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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/VolumetricConvolution.c"
#else
static int nn_(VolumetricConvolution_updateOutput)(lua_State *L)
{
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
int dT = luaT_getfieldcheckint(L, 1, "dT");
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 == 4, 2, "4D tensor expected");
long nOutputPlane = weight->size[0];
long kT = weight->size[2];
long kH = weight->size[3];
long kW = weight->size[4];
long inputDepth = input->size[1];
long inputHeight = input->size[2];
long inputWidth = input->size[3];
long outputDepth = (inputDepth - kT) / dT + 1;
long outputWidth = (inputWidth - kW) / dW + 1;
long outputHeight = (inputHeight - kH) / dH + 1;
THTensor_(resize4d)(output, nOutputPlane, outputDepth, outputHeight, outputWidth);
/* add bias */
long i;
THTensor *outn = THTensor_(new)();
for (i=0; i<bias->size[0]; i++) {
THTensor_(select)(outn,output,0,i);
THTensor_(fill)(outn, THTensor_(get1d)(bias, i));
}
THTensor_(free)(outn);
/* do convolutions */
THTensor_(conv3Dmv)(output, 1.0, 1.0, input, weight, dT, dH, dW, "V", "X");
return 1;
}
static int nn_(VolumetricConvolution_updateGradInput)(lua_State *L)
{
THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
int dT = luaT_getfieldcheckint(L, 1, "dT");
int dW = luaT_getfieldcheckint(L, 1, "dW");
int dH = luaT_getfieldcheckint(L, 1, "dH");
int nOutputPlane = luaT_getfieldcheckint(L, 1, "nOutputPlane");
THTensor *weight = luaT_getfieldcheckudata(L, 1, "weight", torch_Tensor);
THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_Tensor);
THArgCheck( nOutputPlane == gradOutput->size[0], 1, "Number of output features is not equal to nOutputPlane" );
/* gradient to input */
THTensor *tweight = THTensor_(newTranspose)(weight,0,1);
THTensor_(conv3Dmv)(gradInput, 0.0, 1.0, gradOutput, tweight, dT, dH, dW, "F", "C");
THTensor_(free)(tweight);
return 1;
}
static int nn_(VolumetricConvolution_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 dT = luaT_getfieldcheckint(L, 1, "dT");
int dW = luaT_getfieldcheckint(L, 1, "dW");
int dH = luaT_getfieldcheckint(L, 1, "dH");
int nOutputPlane = luaT_getfieldcheckint(L, 1, "nOutputPlane");
THTensor *gradWeight = luaT_getfieldcheckudata(L, 1, "gradWeight", torch_Tensor);
THTensor *gradBias = luaT_getfieldcheckudata(L, 1, "gradBias", torch_Tensor);
THArgCheck( nOutputPlane == gradOutput->size[0], 1, "Number of output features is not equal to nOutputPlane" );
long k;
/* gradient to bias */
real *gradBias_data = THTensor_(data)(gradBias);
THTensor* gradOutSlice = THTensor_(new)();
for(k = 0; k < nOutputPlane; k++)
{
THTensor_(select)(gradOutSlice, gradOutput, 0, k);
gradBias_data[k] += scale*THTensor_(sumall)(gradOutSlice);
}
THTensor_(free)(gradOutSlice);
/* gradient to kernels */
THTensor_(conv3DRevger)(gradWeight, 1.0, scale, input, gradOutput, dT, dH, dW);
return 0;
}
static const struct luaL_Reg nn_(VolumetricConvolution__) [] = {
{"VolumetricConvolution_updateOutput", nn_(VolumetricConvolution_updateOutput)},
{"VolumetricConvolution_updateGradInput", nn_(VolumetricConvolution_updateGradInput)},
{"VolumetricConvolution_accGradParameters", nn_(VolumetricConvolution_accGradParameters)},
{NULL, NULL}
};
static void nn_(VolumetricConvolution_init)(lua_State *L)
{
luaT_pushmetatable(L, torch_Tensor);
luaT_registeratname(L, nn_(VolumetricConvolution__), "nn");
lua_pop(L,1);
}
#endif
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