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deepin-ocr/3rdparty/opencv-4.5.4/modules/dnn/src/layers/elementwise_layers.cpp
wangzhengyang 718c41634f feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake 
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试
2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程
3.重整权利声明文件，重整代码工程，确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
2022-05-10 10:22:11 +08:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_cuda.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include "../op_vkcom.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <iostream>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/activation.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv
{
namespace dnn
{
using std::abs;
using std::exp;
using std::tanh;
using std::pow;
template<typename Func>
class ElementWiseLayer : public Func::Layer
{
public:
class PBody : public cv::ParallelLoopBody
{
public:
const Func* func_;
const Mat* src_;
Mat* dst_;
int nstripes_;
PBody(const Func &func, const Mat &src, Mat& dst, int nstripes)
{
func_ = &func;
src_ = &src;
dst_ = &dst;
nstripes_ = nstripes;
}
void operator()(const Range &r) const CV_OVERRIDE
{
int nstripes = nstripes_, nsamples = 1, outCn = 1;
size_t planeSize = 1;
if (src_->dims > 1)
{
nsamples = src_->size[0];
outCn = src_->size[1];
}
else
outCn = src_->size[0];
for (int i = 2; i < src_->dims; ++i)
planeSize *= src_->size[i];
size_t stripeSize = (planeSize + nstripes - 1)/nstripes;
size_t stripeStart = r.start*stripeSize;
size_t stripeEnd = std::min(r.end*stripeSize, planeSize);
for( int i = 0; i < nsamples; i++ )
{
const float* srcptr = src_->ptr<float>(i) + stripeStart;
float* dstptr = dst_->ptr<float>(i) + stripeStart;
func_->apply(srcptr, dstptr, (int)(stripeEnd - stripeStart), planeSize, 0, outCn);
}
}
};
ElementWiseLayer(const Func &f=Func()) { func = f; }
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return func.supportBackend(backendId, this->preferableTarget);
}
virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE
{
func.finalize();
}
virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node) CV_OVERRIDE
{
switch (node->backendId)
{
case DNN_BACKEND_HALIDE:
{
#ifdef HAVE_HALIDE
auto base = node.dynamicCast<HalideBackendNode>();
Halide::Func& input = base->funcs.back();
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (this->name.empty() ? Halide::Func() : Halide::Func(this->name));
func.attachHalide(input(x, y, c, n), top);
return Ptr<BackendNode>(new HalideBackendNode(base, top));
#endif // HAVE_HALIDE
break;
}
}
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> input = halideBuffer(inputs[0]);
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (this->name.empty() ? Halide::Func() : Halide::Func(this->name));
func.attachHalide(input(x, y, c, n), top);
return Ptr<BackendNode>(new HalideBackendNode(top));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer = func.initInfEngineBuilderAPI();
ieLayer.setName(this->name);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto node = func.initNgraphAPI(ieInpNode);
return Ptr<BackendNode>(new InfEngineNgraphNode(node));
}
#endif // HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
#ifdef HAVE_VULKAN
return Ptr<BackendNode>(new VkComBackendNode(inputs, func.initVkCom()));
#endif // HAVE_VULKAN
return Ptr<BackendNode>();
}
virtual bool tryFuse(Ptr<dnn::Layer>& top) CV_OVERRIDE
{
return func.tryFuse(top);
}
void getScaleShift(Mat& scale_, Mat& shift_) const CV_OVERRIDE
{
func.getScaleShift(scale_, shift_);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
return true;
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(this->preferableTarget),
func.applyOCL(inputs_arr, outputs_arr, internals_arr))
if (inputs_arr.depth() == CV_16S)
{
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
for (size_t i = 0; i < inputs.size(); i++)
{
const Mat &src = inputs[i];
Mat &dst = outputs[i];
CV_Assert(src.size == dst.size && src.type() == dst.type() &&
src.isContinuous() && dst.isContinuous() && src.type() == CV_32F);
const int nstripes = getNumThreads();
PBody body(func, src, dst, nstripes);
parallel_for_(Range(0, nstripes), body, nstripes);
}
}
void forwardSlice(const float* src, float* dst, int len, size_t planeSize, int cn0, int cn1) const CV_OVERRIDE
