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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialConvolutionMap.c"
#else
static int nn_(SpatialConvolutionMap_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");
int nInputPlane = luaT_getfieldcheckint(L, 1, "nInputPlane");
int nOutputPlane = luaT_getfieldcheckint(L, 1, "nOutputPlane");
THTensor *connTable = luaT_getfieldcheckudata(L, 1, "connTable", torch_Tensor);
THTensor *weight = luaT_getfieldcheckudata(L, 1, "weight", torch_Tensor);
THTensor *bias = luaT_getfieldcheckudata(L, 1, "bias", torch_Tensor);
THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);
luaL_argcheck(L, input->nDimension == 3, 2, "3D tensor expected");
luaL_argcheck(L, input->size[0] >= nInputPlane, 2, "invalid number of input planes");
luaL_argcheck(L, input->size[2] >= kW && input->size[1] >= kH, 2, "input image smaller than kernel size");
THTensor_(resize3d)(output, nOutputPlane,
(input->size[1] - kH) / dH + 1,
(input->size[2] - kW) / dW + 1);
// contiguous
input = THTensor_(newContiguous)(input);
output = THTensor_(newContiguous)(output);
// get raw pointers
real *input_data = THTensor_(data)(input);
real *output_data = THTensor_(data)(output);
real *weight_data = THTensor_(data)(weight);
real *bias_data = THTensor_(data)(bias);
real *connTable_data = THTensor_(data)(connTable);
// and dims
long input_h = input->size[1];
long input_w = input->size[2];
long output_h = output->size[1];
long output_w = output->size[2];
long weight_h = weight->size[1];
long weight_w = weight->size[2];
long p;
#pragma omp parallel for private(p)
for (p = 0; p < nOutputPlane; p++) {
// add bias
real *ptr_output = output_data + p*output_w*output_h;
long j;
for(j = 0; j < output_h*output_w; j++)
ptr_output[j] = bias_data[p];
// convolve all maps
int nweight = connTable->size[0];
long k;
for (k = 0; k < nweight; k++) {
// get offsets for input/output
int o = (int)connTable_data[k*2+1]-1;
int i = (int)connTable_data[k*2+0]-1;
if (o == p)
{
THTensor_(validXCorr2Dptr)(output_data + o*output_w*output_h,
1.0,
input_data + i*input_w*input_h, input_h, input_w,
weight_data + k*weight_w*weight_h, weight_h, weight_w,
dH, dW);
}
}
}
// clean up
THTensor_(free)(input);
THTensor_(free)(output);
return 1;
}
static int nn_(SpatialConvolutionMap_updateGradInput)(lua_State *L)
{
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
int dW = luaT_getfieldcheckint(L, 1, "dW");
int dH = luaT_getfieldcheckint(L, 1, "dH");
int nInputPlane = luaT_getfieldcheckint(L, 1, "nInputPlane");
THTensor *connTable = luaT_getfieldcheckudata(L, 1, "connTable", torch_Tensor);
THTensor *weight = luaT_getfieldcheckudata(L, 1, "weight", torch_Tensor);
THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_Tensor);
// contiguous
gradInput = THTensor_(newContiguous)(gradInput);
gradOutput = THTensor_(newContiguous)(gradOutput);
// Resize/Zero
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
// get raw pointers
real *gradInput_data = THTensor_(data)(gradInput);
real *gradOutput_data = THTensor_(data)(gradOutput);
real *weight_data = THTensor_(data)(weight);
real *connTable_data = THTensor_(data)(connTable);
// and dims
long input_h = input->size[1];
long input_w = input->size[2];
long output_h = gradOutput->size[1];
long output_w = gradOutput->size[2];
long weight_h = weight->size[1];
long weight_w = weight->size[2];
long p;
#pragma omp parallel for private(p)
for(p = 0; p < nInputPlane; p++)
{
long k;
// backward all
int nkernel = connTable->size[0];
for(k = 0; k < nkernel; k++)
{
int o = (int)connTable_data[k*2+1]-1;
int i = (int)connTable_data[k*2+0]-1;
if (i == p)
{
// gradient to input
THTensor_(fullConv2Dptr)(gradInput_data + i*input_w*input_h,
1.0,
gradOutput_data + o*output_w*output_h, output_h, output_w,
weight_data + k*weight_w*weight_h, weight_h, weight_w,
dH, dW);
}
}
}
// clean up
THTensor_(free)(gradInput);
THTensor_(free)(gradOutput);
return 1;
}
static int nn_(SpatialConvolutionMap_accGradParameters)(lua_State *L)
{
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
int dW = luaT_getfieldcheckint(L, 1, "dW");
int dH = luaT_getfieldcheckint(L, 1, "dH");
int nOutputPlane = luaT_getfieldcheckint(L, 1, "nOutputPlane");
real scale = luaL_optnumber(L, 4, 1);
THTensor *connTable = luaT_getfieldcheckudata(L, 1, "connTable", torch_Tensor);
THTensor *weight = luaT_getfieldcheckudata(L, 1, "weight", torch_Tensor);
THTensor *gradWeight = luaT_getfieldcheckudata(L, 1, "gradWeight", torch_Tensor);
THTensor *gradBias = luaT_getfieldcheckudata(L, 1, "gradBias", torch_Tensor);
// contiguous
input = THTensor_(newContiguous)(input);
gradOutput = THTensor_(newContiguous)(gradOutput);
// get raw pointers
real *input_data = THTensor_(data)(input);
real *gradOutput_data = THTensor_(data)(gradOutput);
real *gradWeight_data = THTensor_(data)(gradWeight);
real *gradBias_data = THTensor_(data)(gradBias);
// and dims
long input_h = input->size[1];
long input_w = input->size[2];
long output_h = gradOutput->size[1];
long output_w = gradOutput->size[2];
long weight_h = weight->size[1];
long weight_w = weight->size[2];
// gradients wrt bias
long k;
#pragma omp parallel for private(k)
for(k = 0; k < nOutputPlane; k++) {
real *ptr_gradOutput = gradOutput_data + k*output_w*output_h;
long l;
for(l = 0; l < output_h*output_w; l++)
gradBias_data[k] += scale*ptr_gradOutput[l];
}
// gradients wrt weight
int nkernel = connTable->size[0];
#pragma omp parallel for private(k)
for(k = 0; k < nkernel; k++)
{
int o = (int)THTensor_(get2d)(connTable,k,1)-1;
int i = (int)THTensor_(get2d)(connTable,k,0)-1;
// gradient to kernel
THTensor_(validXCorr2DRevptr)(gradWeight_data + k*weight_w*weight_h,
scale,
input_data + i*input_w*input_h, input_h, input_w,
gradOutput_data + o*output_w*output_h, output_h, output_w,
dH, dW);
}
// clean up
THTensor_(free)(input);
THTensor_(free)(gradOutput);
return 0;
}
static const struct luaL_Reg nn_(SpatialConvolutionMap__) [] = {
{"SpatialConvolutionMap_updateOutput", nn_(SpatialConvolutionMap_updateOutput)},
{"SpatialConvolutionMap_updateGradInput", nn_(SpatialConvolutionMap_updateGradInput)},
{"SpatialConvolutionMap_accGradParameters", nn_(SpatialConvolutionMap_accGradParameters)},
{NULL, NULL}
};
static void nn_(SpatialConvolutionMap_init)(lua_State *L)
{
luaT_pushmetatable(L, torch_Tensor);
luaT_registeratname(L, nn_(SpatialConvolutionMap__), "nn");
lua_pop(L,1);
}
#endif
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