalTexture.logDesc()
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/DwConvBNReluOp.swift
浏览文件 @ e273b9dd
......@@ -17,10 +17,6 @@ import Foundation
class DwConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNReluParam<P>>, Runable, Creator, InferShaperable, Fusion{
typealias OpType = ConvBNReluOp<P>
func inputs() -> [Variant] {
return [para.input, para.bias, para.mean, para.filter, para.variance, para.scale]
}
func inferShape() {
let inDims = para.input.dim
let filterDim = para.filter.dim
......@@ -68,26 +64,7 @@ class DwConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNRelu
}
func delogOutput() {
// let _: P? = para.input.metalTexture.logDesc(header: "conv add batchnorm relu input: ", stridable: false)
// para.filter.logDataPointer(header: "filter data pointer: ")
// print("filter: \(para.filter)")
// print("biase: \(para.y)")
// print("padding: \(para.paddings)")
// print("stride: \(para.stride)")
// let _: P? = para.y.buffer?.logDesc(header: " biase: ", stridable: false)
// let _: P? = para.newBiase?.logDesc(header: "new biase: ", stridable: false)
// let _: P? = para.newScale?.logDesc(header: "new scale: ", stridable: false)
// let output = para.output.metalTexture.floatArray { (p: P) -> P in
// return p
// }
//
// writeToLibrary(fileName: "batch_norm_19.tmp_2", array: output)
// print(" write done")
// let _: P? = para.output.metalTexture.logDesc(header: "conv add batchnorm relu output: ", stridable: false)
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ElementwiseAddOp.swift
浏览文件 @ e273b9dd
......@@ -34,16 +34,12 @@ class ElementwiseAddParam<P: PrecisionType>: OpParam {
}
class ElementwiseAddOp<P: PrecisionType>: Operator<ElementwiseAddKernel<P>, ElementwiseAddParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input, para.inputY]
}
typealias OpType = ElementwiseAddOp<P>
func inferShape() {
para.output.dim = para.input.dim
}
typealias OpType = ElementwiseAddOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/FeedOp.swift
浏览文件 @ e273b9dd
......@@ -35,11 +35,7 @@ class FeedParam<P: PrecisionType>: OpParam{
class FeedOp<P: PrecisionType>: Operator<Texture2DTo2DArrayKernel<P>, FeedParam<P>>, Runable, Creator, InferShaperable {
typealias OpType = FeedOp<P>
func inputs() -> [Variant] {
return [para.input]
}
func inferShape() {
// print("feed input: \(para.input.expectDim)")
print("feed output: \(para.output.dim)")
......
metal/paddle-mobile/paddle-mobile/Operators/FetchOp.swift
浏览文件 @ e273b9dd
......@@ -37,20 +37,18 @@ class FetchKernel<P: PrecisionType>: Kernel, Computable {
}
required init(device: MTLDevice, param: FetchParam<P>) {
super.init(device: device, inFunctionName: "texture2d_to_2d_array")
super.init(device: device, inFunctionName: "place_holder")
}
}
class FetchOp<P: PrecisionType>: Operator< FetchKernel<P>, FetchParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input]
}
typealias OpType = FetchOp<P>
func inferShape() {
print(para.input.dim)
}
typealias OpType = FetchOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
scope.setOutput(output: para.output)
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvTransposeKernel.swift
浏览文件 @ e273b9dd
......@@ -43,7 +43,6 @@ class ConvTransposeKernel<P: PrecisionType>: Kernel, Computable{
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 {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/MulticlassNMSKernel.swift
浏览文件 @ e273b9dd
......@@ -15,17 +15,11 @@
import Foundation
class MulticlassNMSKernel<P: PrecisionType>: Kernel, Computable{
func compute(commandBuffer: MTLCommandBuffer, param: MulticlassNMSParam<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.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
required init(device: MTLDevice, param: MulticlassNMSParam<P>) {
super.init(device: device, inFunctionName: "prior_box")
}
required init(device: MTLDevice, param: MulticlassNMSParam<P>) {
super.init(device: device, inFunctionName: "place_holder")
}
func compute(commandBuffer: MTLCommandBuffer, param: MulticlassNMSParam<P>) throws {
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PoolKernel.swift
浏览文件 @ e273b9dd
......@@ -15,46 +15,48 @@
import Foundation
struct PoolMetalParam {
let ksizeX: Int32
let ksizeY: Int32
let strideX: Int32
let strideY: Int32
let paddingX: Int32
let paddingY: Int32
let poolType: Int32
let ksizeX: Int32
let ksizeY: Int32
let strideX: Int32
let strideY: Int32
let paddingX: Int32
let paddingY: Int32
let poolType: Int32
}
class PoolKernel<P: PrecisionType>: Kernel, Computable{
func compute(commandBuffer: MTLCommandBuffer, param: PoolParam<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)
var poolType: Int32
switch param.poolType {
case "max":
poolType = 0
case "avg":
poolType = 1
default:
throw PaddleMobileError.predictError(message: " unknown pooltype " + param.poolType)
}
var pmp = PoolMetalParam.init(
ksizeX: param.ksize[0],
ksizeY: param.ksize[1],
strideX: param.stride[0],
strideY: param.stride[1],
paddingX: param.padding[0],
paddingY: param.padding[1],
poolType: poolType
)
encoder.setBytes(&pmp, length: MemoryLayout<PoolMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
required init(device: MTLDevice, param: PoolParam<P>) {
super.init(device: device, inFunctionName: "pool")
param.output.initTexture(device: device)
}
func compute(commandBuffer: MTLCommandBuffer, param: PoolParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encoder is nil")
}
required init(device: MTLDevice, param: PoolParam<P>) {
super.init(device: device, inFunctionName: "pool")
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
var poolType: Int32
switch param.poolType {
case "max":
poolType = 0
case "avg":
poolType = 1
default:
throw PaddleMobileError.predictError(message: " unknown pooltype " + param.poolType)
}
var pmp = PoolMetalParam.init(
ksizeX: param.ksize[0],
ksizeY: param.ksize[1],
strideX: param.stride[0],
strideY: param.stride[1],
paddingX: param.padding[0],
paddingY: param.padding[1],
poolType: poolType
)
encoder.setBytes(&pmp, length: MemoryLayout<PoolMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Common.metal
浏览文件 @ e273b9dd
//
// common.metal
// paddle-mobile
//
// Created by liuRuiLong on 2018/8/26.
// 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. */
#include <metal_stdlib>
using namespace metal;
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Kernels.metal
浏览文件 @ e273b9dd
......@@ -16,6 +16,12 @@
#include "Common.metal"
using namespace metal;
// 占位函数, 啥也没干
kernel void place_holder(texture2d<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) {
}
struct OutputDim {
ushort width;
ushort height;
......@@ -161,9 +167,6 @@ kernel void pool_half(texture2d_array<half, access::read> inTexture [[texture(0)
outTexture.write(r, gid.xy, gid.z);
}
struct TransposeParam {
int iC;
int oC;
......
metal/paddle-mobile/paddle-mobile/Operators/MulticlassNMSOp.swift
浏览文件 @ e273b9dd
......@@ -15,39 +15,39 @@
import Foundation
class MulticlassNMSParam<P: PrecisionType>: OpParam {
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
scores = try MulticlassNMSParam.getFirstTensor(key: "Scores", map: opDesc.inputs, from: inScope)
bboxes = try MulticlassNMSParam.getFirstTensor(key: "BBoxes", map: opDesc.inputs, from: inScope)
output = try MulticlassNMSParam.outputOut(outputs: opDesc.outputs, from: inScope)
} catch let error {
throw error
}
typealias ParamPrecisionType = P
required init(opDesc: OpDesc, inScope: Scope) throws {
do {
scores = try MulticlassNMSParam.getFirstTensor(key: "Scores", map: opDesc.inputs, from: inScope)
bboxes = try MulticlassNMSParam.getFirstTensor(key: "BBoxes", map: opDesc.inputs, from: inScope)
output = try MulticlassNMSParam.outputOut(outputs: opDesc.outputs, from: inScope)
} catch let error {
throw error
}
let scores: Texture<P>
let bboxes: Texture<P>
var output: Texture<P>
}
let scores: Texture<P>
let bboxes: Texture<P>
var output: Texture<P>
}
class MulticlassNMSOp<P: PrecisionType>: Operator<MulticlassNMSKernel<P>, MulticlassNMSParam<P>>, Runable, Creator, InferShaperable{
func inputVariant() -> [String : [Variant]] {
return ["Scores" : [para.scores], "BBoxes" : [para.bboxes]]
}
func inputs() -> [Variant] {
return [para.scores,para.bboxes]
func inferShape() {
// para.output.dim = para.input.dim
}
func inferShape() {
// para.output.dim = para.input.dim
}
typealias OpType = MulticlassNMSOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
typealias OpType = MulticlassNMSOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/PoolOp.swift
浏览文件 @ e273b9dd
......@@ -43,15 +43,12 @@ class PoolParam<P: PrecisionType>: OpParam {
class PoolOp<P: PrecisionType>: Operator<PoolKernel<P>, PoolParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input]
}
typealias OpType = PoolOp<P>
func inferShape() {
// para.output.dim = para.input.dim
}
typealias OpType = PoolOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......
metal/paddle-mobile/paddle-mobile/Operators/PreluOp.swift
浏览文件 @ e273b9dd
......@@ -35,15 +35,12 @@ class PreluParam<P: PrecisionType>: OpParam {
class PreluOp<P: PrecisionType>: Operator<PreluKernel<P>, PreluParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.alpha, para.input]
}
typealias OpType = PreluOp<P>
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)
......@@ -51,6 +48,7 @@ class PreluOp<P: PrecisionType>: Operator<PreluKernel<P>, PreluParam<P>>, Runabl
throw error
}
}
func delogOutput() {
print("softmax delog")
let _: P? = para.input.metalTexture.logDesc(header: "softmax input: ", stridable: false)
......
