void THNN_(ClassNLLCriterion_updateOutput)(THNNState *state, THTensor *input,
THIndexTensor *target,
THTensor *output, bool sizeAverage,
THTensor *weights,
THTensor *total_weight)
{
int n_dims = THTensor_(nDimension)(input);
int n_classes = THTensor_(size)(input, n_dims - 1);
if (THIndexTensor_(nDimension)(target) > 1) {
THError("multi-target not supported");
}
if (THTensor_(nDimension)(input) > 2) {
THError("input tensor should be 1D or 2D");
}
input = THTensor_(newContiguous)(input);
target = THIndexTensor_(newContiguous)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
real *input_data = THTensor_(data)(input);
THIndex_t *target_data = THIndexTensor_(data)(target);
real *weights_data = weights ? THTensor_(data)(weights) : NULL;
real *output_data = THTensor_(data)(output);
real *total_weight_data = THTensor_(data)(total_weight);
output_data[0] = total_weight_data[0] = 0.0;
if (THTensor_(nDimension)(input) == 1) {
int cur_target = target_data[0] - 1;
THAssert(cur_target >= 0 && cur_target < n_classes);
total_weight_data[0] = weights ? weights_data[cur_target] : 1.0f;
output_data[0] = -input_data[cur_target] * total_weight_data[0];
} else if (THTensor_(nDimension)(input) == 2) {
int batch_size = THTensor_(size)(input, 0);
THAssert(THIndexTensor_(size)(target, 0) == batch_size);
int n_target = THTensor_(size)(input, 1);
int i;
for (i = 0; i < batch_size; i++) {
int cur_target = target_data[i] - 1;
THAssert(cur_target >= 0 && cur_target < n_classes);
real cur_weight = weights ? weights_data[cur_target] : 1.0f;
total_weight_data[0] += cur_weight;
output_data[0] -= input_data[i * n_target + cur_target] * cur_weight;
}
}
if (sizeAverage && total_weight_data[0]) {
output_data[0] /= total_weight_data[0];
}
if (weights) {
THTensor_(free)(weights);
}
THTensor_(free)(input);
THIndexTensor_(free)(target);
}
void THNN_(LookupTable_renorm)(
THNNState *state,
THIndexTensor *idx,
THTensor *weight,
real maxNorm,
real normType)
{
if (!THTensor_(isContiguous)(weight))
THError("weight must be contiguous");
if (!THIndexTensor_(isContiguous)(idx))
THError("input must be contiguous");
if (THIndexTensor_(nDimension)(idx) != 1)
THError("idx must be a vector");
if (normType <= 0)
THError("non-positive-norm not supported");
long i;
THIndex_t *row_idx = THIndexTensor_(data)(idx);
long numel = THIndexTensor_(nElement)(idx);
long numw = THTensor_(size)(weight, 0);
long stride = THTensor_(stride)(weight, 0);
real *gw = THTensor_(data)(weight);
for (i=0; i<numel; i++)
if (row_idx[i] < 1 || row_idx[i] > numw)
THError("input out of range");
// get unique indices
qsort(row_idx, numel, sizeof(THIndex_t), THNN_(compare_THIndex));
long ptr = 0;
for (i=0; i<numel; i++)
if (i == 0 || row_idx[i] != row_idx[i-1])
row_idx[ptr++] = row_idx[i];
numel = ptr;
#ifdef _OPENMP
if (numel > 1000)
{
// The strategy is to parallelize over the rows that appear in
// row_idx, so that thread 1 handles the rows in row_idx[0..numel/nThreads].
// This distributes the work evenly to each thread.
#pragma omp parallel for private(i)
for (i=0; i<numel; i++)
{
long k = row_idx[i] - 1;
THNN_(LookupTable_renormRow)(gw + k*stride, stride, maxNorm, normType);
}
return;
}
#endif
for (i=0; i<numel; i++)
{
long k = row_idx[i] - 1;
THNN_(LookupTable_renormRow)(gw + k*stride, stride, maxNorm, normType);
}
}
void THNN_(SpatialClassNLLCriterion_updateOutput)(
THNNState *state,
THTensor *input,
THIndexTensor *target,
THTensor *output,
bool sizeAverage,
THTensor *weights,
THTensor *total_weight)
{
INITIAL_CHECK;
input = THTensor_(newContiguous)(input);
target = THIndexTensor_(newContiguous)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
real *input_data = THTensor_(data)(input);
THIndex_t *target_data = THIndexTensor_(data)(target);
real *weights_data = weights ? THTensor_(data)(weights) : NULL;
real *output_data = THTensor_(data)(output);
real *total_weight_data = THTensor_(data)(total_weight);
long batch_size = THTensor_(size)(input, 0);
long n_classes = THTensor_(size)(input, 1);
long map_size = THTensor_(size)(input, 2) * THTensor_(size)(input, 3);
long sample_size = map_size * n_classes;
real total_weight_acc = 0;
real output_acc = 0;
for (int b = 0; b < batch_size; b++) {
for (int elem = 0; elem < map_size; elem++) {
int cur_target = target_data[b * map_size + elem] - TH_INDEX_BASE;
THAssert(cur_target >= 0 && cur_target < n_classes);
real cur_weight = weights ? weights_data[cur_target] : 1.0f;
total_weight_acc += cur_weight;
output_acc -= input_data[b * sample_size + cur_target * map_size + elem] * cur_weight;
}
}
*total_weight_data = total_weight_acc;
*output_data = output_acc;
if (sizeAverage && *total_weight_data)
*output_data /= *total_weight_data;
THTensor_(free)(input);
THIndexTensor_(free)(target);
if (weights)
THTensor_(free)(weights);
}
void THNN_(SpatialClassNLLCriterion_updateGradInput)(
THNNState *state,
THTensor *input,
THIndexTensor *target,
THTensor *gradInput,
bool sizeAverage,
THTensor *weights,
THTensor *total_weight)
{
INITIAL_CHECK;
THArgCheck(THTensor_(isContiguous)(gradInput), 4,
"gradInput must be contiguous");
real *total_weight_data = THTensor_(data)(total_weight);
if (*total_weight_data <= 0)
return;
target = THIndexTensor_(newContiguous)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
THIndex_t *target_data = THIndexTensor_(data)(target);
real *weights_data = weights ? THTensor_(data)(weights) : NULL;
