finish

metal/paddle-mobile-demo/paddle-mobile-demo.xcodeproj/project.pbxproj

@@ -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

+/* 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

-//
-//  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

+//
+//  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

@@ -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

@@ -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

@@ -442,10 +442,5 @@ public extension MTLBuffer {
     }
     return array;
   }
-  
 }
 
-
-
-
-

metal/paddle-mobile/paddle-mobile/Common/Types.swift

@@ -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

@@ -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

@@ -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

@@ -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

-  ///* 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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

-//
-//  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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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())
   }
-  
 }