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nary_eltwise_layers.cpp
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nary_eltwise_layers.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_cuda.hpp"
#include "../op_cann.hpp"
#include "../ie_ngraph.hpp"
#include "../op_vkcom.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <algorithm>
#include <iterator>
#include <numeric>
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/eltwise.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv
{
namespace dnn
{
namespace {
static int _mod(int x, int y) {
int res = x % y;
if ((res < 0 && y > 0) || (res > 0 && y < 0)) {
res += y;
}
return res;
}
}
class NaryEltwiseHelper CV_FINAL
{
public:
int ninputs;
int narrays;
int max_ndims;
std::vector<int> all_ndims;
std::vector<std::vector<int>> orig_shapes;
std::vector<std::vector<size_t>> orig_steps;
std::vector<char*> ptrs;
std::vector<std::vector<int>> shapes;
std::vector<std::vector<size_t>> steps;
std::vector<size_t> elemsize;
NaryEltwiseHelper() {
}
void init(const std::vector<Mat>& inputs, const std::vector<Mat>& outputs)
{
narrays = 0;
max_ndims = 0;
all_ndims.clear();
orig_shapes.clear();
orig_steps.clear();
ptrs.clear();
shapes.clear();
steps.clear();
elemsize.clear();
ninputs = inputs.size();
narrays = ninputs + 1;
// collect ndims
std::vector<int> v_inp_dims;
std::transform(inputs.begin(), inputs.end(), std::back_inserter(v_inp_dims), [] (const Mat& m) { return m.dims; });
const int* inp_ndims = v_inp_dims.data();
int out_ndims = outputs[0].dims;
// find max ndims for broadcasting
int i;
max_ndims = out_ndims > 2 ? out_ndims : 2;
for(i = 0; i < ninputs; i++)
max_ndims = max_ndims > inp_ndims[i] ? max_ndims : inp_ndims[i];
shapes = std::vector<std::vector<int>>(narrays, std::vector<int>(max_ndims, 0));
steps = std::vector<std::vector<size_t>>(narrays, std::vector<size_t>(max_ndims, 0));
ptrs = std::vector<char*>(narrays, nullptr);
for(i = 0; i <= ninputs; i++) {
all_ndims.push_back(i == 0 ? out_ndims : inp_ndims[i-1]);
std::vector<int> _size;
std::vector<size_t> _step;
if (!i) {
std::transform(outputs[0].size.p, outputs[0].size.p + outputs[0].dims, std::back_inserter(_size), [](int s) { return s; });
std::transform(outputs[0].step.p, outputs[0].step.p + outputs[0].dims, std::back_inserter(_step), [](size_t s) { return s; });
}
else {
std::transform(inputs[i-1].size.p, inputs[i-1].size.p + inputs[i-1].dims, std::back_inserter(_size), [](int s) { return s; });
std::transform(inputs[i-1].step.p, inputs[i-1].step.p + inputs[i-1].dims, std::back_inserter(_step), [](size_t s) { return s; });
}
orig_shapes.push_back(_size);
orig_steps.push_back(_step);
int esz = i == 0 ? outputs[0].elemSize() : inputs[i - 1].elemSize();
elemsize.push_back(esz);
}
}
void reInit(size_t newElemSize) {
std::vector<size_t> newElemSizes(elemsize.size(), newElemSize);
reInit(newElemSizes);
}
void reInit(std::vector<size_t> newElemSizes) {
for (size_t array_index = 0; array_index < orig_steps.size(); array_index++) {
auto &step = orig_steps[array_index];
int esz = elemsize[array_index];
int new_esz = newElemSizes[array_index];
for (size_t step_index = 0; step_index < step.size(); step_index++) {
step[step_index] = static_cast<size_t>(step[step_index] / esz * new_esz);
}
elemsize[array_index] = newElemSizes[array_index];
}
prepare_for_broadcast_op();
}
bool prepare_for_broadcast_op()
{
int i, j, k;
// step 1.
// * make all inputs and the output max_ndims-dimensional.
