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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialUpSamplingNearest.c"
#else
static inline void THNN_(SpatialUpSamplingNearest_shapeCheck)
(THTensor *input, THTensor *gradOutput,
int scale_factor) {
THArgCheck(input != NULL, 2, "4D input tensor expected but got NULL");
THArgCheck(scale_factor > 1, 4,
"scale_factor must be greater than 1, but got: %d", scale_factor);
THNN_ARGCHECK(input->nDimension == 3 || input->nDimension == 4, 2, input,
"3D or 4D input tensor expected but got: %s");
if (input->nDimension == 3) {
int nChannels = THTensor_(size)(input, 0);
int inputHeight = THTensor_(size)(input, 1);
int inputWidth = THTensor_(size)(input, 2);
int outputHeight = inputHeight * scale_factor;
int outputWidth = inputWidth * scale_factor;
if (gradOutput != NULL) {
THNN_CHECK_DIM_SIZE(gradOutput, 3, 0, nChannels);
THNN_CHECK_DIM_SIZE(gradOutput, 3, 1, outputHeight);
THNN_CHECK_DIM_SIZE(gradOutput, 3, 2, outputWidth);
}
} else {
int nBatch = THTensor_(size)(input, 0);
int nChannels = THTensor_(size)(input, 1);
int inputHeight = THTensor_(size)(input, 2);
int inputWidth = THTensor_(size)(input, 3);
int outputHeight = inputHeight * scale_factor;
int outputWidth = inputWidth * scale_factor;
if (gradOutput != NULL) {
THNN_CHECK_DIM_SIZE(gradOutput, 4, 0, nBatch);
THNN_CHECK_DIM_SIZE(gradOutput, 4, 1, nChannels);
THNN_CHECK_DIM_SIZE(gradOutput, 4, 2, outputHeight);
THNN_CHECK_DIM_SIZE(gradOutput, 4, 3, outputWidth);
}
}
}
void THNN_(SpatialUpSamplingNearest_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
int scale_factor)
{
THNN_(SpatialUpSamplingNearest_shapeCheck)(input, NULL, scale_factor);
int inputHeight = THTensor_(size)(input, input->nDimension-2);
int inputWidth = THTensor_(size)(input, input->nDimension-1);
int outputHeight = inputHeight * scale_factor;
int outputWidth = inputWidth * scale_factor;
if (input->nDimension == 3) {
THTensor_(resize3d)(output,
THTensor_(size)(input, 0),
outputHeight, outputWidth);
} else {
THTensor_(resize4d)(output,
THTensor_(size)(input, 0),
THTensor_(size)(input, 1),
outputHeight, outputWidth);
}
int dW = scale_factor;
int dH = scale_factor;
int xDim = input->nDimension-2;
int yDim = input->nDimension-1;
// dims
int idim = input->nDimension;
int osz0 = output->size[0];
int osz1 = output->size[1];
int osz2 = output->size[2];
int osz3 = 1;
if (idim > 3) {
osz3 = output->size[3];
}
// get strides
long *is = input->stride;
long *os = output->stride;
// get raw pointers
real *pin = THTensor_(data)(input);
real *pout = THTensor_(data)(output);
// perform the upsampling
int i0, i1, i2, i3, isrc, idst;
int iout[4]; // Output indices
int iin[4]; // Input indices
for (i0 = 0; i0 < osz0; i0++) {
iout[0] = i0;
iin[0] = i0;
for (i1 = 0; i1 < osz1; i1++) {
iout[1] = i1;
iin[1] = i1;
for (i2 = 0; i2 < osz2; i2++) {
iout[2] = i2;
iin[2] = i2;
for (i3 = 0; i3 < osz3; i3++) {
iout[3] = i3;
iin[3] = i3;
// set the indices for the upsampled dimensions
iin[xDim] = iout[xDim] / dW;
iin[yDim] = iout[yDim] / dH;
idst = i0*os[0] + i1*os[1] + i2*os[2];
isrc = iin[0]*is[0] + iin[1]*is[1] + iin[2]*is[2];
if (idim > 3) {
idst += i3*os[3];
isrc += iin[3]*is[3];
}
pout[idst] = pin[isrc];
}
}
}
}
}
void THNN_(SpatialUpSamplingNearest_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
int scale_factor)
{
THNN_(SpatialUpSamplingNearest_shapeCheck)(input, gradOutput, scale_factor);
THTensor_(resizeAs)(gradInput, input);
int dW = scale_factor;
int dH = scale_factor;
int xDim = gradInput->nDimension-2;
int yDim = gradInput->nDimension-1;
// dims
int idim = gradInput->nDimension; // Guaranteed to be between 3 and 5
int isz0 = gradInput->size[0];
int isz1 = gradInput->size[1];
int isz2 = gradInput->size[2];
int isz3 = 1;
if (idim > 3) {
isz3 = gradInput->size[3];
}
// get strides
long *is = gradInput->stride;
long *os = gradOutput->stride;
// get raw pointers
real *pin = THTensor_(data)(gradInput);
real *pout = THTensor_(data)(gradOutput);
// perform the upsampling
int i0, i1, i2, i3, isrc, idst, x, y;
int iin[4]; // Input indices
int iout[4]; // Output indices
THTensor_(zero)(gradInput);
for (i0 = 0; i0 < isz0; i0++) {
iin[0] = i0;
iout[0] = i0;
for (i1 = 0; i1 < isz1; i1++) {
iin[1] = i1;
iout[1] = i1;
for (i2 = 0; i2 < isz2; i2++) {
iin[2] = i2;
iout[2] = i2;
for (i3 = 0; i3 < isz3; i3++) {
iin[3] = i3;
iout[3] = i3;
idst = i0*is[0] + i1*is[1] + i2*is[2];
if (idim > 3) {
idst += i3*is[3];
}
// Now accumulate the gradients from gradOutput
for (y = 0; y < dH; y++) {
for (x = 0; x < dW; x++) {
iout[xDim] = dW * iin[xDim] + x;
iout[yDim] = dH * iin[yDim] + y;
isrc = iout[0]*os[0] + iout[1]*os[1] + iout[2]*os[2];
if (idim > 3) {
isrc += iout[3]*os[3];
}
pin[idst] += pout[isrc];
}
}
}
}
}
}
}
#endif
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