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  #include "../precomp.hpp"
  #include "layers_common.hpp"
  #include "../op_inf_engine.hpp"
  #include <float.h>
  #include <algorithm>
  
  #ifdef HAVE_OPENCL
  #include "opencl_kernels_dnn.hpp"
  #endif
  
  namespace cv
  {
  namespace dnn
  {
  class PermuteLayerImpl CV_FINAL : public PermuteLayer
  {
  public:
      void checkCurrentOrder(int currentOrder)
      {
          if(currentOrder < 0 || currentOrder > 3)
          {
              CV_Error(
                       Error::StsBadArg,
                       "Orders of dimensions in Permute layer parameter"
                       "must be in [0...3] interval");
          }
  
          if(std::find(_order.begin(), _order.end(), currentOrder) != _order.end())
          {
              CV_Error(Error::StsBadArg,
                       "Permute layer parameter contains duplicated orders.");
          }
      }
  
      void checkNeedForPermutation()
      {
          _needsPermute = false;
          for (size_t i = 0; i < _numAxes; ++i)
          {
              if (_order[i] != i)
              {
                  _needsPermute = true;
                  break;
              }
          }
      }
  
      PermuteLayerImpl(const LayerParams &params)
          : _count(0), _needsPermute(false), _numAxes(0)
      {
          if (!params.has("order"))
          {
              return;
          }
  
          DictValue paramOrder = params.get("order");
          if(paramOrder.size() > 4)
          {
              CV_Error(
                       Error::StsBadArg,
                       "Too many (> 4) orders of dimensions in Permute layer");
          }
  
          _numAxes = paramOrder.size();
  
          for (size_t i = 0; i < _numAxes; i++)
          {
              int currentOrder = paramOrder.get<int>(i);
              checkCurrentOrder(currentOrder);
              _order.push_back(currentOrder);
          }
  
          setParamsFrom(params);
          checkNeedForPermutation();
      }
  
      virtual bool supportBackend(int backendId) CV_OVERRIDE
      {
          return backendId == DNN_BACKEND_DEFAULT ||
                 backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
      }
  
      bool getMemoryShapes(const std::vector<MatShape> &inputs,
                           const int requiredOutputs,
                           std::vector<MatShape> &outputs,
                           std::vector<MatShape> &internals) const CV_OVERRIDE
      {
          if(!_needsPermute)
          {
              Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
              return true;
          }
  
          CV_Assert(inputs.size() > 0);
          CV_Assert((int)_numAxes == inputs[0].size());
  
          MatShape shapeBefore = inputs[0], shapeAfter;
          for (size_t i = 0; i < _numAxes; i++)
          {
              shapeAfter.push_back(shapeBefore[_order[i]]);
          }
  
          outputs.clear();
  
          for (size_t i = 0; i < inputs.size(); i++)
          {
              CV_Assert(inputs[i].size() == 4);
              CV_Assert(inputs[i][2] == shapeBefore[2] && inputs[i][3] == shapeBefore[3]);
              CV_Assert(total(inputs[i]) == total(shapeAfter));
              outputs.push_back(shapeAfter);
          }
  
          return false;
      }
  
      void computeStrides(const MatShape &shapeBefore, const MatShape &shapeAfter)
      {
          _oldStride.resize(_numAxes);
          _newStride.resize(_numAxes);
  
          _oldStride[_numAxes - 1] = 1;
          _newStride[_numAxes - 1] = 1;
  
          for(int i = _numAxes - 2; i >= 0; i--)
          {
              _oldStride[i] = _oldStride[i + 1] * shapeBefore[i + 1];
              _newStride[i] = _newStride[i + 1] * shapeAfter[i + 1];
          }
  
          _count = _oldStride[0] * shapeBefore[0];
      }
  
      void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
      {
          if(!_needsPermute)
          {
              return;
          }
  
          CV_Assert(inputs.size() > 0);
          const Mat& inp0 = *inputs[0];
          CV_Assert((int)_numAxes == inp0.dims);
  
          computeStrides(shape(*inputs[0]), shape(outputs[0]));
  
