text: /*
   * Licensed to the Apache Software Foundation (ASF) under one
   * or more contributor license agreements.  See the NOTICE file
   * distributed with this work for additional information
   * regarding copyright ownership.  The ASF licenses this file
   * to you 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) 2017, Open AI Lab
   * Author: haitao@openailab.com
   */
  #include <iostream>
  #include <functional>
  #include <cstring>
  #include <algorithm>
  
  #include "logger.hpp"
  #include "executor.hpp"
  #include "tensor_mem.hpp"
  #include "graph.hpp"
  #include "operator/scale.hpp"
  
  namespace TEngine {
  
  
  namespace ScaleImpl {
  
  bool OnBind(Node * node, ExecEngine * engine)
  {
      //set the inplace feature
      inplace_t io_map;
     
      io_map[0]=0;
  
      node->SetAttr(ATTR_INPLACE,io_map);
  
      return true;
  }
  
  extern "C" void scale_neon(float * input, float * gamma, int channel,  int channel_size,float * output);
  extern "C" void scale_neon_bias(float * input, float * gamma, int channel, int channel_size,float * output, float * bias);
  
  bool Run(Node * node, ExecEngine * engine)
  {
      const Tensor * input_tensor=node->GetInputTensor(0);
      Tensor * output_tensor=node->GetOutputTensor(0);
      const Tensor * gamma_tensor=node->GetInputTensor(1);
      const Tensor * beta_tensor=node->GetInputTensor(2);
  
      const TShape&  shape=input_tensor->GetShape();
      const std::vector<int> dims=shape.GetDim();
  
      int batch_number=dims[0];
      int channel_num=dims[1];
      int channel_size=dims[2]*dims[3];
      int img_size=channel_num*channel_size;
  
  
      float * input=(float *)get_tensor_mem(input_tensor);
      float * output=(float *)get_tensor_mem(output_tensor);
      float * gamma=(float *)get_tensor_mem(gamma_tensor);
  
      if(beta_tensor==nullptr)
      {
          for(int i=0;i<batch_number;i++)
          {
             scale_neon(input,gamma,channel_num,channel_size,output);
              input+=img_size;
              output+=img_size;
          }
      }
      else
      {
          float * beta=(float *)get_tensor_mem(beta_tensor); 
  
          for(int i=0;i<batch_number;i++)
          {
              scale_neon_bias(input,gamma,channel_num,channel_size,output,beta);
              input+=img_size;
              output+=img_size;
          }
      }
  
      return true;
  }
  
  } //namespace ScaleImpl
  
  void RegisterScaleNodeExec(void)
  {
      NodeExec scale_exec={ScaleImpl::OnBind,nullptr,ScaleImpl::Run,nullptr};
  
      RegisterNodeExec("Scale",scale_exec);
  }
  
  
  } //namespace TEngine
  