real *gradInput_data = THTensor_(data)(gradInput);
long batch_size = THTensor_(size)(input, 0);
long n_classes = THTensor_(size)(input, 1);
long map_size = THTensor_(size)(input, 2) * THTensor_(size)(input, 3);
long sample_size = map_size * n_classes;
real normalize = sizeAverage ? *total_weight_data : 1.0f;
int b;
#pragma omp parallel for
for (b = 0; b < batch_size; b++) {
int elem;
for (elem = 0; elem < map_size; elem++) {
int cur_target = target_data[b * map_size + elem] - TH_INDEX_BASE;
THAssert(cur_target >= 0 && cur_target < n_classes);
gradInput_data[b * sample_size + cur_target * map_size + elem] =
-(weights ? weights_data[cur_target] : 1.0f) / normalize;
}
}
THIndexTensor_(free)(target);
if (weights)
THTensor_(free)(weights);
}
static void JHNN_(LookupTable_resetCount)(
THInteger_t *count_data,
THIndexTensor *input)
{
int i;
THIndex_t *input_data = THIndexTensor_(data)(input);
long numel = THIndexTensor_(nElement)(input);
for (i = 0; i<numel; i++) {
long k = input_data[i] - 1;
count_data[k] = 0;
}
for (i = 0; i<numel; i++) {
long k = input_data[i] - 1;
count_data[k]++;
}
}
static void THNN_(LookupTable_resetCount)(
THInteger_t *count_data,
THIndexTensor *input)
{
ptrdiff_t i;
THIndex_t *input_data = THIndexTensor_(data)(input);
ptrdiff_t numel = THIndexTensor_(nElement)(input);
for (i = 0; i<numel; i++)
{
long k = input_data[i] - TH_INDEX_BASE;
count_data[k] = 0;
}
for (i = 0; i<numel; i++)
{
long k = input_data[i] - TH_INDEX_BASE;
count_data[k]++;
}
}
void THNN_(SpatialMaxUnpooling_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THIndexTensor *indices,
int owidth, int oheight)
{
int dimw = 2;
int dimh = 1;
int nbatch = 1;
int nslices;
int iheight;
int iwidth;
real *input_data;
real *output_data;
THIndex_t *indices_data;
THNN_ARGCHECK(input->nDimension == 3 || input->nDimension == 4, 2, input,
"3D or 4D (batch mode) tensor expected for input, but got: %s");
THNN_CHECK_SHAPE_INDICES(input, indices);
if (input->nDimension == 4)
{
nbatch = input->size[0];
dimw++;
dimh++;
}
/* sizes */
nslices = input->size[dimh-1];
iheight = input->size[dimh];
iwidth = input->size[dimw];
/* get contiguous input and indices */
input = THTensor_(newContiguous)(input);
indices = THIndexTensor_(newContiguous)(indices);
/* resize output */
if (input->nDimension == 3)
{
THTensor_(resize3d)(output, nslices, oheight, owidth);
THTensor_(zero)(output);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
THNN_(SpatialMaxUnpooling_updateOutput_frame)(input_data, output_data,
indices_data,
nslices,
iwidth, iheight,
owidth, oheight);
}
else
{
long p;
THTensor_(resize4d)(output, nbatch, nslices, oheight, owidth);
THTensor_(zero)(output);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
#pragma omp parallel for private(p)
for (p = 0; p < nbatch; p++)
{
THNN_(SpatialMaxUnpooling_updateOutput_frame)(input_data+p*nslices*iwidth*iheight, output_data+p*nslices*owidth*oheight,
indices_data+p*nslices*iwidth*iheight,
nslices,
iwidth, iheight,
owidth, oheight);
}
}
/* cleanup */
THTensor_(free)(input);
THIndexTensor_(free)(indices);
}
void THNN_(SpatialMaxUnpooling_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THIndexTensor *indices,
int owidth, int oheight)
{
int dimw = 2;
int dimh = 1;
int nbatch = 1;
int nslices;
int iheight;
int iwidth;
real *gradInput_data;
real *gradOutput_data;
THIndex_t *indices_data;
THNN_CHECK_SHAPE_INDICES(input, indices);
/* get contiguous gradOutput and indices */
gradOutput = THTensor_(newContiguous)(gradOutput);
indices = THIndexTensor_(newContiguous)(indices);
/* resize */
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
if (input->nDimension == 4) {
nbatch = input->size[0];
dimw++;
dimh++;
}
/* sizes */
nslices = input->size[dimh-1];
iheight = input->size[dimh];
iwidth = input->size[dimw];
if(owidth!=gradOutput->size[dimw] || oheight!=gradOutput->size[dimh]){
THError("Inconsistent gradOutput size. oheight= %d, owidth= %d, gradOutput: %dx%d",
oheight, owidth,gradOutput->size[dimh],gradOutput->size[dimw]);
}
/* get raw pointers */
gradInput_data = THTensor_(data)(gradInput);
gradOutput_data = THTensor_(data)(gradOutput);
indices_data = THIndexTensor_(data)(indices);
/* backprop */
if (input->nDimension == 3)
{
THNN_(SpatialMaxUnpooling_updateGradInput_frame)(gradInput_data, gradOutput_data,
indices_data,
nslices,
iwidth, iheight,
owidth, oheight);
}
else
{
long p;
#pragma omp parallel for private(p)
for (p = 0; p < nbatch; p++)
{
THNN_(SpatialMaxUnpooling_updateGradInput_frame)(gradInput_data+p*nslices*iwidth*iheight, gradOutput_data+p*nslices*owidth*oheight,
indices_data+p*nslices*iwidth*iheight,
nslices,
iwidth, iheight,
owidth, oheight);
}
}
/* cleanup */
THTensor_(free)(gradOutput);
THIndexTensor_(free)(indices);
}
// TODO: improve error messages
void THNN_(MultiLabelMarginCriterion_updateOutput)(
THNNState *state,
THTensor *input,
THIndexTensor *target,
THTensor *output,
THTensor *isTarget,
bool sizeAverage)
{
real *input_data, *isTarget_data;
THIndex_t *target_data;
long nframe, dim;
long t, d, dt, ddt;
real sum;
THArgCheck((input->nDimension == 1) || (input->nDimension == 2), 2,
"vector or matrix expected");
if (input->nDimension == 1)
{
nframe = 1;
dim = input->size[0];
THArgCheck((target->nDimension == 1) && (target->size[0] == dim), 3,
"inconsistent target size");
}
else
{
nframe = input->size[0];
dim = input->size[1];