{
func.apply(src, dst, len, planeSize, cn0, cn1);
}
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(
void *context_,
const std::vector<Ptr<BackendWrapper>>& inputs,
const std::vector<Ptr<BackendWrapper>>& outputs
) override
{
auto context = reinterpret_cast<csl::CSLContext*>(context_);
return func.initCUDA(Layer::preferableTarget, context->stream);
}
#endif
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
{
return func.tryQuantize(scales, zeropoints, params);
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
long flops = 0;
for (int i = 0; i < outputs.size(); i++)
{
flops += total(outputs[i]) * func.getFLOPSPerElement();
}
return flops;
}
Func func;
};
#ifdef HAVE_OPENCL
static String oclGetTMacro(const UMat &m)
{
String str_name = ocl::typeToStr(m.type());
if (str_name == "short")
str_name = "half";
return format("-DT=%s -Dconvert_T=convert_%s ", str_name.c_str(), str_name.c_str());
}
#endif
struct BaseFunctor
{
void finalize() {}
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
bool tryQuantize(const std::vector<std::vector<float>>&, const std::vector<std::vector<int>>&, LayerParams&) { return false; }
};
struct ReLUFunctor : public BaseFunctor
{
typedef ReLULayer Layer;
float slope;
explicit ReLUFunctor(float slope_=1.f) : slope(slope_) {}
bool supportBackend(int backendId, int)
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
#endif
#ifdef HAVE_DNN_NGRAPH
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_VKCOM;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
{
float s = slope;
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
int i = 0;
#if CV_SIMD128
v_float32x4 s4 = v_setall_f32(s), z = v_setzero_f32();
for( ; i <= len - 16; i += 16 )
{
v_float32x4 x0 = v_load(srcptr + i);
v_float32x4 x1 = v_load(srcptr + i + 4);
v_float32x4 x2 = v_load(srcptr + i + 8);
v_float32x4 x3 = v_load(srcptr + i + 12);
x0 = v_select(x0 >= z, x0, x0*s4);
x1 = v_select(x1 >= z, x1, x1*s4);
x2 = v_select(x2 >= z, x2, x2*s4);
x3 = v_select(x3 >= z, x3, x3*s4);
v_store(dstptr + i, x0);
v_store(dstptr + i + 4, x1);
v_store(dstptr + i + 8, x2);
v_store(dstptr + i + 12, x3);
}
#endif
for( ; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = x >= 0.f ? x : s*x;
}
}
}
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::ReLUOp>(target, stream, slope);
}
#endif
#ifdef HAVE_OPENCL
bool initKernel(ocl::Kernel &ker, const UMat &src) const
{
const char *buildoptSlope = (slope == 0) ? "-DRELU_NO_SLOPE" : "";
String buildopt = oclGetTMacro(src) + buildoptSlope;
if (!ker.create("ReLUForward", ocl::dnn::activations_oclsrc, buildopt))
return false;
if (slope != 0)
ker.set(3, (float)slope);
return true;
}
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
for (size_t i = 0; i < inputs.size(); i++)
{
UMat& src = inputs[i];
UMat& dst = outputs[i];
CV_Assert(src.isContinuous() && dst.isContinuous() && !src.offset && !dst.offset);
ocl::Kernel kernel;
CV_Assert(initKernel(kernel, src));
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)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
if (slope)
{
top(x, y, c, n) = select(input >= 0.0f, input, slope * input);
}
else
{
top(x, y, c, n) = max(input, 0.0f);
}
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(slope);
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
if (slope) {
auto param = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &slope);
return std::make_shared<ngraph::op::PRelu>(node, param);
}
return std::make_shared<ngraph::op::Relu>(node);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
std::shared_ptr<vkcom::OpBase> op(new vkcom::OpReLU(slope));
return op;
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
if (slope != 0.f)
{
float inpScale = scales[0][0], outScale = scales[1][0];
int inpZp = zeropoints[0][0], outZp = zeropoints[1][0];
Mat lookUpTable(1, 256, CV_8S);
int8_t* table = lookUpTable.ptr<int8_t>();
for (int i = -128; i < 128; i++)
{
float x = inpScale*(i - inpZp);
float y = x >= 0.f ? x : slope*x;
int quantized = outZp + (int)std::round(y/outScale);
table[i+128] = saturate_cast<int8_t>(quantized);
}
params.blobs.clear();
params.blobs.push_back(lookUpTable);
}
return true;
}
int64 getFLOPSPerElement() const { return 1; }
};
