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opencv/modules/dnn/src/layers/eltwise_layer.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// are permitted provided that the following conditions are met:
//
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// this list of conditions and the following disclaimer.
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// 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.
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//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 <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/eltwise.hpp"
#include "../cuda4dnn/primitives/shortcut.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv
{
namespace dnn
{
class EltwiseLayerImpl CV_FINAL : public EltwiseLayer
{
public:
enum EltwiseOp
{
PROD = 0,
SUM = 1,
MAX = 2,
DIV = 3
} op;
std::vector<float> coeffs;
enum OutputChannelsMode
{
ELTWISE_CHANNNELS_SAME = 0, //!< number of channels from inputs must be the same and equal to output's number of channels
ELTWISE_CHANNNELS_INPUT_0, //!< number of channels from inputs may be different,
//!< output's number of channels is equal to number of channels of first input
//!< number of channels of other inputs should not be greater than number of channels of first input
ELTWISE_CHANNNELS_INPUT_0_TRUNCATE, //!< number of channels from inputs may be different,
//!< output's number of channels is equal to number of channels of first input
//!< there is restriction on number of channels of other inputs
//!< extra channels of other inputs is ignored
ELTWISE_CHANNNELS_USE_MAX, //!< number of channels from inputs may be different,
//!< output's number of channels is equal to maximal number of input channels
//!< @note supported operation: `SUM`
} channelsModeInput;
mutable OutputChannelsMode channelsMode; //!< "optimized" channels mode (switch to ELTWISE_CHANNNELS_SAME if number of input channels are equal)
mutable /*size_t*/int outputChannels;
EltwiseLayerImpl(const LayerParams& params)
: outputChannels(0)
{
setParamsFrom(params);
hasVecInput = false;
op = SUM;
if (params.has("operation"))
{
String operation = toLowerCase(params.get<String>("operation"));
if (operation == "prod")
op = PROD;
else if (operation == "sum")
op = SUM;
else if (operation == "max")
op = MAX;
else if (operation == "div")
op = DIV;
else
CV_Error(cv::Error::StsBadArg, "Unknown operation type \"" + operation + "\"");
}
if (params.has("coeff"))
{
DictValue paramCoeff = params.get("coeff");
int i, n = paramCoeff.size();
coeffs.resize(n);
for (i = 0; i < n; i++)
{
coeffs[i] = paramCoeff.get<float>(i);
}
}
channelsModeInput = ELTWISE_CHANNNELS_SAME;
if (params.has("output_channels_mode"))
{
String v = toLowerCase(params.get<String>("output_channels_mode"));
if (v == "same")
{
channelsModeInput = ELTWISE_CHANNNELS_SAME;
}
else if (v == "input_0")
{
channelsModeInput = ELTWISE_CHANNNELS_INPUT_0;
}
else if (v == "input_0_truncate")
{
channelsModeInput = ELTWISE_CHANNNELS_INPUT_0_TRUNCATE;
}
else if (v == "max_input_channels")
{
channelsModeInput = ELTWISE_CHANNNELS_USE_MAX;
if (op != SUM)
CV_Error(cv::Error::StsBadArg, "[" + type + "]:(" + name + ") 'max' channels mode is limited to SUM operation only");
}
else
CV_Error(cv::Error::StsBadArg, "[" + type + "]:(" + name + ") unknown channels mode: \"" + v + "\"");
}
channelsMode = channelsModeInput;
// TODO Must have checks for other unknown options
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (hasVecInput && ELTWISE_CHANNNELS_SAME)
return backendId == DNN_BACKEND_OPENCV;
if (backendId == DNN_BACKEND_CUDA)
{
if(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 || channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
return op == SUM && coeffs.empty();
return channelsModeInput == ELTWISE_CHANNNELS_SAME;
}
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && op != DIV) || // TODO: not implemented, see PR #15811
((((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()))
|| backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && channelsMode == ELTWISE_CHANNNELS_SAME));
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() >= 2);
CV_Assert(inputs[0].size() >= 2);
CV_Assert(coeffs.size() == 0 || coeffs.size() == inputs.size());
CV_Assert(op == SUM || coeffs.size() == 0);
int dims = inputs[0].size();
// Number of channels in output shape is determined by the first input tensor.