// ** prepend dimension 1 to the mat of less dims
// * compute proper step's
for (i = this->max_ndims-1; i >= 0; i--) {
for (k = 0; k < this->narrays; k++) {
j = this->all_ndims[k] - (this->max_ndims - i);
int sz_i = j >= 0 ? this->orig_shapes[k][j] : 1;
size_t st_i = j >= 0 && this->orig_steps[k][j] > 0 ? this->orig_steps[k][j] :
i == this->max_ndims-1 ? elemsize[k] : this->steps[k][i+1]*this->shapes[k][i+1];
assert(st_i % elemsize[k] == 0);
this->shapes[k][i] = sz_i;
this->steps[k][i] = st_i;
if (this->shapes[k][i] == 0)
return false;
}
}
// step 3. Let's do the flattening first,
// since we'd need proper values of steps to check continuity.
// this loop is probably the most tricky part
// in the whole implementation of broadcasting.
j = this->max_ndims-1;
for (i = j - 1; i >= 0; i--) {
bool all_contiguous = true, all_scalars = true, all_consistent = true;
for(k = 0; k < this->narrays; k++) {
size_t st = this->steps[k][j]*this->shapes[k][j];
bool prev_scalar = this->shapes[k][j] == 1;
bool scalar = this->shapes[k][i] == 1;
all_contiguous = all_contiguous && (st == this->steps[k][i]);
all_scalars = all_scalars && scalar;
all_consistent = all_consistent && (scalar == prev_scalar);
}
if (all_contiguous && (all_consistent || all_scalars)) {
for(k = 0; k < this->narrays; k++)
this->shapes[k][j] *= this->shapes[k][i];
} else {
j--;
if (i < j) {
for(k = 0; k < this->narrays; k++) {
this->shapes[k][j] = this->shapes[k][i];
this->steps[k][j] = this->steps[k][i];
}
}
}
}
// step 2. Set some step's to 0's.
for (i = this->max_ndims-1; i >= j; i--) {
for (k = 0; k < this->narrays; k++)
this->steps[k][i] = this->shapes[k][i] == 1 ? 0 : this->steps[k][i];
}
for (; i >= 0; i--) {
for (k = 0; k < this->narrays; k++) {
this->steps[k][i] = 0;
this->shapes[k][i] = 1;
}
}
return true;
}
};
class NaryEltwiseLayerImpl CV_FINAL : public NaryEltwiseLayer
{
NaryEltwiseHelper helper;
public:
enum class OPERATION
{
AND = 0,
EQUAL,
GREATER,
GREATER_EQUAL,
LESS,
LESS_EQUAL,
OR,
POW,
XOR,
BITSHIFT,
MAX,
MEAN,
MIN,
MOD, // Integer Mod. Reminder's sign = Divisor's sign.
FMOD, // Floating-point Mod. Reminder's sign = Dividend's sign.
PROD,
SUB,
SUM,
ADD,
DIV,
WHERE,
} op;
NaryEltwiseLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
String operation = toLowerCase(params.get<String>("operation", "sum"));
if (operation == "equal")
op = OPERATION::EQUAL;
else if (operation == "greater")
op = OPERATION::GREATER;
else if (operation == "greaterorequal")
op = OPERATION::GREATER_EQUAL;
else if (operation == "less")
op = OPERATION::LESS;
else if (operation == "lessorequal")
op = OPERATION::LESS_EQUAL;
else if (operation == "pow")
op = OPERATION::POW;
else if (operation == "bitshift")
op = OPERATION::BITSHIFT;
else if (operation == "max")
op = OPERATION::MAX;
else if (operation == "mean")
op = OPERATION::MEAN;
else if (operation == "min")
op = OPERATION::MIN;
else if (operation == "mod")
op = OPERATION::MOD;
else if (operation == "fmod")
op = OPERATION::FMOD;
else if (operation == "mul")
op = OPERATION::PROD;
else if (operation == "sub")
op = OPERATION::SUB;
else if (operation == "sum")
op = OPERATION::SUM;
else if (operation == "add")
op = OPERATION::ADD;