THArgCheck((target->nDimension == 2) && (target->size[0] == nframe)
&& (target->size[1] == dim), 3, "inconsistent target size");
}
THArgCheck(THIndexTensor_(minall)(target) >= 0, 3, "target out of range");
THArgCheck(THIndexTensor_(maxall)(target) <= dim, 3, "target out of range");
target = THIndexTensor_(newContiguous)(target);
input = THTensor_(newContiguous)(input);
input_data = THTensor_(data)(input);
target_data = THIndexTensor_(data)(target);
THNN_resizeAs_indices(isTarget, target);
THTensor_(zero)(isTarget);
isTarget_data = THTensor_(data)(isTarget);
sum = 0;
for (t = 0; t < nframe; t++)
{
for (ddt = 0; ddt < dim; ddt++)
{
THIndex_t target_idx = target_data[ddt] - TH_INDEX_BASE;
if (target_idx < 0)
break;
isTarget_data[target_idx] = 1;
}
for (dt = 0; dt < dim; dt++)
{
THIndex_t target_idx = target_data[dt] - TH_INDEX_BASE;
real input_target;
if (target_idx < 0)
break;
input_target = input_data[target_idx];
for (d = 0; d < dim; d++)
{
if (!isTarget_data[d])
{
real z = 1 - input_target + input_data[d];
if (z > 0)
sum += z;
}
}
}
input_data += dim;
target_data += dim;
isTarget_data += dim;
}
sum /= dim;
if (sizeAverage)
sum /= nframe;
THTensor_(set1d)(output, 0, sum);
THTensor_(free)(input);
THIndexTensor_(free)(target);
}
void THNN_(SpatialMaxUnpooling_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THIndexTensor *indices,
int owidth, int oheight)
{
int dimw = 2;
int dimh = 1;
int nbatch = 1;
int nslices;
int iheight;
int iwidth;
scalar_t *gradInput_data;
scalar_t *gradOutput_data;
THIndex_t *indices_data;
THNN_CHECK_SHAPE_INDICES(input, indices);
/* get contiguous gradOutput and indices */
gradOutput = THTensor_(newContiguous)(gradOutput);
indices = THIndexTensor_(newContiguous)(indices);
/* resize */
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
if (input->dim() == 4) {
nbatch = input->size(0);
dimw++;
dimh++;
}
/* sizes */
nslices = input->size(dimh-1);
iheight = input->size(dimh);
iwidth = input->size(dimw);
if(owidth!=gradOutput->size(dimw) || oheight!=gradOutput->size(dimh)){
THError("Inconsistent gradOutput size. oheight= %d, owidth= %d, gradOutput: %dx%d",
oheight, owidth, gradOutput->size(dimh), gradOutput->size(dimw));
}
/* get raw pointers */
gradInput_data = gradInput->data<scalar_t>();
gradOutput_data = gradOutput->data<scalar_t>();
indices_data = THIndexTensor_(data)(indices);
/* backprop */
if (input->dim() == 3)
{
THNN_(SpatialMaxUnpooling_updateGradInput_frame)(gradInput_data, gradOutput_data,
indices_data,
nslices,
iwidth, iheight,
owidth, oheight);
}
else
{
int p;
for (p = 0; p < nbatch; p++)
{
THNN_(SpatialMaxUnpooling_updateGradInput_frame)(gradInput_data+p*nslices*iwidth*iheight, gradOutput_data+p*nslices*owidth*oheight,
indices_data+p*nslices*iwidth*iheight,
nslices,
iwidth, iheight,
owidth, oheight);
}
}
/* cleanup */
c10::raw::intrusive_ptr::decref(gradOutput);
THIndexTensor_(free)(indices);
}
void THNN_(VolumetricAdaptiveMaxPooling_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THIndexTensor *indices)
{
int dimD = 0;
int dimT = 1;
int dimH = 2;
int dimW = 3;
int64_t sizeB = 1;
int64_t sizeD;
int64_t isizeT;
int64_t isizeH;
int64_t isizeW;
int64_t osizeT;
int64_t osizeH;
int64_t osizeW;
real *gradInput_data;
real *gradOutput_data;
THIndex_t *indices_data;
/* get contiguous gradOutput */
gradOutput = THTensor_(newContiguous)(gradOutput);
/* resize */
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
if (input->dim() == 5) {
sizeB = input->size[0];
dimD++;
dimT++;
dimH++;
dimW++;
}
/* sizes */
sizeD = input->size[dimD];
isizeT = input->size[dimT];
isizeH = input->size[dimH];
isizeW = input->size[dimW];
osizeT = gradOutput->size[dimT];
osizeH = gradOutput->size[dimH];
osizeW = gradOutput->size[dimW];
/* get raw pointers */
gradInput_data = THTensor_(data)(gradInput);
gradOutput_data = THTensor_(data)(gradOutput);
indices_data = THIndexTensor_(data)(indices);
/* backprop */
if (input->dim() == 4)
{
THNN_(VolumetricAdaptiveMaxPooling_updateGradInput_frame)(gradInput_data, gradOutput_data,
indices_data,
sizeD,
isizeT, isizeH, isizeW,
osizeT, osizeH, osizeW);
}
else
{
int64_t b;
#pragma omp parallel for private(b)
for (b = 0; b < sizeB; b++)
{
THNN_(VolumetricAdaptiveMaxPooling_updateGradInput_frame)(gradInput_data+b*sizeD*isizeT*isizeH*isizeW, gradOutput_data+b*sizeD*osizeT*osizeH*osizeW,
indices_data+b*sizeD*osizeT*osizeH*osizeW,
sizeD,
isizeT, isizeH, isizeW,
osizeT, osizeH, osizeW);
}
}
/* cleanup */
THTensor_(free)(gradOutput);
}
void THNN_(SpatialDilatedMaxPooling_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THIndexTensor *indices,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH,
int dilationW,
int dilationH,
bool ceil_mode)
{
int dimw = 2;
int dimh = 1;
long nbatch = 1;
int nInputPlane;
int inputHeight;
int inputWidth;
int outputHeight;
int outputWidth;
real *gradInput_data;
real *gradOutput_data;
THIndex_t *indices_data;
THNN_(SpatialDilatedMaxPooling_shapeCheck)
(input, gradOutput, indices, kH, kW, dH, dW,
padH, padW, dilationH, dilationW, ceil_mode);
/* get contiguous gradOutput */
gradOutput = THTensor_(newContiguous)(gradOutput);
/* resize */
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
if (input->nDimension == 4) {
nbatch = input->size[0];
dimw++;
dimh++;
}
/* sizes */
nInputPlane = input->size[dimh-1];
inputHeight = input->size[dimh];
inputWidth = input->size[dimw];
outputHeight = gradOutput->size[dimh];
