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Merge pull request #20883 from rogday:eltwise_refactoring
* backport elementwise_layers refactor * keep NULL
This commit is contained in:
rogday
GitHub
parent
3f191e1b75
commit
b3f966e2ca
@ -500,16 +500,9 @@ struct ReLU6Functor : public BaseFunctor
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int64 getFLOPSPerElement() const { return 2; }
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};
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struct TanHFunctor : public BaseFunctor
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template <class T>
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struct BaseDefaultFunctor : public BaseFunctor
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{
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typedef TanHLayer Layer;
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bool supportBackend(int backendId, int)
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{
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return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
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}
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void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
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{
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for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
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@ -517,7 +510,7 @@ struct TanHFunctor : public BaseFunctor
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for( int i = 0; i < len; i++ )
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{
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float x = srcptr[i];
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dstptr[i] = tanh(x);
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dstptr[i] = static_cast<T const*>(this)->calculate(x);
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}
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}
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}
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@ -537,10 +530,11 @@ struct TanHFunctor : public BaseFunctor
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UMat& src = inputs[i];
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UMat& dst = outputs[i];
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ocl::Kernel kernel("TanHForward", ocl::dnn::activations_oclsrc, buildopt);
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kernel.set(0, (int)src.total());
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ocl::Kernel kernel(ocl_kernel_name, ocl::dnn::activations_oclsrc, buildopt);
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kernel.set(0, static_cast<int>(src.total()));
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kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
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kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
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static_cast<T const*>(this)->setKernelParams(kernel);
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size_t gSize = src.total();
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CV_Assert(kernel.run(1, &gSize, NULL, false));
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@ -550,6 +544,41 @@ struct TanHFunctor : public BaseFunctor
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}
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#endif
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inline void setKernelParams(ocl::Kernel& kernel) const {}
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#ifdef HAVE_DNN_IE_NN_BUILDER_2019
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InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
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{
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CV_Error(Error::StsNotImplemented, "");
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}
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#endif // HAVE_DNN_IE_NN_BUILDER_2019
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#ifdef HAVE_DNN_NGRAPH
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std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
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{
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CV_Error(Error::StsNotImplemented, "");
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}
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#endif // HAVE_DNN_NGRAPH
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private:
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static const char* const ocl_kernel_name;
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};
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struct TanHFunctor : public BaseDefaultFunctor<TanHFunctor>
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{
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typedef TanHLayer Layer;
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bool supportBackend(int backendId, int)
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{
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return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
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}
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inline float calculate(float x) const
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{
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return tanh(x);
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}
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#ifdef HAVE_HALIDE
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void attachHalide(const Halide::Expr& input, Halide::Func& top)
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{
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@ -575,56 +604,24 @@ struct TanHFunctor : public BaseFunctor
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int64 getFLOPSPerElement() const { return 1; }
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};
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struct SwishFunctor : public BaseFunctor
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template<>
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const char* const TanHFunctor::BaseDefaultFunctor<TanHFunctor>::ocl_kernel_name = "TanHForward";
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struct SwishFunctor : public BaseDefaultFunctor<SwishFunctor>
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{
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typedef SwishLayer Layer;
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bool supportBackend(int backendId, int)
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{
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return backendId == DNN_BACKEND_OPENCV ||
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backendId == DNN_BACKEND_HALIDE || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;;
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backendId == DNN_BACKEND_HALIDE || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
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}
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void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
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inline float calculate(float x) const
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{
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for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
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{
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for( int i = 0; i < len; i++ )
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{
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float x = srcptr[i];
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dstptr[i] = x / (1.0f + exp(-x));
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}
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}
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return x / (1.f + exp(-x));
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}