else if (operation == "div")
op = OPERATION::DIV;
else if (operation == "and")
op = OPERATION::AND;
else if (operation == "or")
op = OPERATION::OR;
else if (operation == "xor")
op = OPERATION::XOR;
else if (operation == "where")
op = OPERATION::WHERE;
else
CV_Error(cv::Error::StsBadArg, "Unknown operation type \"" + operation + "\"");
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_CANN
if (backendId == DNN_BACKEND_CANN)
return op == OPERATION::ADD || op == OPERATION::PROD || op == OPERATION::SUB ||
op == OPERATION::DIV || op == OPERATION::MAX || op == OPERATION::MIN ||
op == OPERATION::MOD || op == OPERATION::FMOD;
#endif
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return (op == OPERATION::ADD ||
op == OPERATION::PROD ||
op == OPERATION::GREATER_EQUAL ||
op == OPERATION::LESS_EQUAL ||
op == OPERATION::MOD ||
op == OPERATION::FMOD
);
#ifdef HAVE_VULKAN
if (backendId == DNN_BACKEND_VKCOM)
return op == OPERATION::ADD || op == OPERATION::PROD || op == OPERATION::SUB ||
op == OPERATION::DIV ;
#endif
if (backendId == DNN_BACKEND_CUDA) {
return op == OPERATION::MAX || op == OPERATION::MIN || op == OPERATION::SUM ||
op == OPERATION::PROD || op == OPERATION::DIV || op == OPERATION::ADD ||
op == OPERATION::SUB || op == OPERATION::MOD || op == OPERATION::FMOD;
}
return backendId == DNN_BACKEND_OPENCV;
}
static MatShape findCommonShape(std::vector<MatShape> shapes)
{
CV_Assert(!shapes.empty());
const size_t dim = std::max_element(shapes.begin(), shapes.end(),
[](const MatShape& a, const MatShape& b)
{ return a.size() < b.size(); })->size();
for (auto& shape : shapes)
{
shape.insert(shape.begin(), dim - shape.size(), 1);
}
MatShape outShape(dim, 1);
for (size_t i = 0; i < dim; ++i)
{
for (const auto& shape : shapes)
{
if (shape[i] != outShape[i])
{
CV_Assert(shape[i] == 1 || outShape[i] == 1);
outShape[i] = std::max(outShape[i], shape[i]);
}
}
}
return outShape;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE {
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
helper.init(inputs, outputs);
CV_Assert(helper.prepare_for_broadcast_op());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
MatShape outShape = findCommonShape(inputs);
outputs.assign(1, outShape);
return false;
}
template <typename T, typename Functor>
void binary_forward_impl(
int ndims, const std::vector<int>& shape,
const char* data1, const std::vector<size_t>& step1,
const char* data2, const std::vector<size_t>& step2,
char* data, const std::vector<size_t>& step,
const Functor& op)
{
assert(ndims >= 2);
size_t dp1 = step1[ndims-1]/sizeof(T);
size_t dp2 = step2[ndims-1]/sizeof(T);
size_t dp = step[ndims-1]/sizeof(T);
int k, n1 = shape[ndims-1], n2 = shape[ndims-2];
size_t plane_idx, nplanes = 1;
for (k = 0; k < ndims-2; k++) nplanes *= shape[k];
for (plane_idx = 0; plane_idx < nplanes; plane_idx++) {
const char* ptr1_ = data1;
const char* ptr2_ = data2;
char* ptr_ = data;
size_t idx = plane_idx;
for (k = ndims-3; k >= 0; k--) {
size_t next_idx = idx/shape[k];
int i_k = (int)(idx - next_idx*shape[k]);
ptr1_ += i_k*step1[k];
ptr2_ += i_k*step2[k];
ptr_ += i_k*step[k];
idx = next_idx;
}
for (int i2 = 0; i2 < n2; i2++, ptr1_ += step1[ndims-2],
ptr2_ += step2[ndims-2],