outputWidth = gradOutput->size[dimw];
/* get raw pointers */
gradInput_data = THTensor_(data)(gradInput);
gradOutput_data = THTensor_(data)(gradOutput);
indices_data = THIndexTensor_(data)(indices);
/* backprop */
if (input->nDimension == 3)
{
THNN_(SpatialDilatedMaxPooling_updateGradInput_frame)
(gradInput_data, gradOutput_data,
indices_data,
nInputPlane,
inputWidth, inputHeight,
outputWidth, outputHeight,
dW, dH);
}
else
{
long p;
#pragma omp parallel for private(p)
for (p = 0; p < nbatch; p++)
{
THNN_(SpatialDilatedMaxPooling_updateGradInput_frame)
(gradInput_data+p*nInputPlane*inputWidth*inputHeight,
gradOutput_data+p*nInputPlane*outputWidth*outputHeight,
indices_data+p*nInputPlane*outputWidth*outputHeight,
nInputPlane,
inputWidth, inputHeight,
outputWidth, outputHeight,
dW, dH);
}
}
/* cleanup */
THTensor_(free)(gradOutput);
}
void THNN_(SpatialDilatedMaxPooling_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THIndexTensor *indices,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH,
int dilationW,
int dilationH,
bool ceil_mode)
{
int dimw = 2;
int dimh = 1;
long nbatch = 1;
long nInputPlane;
long inputHeight;
long inputWidth;
long outputHeight;
long outputWidth;
real *input_data;
real *output_data;
THIndex_t *indices_data;
THNN_(SpatialDilatedMaxPooling_shapeCheck)
(input, NULL, NULL, kH, kW, dH, dW,
padH, padW, dilationH, dilationW, ceil_mode);
if (input->nDimension == 4)
{
nbatch = input->size[0];
dimw++;
dimh++;
}
/* sizes */
nInputPlane = input->size[dimh-1];
inputHeight = input->size[dimh];
inputWidth = input->size[dimw];
if (ceil_mode)
{
outputHeight = (long)(ceil((float)(inputHeight - (dilationH * (kH - 1) + 1) + 2*padH) / dH)) + 1;
outputWidth = (long)(ceil((float)(inputWidth - (dilationW * (kW - 1) + 1) + 2*padW) / dW)) + 1;
}
else
{
outputHeight = (long)(floor((float)(inputHeight - (dilationH * (kH - 1) + 1) + 2*padH) / dH)) + 1;
outputWidth = (long)(floor((float)(inputWidth - (dilationW * (kW - 1) + 1) + 2*padW) / dW)) + 1;
}
if (padW || padH)
{
// ensure that the last pooling starts inside the image
if ((outputHeight - 1)*dH >= inputHeight + padH)
--outputHeight;
if ((outputWidth - 1)*dW >= inputWidth + padW)
--outputWidth;
}
/* get contiguous input */
input = THTensor_(newContiguous)(input);
/* resize output */
if (input->nDimension == 3)
{
THTensor_(resize3d)(output, nInputPlane, outputHeight, outputWidth);
/* indices will contain the locations for each output point */
THIndexTensor_(resize3d)(indices, nInputPlane, outputHeight, outputWidth);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
THNN_(SpatialDilatedMaxPooling_updateOutput_frame)
(input_data, output_data,
indices_data,
nInputPlane,
inputWidth, inputHeight,
outputWidth, outputHeight,
kW, kH, dW, dH,
padW, padH,
dilationW, dilationH
);
}
else
{
long p;
THTensor_(resize4d)(output, nbatch, nInputPlane, outputHeight, outputWidth);
/* indices will contain the locations for each output point */
THIndexTensor_(resize4d)(indices, nbatch, nInputPlane, outputHeight, outputWidth);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
#pragma omp parallel for private(p)
for (p = 0; p < nbatch; p++)
{
THNN_(SpatialDilatedMaxPooling_updateOutput_frame)
(input_data+p*nInputPlane*inputWidth*inputHeight,
output_data+p*nInputPlane*outputWidth*outputHeight,
indices_data+p*nInputPlane*outputWidth*outputHeight,
nInputPlane,
inputWidth, inputHeight,
outputWidth, outputHeight,
kW, kH, dW, dH,
padW, padH,
dilationW, dilationH
);
}
}
/* cleanup */
THTensor_(free)(input);
}
void THNN_(SpatialDilatedMaxPooling_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THIndexTensor *indices,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH,
int dilationW,
int dilationH,
bool ceil_mode)
{
int dimw = 2;
int dimh = 1;
int64_t nbatch = 1;
int nInputPlane;
int inputHeight;
int inputWidth;
int outputHeight;
int outputWidth;
scalar_t *gradInput_data;
scalar_t *gradOutput_data;
THIndex_t *indices_data;
THNN_(SpatialDilatedMaxPooling_shapeCheck)
(input, gradOutput, indices, kH, kW, dH, dW,
padH, padW, dilationH, dilationW, ceil_mode);
/* get contiguous gradOutput */
gradOutput = THTensor_(newContiguous)(gradOutput);
/* resize */
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
if (input->dim() == 4) {
nbatch = input->size(0);
dimw++;
dimh++;
}
/* sizes */
nInputPlane = input->size(dimh-1);
inputHeight = input->size(dimh);
inputWidth = input->size(dimw);
outputHeight = gradOutput->size(dimh);
outputWidth = gradOutput->size(dimw);
/* get raw pointers */
gradInput_data = gradInput->data<scalar_t>();
gradOutput_data = gradOutput->data<scalar_t>();
indices_data = THIndexTensor_(data)(indices);
/* backprop */
if (input->dim() == 3)
{
THNN_(SpatialDilatedMaxPooling_updateGradInput_frame)
(gradInput_data, gradOutput_data,
indices_data,
nInputPlane,
inputWidth, inputHeight,
outputWidth, outputHeight,
dW, dH);
}
else
{
at::parallel_for(0, nbatch, 0, [&](int64_t start, int64_t end) {
for (auto p = start; p < end; p++)
{
THNN_(SpatialDilatedMaxPooling_updateGradInput_frame)
(gradInput_data+p*nInputPlane*inputWidth*inputHeight,
gradOutput_data+p*nInputPlane*outputWidth*outputHeight,
indices_data+p*nInputPlane*outputWidth*outputHeight,
nInputPlane,
inputWidth, inputHeight,
outputWidth, outputHeight,
dW, dH);
}
});
}
/* cleanup */
c10::raw::intrusive_ptr::decref(gradOutput);
}