  #ifdef HAVE_OPENCL
          if (uorder.empty())
          {
              std::vector<int> orderVec(_order.begin(), _order.end());;
              Mat morder(1, orderVec.size(), CV_32SC1, &orderVec[0]);
  
              std::vector<int> oldStrideVec(_oldStride.begin(), _oldStride.end());
              Mat mold_stride(1, _oldStride.size(), CV_32SC1, &oldStrideVec[0]);
  
              std::vector<int> newStrideVec(_newStride.begin(), _newStride.end());
              Mat mnew_stride(1, newStrideVec.size(), CV_32SC1, &newStrideVec[0]);
  
              morder.copyTo(uorder);
              mold_stride.copyTo(uold_stride);
              mnew_stride.copyTo(unew_stride);
          }
  #endif
      }
  
      class PermuteInvoker : public ParallelLoopBody
      {
      public:
          const Mat* inp;
          Mat* out;
          const std::vector<size_t>* order;
          int nstripes;
  
          static void run(const Mat& inp, Mat& out, const std::vector<size_t>& order, int nstripes)
          {
              PermuteInvoker p;
              p.inp = &inp;
              p.out = &out;
              p.order = &order;
              p.nstripes = nstripes;
  
              CV_Assert( out.size[0] == inp.size[order[0]] &&
                        out.size[1] == inp.size[order[1]] &&
                        out.size[2] == inp.size[order[2]] &&
                        out.size[3] == inp.size[order[3]]);
  
              parallel_for_(Range(0, nstripes), p, nstripes);
          }
  
          PermuteInvoker() : inp(0), out(0), order(0), nstripes(0) {}
  
          void operator()(const Range& r) const CV_OVERRIDE
          {
              int n0 = out->size[0], n1 = out->size[1], n2 = out->size[2], n3 = out->size[3];
  
              size_t orows = (size_t)n0*n1*n2;
              size_t stripeSize = (orows + nstripes - 1)/nstripes;
              size_t stripeStart = r.start*stripeSize;
              size_t stripeEnd = std::min(r.end*stripeSize, orows);
  
              const size_t esz = sizeof(float);
              size_t ostep0 = out->step[0]/esz, ostep1 = out->step[1]/esz, ostep2 = out->step[2]/esz;
              const size_t* ord = &order->at(0);
              size_t istep0 = inp->step[ord[0]]/esz, istep1 = inp->step[ord[1]]/esz,
              istep2 = inp->step[ord[2]]/esz, istep3 = inp->step[ord[3]]/esz;
  
              size_t val = stripeStart;
              int i2 = (int)(val % n2);
              val /= n2;
              int i1 = (int)(val % n1);
              int i0 = (int)(val / n1);
  
              const float* inptr_orig = inp->ptr<float>();
              float* outptr_orig = out->ptr<float>();
  
              for( size_t ofs = stripeStart; ofs < stripeEnd; ofs++ )
              {
                  const float* inptr = inptr_orig + i0*istep0 + i1*istep1 + i2*istep2;
                  float* outptr = outptr_orig + i0*ostep0 + i1*ostep1 + i2*ostep2;
  
                  for( int i3 = 0; i3 < n3; i3++ )
                      outptr[i3] = inptr[i3*istep3];
  
                  if( ++i2 >= n2 )
                  {
                      i2 = 0;
                      if( ++i1 >= n1 )
                      {
                          i1 = 0;
                          if( ++i0 >= n0 )
                              break;
                      }
                  }
              }
          }
      };
  
