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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialFullConvolution.c"
#else
static int nn_(SpatialFullConvolution_updateOutput)(lua_State *L)
{
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
int dW = luaT_getfieldcheckint(L, 1, "dW");
int dH = luaT_getfieldcheckint(L, 1, "dH");
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 || input->nDimension == 4, 2, "3D or 4D(batch mode) tensor expected");
int dimw = 2;
int dimh = 1;
if (input->nDimension == 4) {
dimw++;
dimh++;
}
long nOutputPlane = weight->size[1];
long kW = weight->size[3];
long kH = weight->size[2];
long inputWidth = input->size[dimw];
long inputHeight = input->size[dimh];
long outputWidth = (inputWidth - 1) * dW + kW;
long outputHeight = (inputHeight - 1) * dH + kH;
if (input->nDimension == 3)
{
THTensor_(resize3d)(output, nOutputPlane, outputHeight, outputWidth);
/* add bias */
long i;
real* bias_data = THTensor_(data)(bias);
real* output_data = THTensor_(data)(output);
#pragma omp parallel for private(i)
for (i=0; i<bias->size[0]; i++)
{
/*THTensor_(select)(outn,output,0,i);*/
/*TH_TENSOR_APPLY(real,outn, *outn_data = bias_data[i];);*/
real *ptr_output = output_data + i*outputWidth*outputHeight;
long j;
for(j = 0; j < outputWidth*outputHeight; j++)
ptr_output[j] = bias_data[i];
}
/* do convolutions */
THTensor *tweight = THTensor_(newTranspose)(weight,0,1);
THTensor_(conv2Dmv)(output, 1.0, 1.0, input, tweight, dH, dW, "F", "C");
THTensor_(free)(tweight);
}
else
{
THTensor_(resize4d)(output, input->size[0], nOutputPlane, outputHeight, outputWidth);
real* bias_data = THTensor_(data)(bias);
real* output_data = THTensor_(data)(output);
long p;
#pragma omp parallel for private(p)
for (p=0; p<input->size[0]; p++)
{
/* BIAS */
long i;
for (i=0; i<bias->size[0]; i++)
{
real *ptr_output = output_data + p*nOutputPlane*outputWidth*outputHeight + i*outputWidth*outputHeight;
long j;
for(j = 0; j < outputWidth*outputHeight; j++)
ptr_output[j] = bias_data[i];
}
}
/* do convolutions */
THTensor *tweight = THTensor_(newTranspose)(weight,0,1);
THTensor_(conv2Dmm)(output, 1.0, 1.0, input, tweight, dH, dW, "F", "C");
THTensor_(free)(tweight);
}
return 1;
}
static int nn_(SpatialFullConvolution_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");
THTensor *weight = luaT_getfieldcheckudata(L, 1, "weight", torch_Tensor);
THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_Tensor);
long nOutputPlane = weight->size[1];
THArgCheck( nOutputPlane == gradOutput->size[input->nDimension == 4 ? 1 : 0], 1, "Number of output features is not equal to nOutputPlane" );
if (input->nDimension == 3)
{
/* gradient to input */
THTensor_(conv2Dmv)(gradInput, 0.0, 1.0, gradOutput, weight, dH, dW, "V", "X");
}
else
{
/* gradient to input */
THTensor_(conv2Dmm)(gradInput, 0.0, 1.0, gradOutput, weight, dH, dW, "V", "X");
}
return 1;
}
static int nn_(SpatialFullConvolution_accGradParameters)(lua_State *L)
{
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
real scale = luaL_optnumber(L, 4, 1);
int dW = luaT_getfieldcheckint(L, 1, "dW");
int dH = luaT_getfieldcheckint(L, 1, "dH");
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);
long nOutputPlane = weight->size[1];
THArgCheck( nOutputPlane == gradOutput->size[input->nDimension == 4 ? 1 : 0], 1, "Number of output features is not equal to nOutputPlane" );
int dimw = 2;
int dimh = 1;
if (input->nDimension == 4)
{
dimw++;
dimh++;
}
/* gradient to bias */
real *gradBias_data = THTensor_(data)(gradBias);
real *gradOutput_data = THTensor_(data)(gradOutput);
long noutSlice = gradOutput->size[dimh]*gradOutput->size[dimw];
/*THTensor* gradOutSlice = THTensor_(new)();*/
if (input->nDimension == 3)
{
long k;
#pragma omp parallel for private(k)
for(k = 0; k < nOutputPlane; k++)
{
/*THTensor_(select)(gradOutSlice, gradOutput, 0, k);*/
real *ptr_gradOutput = gradOutput_data + k*noutSlice;
long l;
for(l = 0; l < noutSlice; l++)
gradBias_data[k] += scale*ptr_gradOutput[l];
}
/* gradient to kernels */
THTensor_(conv2DRevger)(gradWeight, 1.0, scale, gradOutput, input, dH, dW);
}
else
{
long k;
#pragma omp parallel for private(k)
for(k = 0; k < nOutputPlane; k++)
{
long p;
for(p = 0; p < input->size[0]; p++)
{
/* BIAS */
real *ptr_gradOutput = gradOutput_data + p*nOutputPlane*noutSlice + k*noutSlice;
long l;
for(l = 0; l < noutSlice; l++)
gradBias_data[k] += scale*ptr_gradOutput[l];
}
}
/* gradient to kernels */
THTensor_(conv2DRevgerm)(gradWeight, 1.0, scale, gradOutput, input, dH, dW);
}
return 0;
}
static const struct luaL_Reg nn_(SpatialFullConvolution__) [] = {
{"SpatialFullConvolution_updateOutput", nn_(SpatialFullConvolution_updateOutput)},
{"SpatialFullConvolution_updateGradInput", nn_(SpatialFullConvolution_updateGradInput)},
{"SpatialFullConvolution_accGradParameters", nn_(SpatialFullConvolution_accGradParameters)},
{NULL, NULL}
};
static void nn_(SpatialFullConvolution_init)(lua_State *L)
{
luaT_pushmetatable(L, torch_Tensor);
luaT_registeratname(L, nn_(SpatialFullConvolution__), "nn");
lua_pop(L,1);
}
#endif
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