void THNN_(SpatialDilatedMaxPooling_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THIndexTensor *indices,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH,
int dilationW,
int dilationH,
bool ceil_mode)
{
int dimw = 2;
int dimh = 1;
int64_t nbatch = 1;
int64_t nInputPlane;
int64_t inputHeight;
int64_t inputWidth;
int64_t outputHeight;
int64_t outputWidth;
scalar_t *input_data;
scalar_t *output_data;
THIndex_t *indices_data;
THNN_(SpatialDilatedMaxPooling_shapeCheck)
(input, NULL, NULL, kH, kW, dH, dW,
padH, padW, dilationH, dilationW, ceil_mode);
if (input->dim() == 4)
{
nbatch = input->size(0);
dimw++;
dimh++;
}
/* sizes */
nInputPlane = input->size(dimh-1);
inputHeight = input->size(dimh);
inputWidth = input->size(dimw);
outputHeight = pooling_output_shape<int64_t>(inputHeight, kH, padH, dH, dilationH, ceil_mode);
outputWidth = pooling_output_shape<int64_t>(inputWidth, kW, padW, dW, dilationW, ceil_mode);
/* get contiguous input */
input = THTensor_(newContiguous)(input);
/* resize output */
if (input->dim() == 3)
{
THTensor_(resize3d)(output, nInputPlane, outputHeight, outputWidth);
/* indices will contain the locations for each output point */
THIndexTensor_(resize3d)(indices, nInputPlane, outputHeight, outputWidth);
input_data = input->data<scalar_t>();
output_data = output->data<scalar_t>();
indices_data = THIndexTensor_(data)(indices);
THNN_(SpatialDilatedMaxPooling_updateOutput_frame)
(input_data, output_data,
indices_data,
nInputPlane,
inputWidth, inputHeight,
outputWidth, outputHeight,
kW, kH, dW, dH,
padW, padH,
dilationW, dilationH
);
}
else
{
THTensor_(resize4d)(output, nbatch, nInputPlane, outputHeight, outputWidth);
/* indices will contain the locations for each output point */
THIndexTensor_(resize4d)(indices, nbatch, nInputPlane, outputHeight, outputWidth);
input_data = input->data<scalar_t>();
output_data = output->data<scalar_t>();
indices_data = THIndexTensor_(data)(indices);
at::parallel_for(0, nbatch, 0, [&](int64_t start, int64_t end) {
for (auto p = start; p < end; p++)
{
THNN_(SpatialDilatedMaxPooling_updateOutput_frame)
(input_data+p*nInputPlane*inputWidth*inputHeight,
output_data+p*nInputPlane*outputWidth*outputHeight,
indices_data+p*nInputPlane*outputWidth*outputHeight,
nInputPlane,
inputWidth, inputHeight,
outputWidth, outputHeight,
kW, kH, dW, dH,
padW, padH,
dilationW, dilationH
);
}
});
}
/* cleanup */
c10::raw::intrusive_ptr::decref(input);
}
void THNN_(ClassNLLCriterion_updateGradInput)(THNNState *state, THTensor *input, THIndexTensor *target, THTensor *gradInput, bool sizeAverage, THTensor *weights, THTensor *total_weight)
{
int n_dims = THTensor_(nDimension)(input);
int n_classes = THTensor_(size)(input, n_dims - 1);
if (!THTensor_(isContiguous)(gradInput)) {
THError("gradInput must be contiguous");
}
real *total_weight_data = THTensor_(data)(total_weight);
if (!(*total_weight_data > 0)) {
return;
}
if (THIndexTensor_(nDimension)(target) > 1) {
THError("multi-target not supported");
}
if (THTensor_(nDimension)(input) > 2) {
THError("input tensor should be 1D or 2D");
}
target = THIndexTensor_(newContiguous)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
THIndex_t *target_data = THIndexTensor_(data)(target);
real *weights_data = weights ? THTensor_(data)(weights) : NULL;
real *gradInput_data = THTensor_(data)(gradInput);
if (THTensor_(nDimension)(input) == 1) {
int cur_target = target_data[0] - 1;
THAssert(cur_target >= 0 && cur_target < n_classes);
gradInput_data[cur_target] =
(!sizeAverage && weights) ? -weights_data[cur_target] : -1;
} else if (THTensor_(nDimension)(input) == 2) {
int batch_size = THTensor_(size)(input, 0);
int n_target = THTensor_(size)(input, 1);
int i;
for (i = 0; i < batch_size; i++){
int cur_target = target_data[i] - 1;
THAssert(cur_target >= 0 && cur_target < n_classes);
gradInput_data[i * n_target + cur_target] =
-(weights ? weights_data[cur_target] : 1.0f);
if (sizeAverage && *total_weight_data) {
gradInput_data[i * n_target + cur_target] /= *total_weight_data;
}
}
}
THIndexTensor_(free)(target);
if (weights) {
THTensor_(free)(weights);
}
}
void THNN_(VolumetricAdaptiveMaxPooling_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THIndexTensor *indices,
int osizeT,
int osizeW,
int osizeH)
{
int dimD = 0;
int dimT = 1;
int dimH = 2;
int dimW = 3;
int64_t sizeB = 1;
int64_t sizeD = 0;
int64_t isizeT = 0;
int64_t isizeH = 0;
int64_t isizeW = 0;
int64_t istrideB = 0;
int64_t istrideD = 0;
int64_t istrideT = 0;
int64_t istrideH = 0;
int64_t istrideW = 0;
real *input_data = nullptr;
real *output_data = nullptr;
THIndex_t *indices_data = nullptr;
THNN_ARGCHECK(!input->is_empty() && (input->dim() == 4 || input->dim() == 5), 2, input,
"non-empty 4D or 5D (batch mode) tensor expected for input, but got: %s");
if (input->dim() == 5)
{
istrideB = input->stride[0];
sizeB = input->size[0];
dimD++;
dimT++;
dimH++;
dimW++;
}
/* sizes */
sizeD = input->size[dimD];
isizeT = input->size[dimT];
isizeH = input->size[dimH];
isizeW = input->size[dimW];
/* strides */
istrideD = input->stride[dimD];
istrideT = input->stride[dimT];
istrideH = input->stride[dimH];
istrideW = input->stride[dimW];
/* resize output */
if (input->dim() == 4)
{
THTensor_(resize4d)(output, sizeD, osizeT, osizeH, osizeW);
/* indices will contain max input locations for each output point */
THIndexTensor_(resize4d)(indices, sizeD, osizeT, osizeH, osizeW);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