ptr_ += step[ndims-2])
{
const T* ptr1 = (const T*)ptr1_;
const T* ptr2 = (const T*)ptr2_;
T* ptr = (T*)ptr_;
if (dp1 == 1 && dp2 == 1 && dp == 1) {
for(int i1 = 0; i1 < n1; i1++)
ptr[i1] = op(ptr1[i1], ptr2[i1]);
} else if (dp1 == 1 && dp2 == 0 && dp == 1){
T x2 = *ptr2;
for(int i1 = 0; i1 < n1; i1++)
ptr[i1] = op(ptr1[i1], x2);
} else if (dp1 == 0 && dp2 == 1 && dp == 1){
T x1 = *ptr1;
for(int i1 = 0; i1 < n1; i1++)
ptr[i1] = op(x1, ptr2[i1]);
} else {
for(int i1 = 0; i1 < n1; i1++, ptr1 += dp1, ptr2 += dp2, ptr += dp)
*ptr = op(*ptr1, *ptr2);
}
}
}
}
template <typename T, typename Functor>
void binary_forward(const Functor& f, const std::vector<Mat>& inputs, std::vector<Mat>& outputs)
{
const Mat& a = inputs[0];
const Mat& b = inputs[1];
Mat& out = outputs[0];
CV_Assert(helper.shapes.size() == 3 && helper.steps.size() == 3);
binary_forward_impl<T, Functor>(
helper.max_ndims, helper.shapes[0], a.ptr<char>(), helper.steps[1],
b.ptr<char>(), helper.steps[2], out.ptr<char>(), helper.steps[0],
f);
}
template<typename T, typename Functor>
void nary_forward_impl(
const Functor& f, const T scale, int ninputs, int ndims, const std::vector<int>& shape,
const char** inp, char* out,
const std::vector<std::vector<size_t>>& steps, std::vector<char*>& ptrs)
{
CV_Assert(ndims >= 2);
size_t dp = steps[0][ndims-1]/sizeof(T);
size_t dp1 = steps[1][ndims-1]/sizeof(T);
size_t dp2 = steps[2][ndims-1]/sizeof(T);
enum { BLOCK_SIZE = 1024 };
T blck[BLOCK_SIZE];
int k, i, di1=0, n1 = shape[ndims-1], n2 = shape[ndims-2];
int second = ninputs == 1 ? 1 : 2;
size_t plane_idx, nplanes = 1;
for (k = 0; k < ndims-2; k++) nplanes *= shape[k];
for (plane_idx = 0; plane_idx < nplanes; plane_idx++) {
ptrs[0] = out;
for (i = 0; i < ninputs; i++) ptrs[i+1] = (char*)inp[i];
size_t idx = plane_idx;
for (k = ndims-3; k >= 0; k--) {
size_t next_idx = idx/shape[k];
int i_k = (int)(idx - next_idx*shape[k]);
for (i = 0; i < ninputs; i++)
ptrs[i] += i_k*steps[i][k];
idx = next_idx;
}
for (int i2 = 0; i2 < n2; i2++)
{
const T* ptr1 = (const T*)(ptrs[1] + steps[1][ndims-2]*i2);
const T* ptr2 = (const T*)(ptrs[second] + steps[second][ndims-2]*i2);
T* ptr = (T*)(ptrs[0] + steps[0][ndims-2]*i2);
if (ninputs <= 2) {
if (dp1 == 1 && dp2 == 1) {
for (int i1 = 0; i1 < n1; i1++)
ptr[i1] = saturate_cast<T>(f(ptr1[i1], ptr2[i1])*scale);
} else {
for(int i1 = 0; i1 < n1; i1++, ptr1 += dp1, ptr2 += dp2, ptr += dp)
*ptr = saturate_cast<T>(f(*ptr1, *ptr2)*scale);
}
} else {
for (int i1 = 0; i1 < n1; i1 += di1, ptr += di1) {
di1 = BLOCK_SIZE < n1-i1 ? BLOCK_SIZE : n1-i1;
if (dp1 == 1 && dp2 == 1) {
for (int j = 0; j < di1; j++)
blck[j] = f(ptr1[j], ptr2[j]);
ptr1 += di1;
ptr2 += di1;
} else {
for(int j = 0; j < di1; j++, ptr1 += dp1, ptr2 += dp2)
blck[j] = f(*ptr1, *ptr2);
}
for(i = 2; i < ninputs; i++) {
int dp_i = steps[i+1][ndims-1]/sizeof(T);
const T* ptr_i = (const T*)(ptrs[i+1] +
steps[i+1][ndims-2]*i2) + i1*dp_i;
if (dp_i == 1) {
if (i < ninputs-1) {
for (int j = 0; j < di1; j++)
blck[j] = f(blck[j], ptr_i[j]);
} else {
for (int j = 0; j < di1; j++)
ptr[j] = saturate_cast<T>(f(blck[j], ptr_i[j]) * scale);
}
} else {
if (i < ninputs-1) {
for (int j = 0; j < di1; j++, ptr_i += dp_i)
