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// Adapted from interp.cpp from Caffe util by Pauline Luc
// Originally developed by George Papandreou
#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/VolumetricUpSamplingTrilinear.c"
#else
static inline void THNN_(VolumetricUpSamplingTrilinear_shapeCheck)
(THTensor *input, THTensor *gradOutput,
int nBatch, int nChannels,
int inputDepth, int inputHeight, int inputWidth,
int outputDepth, int outputHeight, int outputWidth) {
THArgCheck(inputDepth > 0 && inputHeight > 0 && inputWidth > 0
&& outputDepth > 0 && outputHeight > 0 && outputWidth > 0, 2,
"input and output sizes should be greater than 0,"
" but got input (D: %d, H: %d, W: %d) output (D: %d, H: %d, W: %d)",
inputDepth, inputHeight, inputWidth, outputDepth, outputHeight, outputWidth);
if (input != NULL) {
THNN_ARGCHECK(input->nDimension == 5, 2, input,
"5D input tensor expected but got: %s");
}
if (gradOutput != NULL) {
THNN_CHECK_DIM_SIZE(gradOutput, 5, 0, nBatch);
THNN_CHECK_DIM_SIZE(gradOutput, 5, 1, nChannels);
THNN_CHECK_DIM_SIZE(gradOutput, 5, 2, outputDepth);
THNN_CHECK_DIM_SIZE(gradOutput, 5, 3, outputHeight);
THNN_CHECK_DIM_SIZE(gradOutput, 5, 4, outputWidth);
}
}
void THNN_(VolumetricUpSamplingTrilinear_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
int outputDepth,
int outputHeight,
int outputWidth){
int nbatch = THTensor_(size)(input, 0);
int channels = THTensor_(size)(input, 1);
int inputDepth = THTensor_(size)(input, 2);
int inputHeight = THTensor_(size)(input, 3);
int inputWidth = THTensor_(size)(input, 4);
THNN_(VolumetricUpSamplingTrilinear_shapeCheck)
(input, NULL,
nbatch, channels,
inputDepth, inputHeight, inputWidth,
outputDepth, outputHeight, outputWidth);
input = THTensor_(newContiguous)(input);
THTensor_(resize5d)(output,
THTensor_(size)(input, 0),
THTensor_(size)(input, 1),
outputDepth, outputHeight, outputWidth);
THTensor_(zero)(output);
real *idata = THTensor_(data)(input);
real *odata = THTensor_(data)(output);
channels = nbatch * channels;
THAssert(inputDepth > 0 && inputHeight > 0 && inputWidth > 0 &&
outputDepth > 0 && outputHeight > 0 && outputWidth > 0);
// special case: just copy
if (inputDepth == outputDepth && inputHeight == outputHeight && inputWidth == outputWidth) {
for (int t2 = 0; t2 < outputDepth; ++t2) {
const int t1 = t2;
for (int h2 = 0; h2 < outputHeight; ++h2) {
const int h1 = h2;
for (int w2 = 0; w2 < outputWidth; ++w2) {
const int w1 = w2;
const real* pos1 = &idata[t1 * inputHeight * inputWidth + h1 * inputWidth + w1];
real* pos2 = &odata[t2 * outputHeight * outputWidth + h2 * outputWidth + w2];
for (int c = 0; c < channels; ++c) {
pos2[0] = pos1[0];
pos1 += inputWidth * inputHeight * inputDepth;
pos2 += outputWidth * outputHeight * outputDepth;
}
}
}
}
return;
}
const float rdepth = (outputDepth > 1) ? (float)(inputDepth - 1)/(outputDepth - 1) : 0.f;
const float rheight = (outputHeight > 1) ? (float)(inputHeight - 1)/(outputHeight - 1) : 0.f;
const float rwidth = (outputWidth > 1) ? (float)(inputWidth - 1) / (outputWidth - 1) : 0.f;
for (int t2 = 0; t2 < outputDepth; ++t2) {
const float t1r = rdepth * t2;
const int t1 = t1r;
const int t1p = (t1 < inputDepth - 1) ? 1 : 0;
const real t1lambda = t1r - t1;
const real t0lambda = (real)1. - t1lambda;
for (int h2 = 0; h2 < outputHeight; ++h2) {
const float h1r = rheight * h2;
const int h1 = h1r;
const int h1p = (h1 < inputHeight - 1) ? 1 : 0;
const real h1lambda = h1r - h1;
const real h0lambda = (real)1. - h1lambda;
for (int w2 = 0; w2 < outputWidth; ++w2) {
const float w1r = rwidth * w2;
const int w1 = w1r;
const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
const real w1lambda = w1r - w1;
const real w0lambda = (real)1. - w1lambda;
const real* pos1 = &idata[t1 * inputHeight * inputWidth + h1 * inputWidth + w1];
real* pos2 = &odata[t2 * outputHeight * outputWidth + h2 * outputWidth + w2];
for (int c = 0; c < channels; ++c) {
