SpatialAveragePooling.c

#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialAveragePooling.c"
#else

static int nn_(SpatialAveragePooling_updateOutput)(lua_State *L)
{
  THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
  int kW = luaT_getfieldcheckint(L, 1, "kW");
  int kH = luaT_getfieldcheckint(L, 1, "kH");
  int dW = luaT_getfieldcheckint(L, 1, "dW");
  int dH = luaT_getfieldcheckint(L, 1, "dH");
  
  THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);

  real *output_data;
  real *input_data;

  int dimw = 2;
  int dimh = 1;
  int dimc = 0;
  long nbatch = 1;

  long inputWidth;
  long inputHeight;
  long outputWidth;
  long outputHeight;
  long nInputPlane; // number of channels (or colors)

  long k;

  luaL_argcheck(L, input->nDimension == 3 || input->nDimension == 4, 2, "3D or 4D(batch mode) tensor expected");

  if (input->nDimension == 4) {
    nbatch = input->size[0];
    dimw++;
    dimh++;
    dimc++;
  }

  inputWidth = input->size[dimw];
  inputHeight = input->size[dimh];
  nInputPlane = input->size[dimc];
  outputWidth = (inputWidth - kW) / dW + 1;
  outputHeight = (inputHeight - kH) / dH + 1;

  luaL_argcheck(L, inputWidth >= kW && inputHeight >= kH, 2, "input image smaller than kernel size");

  if (input->nDimension == 3)
    THTensor_(resize3d)(output, nInputPlane, outputHeight, outputWidth);
  else
    THTensor_(resize4d)(output, input->size[0], nInputPlane, outputHeight, outputWidth);
  
  input = THTensor_(newContiguous)(input);
  input_data = THTensor_(data)(input);
  output_data = THTensor_(data)(output);
  
#pragma omp parallel for private(k)
  for(k = 0; k < nInputPlane; k++)
  {
    long p;
    for(p = 0; p < nbatch; p++)
    {
      long xx, yy;
      /* For all output pixels... */
      real *ptr_output = output_data + p*nInputPlane*outputWidth*outputHeight + k*outputWidth*outputHeight;
      long i;
      for(i = 0; i < outputWidth*outputHeight; i++)
        ptr_output[i] = 0;
      
      for(yy = 0; yy < outputHeight; yy++)
      {
        for(xx = 0; xx < outputWidth; xx++)
        {
          /* Compute the mean of the input image... */
          real *ptr_input = input_data + p*nInputPlane*inputWidth*inputHeight + k*inputWidth*inputHeight + yy*dH*inputWidth+xx*dW;
          real sum = 0;
          long kx, ky;

          for(ky = 0; ky < kH; ky++)
          {
            for(kx = 0; kx < kW; kx++)
              sum += ptr_input[kx];
            ptr_input += inputWidth; /* next input line */
          }
          /* Update output */
          *ptr_output++ += sum/(kW*kH);
        }
      }
    }
  }
  THTensor_(free)(input);

  return 1;
}

static int nn_(SpatialAveragePooling_updateGradInput)(lua_State *L)
{
  THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
  THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
  int kW = luaT_getfieldcheckint(L, 1, "kW");
  int kH = luaT_getfieldcheckint(L, 1, "kH");
  int dW = luaT_getfieldcheckint(L, 1, "dW");
  int dH = luaT_getfieldcheckint(L, 1, "dH");
  THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_Tensor);

  int dimw = 2;
  int dimh = 1;
  int dimc = 0;
  long nbatch = 1;

  long inputWidth;
  long inputHeight;
  long outputWidth;
  long outputHeight;
  long nInputPlane; // number of channels (or colors)

  real *gradOutput_data;
  real *input_data, *gradInput_data;

  long k;

  if (input->nDimension == 4) {
    nbatch = input->size[0];
    dimw++;
    dimh++;
    dimc++;
  }

  inputWidth = input->size[dimw];
  inputHeight = input->size[dimh];
  nInputPlane = input->size[dimc];
  outputWidth = (inputWidth - kW) / dW + 1;
  outputHeight = (inputHeight - kH) / dH + 1;

  input_data = THTensor_(data)(input);

  THTensor_(resizeAs)(gradInput, input);
  gradInput_data = THTensor_(data)(gradInput);
  gradOutput_data = THTensor_(data)(gradOutput);

#pragma omp parallel for private(k)
  for(k = 0; k < nInputPlane; k++)
  {
    long p;
    for(p = 0; p < nbatch; p++)
    {
      real *ptr_gradOutput = gradOutput_data + p*nInputPlane*outputHeight*outputWidth + k*outputWidth*outputHeight;
      long xx, yy;

      real* ptr_gi = gradInput_data + p*nInputPlane*inputWidth*inputHeight + k*inputWidth*inputHeight;
      long i;
      for(i=0; i<inputWidth*inputHeight; i++)
        ptr_gi[i] = 0.0;

      for(yy = 0; yy < outputHeight; yy++)
      {
        for(xx = 0; xx < outputWidth; xx++)
        {
          real *ptr_gradInput = gradInput_data + p*nInputPlane*inputWidth*inputHeight + k*inputWidth*inputHeight + yy*dH*inputWidth+xx*dW;
          real z = *ptr_gradOutput++;
          long kx, ky;

          for(ky = 0; ky < kH; ky++)
          {
            for(kx = 0; kx < kW; kx++)
              ptr_gradInput[kx] += z/(kW*kH);
            ptr_gradInput += inputWidth;
          }
        }
      }
    }
  }

  return 1;
}

static const struct luaL_Reg nn_(SpatialAveragePooling__) [] = {
  {"SpatialAveragePooling_updateOutput", nn_(SpatialAveragePooling_updateOutput)},
  {"SpatialAveragePooling_updateGradInput", nn_(SpatialAveragePooling_updateGradInput)},
  {NULL, NULL}
};

static void nn_(SpatialAveragePooling_init)(lua_State *L)
{
  luaT_pushmetatable(L, torch_Tensor);
  luaT_registeratname(L, nn_(SpatialAveragePooling__), "nn");
  lua_pop(L,1);
}

#endif