diff options
Diffstat (limited to 'generic/MultiLabelMarginCriterion.c')
| -rw-r--r-- | generic/MultiLabelMarginCriterion.c | 185 |
1 files changed, 185 insertions, 0 deletions
diff --git a/generic/MultiLabelMarginCriterion.c b/generic/MultiLabelMarginCriterion.c new file mode 100644 index 0000000..f4c3914 --- /dev/null +++ b/generic/MultiLabelMarginCriterion.c @@ -0,0 +1,185 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "generic/MultiLabelMarginCriterion.c" +#else + +static int nn_(MultiLabelMarginCriterion_updateOutput)(lua_State *L) +{ + THTensor *input = luaT_checkudata(L, 2, torch_(Tensor_id)); + int sizeAverage = luaT_getfieldcheckboolean(L, 1, "sizeAverage"); + real *input_data, *target_data; + long nframe, dim; + long t, d, dt, ddt; + THTensor *target; + real sum; + + THArgCheck((input->nDimension == 1) || (input->nDimension == 2), 2, "vector or matrix expected"); + + if(input->nDimension == 1) + { + nframe = 1; + dim = input->size[0]; + target = luaT_checkudata(L, 3, torch_(Tensor_id)); + THArgCheck((target->nDimension == 1) && (target->size[0] == dim), 3, "inconsistent target size"); + } + else + { + nframe = input->size[0]; + dim = input->size[1]; + target = luaT_checkudata(L, 3, torch_(Tensor_id)); + THArgCheck((target->nDimension == 2) && (target->size[0] == nframe) && (target->size[1] == dim), 3, "inconsistent target size"); + } + + THArgCheck(THTensor_(minall)(target) >= 0, 3, "target out of range"); + THArgCheck(THTensor_(maxall)(target) <= dim, 3, "target out of range"); + + target = THTensor_(newContiguous)(target); + input = THTensor_(newContiguous)(input); + input_data = THTensor_(data)(input); + target_data = THTensor_(data)(target); + + sum = 0; + for(t = 0; t < nframe; t++) + { + for(dt = 0; dt < dim; dt++) + { + long target_idx = (long)target_data[dt]-1; + real input_target; + if(target_idx < 0) + break; + + input_target = input_data[target_idx]; + for(d = 0; d < dim; d++) + { + int istarget = 0; + for(ddt = 0; ddt < dim; ddt++) + { + if(!target_data[ddt]) + break; + if(((long)target_data[ddt])-1 == d) + istarget = 1; + } + + if(!istarget) + { + real z = 1 - input_target + input_data[d]; + if(z > 0) + sum += z; + } + } + } + input_data += dim; + target_data += dim; + } + + if(sizeAverage) + sum /= dim; + + lua_pushnumber(L, sum); + lua_setfield(L, 1, "output"); + + THTensor_(free)(input); + THTensor_(free)(target); + lua_pushnumber(L, sum); + return 1; +} + +static int nn_(MultiLabelMarginCriterion_updateGradInput)(lua_State *L) +{ + THTensor *input = luaT_checkudata(L, 2, torch_(Tensor_id)); + int sizeAverage = luaT_getfieldcheckboolean(L, 1, "sizeAverage"); + THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_(Tensor_id)); + real *input_data; + real *gradInput_data; + real *target_data; + long nframe, dim; + long t, d, dt, ddt; + THTensor *target; + real g; + + THArgCheck((input->nDimension == 1) || (input->nDimension == 2), 2, "vector or matrix expected"); + + if(input->nDimension == 1) + { + nframe = 1; + dim = input->size[0]; + target = luaT_checkudata(L, 3, torch_(Tensor_id)); + THArgCheck((target->nDimension == 1) && (target->size[0] == dim), 3, "inconsistent target size"); + } + else + { + nframe = input->size[0]; + dim = input->size[1]; + target = luaT_checkudata(L, 3, torch_(Tensor_id)); + THArgCheck((target->nDimension == 2) && (target->size[0] == nframe) && (target->size[1] == dim), 3, "inconsistent target size"); + } + + THArgCheck(THTensor_(minall)(target) >= 0, 3, "target out of range"); + THArgCheck(THTensor_(maxall)(target) <= dim, 3, "target out of range"); + + target = THTensor_(newContiguous)(target); + input = THTensor_(newContiguous)(input); + input_data = THTensor_(data)(input); + target_data = THTensor_(data)(target); + + g = (sizeAverage ? 1./((real)dim) : 1.); + + THTensor_(resizeAs)(gradInput, input); + THTensor_(zero)(gradInput); + gradInput_data = THTensor_(data)(gradInput); + + for(t = 0; t < nframe; t++) + { + for(dt = 0; dt < dim; dt++) + { + long target_idx = (long)target_data[dt]-1; + real input_target; + if(target_idx < 0) + break; + + input_target = input_data[target_idx]; + for(d = 0; d < dim; d++) + { + int istarget = 0; + for(ddt = 0; ddt < dim; ddt++) + { + if(!target_data[ddt]) + break; + if(((long)target_data[ddt])-1 == d) + istarget = 1; + } + + if(!istarget) + { + real z = 1 - input_target + input_data[d]; + if(z > 0) + { + gradInput_data[target_idx] -= g; + gradInput_data[d] += g; + } + } + } + } + input_data += dim; + target_data += dim; + gradInput_data += dim; + } + + THTensor_(free)(input); + THTensor_(free)(target); + return 1; +} + +static const struct luaL_Reg nn_(MultiLabelMarginCriterion__) [] = { + {"MultiLabelMarginCriterion_updateOutput", nn_(MultiLabelMarginCriterion_updateOutput)}, + {"MultiLabelMarginCriterion_updateGradInput", nn_(MultiLabelMarginCriterion_updateGradInput)}, + {NULL, NULL} +}; + +static void nn_(MultiLabelMarginCriterion_init)(lua_State *L) +{ + luaT_pushmetaclass(L, torch_(Tensor_id)); + luaT_registeratname(L, nn_(MultiLabelMarginCriterion__), "nn"); + lua_pop(L,1); +} + +#endif |