pos2[0] = t0lambda * (h0lambda * (w0lambda * pos1[0] + w1lambda * pos1[w1p])
+ h1lambda * (w0lambda * pos1[h1p * inputWidth]
+ w1lambda * pos1[h1p * inputWidth + w1p]))
+ t1lambda * (h0lambda * (w0lambda * pos1[t1p * inputHeight * inputWidth]
+ w1lambda * pos1[t1p * inputHeight * inputWidth
+ w1p])
+ h1lambda * (w0lambda * pos1[t1p * inputHeight * inputWidth
+ h1p * inputWidth]
+ w1lambda * pos1[t1p * inputHeight * inputWidth
+ h1p * inputWidth + w1p]));
pos1 += inputWidth * inputHeight * inputDepth;
pos2 += outputWidth * outputHeight * outputDepth;
}
}
}
}
THTensor_(free)(input);
}
void THNN_(VolumetricUpSamplingTrilinear_updateGradInput)(
THNNState *state,
THTensor *gradOutput,
THTensor *gradInput,
int nbatch,
int channels,
int inputDepth,
int inputHeight,
int inputWidth,
int outputDepth,
int outputHeight,
int outputWidth){
THNN_(VolumetricUpSamplingTrilinear_shapeCheck)
(NULL, gradOutput,
nbatch, channels,
inputDepth, inputHeight, inputWidth,
outputDepth, outputHeight, outputWidth);
THTensor_(resize5d)(gradInput, nbatch, channels, inputDepth, inputHeight, inputWidth);
THTensor_(zero)(gradInput);
gradOutput = THTensor_(newContiguous)(gradOutput);
real *data1 = THTensor_(data)(gradInput);
real *data2 = THTensor_(data)(gradOutput);
channels = nbatch * channels;
// special case: same-size matching grids
if (inputDepth == outputDepth && inputHeight == outputHeight && inputWidth == outputWidth) {
for (int t2 = 0; t2 < outputDepth; ++t2) {
const int t1 = t2;
for (int h2 = 0; h2 < outputHeight; ++h2) {
const int h1 = h2;
for (int w2 = 0; w2 < outputWidth; ++w2) {
const int w1 = w2;
real* pos1 = &data1[t1 * inputHeight * inputWidth + h1 * inputWidth + w1];
const real* pos2 = &data2[t2 * outputHeight * outputWidth + h2 * outputWidth + w2];
for (int c = 0; c < channels; ++c) {
pos1[0] += pos2[0];
pos1 += inputWidth * inputHeight * inputDepth;
pos2 += outputWidth * outputHeight * outputDepth;
}
}
}
}
return;
}
const float rdepth = (outputDepth > 1) ? (float)(inputDepth - 1)/(outputDepth - 1) : 0.f;
const float rheight = (outputHeight > 1) ? (float)(inputHeight - 1)/(outputHeight - 1) : 0.f;
const float rwidth = (outputWidth > 1) ? (float)(inputWidth - 1)/(outputWidth - 1) : 0.f;
for (int t2 = 0; t2 < outputDepth; ++t2) {
const float t1r = rdepth * t2;
const int t1 = t1r;
const int t1p = (t1 < inputDepth - 1) ? 1 : 0;
const real t1lambda = t1r - t1;
const real t0lambda = (real)1. - t1lambda;
for (int h2 = 0; h2 < outputHeight; ++h2) {
const float h1r = rheight * h2;
const int h1 = h1r;
const int h1p = (h1 < inputHeight - 1) ? 1 : 0;
const real h1lambda = h1r - h1;
const real h0lambda = (real)1. - h1lambda;
for (int w2 = 0; w2 < outputWidth; ++w2) {
const float w1r = rwidth * w2;
const int w1 = w1r;
const int w1p = (w1 < inputWidth - 1) ? 1 : 0;
const real w1lambda = w1r - w1;
const real w0lambda = (real)1. - w1lambda;
real* pos1 = &data1[t1 * inputHeight * inputWidth + h1 * inputWidth + w1];
const real* pos2 = &data2[t2 * outputHeight * outputWidth + h2 * outputWidth + w2];
for (int c = 0; c < channels; ++c) {
pos1[0] += t0lambda * h0lambda * w0lambda * pos2[0];
pos1[w1p] += t0lambda * h0lambda * w1lambda * pos2[0];
pos1[h1p * inputWidth] += t0lambda * h1lambda * w0lambda * pos2[0];
pos1[h1p * inputWidth + w1p] += t0lambda * h1lambda * w1lambda * pos2[0];
pos1[t1p * inputHeight * inputWidth] += t1lambda * h0lambda * w0lambda * pos2[0];
pos1[t1p * inputHeight * inputWidth + w1p] += t1lambda * h0lambda * w1lambda * pos2[0];
pos1[t1p * inputHeight * inputWidth + h1p * inputWidth] += t1lambda * h1lambda * w0lambda * pos2[0];
pos1[t1p * inputHeight * inputWidth + h1p * inputWidth + w1p] += t1lambda * h1lambda * w1lambda * pos2[0];
pos1 += inputWidth * inputHeight * inputDepth;
pos2 += outputWidth * outputHeight * outputDepth;
}
}
}
}
THTensor_(free)(gradOutput);
}
#endif
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