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Power, Tanh and Channels ReLU layer ocl support

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Signed-off-by: Li Peng <peng.li@intel.com>
This commit is contained in:
Li Peng
2017-11-23 22:39:10 +08:00
parent 4189214d04
commit 7bc017601f
3 changed files with 90 additions and 8 deletions
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modules/dnn/src/layers/elementwise_layers.cpp
+75 -8
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@@ -267,7 +267,6 @@ struct ReLUFunctor
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
size_t wgSize = ocl::Device::getDefault().maxWorkGroupSize();
std::vector<UMat> inputs;
std::vector<UMat> outputs;
@@ -287,7 +286,7 @@ struct ReLUFunctor
kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
size_t gSize = src.total();
CV_Assert(kernel.run(1, &gSize, &wgSize, false));
CV_Assert(kernel.run(1, &gSize, NULL, false));
}
return true;
@@ -395,8 +394,28 @@ struct TanHFunctor
#ifdef HAVE_OPENCL
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
// TODO: implement OCL version
return false;
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
String buildopt = oclGetTMacro(inputs[0]);
for (size_t i = 0; i < inputs.size(); i++)
{
UMat& src = inputs[i];
UMat& dst = outputs[i];
ocl::Kernel kernel("TanHForward", ocl::dnn::activations_oclsrc, buildopt);
kernel.set(0, (int)src.total());
kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
size_t gSize = src.total();
CV_Assert(kernel.run(1, &gSize, NULL, false));
}
return true;
}
#endif
@@ -594,8 +613,31 @@ struct PowerFunctor
#ifdef HAVE_OPENCL
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
// TODO: implement OCL version
return false;
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
String buildopt = oclGetTMacro(inputs[0]);
for (size_t i = 0; i < inputs.size(); i++)
{
UMat& src = inputs[i];
UMat& dst = outputs[i];
ocl::Kernel kernel("PowForward", ocl::dnn::activations_oclsrc, buildopt);
kernel.set(0, (int)src.total());
kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
kernel.set(3, (float)power);
kernel.set(4, (float)scale);
kernel.set(5, (float)shift);
size_t gSize = src.total();
CV_Assert(kernel.run(1, &gSize, NULL, false));
}
return true;
}
#endif
@@ -624,9 +666,11 @@ struct ChannelsPReLUFunctor
{
typedef ChannelsPReLULayer Layer;
Mat scale;
UMat scale_umat;
explicit ChannelsPReLUFunctor(const Mat& scale_=Mat()) : scale(scale_)
{
scale_umat = scale.getUMat(ACCESS_READ);
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@@ -669,8 +713,31 @@ struct ChannelsPReLUFunctor
#ifdef HAVE_OPENCL
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
// TODO: implement OCL version
return false;
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
String buildopt = oclGetTMacro(inputs[0]);
for (size_t i = 0; i < inputs.size(); i++)
{
UMat& src = inputs[i];
UMat& dst = outputs[i];
ocl::Kernel kernel("PReLUForward", ocl::dnn::activations_oclsrc, buildopt);
kernel.set(0, (int)src.total());
kernel.set(1, (int)src.size[1]);
kernel.set(2, (int)total(shape(src), 2));
kernel.set(3, ocl::KernelArg::PtrReadOnly(src));
kernel.set(4, ocl::KernelArg::PtrWriteOnly(dst));
kernel.set(5, ocl::KernelArg::PtrReadOnly(scale_umat));
size_t gSize = src.total();
CV_Assert(kernel.run(1, &gSize, NULL, false));
}
return true;
}
#endif