bool variableChannels = false;
int numChannels = inputs[0][1];
for (size_t i = 1; i < inputs.size(); i++)
{
CV_Assert(inputs[0][0] == inputs[i][0]); // batch sizes are equal
int input_channels = inputs[i][1];
if (numChannels != input_channels)
variableChannels = true;
if (channelsModeInput == ELTWISE_CHANNNELS_SAME)
{
CV_Assert(numChannels == input_channels);
}
else if (channelsModeInput == ELTWISE_CHANNNELS_INPUT_0)
{
CV_Assert(numChannels >= input_channels);
}
else if (channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
{
// nothing to check
}
else if (channelsModeInput == ELTWISE_CHANNNELS_USE_MAX)
{
numChannels = std::max(numChannels, input_channels);
}
else
{
CV_Assert(0 && "Internal error");
}
}
channelsMode = variableChannels ? channelsModeInput : ELTWISE_CHANNNELS_SAME;
outputChannels = numChannels;
outputs.assign(1, inputs[0]);
outputs[0][1] = numChannels;
if (dims > 2)
{
size_t vecIdx = 0;
bool isVecFound = false;
for (size_t i = 0; i < inputs.size(); i++)
{
bool allOnes = isAllOnes(inputs[i], 2, dims);
if (!allOnes && !isVecFound)
{
vecIdx = i;
isVecFound = true;
}
if (!allOnes && i != vecIdx)
{
for (size_t j = 2; j < dims; j++)
{
CV_Assert(inputs[vecIdx][j] == inputs[i][j]);
}
}
}
if (channelsModeInput == ELTWISE_CHANNNELS_SAME && isVecFound)
{
for (size_t j = 2; j < dims; j++)
{
outputs[0][j] = inputs[vecIdx][j];
}
}
}
return false;
}
void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
for (size_t i = 0; i < inputs.size(); i++)
{
MatShape inpShape = shape(inputs[i].size);
if (isAllOnes(inpShape, 2, inputs[i].dims))
{
hasVecInput = true;
return;
}
}
}
class EltwiseInvoker : public ParallelLoopBody
{
EltwiseLayerImpl& self;
std::vector<const Mat*> srcs;
std::vector<int> srcNumChannels;
int nsrcs;
Mat* dst;
std::vector<float> coeffs;
int nstripes;
const ActivationLayer* activ;
int channels;
size_t planeSize;
EltwiseInvoker(EltwiseLayerImpl& self_)
: self(self_)
, nsrcs(0), dst(0), nstripes(0), activ(0), channels(0)
, planeSize(0)
{}
public:
static void run(EltwiseLayerImpl& self,
const Mat* srcs, int nsrcs, Mat& dst,
int nstripes)
{
const EltwiseOp op = self.op;
CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 5, ""); CV_CheckTypeEQ(dst.type(), CV_32FC1, ""); CV_Assert(dst.isContinuous());
CV_Assert(self.coeffs.empty() || self.coeffs.size() == (size_t)nsrcs);
CV_CheckGE(nsrcs, 2, "");
CV_Assert(self.outputChannels == dst.size[1]);
EltwiseInvoker p(self);
p.srcs.resize(nsrcs);
p.srcNumChannels.resize(nsrcs);
p.coeffs = self.coeffs; // can be sorted
bool sortInputs = false;
for( int i = 0; i < nsrcs; i++ )
{
p.srcs[i] = &srcs[i];
CV_CheckEQ(srcs[i].dims, dst.dims, "");
CV_Assert(srcs[i].isContinuous());
CV_Assert(srcs[i].type() == dst.type());
p.srcNumChannels[i] = (srcs[i].dims >= 4) ? srcs[i].size[1] : 1;
if (self.channelsMode == ELTWISE_CHANNNELS_SAME)
{
CV_Assert(srcs[i].size == dst.size);
}
else if (self.channelsMode == ELTWISE_CHANNNELS_INPUT_0)
{
if (i == 0)
CV_Assert(srcs[0].size == dst.size);
CV_Assert(self.outputChannels >= p.srcNumChannels[i]);
sortInputs = true;
}
else if (self.channelsMode == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
{
if (i == 0)
CV_Assert(srcs[0].size == dst.size);
sortInputs = true;
}
else if (self.channelsMode == ELTWISE_CHANNNELS_USE_MAX)
{
CV_Assert(op == SUM);
CV_Assert(self.outputChannels >= p.srcNumChannels[i]);
sortInputs = true;
}
else
{
CV_Assert(0 && "Internal error");
}
if (sortInputs)
{
// Sort srcs and coefficients in the desc order by number of channels
for (int j = i; j >= 1; j--)
{
if (std::min(self.outputChannels, p.srcs[j - 1]->size[1]) < std::min(self.outputChannels, p.srcs[j]->size[1]))
{
std::swap(p.srcs[j - 1], p.srcs[j]);
std::swap(p.srcNumChannels[j - 1], p.srcNumChannels[j]);
if (!p.coeffs.empty())
std::swap(p.coeffs[j - 1], p.coeffs[j]);
}
else
break;
}
}
}
p.nsrcs = nsrcs;
p.dst = &dst;
p.nstripes = nstripes;
p.channels = (dst.dims >= 4 ? dst.size[1] : 1);
p.planeSize = dst.total(dst.dims >= 4 ? 2 : 1);
