diff options
Diffstat (limited to 'contrib/lua-torch/nn/lib/THNN/generic/SpatialUpSamplingNearest.c')
| -rw-r--r-- | contrib/lua-torch/nn/lib/THNN/generic/SpatialUpSamplingNearest.c | 199 |
1 files changed, 199 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/lib/THNN/generic/SpatialUpSamplingNearest.c b/contrib/lua-torch/nn/lib/THNN/generic/SpatialUpSamplingNearest.c new file mode 100644 index 000000000..b4699ff3e --- /dev/null +++ b/contrib/lua-torch/nn/lib/THNN/generic/SpatialUpSamplingNearest.c @@ -0,0 +1,199 @@ +#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 |