metal/paddle-mobile/paddle-mobile/Operators/PriorBoxOp.swift
浏览文件 @ e273b9dd
......@@ -55,15 +55,11 @@ class PriorBoxParam<P: PrecisionType>: OpParam {
class PriorBoxOp<P: PrecisionType>: Operator<PriorBoxKernel<P>, PriorBoxParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input, para.inputImage]
}
typealias OpType = PriorBoxOp<P>
func inferShape() {
}
typealias OpType = PriorBoxOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......@@ -73,8 +69,8 @@ class PriorBoxOp<P: PrecisionType>: Operator<PriorBoxKernel<P>, PriorBoxParam<P>
}
func delogOutput() {
print("pribox: ")
print("output: ")
print(" \(type) output: ")
// output
let outputArray = para.output.metalTexture.floatArray { (o: Float32) -> Float32 in
return o
......@@ -93,9 +89,6 @@ class PriorBoxOp<P: PrecisionType>: Operator<PriorBoxKernel<P>, PriorBoxParam<P>
// writeToLibrary(fileName: "variance_out", array: outputVarianceArray)
print("pribox write done ")
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ReluOp.swift
浏览文件 @ e273b9dd
......@@ -30,15 +30,12 @@ class ReluParam<P: PrecisionType>: OpParam {
class ReluOp<P: PrecisionType>: Operator<ReluKernel<P>, ReluParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input]
}
typealias OpType = ReluOp<P>
func inferShape() {
para.output.dim = para.input.dim
}
typealias OpType = ReluOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......
metal/paddle-mobile/paddle-mobile/Operators/ReshapeOp.swift
浏览文件 @ e273b9dd
......@@ -57,15 +57,12 @@ class ReshapeParam<P: PrecisionType>: OpParam {
class ReshapeOp<P: PrecisionType>: Operator<ReshapeKernel<P>, ReshapeParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input]
}
typealias OpType = ReshapeOp<P>
func inferShape() {
// para.output.dim = para.input.dim
}
typealias OpType = ReshapeOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......
metal/paddle-mobile/paddle-mobile/Operators/SoftmaxOp.swift
浏览文件 @ e273b9dd
......@@ -36,16 +36,12 @@ class SoftmaxParam<P: PrecisionType>: OpParam {
}
class SoftmaxOp<P: PrecisionType>: Operator<SoftmaxKernel<P>, SoftmaxParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input]
}
typealias OpType = SoftmaxOp<P>
func inferShape() {
// para.output.dim = para.input.dim
}
typealias OpType = SoftmaxOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......@@ -53,6 +49,7 @@ class SoftmaxOp<P: PrecisionType>: Operator<SoftmaxKernel<P>, SoftmaxParam<P>>,
throw error
}
}
func delogOutput() {
print("softmax delog")
......
metal/paddle-mobile/paddle-mobile/Operators/TransposeOp.swift
浏览文件 @ e273b9dd
......@@ -32,15 +32,12 @@ class TransposeParam<P: PrecisionType>: OpParam {
class TransposeOp<P: PrecisionType>: Operator<TransposeKernel<P>, TransposeParam<P>>, Runable, Creator, InferShaperable{
func inputs() -> [Variant] {
return [para.input]
}
typealias OpType = TransposeOp<P>
func inferShape() {
//para.output.dim = para.input.dim
}
typealias OpType = TransposeOp<P>
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
......@@ -48,32 +45,13 @@ class TransposeOp<P: PrecisionType>: Operator<TransposeKernel<P>, TransposeParam
throw error
}
}
func delogOutput() {
print(para.output.metalTexture.realNHWC(dim: (n: para.output.originDim[0], h: para.output.originDim[1], w: para.output.originDim[2], c: para.output.originDim[3])).strideArray())
// print(para.input.metalTexture.toTensor(dim: (n: para.input.originDim[0], c: para.input.originDim[1], h: para.input.originDim[2], w: para.input.originDim[3])).strideArray())
//
//
// let originDim = para.output.tensorDim
// let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
// print(outputArray.strideArray())
// let inputArray: [Float32] = para.input.metalTexture.floatArray { (ele: Float32) -> Float32 in
// return ele
// }
//
// print(inputArray.strideArray())
//
// let outputArray: [Float32] = para.output.metalTexture.floatArray { (ele: Float32) -> Float32 in
// return ele
// }
//
// print(outputArray.strideArray())
// writeToLibrary(fileName: "transpose_ouput", array: outputArray)
print(" \(type) output: ")
let originDim = para.output.tensorDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
}
}
......
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