CV_CheckEQ(dst.total(), dst.size[0] * p.channels * p.planeSize, "");
bool simpleCoeffs = true;
if (op == SUM && !p.coeffs.empty())
{
CV_CheckEQ(p.coeffs.size(), (size_t)nsrcs, "");
for (size_t i = 0; i < p.coeffs.size(); i++)
{
if (p.coeffs[i] != 1)
{
simpleCoeffs = false;
break;
}
}
}
if (simpleCoeffs)
p.coeffs.clear();
p.activ = self.activ.get();
parallel_for_(Range(0, nstripes), p, nstripes);
}
void operator()(const Range& r) const CV_OVERRIDE
{
const EltwiseOp op = self.op;
size_t total = dst->size[0]*planeSize;
size_t stripeSize = (total + nstripes - 1)/nstripes;
size_t stripeStart = r.start*stripeSize;
size_t stripeEnd = std::min(r.end*stripeSize, total);
const float* coeffsptr = !coeffs.empty() ? &coeffs[0] : 0;
float* dstptr0 = dst->ptr<float>();
int blockSize0 = 1 << 12;
for (size_t ofs = stripeStart; ofs < stripeEnd; )
{
int sampleIdx = (int)(ofs / planeSize);
int delta = (int)ofs - sampleIdx * planeSize;
int blockSize = std::min(blockSize0, std::min((int)(stripeEnd - ofs), (int)planeSize - delta));
if( blockSize <= 0 )
break;
ofs += blockSize;
for (int c = 0; c < channels; c++)
{
size_t dstIdx = delta + (sampleIdx*channels + c)*planeSize;
float* dstptr = dstptr0 + dstIdx;
// process first two inputs
{
const float* srcptr0 = srcs[0]->ptr<float>() + dstIdx;
const int inputIdx = 1;
int src1_channels = srcNumChannels[inputIdx];
if (c >= src1_channels)
{
// no data from second input
if (!coeffsptr || coeffsptr[0] == 1.0f)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = srcptr0[j];
}
}
else
{
float c0 = coeffsptr[0];
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = c0*srcptr0[j];
}
}
}
else
{
size_t srcIdx = delta + (sampleIdx * src1_channels + c) * planeSize;
const float* srcptrI = srcs[inputIdx]->ptr<float>() + srcIdx;
if (op == PROD)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = srcptr0[j] * srcptrI[j];
}
}
else if (op == DIV)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = srcptr0[j] / srcptrI[j];
}
}
else if (op == MAX)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = std::max(srcptr0[j], srcptrI[j]);
}
}
else if (op == SUM)
{
if (!coeffsptr || (coeffsptr[0] == 1.0f && coeffsptr[1] == 1.0f))
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = srcptr0[j] + srcptrI[j];
}
}
else
{
float c0 = coeffsptr[0];
float c1 = coeffsptr[1];
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = c0*srcptr0[j] + c1*srcptrI[j];
}
}
}
else
CV_Error(Error::StsInternal, "");
}
}
// aggregate other inputs (3+)
for (size_t inputIdx = 2; inputIdx < nsrcs; inputIdx++)
{
int srcI_channels = srcNumChannels[inputIdx];
if (c >= srcI_channels)
continue; // no data from second input
size_t srcIdx = delta + (sampleIdx * srcI_channels + c) * planeSize;
const float* srcptrI = srcs[inputIdx]->ptr<float>() + srcIdx;
if (op == PROD)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] *= srcptrI[j];
}
}
else if (op == DIV)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] /= srcptrI[j];
}
}
else if (op == MAX)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = std::max(dstptr[j], srcptrI[j]);
}
}
else if (op == SUM)
{
if (!coeffsptr || coeffsptr[inputIdx] == 1.0f)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] += srcptrI[j];
}
}
else
{
float cI = coeffsptr[inputIdx];
for (int j = 0; j < blockSize; j++)
{
dstptr[j] += cI * srcptrI[j];
}
}
}
else
CV_Error(Error::StsInternal, "");
}
}
if( activ )
{
float* ptr = dstptr0 + delta + sampleIdx*channels*planeSize;
activ->forwardSlice(ptr, ptr, blockSize, planeSize, 0, channels);
}
}
}
};
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
if ((inputs_.depth() == CV_16S && op != SUM) || (channelsMode != ELTWISE_CHANNNELS_SAME))
return false;
if (hasVecInput)
return false; // TODO not implemented yet: https://github.com/opencv/opencv/pull/19477
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
switch (op)
{
case SUM:
{
int channels = total(shape(outputs[0]), 0, 2);
int plane_size = total(shape(outputs[0]), 2);