blck[j] = f(blck[j], *ptr_i);
} else {
for (int j = 0; j < di1; j++, ptr_i += dp_i)
ptr[j] = saturate_cast<T>(f(blck[j], *ptr_i) * scale);
}
}
}
}
}
}
}
}
template <typename T, typename Functor>
void nary_forward(
const Functor& f, T scale,
const std::vector<Mat>& inputs, std::vector<Mat>& outputs
)
{
// collect all input info
std::vector<const char*> v_inp;
std::transform(inputs.begin(), inputs.end(), std::back_inserter(v_inp), [] (const Mat& m) { return m.template ptr<const char>(); });
const char** inp = v_inp.data();
// collect output info
char* out = outputs[0].ptr<char>();
nary_forward_impl<T>(
f, scale, helper.ninputs, helper.max_ndims, helper.shapes[0], inp, out, helper.steps, helper.ptrs);
}
template <typename T, typename Functor>
void trinary_forward(const Functor& f, const std::vector<Mat>& inputs, std::vector<Mat>& outputs)
{
const Mat& a = inputs[0];
const Mat& b = inputs[1];
const Mat& c = inputs[2];
Mat& out = outputs[0];
CV_Assert(helper.shapes.size() == 4 && helper.steps.size() == 4);
trinary_forward_impl<T, Functor>(
helper.max_ndims, helper.shapes[0], a.ptr<char>(), helper.steps[1], b.ptr<char>(), helper.steps[2],
c.ptr<char>(), helper.steps[3], out.ptr<char>(), helper.steps[0],
f);
}
template <typename T, typename Functor>
void trinary_forward_impl(
int ndims, const std::vector<int>& shape,
const char* data1, const std::vector<size_t>& step1,
const char* data2, const std::vector<size_t>& step2,
const char* data3, const std::vector<size_t>& step3,
char* data, const std::vector<size_t>& step,
const Functor& op)
{
assert(ndims >= 2);
size_t dp1 = step1[ndims-1]/sizeof(T);
size_t dp2 = step2[ndims-1]/sizeof(T);
size_t dp3 = step3[ndims-1]/sizeof(T);
size_t dp = step[ndims-1]/sizeof(T);
int k, n1 = shape[ndims-1], n2 = shape[ndims-2];
size_t plane_idx, nplanes = 1;
for (k = 0; k < ndims-2; k++) nplanes *= shape[k];
for (plane_idx = 0; plane_idx < nplanes; plane_idx++)
{
const char* ptr1_ = data1;
const char* ptr2_ = data2;
const char* ptr3_ = data3;
char* ptr_ = data;
size_t idx = plane_idx;
for (k = ndims-3; k >= 0; k--)
{
size_t next_idx = idx/shape[k];
int i_k = (int)(idx - next_idx*shape[k]);
ptr1_ += i_k*step1[k];
ptr2_ += i_k*step2[k];
ptr3_ += i_k*step3[k];
ptr_ += i_k*step[k];
idx = next_idx;
}
for (int i2 = 0; i2 < n2; i2++, ptr1_ += step1[ndims-2],
ptr2_ += step2[ndims-2],
ptr3_ += step3[ndims-2],
ptr_ += step[ndims-2])
{
const T* ptr1 = (const T*)ptr1_;
const T* ptr2 = (const T*)ptr2_;
const T* ptr3 = (const T*)ptr3_;
T* ptr = (T*)ptr_;
if (dp1 == 1 && dp2 == 1 && dp3 == 1 && dp == 1)
{
for(int i1 = 0; i1 < n1; i1++)
ptr[i1] = op(ptr1[i1], ptr2[i1], ptr3[i1]);
}
else
{
for(int i1 = 0; i1 < n1; i1++, ptr1 += dp1, ptr2 += dp2, ptr3 += dp3, ptr += dp)
*ptr = op(*ptr1, *ptr2, *ptr3);
}
}
}
}
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());
if (inputs_arr.depth() == CV_16F)
{
helper.reInit(sizeof(float));
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
// TODO: assert types
typeDispatch(outputs[0].type(), inputs.size(), inputs, outputs);
}
template<typename T, typename... Args>
inline void opDispatch(size_t ninputs, Args&&... args)
{
switch (op)
{
case OPERATION::EQUAL:
{
auto equal = [](const T &a, const T &b) { return a == b; };
