run mobilenet

metal/paddle-mobile-demo/paddle-mobile-demo/ViewController.swift

@@ -109,7 +109,7 @@ class ViewController: UIViewController {
     threadPickerView.delegate = self
     threadPickerView.dataSource = self
     
-    selectImage = UIImage.init(named: "hand.jpg")
+    selectImage = UIImage.init(named: "banana.jpeg")
     selectImageView.image = selectImage
     net.getTexture(image: selectImage!.cgImage!) {[weak self] (texture) in
       self?.toPredictTexture = texture

metal/paddle-mobile/paddle-mobile/Executor.swift

@@ -119,7 +119,7 @@ public class Executor<P: PrecisionType> {
 //      })
 //      print(inputArr.strideArray())
 //
-//      writeToLibrary(fileName: "genet_input_hand", array: inputArr)
+//      writeToLibrary(fileName: "banana", array: inputArr)
 //      print("write to library done")
 //      return
       //            print(inputArr)
@@ -128,11 +128,13 @@ public class Executor<P: PrecisionType> {
       //            print(stridableInput)
       
       //            let _: Flo? = input.logDesc(header: "input: ", stridable: true)
-//      for i in 0..<self.ops.count {
-//        let op = self.ops[i]
-//        print(" 第 \(i) 个 op: ")
-//        op.delogOutput()
-//      }
+      for i in 0..<self.ops.count {
+        let op = self.ops[i]
+        print(" 第 \(i) 个 op: ")
+        op.delogOutput()
+      }
+      
+//      return;
 //      self.ops[testTo - 2].delogOutput()
 //      self.ops[testTo - 1].delogOutput()
 //      self.ops[60].delogOutput()

metal/paddle-mobile/paddle-mobile/Operators/ConvAddBatchNormReluOp.swift

@@ -34,7 +34,7 @@ class ConvAddBatchNormReluParam<P: PrecisionType>: OpParam {
       
       scale = try ConvAddBatchNormReluParam.inputScale(inputs: opDesc.paraInputs, from: inScope)
       mean = try ConvAddBatchNormReluParam.inputMean(inputs: opDesc.paraInputs, from: inScope)
-      y = try ConvAddBatchNormReluParam.inputY(inputs: opDesc.inputs, from: inScope)
+      y = try ConvAddBatchNormReluParam.inputY(inputs: opDesc.paraInputs, from: inScope)
     } catch let error {
       throw error
     }

metal/paddle-mobile/paddle-mobile/Operators/ConvAddOp.swift

@@ -93,34 +93,24 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
   }
   
   func delogOutput() {
+//    print("op \(type): ")
+//    print(" padding: ")
+//    print(para.paddings)
+//    print("stride: ")
+//    print(para.stride)
+//    print("dilations: ")
+//    print(para.dilations)
+//    print(" para input dim: ")
+//    print(para.input.dim)
+//    print(" para filter dim: ")
+//    print(para.filter.dim)
+//    print(" para output dim: ")
+//    print(para.output.dim)
+//    print(" biase: ")
+//    let biase: [Float32] = para.y.buffer.array()
+//    print(biase)
     
-    
-    print("op \(type): ")
     print(" \(type) output: ")
-    print(" padding: ")
-    print(para.paddings)
-    print("stride: ")
-    print(para.stride)
-    print("dilations: ")
-    print(para.dilations)
-    print(" para input dim: ")
-    print(para.input.dim)
-    print(" para filter dim: ")
-    print(para.filter.dim)
-    print(" para output dim: ")
-    print(para.output.dim)
-    print(" biase: ")
-    let biase: [Float32] = para.y.buffer.array()
-    print(biase)
-    
-    print(" - filter - ")
-    let array: [Float32] = para.filter.buffer.array()
-    print(array)
-    
-    print(" - y - ")
-    let yArray: [Float32] = para.y.buffer.array()
-    print(yArray)
-    
     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/Kernels/ConvAddBatchNormReluKernel.swift

@@ -81,8 +81,6 @@ class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable
       fatalError()
     }
     
-   
-    
     let offsetX = param.filter.width/2 - Int(param.paddings[0])
     let offsetY = param.filter.height/2 - Int(param.paddings[1])
     
@@ -121,10 +119,10 @@ class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable
     var newBiaseBuffer: MTLBuffer
     var newScaleBuffer: MTLBuffer
     
-    if computePrecision == .Float16 {
+    if computePrecision == .Float32 {
       newBiaseBuffer = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)!
       newScaleBuffer = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)!
-    } else if computePrecision == .Float32 {
+    } else if computePrecision == .Float16 {
       
       newBiaseBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
       newScaleBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
@@ -151,7 +149,6 @@ class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable
       throw PaddleMobileError.predictError(message: " encode is nil")
     }
     
-    
     encoder.setTexture(param.input.metalTexture, index: 0)
     encoder.setTexture(param.output.metalTexture, index: 1)
     encoder.setBytes(&metalParam, length: MemoryLayout<MetalConvParam>.size, index: 0)

metal/paddle-mobile/paddle-mobile/Operators/Kernels/SoftmaxKernel.swift

@@ -21,6 +21,17 @@ struct SoftmaxMetalParam {
 
 class SoftmaxKernel<P: PrecisionType>: Kernel, Computable{
   
+  required init(device: MTLDevice, param: SoftmaxParam<P>) {
+    param.output.initTexture(device: device, computePrecision: computePrecision)
+    if computePrecision == .Float32 {
+      super.init(device: device, inFunctionName: "softmax")
+    } else if computePrecision == .Float16 {
+      super.init(device: device, inFunctionName: "softmax_half")
+    } else {
+      fatalError()
+    }
+  }
+
   func compute(commandBuffer: MTLCommandBuffer, param: SoftmaxParam<P>) throws {
     guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
       throw PaddleMobileError.predictError(message: " encoder is nil")
@@ -32,19 +43,12 @@ class SoftmaxKernel<P: PrecisionType>: Kernel, Computable{
       N: Int32(param.input.tensorDim[0]),
       K: Int32(param.input.tensorDim[1])
     )
+    
+    print(" soft max param: ")
+    print(smp)
     encoder.setBytes(&smp, length: MemoryLayout<SoftmaxMetalParam>.size, index: 0)
     encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
     encoder.endEncoding()
   }
   
-  required init(device: MTLDevice, param: SoftmaxParam<P>) {
-    param.output.initTexture(device: device, computePrecision: computePrecision)
-    if computePrecision == .Float32 {
-      super.init(device: device, inFunctionName: "softmax")
-    } else if computePrecision == .Float16 {
-      super.init(device: device, inFunctionName: "softmax_half")
-    } else {
-      fatalError()
-    }
-  }
 }

metal/paddle-mobile/paddle-mobile/Operators/SoftmaxOp.swift

@@ -52,7 +52,9 @@ class SoftmaxOp<P: PrecisionType>: Operator<SoftmaxKernel<P>, SoftmaxParam<P>>,
   
   func delogOutput() {
     print("softmax delog")
-
+    print(para.input)
+    
+    print(para.output)
     let originDim = para.output.originDim
     let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
     print(outputArray.strideArray())