THNN_(VolumetricAdaptiveMaxPooling_updateOutput_frame)(input_data, output_data,
indices_data,
sizeD,
isizeT, isizeH, isizeW,
osizeT, osizeH, osizeW,
istrideD, istrideT,
istrideH, istrideW);
}
else
{
int64_t b;
THTensor_(resize5d)(output, sizeB, sizeD, osizeT, osizeH, osizeW);
/* indices will contain max input locations for each output point */
THIndexTensor_(resize5d)(indices, sizeB, sizeD, osizeT, osizeH, osizeW);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
#pragma omp parallel for private(b)
for (b = 0; b < sizeB; b++)
{
THNN_(VolumetricAdaptiveMaxPooling_updateOutput_frame)(input_data+b*istrideB, output_data+b*sizeD*osizeT*osizeH*osizeW,
indices_data+b*sizeD*osizeT*osizeH*osizeW,
sizeD,
isizeT, isizeH, isizeW,
osizeT, osizeH, osizeW,
istrideD, istrideT,
istrideH, istrideW);
}
}
}
void THNN_(VolumetricDilatedMaxPooling_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THIndexTensor *indices,
int kT,
int kW,
int kH,
int dT,
int dW,
int dH,
int pT,
int pW,
int pH,
int dilationT,
int dilationW,
int dilationH,
bool ceilMode)
{
int64_t nslices;
int64_t itime;
int64_t iheight;
int64_t iwidth;
int64_t otime;
int64_t oheight;
int64_t owidth;
real *input_data;
real *output_data;
THIndex_t *indices_data;
int dimN = 0;
int dimt = 1;
int dimh = 2;
int dimw = 3;
if (input->nDimension == 5)
{
dimN++;
dimt++;
dimh++;
dimw++;
}
THNN_(VolumetricDilatedMaxPooling_shapeCheck)(
state, input, NULL, NULL,
kT, kW, kH, dT, dW, dH,
pT, pW, pH, dilationT, dilationW, dilationH,
ceilMode);
/* sizes */
nslices = input->size[dimN];
itime = input->size[dimt];
iheight = input->size[dimh];
iwidth = input->size[dimw];
if (ceilMode)
{
otime = (int)(ceil((float)(itime - (dilationT * (kT - 1) + 1) + 2*pT) / dT)) + 1;
oheight = (int)(ceil((float)(iheight - (dilationH * (kH - 1) + 1) + 2*pH) / dH)) + 1;
owidth = (int)(ceil((float)(iwidth - (dilationW * (kW - 1) + 1) + 2*pW) / dW)) + 1;
}
else
{
otime = (int)(floor((float)(itime - (dilationT * (kT - 1) + 1) + 2*pT) / dT)) + 1;
oheight = (int)(floor((float)(iheight - (dilationH * (kH - 1) + 1) + 2*pH) / dH)) + 1;
owidth = (int)(floor((float)(iwidth - (dilationW * (kW - 1) + 1) + 2*pW) / dW)) + 1;
}
if (pT || pW || pH)
{
// ensure that the last pooling starts inside the image
if ((otime - 1)*dT >= itime + pT)
--otime;
if ((oheight - 1)*dH >= iheight + pH)
--oheight;
if ((owidth - 1)*dW >= iwidth + pW)
--owidth;
}
/* get contiguous input */
input = THTensor_(newContiguous)(input);
if (input->nDimension == 4) /* non-batch mode */
{
/* resize output */
THTensor_(resize4d)(output, nslices, otime, oheight, owidth);
/* indices will contain ti,i,j uchar locations packed into float/double */
THIndexTensor_(resize4d)(indices, nslices, otime, oheight, owidth);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
THNN_(VolumetricDilatedMaxPooling_updateOutput_frame)(
input_data, output_data,
indices_data,
nslices,
itime, iwidth, iheight,
otime, owidth, oheight,
kT, kW, kH,
dT, dW, dH,
pT, pW, pH,
dilationT, dilationW, dilationH
);
}
else /* batch mode */
{
int64_t p;
int64_t nBatch = input->size[0];
int64_t istride = nslices * itime * iwidth * iheight;
int64_t ostride = nslices * otime * owidth * oheight;
/* resize output */
THTensor_(resize5d)(output, nBatch, nslices, otime, oheight, owidth);
/* indices will contain ti,i,j locations for each output point */
THIndexTensor_(resize5d)(indices, nBatch, nslices, otime, oheight, owidth);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
#pragma omp parallel for private(p)
for (p=0; p < nBatch; p++)
{
THNN_(VolumetricDilatedMaxPooling_updateOutput_frame)(
input_data + p * istride,
output_data + p * ostride,
indices_data + p * ostride,
nslices,
itime, iwidth, iheight,
otime, owidth, oheight,
kT, kW, kH,
dT, dW, dH,
pT, pW, pH,
dilationT, dilationW, dilationH
);
}
}
/* cleanup */
THTensor_(free)(input);
}
void THNN_(LookupTable_accGradParameters)(
THNNState *state,
THIndexTensor *input,
THTensor *gradOutput,
THTensor *gradWeight,
THIntegerTensor *count,
THTensor *sorted,
THTensor *indices,
bool scaleGradByFreq,
int paddingValue,
real scale)
{
long i;
THInteger_t *count_data = NULL;
if (scaleGradByFreq)
{
THIntegerTensor_(resize1d)(count, gradWeight->size[0]);
count_data = THIntegerTensor_(data)(count);
}
if (!THTensor_(isContiguous)(gradWeight))
THError("gradWeight must be contiguous");
if (!THIndexTensor_(isContiguous)(input))
THError("input must be contiguous");
if (THIndexTensor_(nDimension)(input) != 1 && THIndexTensor_(nDimension)(input) != 2)
THError("input must be a vector or matrix");
THIndex_t *input_data = THIndexTensor_(data)(input);
long numel = THIndexTensor_(nElement)(input);
long numw = THTensor_(size)(gradWeight, 0);
// check that inputs are all within range
for (i=0; i<numel; i++)
if (input_data[i] < 1 || input_data[i] > numw)
THError("input out of range");
gradOutput = THTensor_(newContiguous)(gradOutput);
real *gw = THTensor_(data)(gradWeight);
real *go = THTensor_(data)(gradOutput);
long stride = THTensor_(stride)(gradWeight, 0);
if (count_data)
THNN_(LookupTable_resetCount)(count_data, input);
#ifdef _OPENMP
if (numel > 1000)
{
// The strategy is to parallelize over sections of the vocabulary, so that
// thread 1 handles updates to gradWeight[0..nVocab/nThreads]. Every thread
// has to traverse the entire input, but the dominating factor is the axpy
// BLAS call.