binary_forward<T>(equal, std::forward<Args>(args)...);
break;
}
case OPERATION::GREATER:
{
auto greater = [](const T &a, const T &b) { return a > b; };
binary_forward<T>(greater, std::forward<Args>(args)...);
break;
}
case OPERATION::GREATER_EQUAL:
{
auto greater_equal = [](const T &a, const T &b) { return a >= b; };
binary_forward<T>(greater_equal, std::forward<Args>(args)...);
break;
}
case OPERATION::LESS:
{
auto less = [](const T &a, const T &b) { return a < b; };
binary_forward<T>(less, std::forward<Args>(args)...);
break;
}
case OPERATION::LESS_EQUAL:
{
auto less_equal = [](const T &a, const T &b) { return a <= b; };
binary_forward<T>(less_equal, std::forward<Args>(args)...);
break;
}
case OPERATION::POW:
{
auto pow = [] (const T& a, const T& b) { return std::pow(a, b); };
binary_forward<T>(pow, std::forward<Args>(args)...);
break;
}
case OPERATION::BITSHIFT:
{
auto bitshift = [] (const uint8_t &a, const uint8_t &b) { return a << b; };
binary_forward<T>(bitshift, std::forward<Args>(args)...);
break;
}
case OPERATION::MAX:
{
auto max = [](const T &a, const T &b) { return std::max(a, b); };
nary_forward<T>(max, T{1}, std::forward<Args>(args)...);
break;
}
case OPERATION::MEAN:
{
auto mean = [](const T &a, const T &b) { return (a + b) / T{2}; };
nary_forward<T>(mean, T{1} / ninputs, std::forward<Args>(args)...);
break;
}
case OPERATION::MIN:
{
auto min = [](const T &a, const T &b) { return std::min(a, b); };
nary_forward<T>(min, T{1}, std::forward<Args>(args)...);
break;
}
case OPERATION::MOD:
{
auto mod = [] (const T &a, const T &b) { return static_cast<T>(_mod(int(a), int(b))); };
binary_forward<T>(mod, std::forward<Args>(args)...);
break;
}
case OPERATION::FMOD:
{
auto fmod = [](const T &a, const T &b) { return std::fmod(a, b); };
binary_forward<T>(fmod, std::forward<Args>(args)...);
break;
}
case OPERATION::PROD:
{
auto prod = [](const T &a, const T &b) { return a * b; };
binary_forward<T>(prod, std::forward<Args>(args)...);
break;
}
case OPERATION::SUB:
{
auto sub = [](const T &a, const T &b) { return a - b; };
binary_forward<T>(sub, std::forward<Args>(args)...);
break;
}
case OPERATION::SUM:
{
auto sum = [](const T &a, const T &b) { return a + b; };
nary_forward<T>(sum, T{1}, std::forward<Args>(args)...);
break;
}
case OPERATION::ADD:
{
auto add = [](const T &a, const T &b) { return a + b; };
binary_forward<T>(add, std::forward<Args>(args)...);
break;
}
case OPERATION::DIV:
{
auto div = [](const T &a, const T &b) { return a / b; };
binary_forward<T>(div, std::forward<Args>(args)...);
break;
}
case OPERATION::AND:
{
auto op_and = [](const uint8_t &a, const uint8_t &b) { return a & b; };
binary_forward<T>(op_and, std::forward<Args>(args)...);
break;
}
case OPERATION::OR:
{
auto op_or = [](const uint8_t &a, const uint8_t &b) { return a | b; };
binary_forward<T>(op_or, std::forward<Args>(args)...);
break;
}
case OPERATION::XOR:
{
auto op_xor = [](const uint8_t &a, const uint8_t &b) { return a ^ b; };
binary_forward<T>(op_xor, std::forward<Args>(args)...);
break;
}
case OPERATION::WHERE:
{
auto op_where = [](const T &a, const T &b, const T &c) { return a ? b : c; };
trinary_forward<T>(op_where, std::forward<Args>(args)...);