  #ifdef HAVE_OPENCL
      bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
      {
          std::vector<UMat> inputs;
          std::vector<UMat> outputs;
  
          inps.getUMatVector(inputs);
          outs.getUMatVector(outputs);
  
          if (!_needsPermute)
              return false;
  
          bool use_half = (inps.depth() == CV_16S);
          String opts = format("-DDtype=%s", use_half ? "half" : "float");
          for (size_t i = 0; i < inputs.size(); i++)
          {
              ocl::Kernel kernel("permute", ocl::dnn::permute_oclsrc, opts);
  
              kernel.set(0, (int)_count);
              kernel.set(1, ocl::KernelArg::PtrReadOnly(inputs[i]));
              kernel.set(2, ocl::KernelArg::PtrReadOnly(uorder));
              kernel.set(3, ocl::KernelArg::PtrReadOnly(uold_stride));
              kernel.set(4, ocl::KernelArg::PtrReadOnly(unew_stride));
              kernel.set(5, (int)_numAxes);
              kernel.set(6, ocl::KernelArg::PtrWriteOnly(outputs[i]));
  
              if (!kernel.run(1, &_count, NULL, false))
                  return false;
          }
  
          return true;
      }
  #endif
  
      void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
      {
          CV_TRACE_FUNCTION();
          CV_TRACE_ARG_VALUE(name, "name", name.c_str());
  
          CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                     OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                     forward_ocl(inputs_arr, outputs_arr, internals_arr))
  
          Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
      }
  
      void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
      {
          CV_TRACE_FUNCTION();
          CV_TRACE_ARG_VALUE(name, "name", name.c_str());
  
          size_t k, ninputs = inputs.size();
          if(!_needsPermute)
          {
              for (k = 0; k < ninputs; k++)
              {
                  CV_Assert(outputs[k].total() == inputs[k]->total());
                  if (outputs[k].data != inputs[k]->data)
                      inputs[k]->copyTo(outputs[k]);
              }
          }
          else
          {
              size_t i, j, count = _count, numAxes = _numAxes;
              const size_t* newStride = &_newStride[0];
              const size_t* oldStride = &_oldStride[0];
              const size_t* order = &_order[0];
  
              for (k = 0; k < ninputs; k++)
              {
                  const Mat& inp = *inputs[k];
                  Mat& out = outputs[k];
  
                  CV_Assert(inp.dims == numAxes && inp.size == inputs[0]->size);
                  CV_Assert(out.dims == numAxes && out.size == outputs[0].size);
  
                  CV_Assert(inp.isContinuous() && out.isContinuous());
                  CV_Assert(inp.type() == CV_32F && out.type() == CV_32F);
  
                  if( numAxes == 4 )
                  {
                      int nstripes = getNumThreads();
                      PermuteInvoker::run(inp, out, _order, nstripes);
                  }
                  else
                  {
                      const float *srcData = inp.ptr<float>();
                      float *dstData = out.ptr<float>();
  
                      for (i = 0; i < count; ++i)
                      {
                          size_t oldPosition = 0;
                          size_t newPosition = i;
  
                          for (j = 0; j < numAxes; ++j)
                          {
                              oldPosition += (newPosition / newStride[j]) * oldStride[order[j]];
                              newPosition %= newStride[j];
                          }
                          dstData[i] = srcData[oldPosition];
                      }
                  }
              }
          }
      }
  
      virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
      {
  #ifdef HAVE_INF_ENGINE
          InferenceEngine::LayerParams lp;
          lp.name = name;
          lp.type = "Permute";
          lp.precision = InferenceEngine::Precision::FP32;
          std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
  
          CV_Assert(!_order.empty());
          ieLayer->params["order"] = format("%d", _order[0]);
          for (int i = 1; i < _order.size(); ++i)
              ieLayer->params["order"] += format(",%d", _order[i]);
  
          return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
  #endif  // HAVE_INF_ENGINE
          return Ptr<BackendNode>();
      }
  
      size_t _count;
      std::vector<size_t> _order;
  
      std::vector<int> _oldDimensionSize;
      std::vector<int> _newDimensionSize;
  
      std::vector<size_t> _oldStride;
      std::vector<size_t> _newStride;
      bool _needsPermute;
  
  #ifdef HAVE_OPENCL
      UMat uorder, uold_stride, unew_stride;
  #endif
  
      size_t _numAxes;
  };
  
  Ptr<PermuteLayer> PermuteLayer::create(const LayerParams &params)
  {
      return Ptr<PermuteLayer>(new PermuteLayerImpl(params));
  }
  
  }
  }
  