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#ifdef HAVE_OPENCL
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bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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String buildopt = oclGetTMacro(inputs[0]);
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for (size_t i = 0; i < inputs.size(); i++)
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{
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UMat& src = inputs[i];
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UMat& dst = outputs[i];
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ocl::Kernel kernel("SwishForward", ocl::dnn::activations_oclsrc, buildopt);
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kernel.set(0, (int)src.total());
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kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
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kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
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size_t gSize = src.total();
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CV_Assert(kernel.run(1, &gSize, NULL, false));
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}
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return true;
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}
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#endif
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#ifdef HAVE_HALIDE
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void attachHalide(const Halide::Expr& input, Halide::Func& top)
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{
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@ -633,13 +630,6 @@ struct SwishFunctor : public BaseFunctor
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}
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#endif // HAVE_HALIDE
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#ifdef HAVE_DNN_IE_NN_BUILDER_2019
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InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
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{
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CV_Error(Error::StsNotImplemented, "");
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}
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#endif // HAVE_DNN_IE_NN_BUILDER_2019
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#ifdef HAVE_DNN_NGRAPH
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std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
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{
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@ -651,7 +641,10 @@ struct SwishFunctor : public BaseFunctor
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int64 getFLOPSPerElement() const { return 3; }
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};
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struct MishFunctor : public BaseFunctor
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template<>
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const char* const SwishFunctor::BaseDefaultFunctor<SwishFunctor>::ocl_kernel_name = "SwishForward";
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struct MishFunctor : public BaseDefaultFunctor<MishFunctor>
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{
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typedef MishLayer Layer;
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@ -661,53 +654,18 @@ struct MishFunctor : public BaseFunctor
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backendId == DNN_BACKEND_HALIDE || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
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}
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void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
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inline float calculate(float x) const
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{
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for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
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// Use fast approximation introduced in https://github.com/opencv/opencv/pull/17200
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if (x >= 8.f)
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{
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for( int i = 0; i < len; i++ )
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{
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// Use fast approximation introduced in https://github.com/opencv/opencv/pull/17200
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float x = srcptr[i];
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if (x >= 8.f)
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dstptr[i] = x;
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else
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{
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float eX = exp(x);
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float n = (eX + 2) * eX;
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dstptr[i] = (x * n) / (n + 2);
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}
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}
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}
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}
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#ifdef HAVE_OPENCL
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bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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String buildopt = oclGetTMacro(inputs[0]);
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for (size_t i = 0; i < inputs.size(); i++)
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{
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UMat& src = inputs[i];
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UMat& dst = outputs[i];
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ocl::Kernel kernel("MishForward", ocl::dnn::activations_oclsrc, buildopt);
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kernel.set(0, (int)src.total());
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kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
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kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
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size_t gSize = src.total();
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CV_Assert(kernel.run(1, &gSize, NULL, false));
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return x;
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}
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return true;
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float eX = exp(x);
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float n = (eX + 2.f) * eX;
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return (x * n) / (n + 2.f);
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}
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#endif
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#ifdef HAVE_HALIDE
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void attachHalide(const Halide::Expr& input, Halide::Func& top)
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@ -717,13 +675,6 @@ struct MishFunctor : public BaseFunctor
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}
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#endif // HAVE_HALIDE
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#ifdef HAVE_DNN_IE_NN_BUILDER_2019
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InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
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{
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CV_Error(Error::StsNotImplemented, "");
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}
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#endif // HAVE_DNN_IE_NN_BUILDER_2019
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#ifdef HAVE_DNN_NGRAPH
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std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
