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SpatialConvolutionMM.c
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SpatialConvolutionMM.c
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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialConvolutionMM.c"
#else
static inline void THNN_(SpatialConvolutionMM_shapeCheck)(
THTensor *input, THTensor *gradOutput,
THTensor *weight, THTensor *bias,
int kH, int kW, int dH, int dW, int padH, int padW, int weight_nullable) {
THArgCheck(kW > 0 && kH > 0, 9,
"kernel size should be greater than zero, but got kH: %d kW: %d", kH, kW);
THArgCheck(dW > 0 && dH > 0, 11,
"stride should be greater than zero, but got dH: %d dW: %d", dH, dW);
if (weight != NULL) {
THNN_ARGCHECK(weight->nDimension == 2 || weight->nDimension == 4, 5, weight,
"2D or 4D weight tensor expected, but got: %s");
if (bias != NULL) {
THNN_CHECK_DIM_SIZE(bias, 1, 0, weight->size[0]);
}
} else if (!weight_nullable) {
THError("weight tensor is expected to be non-nullable");
}
int ndim = input->nDimension;
int dimf = 0;
int dimh = 1;
int dimw = 2;
if (ndim == 4) {
dimf++;
dimh++;
dimw++;
}
THNN_ARGCHECK(ndim == 3 || ndim == 4, 2, input,
"3D or 4D input tensor expected but got: %s");
int64_t inputHeight = input->size[dimh];
int64_t inputWidth = input->size[dimw];
int64_t exactInputHeight = inputHeight + 2 * padH;
int64_t exactInputWidth = inputWidth + 2 * padW;
if (exactInputHeight < kH || exactInputWidth < kW) {
THError("Calculated padded input size per channel: (%ld x %ld). "
"Kernel size: (%ld x %ld). Kernel size can't greater than actual input size",
exactInputHeight, exactInputWidth, kH, kW);
}
int64_t outputHeight = (exactInputHeight - kH) / dH + 1;
int64_t outputWidth = (exactInputWidth - kW) / dW + 1;
if (outputWidth < 1 || outputHeight < 1) {
THError("Given input size per channel: (%ld x %ld). "
"Calculated output size per channel: (%ld x %ld). Output size is too small",
inputHeight, inputWidth, outputHeight, outputWidth);
}
if (weight != NULL) {
int64_t nInputPlane = weight->size[1];
if (weight->nDimension == 2) {
nInputPlane /= (kH * kW);
}
THNN_CHECK_DIM_SIZE(input, ndim, dimf, nInputPlane);
}
if (gradOutput != NULL) {
if (weight != NULL) {
int64_t nOutputPlane = weight->size[0];
THNN_CHECK_DIM_SIZE(gradOutput, ndim, dimf, nOutputPlane);
} else if (bias != NULL) {
int64_t nOutputPlane = bias->size[0];
THNN_CHECK_DIM_SIZE(gradOutput, ndim, dimf, nOutputPlane);
}
THNN_CHECK_DIM_SIZE(gradOutput, ndim, dimh, outputHeight);
THNN_CHECK_DIM_SIZE(gradOutput, ndim, dimw, outputWidth);
}
}
static THTensor* THNN_(newViewWeightMM2d)(THTensor *weight) {
weight = THTensor_(newContiguous)(weight);
if (weight->nDimension == 4) {
int64_t s1 = weight->size[0];
int64_t s2 = weight->size[1] * weight->size[2] * weight->size[3];
THTensor *old_weight = weight;
weight = THTensor_(newWithStorage2d)(weight->storage, weight->storageOffset,
s1, -1, s2, -1);
THTensor_(free)(old_weight);
}
return weight;
}
static void THNN_(SpatialConvolutionMM_updateOutput_frame)(
THTensor *input,
THTensor *output,
THTensor *weight,
THTensor *bias,
THTensor *finput,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH,
int64_t nInputPlane,
int64_t inputWidth,
int64_t inputHeight,
int64_t nOutputPlane,
int64_t outputWidth,
int64_t outputHeight)
{
int64_t i;
THTensor *output2d;
THNN_(unfolded_copy)(finput, input, kW, kH, dW, dH, padW, padH,
nInputPlane, inputWidth, inputHeight,
outputWidth, outputHeight);
