bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
int _layerWidth = inputs[0].size[3];
int _layerHeight = inputs[0].size[2];
int _imageWidth = inputs[1].size[3];
int _imageHeight = inputs[1].size[2];
float stepX, stepY;
if (_stepX == 0 || _stepY == 0)
{
stepX = static_cast<float>(_imageWidth) / _layerWidth;
stepY = static_cast<float>(_imageHeight) / _layerHeight;
} else {
stepX = _stepX;
stepY = _stepY;
}
if (umat_offsetsX.empty())
{
Mat offsetsX(1, _offsetsX.size(), CV_32FC1, &_offsetsX[0]);
Mat offsetsY(1, _offsetsX.size(), CV_32FC1, &_offsetsY[0]);
Mat aspectRatios(1, _aspectRatios.size(), CV_32FC1, &_aspectRatios[0]);
Mat variance(1, _variance.size(), CV_32FC1, &_variance[0]);
offsetsX.copyTo(umat_offsetsX);
offsetsY.copyTo(umat_offsetsY);
aspectRatios.copyTo(umat_aspectRatios);
variance.copyTo(umat_variance);
int real_numPriors = _numPriors >> (_offsetsX.size() - 1);
umat_scales = UMat(1, &real_numPriors, CV_32F, 1.0f);
}
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
int _layerWidth = inputs[0].size[3];
int _layerHeight = inputs[0].size[2];
int _imageWidth = inputs[1].size[3];
int _imageHeight = inputs[1].size[2];
if (umat_offsetsX.empty())
{
Mat offsetsX(1, _offsetsX.size(), CV_32FC1, &_offsetsX[0]);
Mat offsetsY(1, _offsetsY.size(), CV_32FC1, &_offsetsY[0]);
Mat variance(1, _variance.size(), CV_32FC1, &_variance[0]);
Mat widths(1, _boxWidths.size(), CV_32FC1, &_boxWidths[0]);
Mat heights(1, _boxHeights.size(), CV_32FC1, &_boxHeights[0]);
offsetsX.copyTo(umat_offsetsX);
offsetsY.copyTo(umat_offsetsY);
variance.copyTo(umat_variance);
widths.copyTo(umat_widths);
heights.copyTo(umat_heights);
}
String opts;
if (use_half)
opts = "-DDtype=half -DDtype4=half4 -Dconvert_T=convert_half4";
else
opts = "-DDtype=float -DDtype4=float4 -Dconvert_T=convert_float4";
size_t nthreads = _layerHeight * _layerWidth;
ocl::Kernel kernel("prior_box", ocl::dnn::prior_box_oclsrc, opts);
kernel.set(0, (int)nthreads);
kernel.set(1, (float)_stepX);
kernel.set(2, (float)_stepY);
kernel.set(3, ocl::KernelArg::PtrReadOnly(umat_offsetsX));
kernel.set(4, ocl::KernelArg::PtrReadOnly(umat_offsetsY));
kernel.set(5, (int)_offsetsX.size());
kernel.set(6, ocl::KernelArg::PtrReadOnly(umat_widths));
kernel.set(7, ocl::KernelArg::PtrReadOnly(umat_heights));
kernel.set(8, (int)_boxWidths.size());
kernel.set(9, ocl::KernelArg::PtrWriteOnly(outputs[0]));
kernel.set(10, (int)_layerHeight);
kernel.set(11, (int)_layerWidth);
kernel.set(12, (int)_imageHeight);
kernel.set(13, (int)_imageWidth);
kernel.run(1, &nthreads, NULL, false);
// clip the prior's coordidate such that it is within [0, 1]
if (_clip)
{
Mat mat = outputs[0].getMat(ACCESS_READ);
int aspect_count = (_maxSize > 0) ? 1 : 0;
int offset = nthreads * 4 * _offsetsX.size() * (1 + aspect_count + _aspectRatios.size());
float* outputPtr = mat.ptr<float>() + offset;
int _outChannelSize = _layerHeight * _layerWidth * _numPriors * 4;
for (size_t d = 0; d < _outChannelSize; ++d)
{
outputPtr[d] = std::min<float>(std::max<float>(outputPtr[d], 0.), 1.);
}
}
// set the variance.
{
ocl::Kernel kernel("set_variance", ocl::dnn::prior_box_oclsrc, opts);
int offset = total(shape(outputs[0]), 2);
size_t nthreads = _layerHeight * _layerWidth * _numPriors;
kernel.set(0, (int)nthreads);
kernel.set(1, (int)offset);
kernel.set(2, (int)_variance.size());
kernel.set(3, ocl::KernelArg::PtrReadOnly(umat_variance));
kernel.set(4, ocl::KernelArg::PtrWriteOnly(outputs[0]));
if (!kernel.run(1, &nthreads, NULL, false))
return false;
}
return true;
}