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{
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@ -740,7 +691,10 @@ struct MishFunctor : public BaseFunctor
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int64 getFLOPSPerElement() const { return 3; }
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};
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struct SigmoidFunctor : public BaseFunctor
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template<>
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const char* const MishFunctor::BaseDefaultFunctor<MishFunctor>::ocl_kernel_name = "MishForward";
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struct SigmoidFunctor : public BaseDefaultFunctor<SigmoidFunctor>
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{
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typedef SigmoidLayer Layer;
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@ -750,46 +704,11 @@ struct SigmoidFunctor : public BaseFunctor
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
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}
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void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
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inline float calculate(float x) const
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{
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for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
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{
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for( int i = 0; i < len; i++ )
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{
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float x = srcptr[i];
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dstptr[i] = 1.f/(1.f + exp(-x));
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}
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}
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return 1.f / (1.f + exp(-x));
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}
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#ifdef HAVE_OPENCL
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bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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String buildopt = oclGetTMacro(inputs[0]);
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for (size_t i = 0; i < inputs.size(); i++)
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{
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UMat& src = inputs[i];
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UMat& dst = outputs[i];
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ocl::Kernel kernel("SigmoidForward", ocl::dnn::activations_oclsrc, buildopt);
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kernel.set(0, (int)src.total());
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kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
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kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
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size_t gSize = src.total();
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CV_Assert(kernel.run(1, &gSize, NULL, false));
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}
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return true;
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}
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#endif
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#ifdef HAVE_HALIDE
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void attachHalide(const Halide::Expr& input, Halide::Func& top)
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{
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@ -815,7 +734,10 @@ struct SigmoidFunctor : public BaseFunctor
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int64 getFLOPSPerElement() const { return 3; }
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};
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struct ELUFunctor : public BaseFunctor
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template<>
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const char* const SigmoidFunctor::BaseDefaultFunctor<SigmoidFunctor>::ocl_kernel_name = "SigmoidForward";
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struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
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{
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typedef ELULayer Layer;
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@ -825,46 +747,11 @@ struct ELUFunctor : public BaseFunctor
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
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}
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void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
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inline float calculate(float x) const
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{
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for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
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{
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for(int i = 0; i < len; i++ )
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{
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float x = srcptr[i];
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dstptr[i] = x >= 0.f ? x : exp(x) - 1;
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}
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}
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return x >= 0.f ? x : exp(x) - 1.f;
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}
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#ifdef HAVE_OPENCL
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bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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String buildopt = oclGetTMacro(inputs[0]);
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for (size_t i = 0; i < inputs.size(); i++)
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{
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UMat& src = inputs[i];
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UMat& dst = outputs[i];
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ocl::Kernel kernel("ELUForward", ocl::dnn::activations_oclsrc, buildopt);
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kernel.set(0, (int)src.total());
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kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
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kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
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size_t gSize = src.total();
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CV_Assert(kernel.run(1, &gSize, NULL, false));
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}
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return true;
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}
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#endif
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#ifdef HAVE_HALIDE
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void attachHalide(const Halide::Expr& input, Halide::Func& top)
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{
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@ -890,7 +777,10 @@ struct ELUFunctor : public BaseFunctor
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int64 getFLOPSPerElement() const { return 2; }
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};
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struct AbsValFunctor : public BaseFunctor
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template<>
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const char* const ELUFunctor::BaseDefaultFunctor<ELUFunctor>::ocl_kernel_name = "ELUForward";
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struct AbsValFunctor : public BaseDefaultFunctor<AbsValFunctor>
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{
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typedef AbsLayer Layer;