struct ReLU6Functor : public BaseFunctor
{
typedef ReLU6Layer Layer;
float minValue, maxValue;
ReLU6Functor(float minValue_ = 0.0f, float maxValue_ = 6.0f)
: minValue(minValue_), maxValue(maxValue_)
{
CV_Assert(minValue <= maxValue);
}
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
int i = 0;
#if CV_SIMD128
v_float32x4 minV = v_setall_f32(minValue), maxV = v_setall_f32(maxValue);
for( ; i <= len - 16; i += 16 )
{
v_float32x4 x0 = v_load(srcptr + i);
v_float32x4 x1 = v_load(srcptr + i + 4);
v_float32x4 x2 = v_load(srcptr + i + 8);
v_float32x4 x3 = v_load(srcptr + i + 12);
x0 = v_min(v_max(minV, x0), maxV);
x1 = v_min(v_max(minV, x1), maxV);
x2 = v_min(v_max(minV, x2), maxV);
x3 = v_min(v_max(minV, x3), maxV);
v_store(dstptr + i, x0);
v_store(dstptr + i + 4, x1);
v_store(dstptr + i + 8, x2);
v_store(dstptr + i + 12, x3);
}
#endif
for( ; i < len; i++ )
{
float x = srcptr[i];
if (x >= minValue)
dstptr[i] = x <= maxValue ? x : maxValue;
else
dstptr[i] = minValue;
}
}
}
#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("ReLU6Forward", 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)minValue);
kernel.set(4, (float)maxValue);
size_t gSize = src.total();
CV_Assert(kernel.run(1, &gSize, NULL, false));
}
return true;
}
#endif
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::ClippedReLUOp>(target, stream, minValue, maxValue);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = clamp(input, minValue, maxValue);
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ClampLayer("").setMinValue(minValue).setMaxValue(maxValue);
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Clamp>(node, minValue, maxValue);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
return true;
}
int64 getFLOPSPerElement() const { return 2; }
};
struct TanHFunctor : public BaseFunctor
{
typedef TanHLayer Layer;
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
for( int i = 0; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = tanh(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("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
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::TanHOp>(target, stream);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = tanh(input);
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::TanHLayer("");
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Tanh>(node);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
float inpScale = scales[0][0], outScale = scales[1][0];
int inpZp = zeropoints[0][0], outZp = zeropoints[1][0];
Mat lookUpTable(1, 256, CV_8S);
int8_t* table = lookUpTable.ptr<int8_t>();
for (int i = -128; i < 128; i++)
{
float x = inpScale*(i - inpZp);
float y = tanh(x);
int quantized = outZp + (int)std::round(y/outScale);
table[i+128] = saturate_cast<int8_t>(quantized);
}
params.blobs.clear();
params.blobs.push_back(lookUpTable);
return true;
}
int64 getFLOPSPerElement() const { return 1; }
};
struct SwishFunctor : public BaseFunctor
{
typedef SwishLayer Layer;
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
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
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
for( int i = 0; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = x / (1.0f + 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("SwishForward", 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_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::SwishOp>(target, stream);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = input / (1.0f + exp(-input));
}
#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)
{
auto sigmoid = std::make_shared<ngraph::op::Sigmoid>(node);
return std::make_shared<ngraph::op::v1::Multiply>(node, sigmoid);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
float inpScale = scales[0][0], outScale = scales[1][0];
int inpZp = zeropoints[0][0], outZp = zeropoints[1][0];
Mat lookUpTable(1, 256, CV_8S);
int8_t* table = lookUpTable.ptr<int8_t>();
for (int i = -128; i < 128; i++)
{
float x = inpScale*(i - inpZp);
float y = x / (1.0f + exp(-x));
int quantized = outZp + (int)std::round(y/outScale);
table[i+128] = saturate_cast<int8_t>(quantized);
}
params.blobs.clear();
params.blobs.push_back(lookUpTable);
return true;
}
int64 getFLOPSPerElement() const { return 3; }
};