output2d = THTensor_(newWithStorage2d)(output->storage, output->storageOffset,
nOutputPlane, -1,
outputHeight*outputWidth, -1);
if (bias) {
for(i = 0; i < nOutputPlane; i++)
THVector_(fill)
(output->storage->data + output->storageOffset + output->stride[0] * i,
THTensor_(get1d)(bias, i), outputHeight*outputWidth);
} else {
THTensor_(zero)(output);
}
THTensor_(addmm)(output2d, 1, output2d, 1, weight, finput);
THTensor_(free)(output2d);
}
void THNN_(SpatialConvolutionMM_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THTensor *weight,
THTensor *bias,
THTensor *finput,
THTensor *fgradInput,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH)
{
weight = THNN_(newViewWeightMM2d)(weight);
THNN_(SpatialConvolutionMM_shapeCheck)
(input, NULL, weight, bias, kH, kW, dH, dW, padH, padW, 0);
input = THTensor_(newContiguous)(input);
int ndim = input->nDimension;
int dimf = 0;
int dimh = 1;
int dimw = 2;
if (ndim == 4) {
dimf++;
dimh++;
dimw++;
}
int64_t nInputPlane = input->size[dimf];
int64_t inputHeight = input->size[dimh];
int64_t inputWidth = input->size[dimw];
int64_t nOutputPlane = weight->size[0];
int64_t outputHeight = (inputHeight + 2*padH - kH) / dH + 1;
int64_t outputWidth = (inputWidth + 2*padW - kW) / dW + 1;
if(input->nDimension == 3)
{
THTensor_(resize2d)(finput, kW*kH*nInputPlane, outputHeight*outputWidth);
THTensor_(resize3d)(output, nOutputPlane, outputHeight, outputWidth);
THNN_(SpatialConvolutionMM_updateOutput_frame)
(input, output, weight, bias, finput,
kW, kH, dW, dH, padW, padH,
nInputPlane, inputWidth, inputHeight,
nOutputPlane, outputWidth, outputHeight);
}
else
{
int64_t T = input->size[0];
int64_t t;
THTensor_(resize3d)(finput, T, kW*kH*nInputPlane, outputHeight*outputWidth);
THTensor_(resize4d)(output, T, nOutputPlane, outputHeight, outputWidth);
#pragma omp parallel for private(t)
for(t = 0; t < T; t++)
{
THTensor *input_t = THTensor_(newSelect)(input, 0, t);
THTensor *output_t = THTensor_(newSelect)(output, 0, t);
THTensor *finput_t = THTensor_(newSelect)(finput, 0, t);
THNN_(SpatialConvolutionMM_updateOutput_frame)
(input_t, output_t, weight, bias, finput_t,
kW, kH, dW, dH, padW, padH,
nInputPlane, inputWidth, inputHeight,
nOutputPlane, outputWidth, outputHeight);
THTensor_(free)(input_t);
THTensor_(free)(output_t);
THTensor_(free)(finput_t);
}
}
THTensor_(free)(input);
THTensor_(free)(weight);
}
static void THNN_(SpatialConvolutionMM_updateGradInput_frame)(
THTensor *gradInput,
THTensor *gradOutput,
THTensor *weight,
THTensor *fgradInput,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH)
{
THTensor *gradOutput2d = THTensor_(newWithStorage2d)
(gradOutput->storage, gradOutput->storageOffset,
gradOutput->size[0], -1,
gradOutput->size[1]*gradOutput->size[2], -1);
THTensor_(addmm)(fgradInput, 0, fgradInput, 1, weight, gradOutput2d);
THTensor_(free)(gradOutput2d);
THTensor_(zero)(gradInput);
THNN_(unfolded_acc)(fgradInput, gradInput, kW, kH, dW, dH,
padW, padH,
gradInput->size[0], gradInput->size[2], gradInput->size[1],
gradOutput->size[2], gradOutput->size[1]);
}
void THNN_(SpatialConvolutionMM_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THTensor *weight,
THTensor *finput,
THTensor *fgradInput,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH)
{
weight = THNN_(newViewWeightMM2d)(weight);
THNN_(SpatialConvolutionMM_shapeCheck)
(input, gradOutput, weight, NULL, kH, kW, dH, dW, padH, padW, 0);
input = THTensor_(newContiguous)(input);
gradOutput = THTensor_(newContiguous)(gradOutput);
THTensor_(resizeAs)(gradInput, input);