#pragma omp parallel private(i)
{
int tid = omp_get_thread_num();
int nthreads = omp_get_num_threads();
long start = tid * (numw/nthreads + 1);
long end = start + (numw/nthreads + 1);
for (i=0; i<numel; i++)
{
if (input_data[i] != paddingValue)
{
long k = input_data[i] - 1;
if (k >= start && k < end)
{
real scale_ = scale;
if (count_data) scale_ /= count_data[k];
THBlas_(axpy)(stride, scale_, go + i*stride, 1, gw + k*stride, 1);
}
}
}
}
THTensor_(free)(gradOutput);
return;
}
#endif
for (i=0; i<numel; i++)
{
if (input_data[i] != paddingValue)
{
long k = input_data[i] - 1;
real scale_ = scale;
if (count_data) scale_ /= count_data[k];
THBlas_(axpy)(stride, scale_, go + i*stride, 1, gw + k*stride, 1);
}
}
THTensor_(free)(gradOutput);
}
void THNN_(VolumetricDilatedMaxPooling_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THIndexTensor *indices,
int kT,
int kW,
int kH,
int dT,
int dW,
int dH,
int pT,
int pW,
int pH,
int dilationT,
int dilationW,
int dilationH,
bool ceilMode)
{
int nslices;
int itime;
int iheight;
int iwidth;
int otime;
int oheight;
int owidth;
real *gradInput_data;
real *gradOutput_data;
THIndex_t *indices_data;
int dimN = 0;
int dimt = 1;
int dimh = 2;
int dimw = 3;
THNN_(VolumetricDilatedMaxPooling_shapeCheck)(
state, input, gradOutput, indices,
kT, kW, kH, dT, dW, dH,
pT, pW, pH, dilationT, dilationW, dilationH,
ceilMode);
// TODO: gradOutput shape check
/* get contiguous gradOutput */
gradOutput = THTensor_(newContiguous)(gradOutput);
/* resize */
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
if (input->nDimension == 5)
{
dimN++;
dimt++;
dimh++;
dimw++;
}
/* sizes */
nslices = input->size[dimN];
itime = input->size[dimt];
iheight = input->size[dimh];
iwidth = input->size[dimw];
otime = gradOutput->size[dimt];
oheight = gradOutput->size[dimh];
owidth = gradOutput->size[dimw];
/* get raw pointers */
gradInput_data = THTensor_(data)(gradInput);
gradOutput_data = THTensor_(data)(gradOutput);
indices_data = THIndexTensor_(data)(indices);
/* backprop */
if (input->nDimension == 4) /* non-batch mode*/
{
THNN_(VolumetricDilatedMaxPooling_updateGradInput_frame)(
gradInput_data, gradOutput_data,
indices_data,
nslices,
itime, iwidth, iheight,
otime, owidth, oheight,
dT, dW, dH,
pT, pW, pH,
dilationT, dilationW, dilationH
);
}
else /* batch mode */
{
int64_t p;
int64_t nBatch = input->size[0];
int64_t istride = nslices * itime * iwidth * iheight;
int64_t ostride = nslices * otime * owidth * oheight;
#pragma omp parallel for private(p)
for (p = 0; p < nBatch; p++)
{
THNN_(VolumetricDilatedMaxPooling_updateGradInput_frame)(
gradInput_data + p * istride,
gradOutput_data + p * ostride,
indices_data + p * ostride,
nslices,
itime, iwidth, iheight,
otime, owidth, oheight,
dT, dW, dH,
pT, pW, pH,
dilationT, dilationW, dilationH
);
}
}
/* cleanup */
THTensor_(free)(gradOutput);
}
void THNN_(SpatialMaxUnpooling_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THIndexTensor *indices,
int owidth, int oheight)
{
int dimw = 2;
int dimh = 1;
int nbatch = 1;
int nslices;
int iheight;
int iwidth;
scalar_t *input_data;
scalar_t *output_data;
THIndex_t *indices_data;
AT_CHECK(!input->is_empty() && (input->dim() == 3 || input->dim() == 4),
"non-empty 3D or 4D (batch mode) tensor expected for input, but got sizes: ", input->sizes());
THNN_CHECK_SHAPE_INDICES(input, indices);
if (input->dim() == 4)
{
nbatch = input->size(0);
dimw++;
dimh++;
}
/* sizes */
nslices = input->size(dimh-1);
iheight = input->size(dimh);
iwidth = input->size(dimw);
/* get contiguous input and indices */
input = THTensor_(newContiguous)(input);
indices = THIndexTensor_(newContiguous)(indices);
/* resize output */
if (input->dim() == 3)
{
THTensor_(resize3d)(output, nslices, oheight, owidth);
THTensor_(zero)(output);
input_data = input->data<scalar_t>();
output_data = output->data<scalar_t>();
indices_data = THIndexTensor_(data)(indices);
THNN_(SpatialMaxUnpooling_updateOutput_frame)(input_data, output_data,
indices_data,
nslices,
iwidth, iheight,
owidth, oheight);
}
else
{
int p;
THTensor_(resize4d)(output, nbatch, nslices, oheight, owidth);
THTensor_(zero)(output);
input_data = input->data<scalar_t>();
output_data = output->data<scalar_t>();
indices_data = THIndexTensor_(data)(indices);
for (p = 0; p < nbatch; p++)
{
THNN_(SpatialMaxUnpooling_updateOutput_frame)(
input_data+p*nslices*iwidth*iheight,
output_data+p*nslices*owidth*oheight,
indices_data+p*nslices*iwidth*iheight,
nslices,
iwidth, iheight,
owidth, oheight);
}
}
/* cleanup */
c10::raw::intrusive_ptr::decref(input);
THIndexTensor_(free)(indices);
}
void THNN_(LookupTable_renorm)(
THNNState *state,
THIndexTensor *idx,
THTensor *weight,
accreal maxNorm_,
accreal normType_)
{
real maxNorm = TH_CONVERT_ACCREAL_TO_REAL(maxNorm_);
real normType = TH_CONVERT_ACCREAL_TO_REAL(normType_);
if (!THTensor_(isContiguous)(weight))
THError("weight must be contiguous");
if (!THIndexTensor_(isContiguous)(idx))
THError("input must be contiguous");
if (THIndexTensor_(nDimension)(idx) != 1)
THError("idx must be a vector");
if (normType <= 0)
THError("non-positive-norm not supported");
ptrdiff_t i;
THIndex_t *row_idx = THIndexTensor_(data)(idx);
ptrdiff_t numel = THIndexTensor_(nElement)(idx);
long numw = THTensor_(size)(weight, 0);
long stride = THTensor_(stride)(weight, 0);
real *gw = THTensor_(data)(weight);
for (i=0; i<numel; i++) {
if (row_idx[i] < TH_INDEX_BASE || row_idx[i] >= numw + TH_INDEX_BASE) {
THError("input need to be in the range %ld <= input < %ld, "
"but got input of value: %ld", TH_INDEX_BASE, (numw + TH_INDEX_BASE),
row_idx[i]);
}
}
// get unique indices
qsort(row_idx, numel, sizeof(THIndex_t), THNN_(compare_THIndex));
ptrdiff_t ptr = 0;
for (i=0; i<numel; i++)
if (i == 0 || row_idx[i] != row_idx[i-1])
row_idx[ptr++] = row_idx[i];
numel = ptr;
#ifdef _OPENMP
if (numel > 1000)
{
// The strategy is to parallelize over the rows that appear in
// row_idx, so that thread 1 handles the rows in row_idx[0..numel/nThreads].