if (channels % 4 == 0 && plane_size % 4 == 0)
{
size_t localsize[] = { 128 };
size_t globalsize[] = { (size_t)channels / 4 * localsize[0] };
String opts;
if (inputs_.depth() == CV_16S)
opts = " -DDtype=half -DDtype4=half4 -DDtype8=half8";
else
opts = " -DDtype=float -DDtype4=float4 -DDtype8=float8";
for (int i = 0; i < (inputs.size() - 1); ++i)
{
String buildopt = format("-DLOOP=%d", i) + opts;
ocl::Kernel kernel("op_sum4", ocl::dnn::eltwise_oclsrc, buildopt);
int idx = 0;
UMat inpMat = (i == 0) ? inputs[0] : UMat();
float coeff1 = (coeffs.empty() || i > 0) ? 1.0f : coeffs[i];
float coeff2 = coeffs.empty() ? 1.0f : coeffs[i + 1];
kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[0]));
kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[1]));
kernel.set(idx++, (int)plane_size);
kernel.set(idx++, (float)coeff1);
kernel.set(idx++, (float)coeff2);
kernel.set(idx++, ocl::KernelArg::PtrReadWrite(outputs[0]));
bool ret = kernel.run(1, globalsize, localsize, false);
if (!ret)
return false;
}
}
else
{
if (inputs_.depth() == CV_16S)
return false;
float coeff1 = coeffs.empty() ? 1.f : coeffs[0];
float coeff2 = coeffs.empty() ? 1.f : coeffs[1];
UMat mul0, mul1;
multiply(coeff1, inputs[0], mul0);
multiply(coeff2, inputs[1], mul1);
add(mul0, mul1, outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
{
float coeff = coeffs.empty() ? 1.f : coeffs[i];
multiply(coeff, inputs[i], mul0);
add(mul0, outputs[0], outputs[0]);
}
}
}
break;
case PROD:
multiply(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
multiply(inputs[i], outputs[0], outputs[0]);
break;
case DIV:
divide(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
divide(outputs[0], inputs[i], outputs[0]);
break;
case MAX:
max(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
max(inputs[i], outputs[0], outputs[0]);
break;
default:
return false;
}
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
if (inputs_arr.depth() == CV_16S)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
CV_Assert(outputs.size() == 1);
const int nstripes = getNumThreads();
if (channelsModeInput == ELTWISE_CHANNNELS_SAME && inputs[0].dims > 2)
{
for (size_t i = 0; i < inputs.size(); i++)
{
MatShape inpShape = shape(inputs[i].size);
bool allOnes = isAllOnes(inpShape, 2, inputs[i].dims);
if (allOnes)
{
Mat tmpInput = inputs[i];
MatShape outShape = shape(outputs[0].size);
size_t xSize = outShape[2];
for (size_t j = 3; j < outShape.size(); j++)
xSize *= outShape[j];
int dimVec[3] = {outShape[0], outShape[1], (int) xSize};
std::vector<int> matSizesVec(&dimVec[0], &dimVec[0] + 3);
inputs[i] = Mat(matSizesVec, tmpInput.type());
std::vector<int> idx(outShape.size(), 0);
std::vector<int> outIdx(inpShape.size(), 0);
for (size_t j = 0; j < outShape[0]; j++)
{
outIdx[0] = idx[0] = j;
for(size_t k = 0; k < outShape[1]; k++)
{
outIdx[1] = idx[1] = k;
for (size_t x = 0; x < xSize; x++)
{
outIdx[2] = x;
inputs[i].at<float>(outIdx.data()) = tmpInput.at<float>(idx.data());
}
}
}
inputs[i] = inputs[i].reshape(0, outShape);
}
}
}
EltwiseInvoker::run(*this,
&inputs[0], (int)inputs.size(), outputs[0],
nstripes);
}
#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_);
CV_Assert(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 ||
channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE ||
channelsModeInput == ELTWISE_CHANNNELS_SAME);
if(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 || channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
{
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
for (int i = 1; i < inputs.size(); i++)
{
auto from_wrapper = inputs[i].dynamicCast<CUDABackendWrapper>();
if (input_wrapper->getShape()[1] != from_wrapper->getShape()[1])
{
CV_Assert(op == SUM);
CV_Assert(coeffs.empty());
return make_cuda_node<cuda4dnn::ShortcutOp>(preferableTarget, std::move(context->stream));
}
}
}
auto op_ = [this] {
switch (op) {
case MAX: return cuda4dnn::EltwiseOpType::MAX;
case SUM: return cuda4dnn::EltwiseOpType::SUM;