struct MishFunctor : public BaseFunctor
{
typedef MishLayer Layer;
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
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
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
for( int i = 0; i < len; i++ )
{
// Use fast approximation introduced in https://github.com/opencv/opencv/pull/17200
float x = srcptr[i];
if (x >= 8.f)
dstptr[i] = x;
else
{
float eX = exp(x);
float n = (eX + 2) * eX;
dstptr[i] = (x * n) / (n + 2);
}
}
}
}
#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("MishForward", 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_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::MishOp>(target, stream);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = input * tanh(log(1.0f + exp(input)));
}
#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)
{
float one = 1.0f;
auto constant = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &one);
auto exp_node = std::make_shared<ngraph::op::v0::Exp>(node);
auto sum = std::make_shared<ngraph::op::v1::Add>(constant, exp_node, ngraph::op::AutoBroadcastType::NUMPY);
auto log_node = std::make_shared<ngraph::op::v0::Log>(sum);
auto tanh_node = std::make_shared<ngraph::op::Tanh>(log_node);
return std::make_shared<ngraph::op::v1::Multiply>(node, tanh_node);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
float inpScale = scales[0][0], outScale = scales[1][0];
int inpZp = zeropoints[0][0], outZp = zeropoints[1][0];
Mat lookUpTable(1, 256, CV_8S);
int8_t* table = lookUpTable.ptr<int8_t>();
for (int i = -128; i < 128; i++)
{
float x = inpScale*(i - inpZp);
float eX = exp(x);
float n = (eX + 2) * eX;
float y = (x * n) / (n + 2);
int quantized = outZp + (int)std::round(y/outScale);
table[i+128] = saturate_cast<int8_t>(quantized);
}
params.blobs.clear();
params.blobs.push_back(lookUpTable);
return true;
}
int64 getFLOPSPerElement() const { return 3; }
};
struct SigmoidFunctor : public BaseFunctor
{
typedef SigmoidLayer Layer;
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
for( int i = 0; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = 1.f/(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("SigmoidForward", 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_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::SigmoidOp>(target, stream);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = 1.0f / (1.0f + exp(-input));
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::SigmoidLayer("");
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Sigmoid>(node);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
float inpScale = scales[0][0], outScale = scales[1][0];
int inpZp = zeropoints[0][0], outZp = zeropoints[1][0];
Mat lookUpTable(1, 256, CV_8S);
int8_t* table = lookUpTable.ptr<int8_t>();
for (int i = -128; i < 128; i++)
{
float x = inpScale*(i - inpZp);
float y = 1.f/(1.f + exp(-x));
int quantized = outZp + (int)std::round(y/outScale);
table[i+128] = saturate_cast<int8_t>(quantized);
}
params.blobs.clear();
params.blobs.push_back(lookUpTable);
return true;
}
int64 getFLOPSPerElement() const { return 3; }
};
struct ELUFunctor : public BaseFunctor
{
typedef ELULayer Layer;
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
for(int i = 0; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = x >= 0.f ? x : exp(x) - 1;
}
}
}
#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("ELUForward", 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_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::ELUOp>(target, stream);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = select(input >= 0.0f, input, exp(input) - 1);
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ELULayer("");
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Elu>(node, 1.0);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
float inpScale = scales[0][0], outScale = scales[1][0];
int inpZp = zeropoints[0][0], outZp = zeropoints[1][0];
Mat lookUpTable(1, 256, CV_8S);
int8_t* table = lookUpTable.ptr<int8_t>();
for (int i = -128; i < 128; i++)
{
float x = inpScale*(i - inpZp);
float y = x >= 0.f ? x : exp(x) - 1;
int quantized = outZp + (int)std::round(y/outScale);
table[i+128] = saturate_cast<int8_t>(quantized);
}
params.blobs.clear();
params.blobs.push_back(lookUpTable);