// This distributes the work evenly to each thread.
#pragma omp parallel for private(i)
for (i=0; i<numel; i++)
{
long k = row_idx[i] - TH_INDEX_BASE;
THNN_(LookupTable_renormRow)(gw + k*stride, stride, maxNorm, normType);
}
return;
}
#endif
for (i=0; i<numel; i++)
{
long k = row_idx[i] - TH_INDEX_BASE;
THNN_(LookupTable_renormRow)(gw + k*stride, stride, maxNorm, normType);
}
}
void THNN_(MultiLabelMarginCriterion_updateGradInput)(
THNNState *state,
THTensor *input,
THIndexTensor *target,
THTensor *gradInput,
THTensor *isTarget,
bool sizeAverage)
{
real *input_data;
real *gradInput_data;
THIndex_t *target_data;
real *isTarget_data;
long nframe, dim;
long t, d, dt;
real g;
THArgCheck((input->nDimension == 1) || (input->nDimension == 2), 2,
"vector or matrix expected");
if (input->nDimension == 1)
{
nframe = 1;
dim = input->size[0];
THArgCheck((target->nDimension == 1) && (target->size[0] == dim), 3,
"inconsistent target size");
THArgCheck((isTarget->nDimension == 1) && (isTarget->size[0] == dim), 3,
"inconsistent isTarget size");
}
else
{
nframe = input->size[0];
dim = input->size[1];
THArgCheck((target->nDimension == 2) && (target->size[0] == nframe)
&& (target->size[1] == dim), 3, "inconsistent target size");
THArgCheck((isTarget->nDimension == 2) && (isTarget->size[0] == nframe)
&& (isTarget->size[1] == dim), 3, "inconsistent isTarget size");
}
THArgCheck(THIndexTensor_(minall)(target) >= 0, 3, "target out of range");
THArgCheck(THIndexTensor_(maxall)(target) <= dim, 3, "target out of range");
THArgCheck(THTensor_(minall)(isTarget) >= 0, 3, "isTarget out of range");
THArgCheck(THTensor_(maxall)(isTarget) <= 1, 3, "isTarget out of range");
target = THIndexTensor_(newContiguous)(target);
input = THTensor_(newContiguous)(input);
isTarget = THTensor_(newContiguous)(isTarget);
input_data = THTensor_(data)(input);
target_data = THIndexTensor_(data)(target);
isTarget_data = THTensor_(data)(isTarget);
g = sizeAverage ? ( 1./((real)(nframe*dim)) ) : ( 1./((real)dim) );
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
gradInput_data = THTensor_(data)(gradInput);
for (t = 0; t < nframe; t++)
{
for (dt = 0; dt < dim; dt++)
{
THIndex_t target_idx = target_data[dt] - TH_INDEX_BASE;
real input_target;
if (target_idx < 0)
break;
input_target = input_data[target_idx];
for (d = 0; d < dim; d++)
{
if (!isTarget_data[d])
{
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;
isTarget_data += dim;
gradInput_data += dim;
}
THTensor_(free)(input);
THIndexTensor_(free)(target);
THTensor_(free)(isTarget);
}
void THNN_(SpatialAdaptiveMaxPooling_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THIndexTensor *indices,
int osizeW,
int osizeH)
{
int dimW = 2;
int dimH = 1;
int64_t sizeB = 1;
int64_t sizeD = 0;
int64_t isizeH = 0;
int64_t isizeW = 0;
int64_t istrideD = 0;
int64_t istrideH = 0;
int64_t istrideW = 0;
int64_t istrideB = 0;
real *input_data = nullptr;
real *output_data = nullptr;
THIndex_t *indices_data = nullptr;
THNN_ARGCHECK(input->nDimension == 3 || input->nDimension == 4, 2, input,
"3D or 4D (batch mode) tensor expected for input, but got: %s");
if (input->nDimension == 4)
{
istrideB = input->stride[0];
sizeB = input->size[0];
dimW++;
dimH++;
}
/* sizes */
sizeD = input->size[dimH-1];
isizeH = input->size[dimH];
isizeW = input->size[dimW];
/* strides */
istrideD = input->stride[dimH-1];
istrideH = input->stride[dimH];
istrideW = input->stride[dimW];
/* resize output */
if (input->nDimension == 3)
{
THTensor_(resize3d)(output, sizeD, osizeH, osizeW);
/* indices will contain i,j locations for each output point */
THIndexTensor_(resize3d)(indices, sizeD, osizeH, osizeW);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
THNN_(SpatialAdaptiveMaxPooling_updateOutput_frame)(input_data, output_data,
indices_data,
sizeD,
isizeH, isizeW,
osizeH, osizeW,
istrideD,
istrideH, istrideW);
}
else
{
int64_t b;
THTensor_(resize4d)(output, sizeB, sizeD, osizeH, osizeW);
/* indices will contain i,j locations for each output point */
THIndexTensor_(resize4d)(indices, sizeB, sizeD, osizeH, osizeW);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THIndexTensor_(data)(indices);
#pragma omp parallel for private(b)
for (b = 0; b < sizeB; b++)
{
THNN_(SpatialAdaptiveMaxPooling_updateOutput_frame)(input_data+b*istrideB, output_data+b*sizeD*osizeH*osizeW,
indices_data+b*sizeD*osizeH*osizeW,
sizeD,
isizeH, isizeW,
osizeH, osizeW,
istrideD,
istrideH, istrideW);
}
}
}