THTensor_(resizeAs)(fgradInput, finput);
// depending on the BLAS library, fgradInput (result tensor) might
// be left uninitialized on zero alpha, which might lead to weird behavior
// hence, to be safe, zero it
THTensor_(zero)(fgradInput);
THTensor *tweight = THTensor_(new)();
THTensor_(transpose)(tweight, weight, 0, 1);
if(input->nDimension == 3)
{
THNN_(SpatialConvolutionMM_updateGradInput_frame)(gradInput, gradOutput,
tweight, fgradInput,
kW, kH, dW, dH, padW, padH);
}
else
{
int64_t T = input->size[0];
int64_t t;
#pragma omp parallel for private(t)
for(t = 0; t < T; t++)
{
THTensor *gradInput_t = THTensor_(newSelect)(gradInput, 0, t);
THTensor *gradOutput_t = THTensor_(newSelect)(gradOutput, 0, t);
THTensor *fgradInput_t = THTensor_(newSelect)(fgradInput, 0, t);
THNN_(SpatialConvolutionMM_updateGradInput_frame)(gradInput_t, gradOutput_t,
tweight, fgradInput_t,
kW, kH, dW, dH, padW, padH);
THTensor_(free)(gradInput_t);
THTensor_(free)(gradOutput_t);
THTensor_(free)(fgradInput_t);
}
}
THTensor_(free)(tweight);
THTensor_(free)(input);
THTensor_(free)(gradOutput);
THTensor_(free)(weight);
}
static void THNN_(SpatialConvolutionMM_accGradParameters_frame)(
THTensor *gradOutput,
THTensor *gradWeight,
THTensor *gradBias,
THTensor *finput,
real scale)
{
int64_t i;
THTensor *gradOutput2d = THTensor_(newWithStorage2d)
(gradOutput->storage, gradOutput->storageOffset,
gradOutput->size[0], -1,
gradOutput->size[1]*gradOutput->size[2], -1);
if (gradWeight) {
THTensor *tfinput = THTensor_(new)();
THTensor_(transpose)(tfinput, finput, 0, 1);
THTensor_(addmm)(gradWeight, 1, gradWeight, scale, gradOutput2d, tfinput);
THTensor_(free)(tfinput);
}
if (gradBias) {
for(i = 0; i < gradBias->size[0]; i++)
{
int64_t k;
real sum = 0;
real *data = gradOutput2d->storage->data + gradOutput2d->storageOffset + i*gradOutput2d->stride[0];
for(k = 0; k < gradOutput2d->size[1]; k++)
sum += data[k];
(gradBias->storage->data + gradBias->storageOffset)[i] += scale*sum;
}
}
THTensor_(free)(gradOutput2d);
}
void THNN_(SpatialConvolutionMM_accGradParameters)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradWeight,
THTensor *gradBias,
THTensor *finput, // can be NULL if gradWeight = NULL
THTensor *fgradInput,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH,
accreal scale_)
{
real scale = TH_CONVERT_ACCREAL_TO_REAL(scale_);
if (gradWeight) {
THArgCheck(THTensor_(isContiguous)(gradWeight), 4, "gradWeight needs to be contiguous");
gradWeight = THNN_(newViewWeightMM2d)(gradWeight);
}
if (gradBias) {
THArgCheck(THTensor_(isContiguous)(gradBias), 5, "gradBias needs to be contiguous");
}
THNN_(SpatialConvolutionMM_shapeCheck)
(input, gradOutput, gradWeight, gradBias, kH, kW, dH, dW, padH, padW, 1);
input = THTensor_(newContiguous)(input);
gradOutput = THTensor_(newContiguous)(gradOutput);
if(input->nDimension == 3)
{
THNN_(SpatialConvolutionMM_accGradParameters_frame)(gradOutput, gradWeight,
gradBias, finput, scale);
}
else
{
int64_t T = input->size[0];
int64_t t;
for(t = 0; t < T; t++)
{
THTensor *gradOutput_t = THTensor_(newSelect)(gradOutput, 0, t);
THTensor *finput_t = NULL;
if (gradWeight) {
finput_t = THTensor_(newSelect)(finput, 0, t);
}
THNN_(SpatialConvolutionMM_accGradParameters_frame)(gradOutput_t, gradWeight,
gradBias, finput_t, scale);
THTensor_(free)(gradOutput_t);
if (gradWeight) {
THTensor_(free)(finput_t);
}
}
}
THTensor_(free)(input);
THTensor_(free)(gradOutput);
if (gradWeight) {
THTensor_(free)(gradWeight);
}
}
#endif