return true;
}
int64 getFLOPSPerElement() const { return 2; }
};
struct AbsValFunctor : public BaseFunctor
{
typedef AbsLayer Layer;
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
for( int i = 0; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = abs(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("AbsValForward", 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_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::AbsValOp>(target, stream);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = abs(input);
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-0.999999f);
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
float coeff = -0.999999f;
// float coeff = preferableTarget == DNN_TARGET_MYRIAD ? -0.999f : -0.999999f;
auto slope = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeff);
return std::make_shared<ngraph::op::PRelu>(node, slope);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
float inpScale = scales[0][0], outScale = scales[1][0];
int inpZp = zeropoints[0][0], outZp = zeropoints[1][0];
Mat lookUpTable(1, 256, CV_8S);
int8_t* table = lookUpTable.ptr<int8_t>();
for (int i = -128; i < 128; i++)
{
float x = inpScale*(i - inpZp);
float y = abs(x);
int quantized = outZp + (int)std::round(y/outScale);
table[i+128] = saturate_cast<int8_t>(quantized);
}
params.blobs.clear();
params.blobs.push_back(lookUpTable);
return true;
}
int64 getFLOPSPerElement() const { return 1; }
};
struct BNLLFunctor : public BaseFunctor
{
typedef BNLLLayer Layer;
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) 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));
}
}
}
#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_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::BNLLOp>(target, stream);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
// https://github.com/BVLC/caffe/blame/1.0/src/caffe/layers/bnll_layer.cpp#L17
top(x, y, c, n) = max(input, 0) + log(1.0f + exp(-abs(input)));
}
#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
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
{
float inpScale = scales[0][0], outScale = scales[1][0];
int inpZp = zeropoints[0][0], outZp = zeropoints[1][0];
Mat lookUpTable(1, 256, CV_8S);
int8_t* table = lookUpTable.ptr<int8_t>();
for (int i = -128; i < 128; i++)
{
float x = inpScale*(i - inpZp);
float y = x > 0 ? x + log(1. + exp(-x)) : log(1. + exp(x));
int quantized = outZp + (int)std::round(y/outScale);
table[i+128] = saturate_cast<int8_t>(quantized);
}
params.blobs.clear();
params.blobs.push_back(lookUpTable);
return true;
}
int64 getFLOPSPerElement() const { return 5; }
};
struct PowerFunctor : public BaseFunctor
{
typedef PowerLayer Layer;
float power, scale, shift;
float originPower, originScale, originShift;
explicit PowerFunctor(float power_ = 1.f, float scale_ = 1.f, float shift_ = 0.f)
: power(power_), scale(scale_), shift(shift_),
originPower(power_), originScale(scale_), originShift(shift_) {}
bool supportBackend(int backendId, int targetId)
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return (targetId != DNN_TARGET_OPENCL && targetId != DNN_TARGET_OPENCL_FP16) || power == 1.0 || power == 0.5;
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
else
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE;
}
void finalize()
{
power = originPower;
scale = originScale;
shift = originShift;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
{
float a = scale, b = shift, p = power;
if( p == 1.f )
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
for( int i = 0; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = a*x + b;
}
}
}
else
{
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
for( int i = 0; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = pow(a*x + b, p);
}
}
}
}
#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("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
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::PowerOp>(target, stream, power, scale, shift);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Expr topExpr = (scale == 1.0f ? input : input * scale);
if (shift)
{
topExpr += shift;
}
if (power != 1.0f)
{
topExpr = pow(topExpr, power);
}
top(x, y, c, n) = topExpr;
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::PowerLayer("").setPower(power)