case PROD: return cuda4dnn::EltwiseOpType::PRODUCT;
case DIV: return cuda4dnn::EltwiseOpType::DIV;
}
return cuda4dnn::EltwiseOpType::SUM;
}();
return make_cuda_node<cuda4dnn::EltwiseOp>(preferableTarget, std::move(context->stream), op_, coeffs);
}
#endif
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input) CV_OVERRIDE
{
#ifdef HAVE_HALIDE
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
Halide::Expr topExpr;
std::vector<Halide::Buffer<> > inputBuffers = halideBuffers(input);
switch (op)
{
case SUM:
if (coeffs.empty())
{
topExpr = inputBuffers[0](x, y, c, n) +
inputBuffers[1](x, y, c, n);
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr += inputBuffers[i](x, y, c, n);
}
else
{
topExpr = coeffs[0] * inputBuffers[0](x, y, c, n) +
coeffs[1] * inputBuffers[1](x, y, c, n);
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr += coeffs[i] * inputBuffers[i](x, y, c, n);
}
break;
case PROD:
topExpr = inputBuffers[0](x, y, c, n) *
inputBuffers[1](x, y, c, n);
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr *= inputBuffers[i](x, y, c, n);
break;
case DIV:
topExpr = inputBuffers[0](x, y, c, n) /
inputBuffers[1](x, y, c, n);
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr /= inputBuffers[i](x, y, c, n);
break;
case MAX:
topExpr = max(inputBuffers[0](x, y, c, n),
inputBuffers[1](x, y, c, n));
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr = max(topExpr, inputBuffers[i](x, y, c, n));
break;
default:
return Ptr<BackendNode>();
}
top(x, y, c, n) = topExpr;
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> >& inputs) CV_OVERRIDE
{
InferenceEngine::Builder::EltwiseLayer ieLayer(name);
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
if (op == SUM)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::SUM);
else if (op == PROD)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MUL);
else if (op == DIV)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::DIV);
else if (op == MAX)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MAX);
else
CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
InferenceEngine::Builder::Layer l = ieLayer;
if (!coeffs.empty())
l.getParameters()["coeff"] = coeffs;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#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 curr_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
if (!coeffs.empty()) {
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[0]);
curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
}
for (size_t i = 1; i < nodes.size(); i++)
{
auto next_node = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
if (!coeffs.empty()) {
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[i]);
next_node = std::make_shared<ngraph::op::v1::Multiply>(next_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
}
switch (op) {
case SUM: curr_node = std::make_shared<ngraph::op::v1::Add>(curr_node, next_node); break;
case PROD: curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, next_node); break;
case DIV: curr_node = std::make_shared<ngraph::op::v1::Divide>(curr_node, next_node); break;
case MAX: curr_node = std::make_shared<ngraph::op::v1::Maximum>(curr_node, next_node); break;
default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
}
}
return Ptr<BackendNode>(new InfEngineNgraphNode(curr_node));
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_UNUSED(outputs); // suppress unused variable warning
CV_Assert(inputs.size());
// FIXIT: handle inputs with different number of channels
long flops = inputs.size() * total(inputs[0]);
return flops;
}
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
if (activ.empty() || layer.empty())
{
activ = layer;
return !activ.empty();
}
else
return false;
}
Ptr<ActivationLayer> activ;
private:
bool hasVecInput;
};
Ptr<EltwiseLayer> EltwiseLayer::create(const LayerParams& params)
{
return Ptr<EltwiseLayer>(new EltwiseLayerImpl(params));
}
}
}
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