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@ -903,46 +793,11 @@ struct AbsValFunctor : public BaseFunctor
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return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
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}
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void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
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inline float calculate(float x) const
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{
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for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
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{
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for( int i = 0; i < len; i++ )
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{
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float x = srcptr[i];
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dstptr[i] = abs(x);
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}
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}
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return abs(x);
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}
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#ifdef HAVE_OPENCL
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bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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String buildopt = oclGetTMacro(inputs[0]);
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for (size_t i = 0; i < inputs.size(); i++)
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{
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UMat& src = inputs[i];
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UMat& dst = outputs[i];
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ocl::Kernel kernel("AbsValForward", ocl::dnn::activations_oclsrc, buildopt);
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kernel.set(0, (int)src.total());
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kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
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kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
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size_t gSize = src.total();
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CV_Assert(kernel.run(1, &gSize, NULL, false));
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}
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return true;
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}
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#endif
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#ifdef HAVE_HALIDE
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void attachHalide(const Halide::Expr& input, Halide::Func& top)
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{
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@ -971,7 +826,10 @@ struct AbsValFunctor : public BaseFunctor
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int64 getFLOPSPerElement() const { return 1; }
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};
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struct BNLLFunctor : public BaseFunctor
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template<>
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const char* const AbsValFunctor::BaseDefaultFunctor<AbsValFunctor>::ocl_kernel_name = "AbsValForward";
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struct BNLLFunctor : public BaseDefaultFunctor<BNLLFunctor>
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{
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typedef BNLLLayer Layer;
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@ -980,47 +838,12 @@ struct BNLLFunctor : public BaseFunctor
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return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
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}
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void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
|
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inline float calculate(float x) const
|
||||
{
|
||||
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
|
||||
{
|
||||
for( int i = 0; i < len; i++ )
|
||||
{
|
||||
float x = srcptr[i];
|
||||
// https://github.com/BVLC/caffe/blame/1.0/src/caffe/layers/bnll_layer.cpp#L17
|
||||
dstptr[i] = x > 0 ? x + log(1. + exp(-x)) : log(1. + exp(x));
|
||||
}
|
||||
}
|
||||
// https://github.com/BVLC/caffe/blame/1.0/src/caffe/layers/bnll_layer.cpp#L17
|
||||
return x > 0 ? x + log(1.f + exp(-x)) : log(1.f + exp(x));
|
||||
}
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
|
||||
{
|
||||
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("BNLLForward", 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
|
||||
|
||||
#ifdef HAVE_HALIDE
|
||||
void attachHalide(const Halide::Expr& input, Halide::Func& top)
|
||||
{
|
||||
@ -1030,23 +853,12 @@ struct BNLLFunctor : public BaseFunctor
|
||||
}
|
||||
#endif // HAVE_HALIDE
|
||||
|
||||
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
|
||||
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
|
||||
{
|
||||
CV_Error(Error::StsNotImplemented, "");
|
||||
}
|
||||
#endif // HAVE_DNN_IE_NN_BUILDER_2019
|
||||
|
||||
#ifdef HAVE_DNN_NGRAPH
|
||||
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
|
||||
{
|
||||
CV_Error(Error::StsNotImplemented, "");
|
||||
}
|
||||
#endif // HAVE_DNN_NGRAPH
|
||||
|
||||
int64 getFLOPSPerElement() const { return 5; }
|
||||
};
|
||||
|
||||
template<>
|
||||
const char* const BNLLFunctor::BaseDefaultFunctor<BNLLFunctor>::ocl_kernel_name = "BNLLForward";
|
||||
|
||||
struct PowerFunctor : public BaseFunctor
|
||||
{
|
||||
typedef PowerLayer Layer;
|
||||
@ -1206,7 +1018,7 @@ struct PowerFunctor : public BaseFunctor
|
||||
int64 getFLOPSPerElement() const { return power == 1 ? 2 : 10; }
|
||||
};
|
||||
|
||||
struct ExpFunctor : public BaseFunctor
|
||||
struct ExpFunctor : public BaseDefaultFunctor<ExpFunctor>
|
||||
{
|
||||
typedef ExpLayer Layer;
|
||||
float base, scale, shift;
|
||||
@ -1232,47 +1044,16 @@ struct ExpFunctor : public BaseFunctor
|
||||
backendId == DNN_BACKEND_HALIDE || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
|
||||
}
|
||||
|
||||
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
|
||||
inline float calculate(float x) const
|
||||
{
|
||||
float a = normScale, b = normShift;
|
||||
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
|
||||
{
|
||||
for( int i = 0; i < len; i++ )
|
||||
{
|
||||
float x = srcptr[i];
|
||||
dstptr[i] = exp(a*x + b);
|
||||
}
|
||||
}
|
||||
return exp(normScale * x + normShift);
|
||||
}
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
|
||||
inline void setKernelParams(ocl::Kernel& kernel) const
|
||||
{
|
||||
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("ExpForward", 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)normScale);
|
||||
kernel.set(4, (float)normShift);
|
||||
|
||||
size_t gSize = src.total();
|
||||
CV_Assert(kernel.run(1, &gSize, NULL, false));
|
||||
}
|
||||
return true;
|
||||
kernel.set(3, normScale);
|
||||
kernel.set(4, normShift);
|
||||
}
|
||||
#endif
|
||||
|
||||
#ifdef HAVE_HALIDE
|
||||
void attachHalide(const Halide::Expr& input, Halide::Func& top)
|
||||
@ -1282,13 +1063,6 @@ struct ExpFunctor : public BaseFunctor
|
||||
}
|
||||
#endif // HAVE_HALIDE
|
||||
|
||||
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
|
||||
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
|
||||
{
|
||||
CV_Error(Error::StsNotImplemented, "");
|
||||
}
|
||||
#endif // HAVE_DNN_IE_NN_BUILDER_2019
|
||||
|
||||
#ifdef HAVE_DNN_NGRAPH
|
||||
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
|
||||
{
|
||||
@ -1305,6 +1079,9 @@ struct ExpFunctor : public BaseFunctor
|
||||
int64 getFLOPSPerElement() const { return 3; }
|
||||
};
|
||||
|
||||
template<>
|
||||
const char* const ExpFunctor::BaseDefaultFunctor<ExpFunctor>::ocl_kernel_name = "ExpForward";
|
||||
|
||||
struct ChannelsPReLUFunctor : public BaseFunctor
|
||||
{
|
||||
typedef ChannelsPReLULayer Layer;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user