diff options
Diffstat (limited to 'generic/VolumetricConvolution.c')
| -rw-r--r-- | generic/VolumetricConvolution.c | 118 |
1 files changed, 118 insertions, 0 deletions
diff --git a/generic/VolumetricConvolution.c b/generic/VolumetricConvolution.c new file mode 100644 index 0000000..0ec2247 --- /dev/null +++ b/generic/VolumetricConvolution.c @@ -0,0 +1,118 @@ +#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_id)); + 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_id)); + THTensor *bias = luaT_getfieldcheckudata(L, 1, "bias", torch_(Tensor_id)); + THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_(Tensor_id)); + + luaL_argcheck(L, input->nDimension == 4, 2, "4D tensor expected"); + + long nOutputPlane = weight->size[0]; + long nInputPlane = weight->size[1]; + 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 *input = luaT_checkudata(L, 2, torch_(Tensor_id)); + THTensor *gradOutput = luaT_checkudata(L, 3, torch_(Tensor_id)); + 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_id)); + THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_(Tensor_id)); + + 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_id)); + THTensor *gradOutput = luaT_checkudata(L, 3, torch_(Tensor_id)); + 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 *weight = luaT_getfieldcheckudata(L, 1, "weight", torch_(Tensor_id)); + THTensor *gradWeight = luaT_getfieldcheckudata(L, 1, "gradWeight", torch_(Tensor_id)); + THTensor *gradBias = luaT_getfieldcheckudata(L, 1, "gradBias", torch_(Tensor_id)); + + 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_pushmetaclass(L, torch_(Tensor_id)); + luaT_registeratname(L, nn_(VolumetricConvolution__), "nn"); + lua_pop(L,1); +} + +#endif |