.setScale(scale)
.setShift(shift);
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
auto scale_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &scale);
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &shift);
auto mul = std::make_shared<ngraph::op::v1::Multiply>(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
if (power == 1)
return scale_shift;
auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &power);
return std::make_shared<ngraph::op::v1::Power>(scale_shift, power_node, ngraph::op::AutoBroadcastType::NUMPY);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
bool tryFuse(Ptr<dnn::Layer>& top)
{
if (power != 1.0f && shift != 0.0f)
return false;
Mat w, b;
top->getScaleShift(w, b);
if ((w.empty() && b.empty()) || w.total() > 1 || b.total() > 1)
return false;
float nextScale = w.empty() ? 1.0f : w.at<float>(0);
float nextShift = b.empty() ? 0.0f : b.at<float>(0);
scale = std::pow(scale, power) * nextScale;
shift = nextScale * shift + nextShift;
return true;
}
void getScaleShift(Mat& _scale, Mat& _shift) const
{
if (power == 1.0f)
{
_scale = Mat(1, 1, CV_32F, Scalar(scale));
_shift = Mat(1, 1, CV_32F, Scalar(shift));
}
}
int64 getFLOPSPerElement() const { return power == 1 ? 2 : 10; }
};
struct ExpFunctor : public BaseFunctor
{
typedef ExpLayer Layer;
float base, scale, shift;
float normScale, normShift;
ExpFunctor(float base_ = -1.f, float scale_ = 1.f, float shift_ = 0.f)
: base(base_), scale(scale_), shift(shift_)
{
// For base > 0 :
// y = base^(scale * input + shift)
// ln(y) = ln(base)*(scale * input + shift)
// y = exp((ln(base)*scale) * input + (ln(base)*shift))
// y = exp(normalized_scale * input + normalized_shift)
CV_Check(base, base == -1.f || base > 0.f, "Unsupported 'base' value");
const float ln_base = (base == -1.f) ? 1.f : log(base);
normScale = scale * ln_base;
normShift = shift * ln_base;
}
bool supportBackend(int backendId, int targetId)
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA ||
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
{
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);
}
}
}
#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("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;
}
#endif
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::ExpOp>(target, stream, normScale, normShift);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = exp(normScale * input + normShift);
}
#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)
{
auto scale_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &normScale);
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &normShift);
auto mul = std::make_shared<ngraph::op::v1::Multiply>(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
return std::make_shared<ngraph::op::v0::Exp>(scale_shift);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
int64 getFLOPSPerElement() const { return 3; }
};
struct ChannelsPReLUFunctor : public BaseFunctor
{
typedef ChannelsPReLULayer Layer;
Mat scale;
#ifdef HAVE_OPENCL
UMat scale_umat;
#endif
explicit ChannelsPReLUFunctor(const Mat& scale_=Mat()) : scale(scale_)
{
}
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
{
CV_Assert(scale.isContinuous() && scale.type() == CV_32F);
const float* scaleptr = scale.ptr<float>();
CV_Assert( 0 <= cn0 && cn0 < cn1 && cn1 <= (int)scale.total() );
for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
{
float s = scaleptr[cn];
int i = 0;
#if CV_SIMD128
v_float32x4 s4 = v_setall_f32(s), z = v_setzero_f32();
for( ; i <= len - 16; i += 16 )
{
v_float32x4 x0 = v_load(srcptr + i);
v_float32x4 x1 = v_load(srcptr + i + 4);
v_float32x4 x2 = v_load(srcptr + i + 8);
v_float32x4 x3 = v_load(srcptr + i + 12);
x0 = v_select(x0 >= z, x0, x0*s4);
x1 = v_select(x1 >= z, x1, x1*s4);
x2 = v_select(x2 >= z, x2, x2*s4);
x3 = v_select(x3 >= z, x3, x3*s4);
v_store(dstptr + i, x0);
v_store(dstptr + i + 4, x1);
v_store(dstptr + i + 8, x2);
v_store(dstptr + i + 12, x3);
}
#endif
for( ; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = x >= 0.f ? x : s*x;
}
}
}
#ifdef HAVE_OPENCL
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
if (scale_umat.empty())
scale.copyTo(scale_umat);
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
