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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/MultiMarginCriterion.c"
#else
// TODO: improve error messages
void THNN_(MultiMarginCriterion_updateOutput)(
THNNState *state,
THTensor *input,
THIndexTensor *target,
THTensor *output,
bool sizeAverage,
int p,
THTensor *weights,
accreal margin_)
{
real margin = TH_CONVERT_ACCREAL_TO_REAL(margin_);
real *input_data, *weights_data;
THIndex_t *target_data;
long nframe, dim;
long t, d;
real sum;
THArgCheck((input->nDimension == 1) || (input->nDimension == 2), 2,
"vector or matrix expected");
if (input->nDimension == 1)
{
nframe = 1;
dim = input->size[0];
}
else
{
nframe = input->size[0];
dim = input->size[1];
THArgCheck((target->nDimension == 1) && (target->size[0] == nframe), 3,
"inconsistent target size");
}
for (t = 0; t < nframe; t++)
{
THIndex_t idx = THIndexTensor_(get1d)(target, t);
THArgCheck((idx >= TH_INDEX_BASE) && (idx < dim + TH_INDEX_BASE), 3,
"target out of range");
}
input = THTensor_(newContiguous)(input);
target = THIndexTensor_(newContiguous)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
input_data = THTensor_(data)(input);
target_data = THIndexTensor_(data)(target);
weights_data = weights ? THTensor_(data)(weights) : NULL;
sum = 0;
for (t = 0; t < nframe; t++)
{
THIndex_t target_idx = target_data[t] - TH_INDEX_BASE;
real input_target = input_data[target_idx];
for (d = 0; d < dim; d++)
{
real z = margin - input_target + input_data[d];
if (d == target_idx)
continue;
if (z > 0) {
real h = (p==1) ? z : z*z;
if(weights_data)
h *= weights_data[target_idx];
sum += h;
}
}
input_data += dim;
}
sum /= dim;
if(sizeAverage)
sum /= nframe;
THTensor_(set1d)(output, 0, sum);
THTensor_(free)(input);
THIndexTensor_(free)(target);
if(weights)
THTensor_(free)(weights);
}
void THNN_(MultiMarginCriterion_updateGradInput)(
THNNState *state,
THTensor *input,
THIndexTensor *target,
THTensor *gradInput,
bool sizeAverage,
int p,
THTensor *weights,
accreal margin_)
{
real margin = TH_CONVERT_ACCREAL_TO_REAL(margin_);
real *input_data;
real *gradInput_data;
THIndex_t *target_data;
real *weights_data;
long nframe, dim;
long t, d;
real g;
THArgCheck((input->nDimension == 1) || (input->nDimension == 2), 2,
"vector or matrix expected");
if (input->nDimension == 1)
{
nframe = 1;
dim = input->size[0];
}
else
{
nframe = input->size[0];
dim = input->size[1];
THArgCheck((target->nDimension == 1) && (target->size[0] == nframe), 3,
"inconsistent target size");
}
g = (sizeAverage ? 1./((real)(nframe*dim)) : 1./((real)dim));
input = THTensor_(newContiguous)(input);
target = THIndexTensor_(newContiguous)(target);
input_data = THTensor_(data)(input);
THTensor_(resizeAs)(gradInput, input);
gradInput_data = THTensor_(data)(gradInput);
target_data = THIndexTensor_(data)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
weights_data = weights ? THTensor_(data)(weights) : NULL;
for (t = 0; t < nframe; t++)
{
THIndex_t target_idx = target_data[t] - TH_INDEX_BASE;
real input_target = input_data[target_idx];
real gradInput_target = 0;
for (d = 0; d < dim; d++)
{
real z = margin - input_target + input_data[d];
if (d == target_idx)
continue;
if (z > 0)
{
real h = (p == 1) ? g : 2*g*z;
if(weights_data)
h *= weights_data[target_idx];
gradInput_target -= h;
gradInput_data[d] = h;
}
else
gradInput_data[d] = 0;
}
gradInput_data[target_idx] = gradInput_target;
input_data += dim;
gradInput_data += dim;
}
THTensor_(free)(input);
THIndexTensor_(free)(target);
if(weights)
THTensor_(free)(weights);
}
#endif
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