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
{
return make_cuda_node<cuda4dnn::ChannelwiseReLUOp>(target, stream, scale);
}
#endif
#ifdef HAVE_HALIDE
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
auto weights = wrapToHalideBuffer(scale, {(int)scale.total()});
top(x, y, c, n) = select(input >= 0.0f, input, weights(c) * input);
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
InferenceEngine::Builder::Layer l = InferenceEngine::Builder::PReLULayer("");
const size_t numChannels = scale.total();
addConstantData("weights", wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C), l);
return l;
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
const size_t numChannels = scale.total();
auto slope = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{numChannels}, scale.data);
return std::make_shared<ngraph::op::PRelu>(node, slope);
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_VULKAN
std::shared_ptr<vkcom::OpBase> initVkCom()
{
// TODO: add vkcom implementation
return std::shared_ptr<vkcom::OpBase>();
}
#endif // HAVE_VULKAN
int64 getFLOPSPerElement() const { return 1; }
};
#define ACTIVATION_CREATOR_FOR(_Layer, _Functor, ...) \
Ptr<_Layer> _Layer::create() { \
return return Ptr<_Layer>( new ElementWiseLayer<_Functor>(_Functor()) ); }
Ptr<ReLULayer> ReLULayer::create(const LayerParams& params)
{
float negativeSlope = params.get<float>("negative_slope", 0.f);
Ptr<ReLULayer> l(new ElementWiseLayer<ReLUFunctor>(ReLUFunctor(negativeSlope)));
l->setParamsFrom(params);
l->negativeSlope = negativeSlope;
return l;
}
Ptr<ReLU6Layer> ReLU6Layer::create(const LayerParams& params)
{
float minValue = params.get<float>("min_value", 0.0f);
float maxValue = params.get<float>("max_value", 6.0f);
Ptr<ReLU6Layer> l(new ElementWiseLayer<ReLU6Functor>(ReLU6Functor(minValue, maxValue)));
l->setParamsFrom(params);
l->minValue = minValue;
l->maxValue = maxValue;
return l;
}
Ptr<TanHLayer> TanHLayer::create(const LayerParams& params)
{
Ptr<TanHLayer> l(new ElementWiseLayer<TanHFunctor>());
l->setParamsFrom(params);
return l;
}
Ptr<SwishLayer> SwishLayer::create(const LayerParams& params)
{
Ptr<SwishLayer> l(new ElementWiseLayer<SwishFunctor>());
l->setParamsFrom(params);
return l;
}
Ptr<MishLayer> MishLayer::create(const LayerParams& params)
{
Ptr<MishLayer> l(new ElementWiseLayer<MishFunctor>());
l->setParamsFrom(params);
return l;
}
Ptr<SigmoidLayer> SigmoidLayer::create(const LayerParams& params)
{
Ptr<SigmoidLayer> l(new ElementWiseLayer<SigmoidFunctor>());
l->setParamsFrom(params);
return l;
}
Ptr<ELULayer> ELULayer::create(const LayerParams& params)
{
Ptr<ELULayer> l(new ElementWiseLayer<ELUFunctor>(ELUFunctor()));
l->setParamsFrom(params);
return l;
}
Ptr<AbsLayer> AbsLayer::create(const LayerParams& params)
{
Ptr<AbsLayer> l(new ElementWiseLayer<AbsValFunctor>());
l->setParamsFrom(params);
return l;
}
Ptr<BNLLLayer> BNLLLayer::create(const LayerParams& params)
{
Ptr<BNLLLayer> l(new ElementWiseLayer<BNLLFunctor>());
l->setParamsFrom(params);
return l;
}
Ptr<PowerLayer> PowerLayer::create(const LayerParams& params)
{
float power = params.get<float>("power", 1.0f);
float scale = params.get<float>("scale", 1.0f);
float shift = params.get<float>("shift", 0.0f);
Ptr<PowerLayer> l(new ElementWiseLayer<PowerFunctor>(PowerFunctor(power, scale, shift)));
l->setParamsFrom(params);
l->power = power;
l->scale = scale;
l->shift = shift;
return l;
}
Ptr<ExpLayer> ExpLayer::create(const LayerParams& params)
{
float base = params.get<float>("base", -1.0f);
float scale = params.get<float>("scale", 1.0f);
float shift = params.get<float>("shift", 0.0f);
Ptr<ExpLayer> l(new ElementWiseLayer<ExpFunctor>(ExpFunctor(base, scale, shift)));
l->setParamsFrom(params);
l->base = base;
l->scale = scale;
l->shift = shift;
return l;
}
Ptr<Layer> ChannelsPReLULayer::create(const LayerParams& params)
{
CV_Assert(params.blobs.size() == 1);
if (params.blobs[0].total() == 1)
{
LayerParams reluParams = params;
reluParams.set("negative_slope", *params.blobs[0].ptr<float>());
return ReLULayer::create(reluParams);
}
Ptr<ChannelsPReLULayer> l(new ElementWiseLayer<ChannelsPReLUFunctor>(ChannelsPReLUFunctor(params.blobs[0])));
l->setParamsFrom(params);
return l;
}
}
}
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