break;
}
default:
CV_Error(Error::StsBadArg, "Unsupported operation.");
};
}
template<typename... Args>
inline void typeDispatch(const int type, Args&&... args)
{
switch (type)
{
case CV_8U:
// TODO: integrate with type inference
helper.reInit(sizeof(uint8_t));
opDispatch<uint8_t>(std::forward<Args>(args)...);
break;
case CV_32S:
// TODO: integrate with type inference
helper.reInit(sizeof(int32_t));
opDispatch<int32_t>(std::forward<Args>(args)...);
break;
case CV_32F:
CV_Assert(op != OPERATION::BITSHIFT && op != OPERATION::AND &&
op != OPERATION::OR && op != OPERATION::XOR);
opDispatch<float>(std::forward<Args>(args)...);
break;
default:
CV_Error(cv::Error::BadDepth, "Unsupported type.");
};
}
#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_);
cuda4dnn::EltwiseOpType op_ = cuda4dnn::EltwiseOpType::SUM;
switch (op) {
case OPERATION::MAX:
op_ = cuda4dnn::EltwiseOpType::MAX;
break;
case OPERATION::MIN:
op_ = cuda4dnn::EltwiseOpType::MIN;
break;
case OPERATION::SUM:
op_ = cuda4dnn::EltwiseOpType::SUM;
break;
case OPERATION::PROD:
op_ = cuda4dnn::EltwiseOpType::PRODUCT;
break;
case OPERATION::DIV:
op_ = cuda4dnn::EltwiseOpType::DIV;
break;
case OPERATION::ADD:
op_ = cuda4dnn::EltwiseOpType::SUM;
break;
case OPERATION::SUB:
op_ = cuda4dnn::EltwiseOpType::SUB;
break;
case OPERATION::MOD:
op_ = cuda4dnn::EltwiseOpType::MOD;
break;
case OPERATION::FMOD:
op_ = cuda4dnn::EltwiseOpType::FMOD;
break;
default: return Ptr<BackendNode>(); // return empty cuda_node if the EltwiseOpType is unsupported type.
};
return make_cuda_node<cuda4dnn::EltwiseOp>(preferableTarget, std::move(context->stream), op_, std::vector<float>());
}
#endif
#ifdef HAVE_CANN
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendWrapper> > &outputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(inputs.size() == 2);
CV_Assert(nodes.size() == 2);
auto op_x1 = nodes[0].dynamicCast<CannBackendNode>()->getOp();
auto x1 = inputs[0].dynamicCast<CannBackendWrapper>();
auto x1_desc = x1->getTensorDesc();
auto op_x2 = nodes[1].dynamicCast<CannBackendNode>()->getOp();
auto x2 = inputs[1].dynamicCast<CannBackendWrapper>();
auto x2_desc = x2->getTensorDesc();
auto output_desc = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
std::shared_ptr<ge::Operator> eltwise_operator = nullptr;
// add, mul, sub, div, max, min
switch (op)
{
#define BUILD_CANN_ELTWISE_OP(op_type, class_name, op_name) \
case op_type: { \
auto eltwise_op = \
std::make_shared<ge::op::class_name>(op_name); \
eltwise_op->set_input_x1_by_name(*op_x1, x1->name.c_str()); \
eltwise_op->set_input_x2_by_name(*op_x2, x2->name.c_str()); \
eltwise_op->update_input_desc_x1(*x1_desc); \
eltwise_op->update_input_desc_x2(*x2_desc); \
eltwise_op->update_output_desc_y(*output_desc); \
eltwise_operator = eltwise_op; \
} break;
BUILD_CANN_ELTWISE_OP(OPERATION::ADD, Add, name);
BUILD_CANN_ELTWISE_OP(OPERATION::PROD, Mul, name);
BUILD_CANN_ELTWISE_OP(OPERATION::SUB, Sub, name);
BUILD_CANN_ELTWISE_OP(OPERATION::DIV, Xdivy, name);
BUILD_CANN_ELTWISE_OP(OPERATION::MAX, Maximum, name);
BUILD_CANN_ELTWISE_OP(OPERATION::MIN, Minimum, name);
BUILD_CANN_ELTWISE_OP(OPERATION::MOD, Mod, name);
BUILD_CANN_ELTWISE_OP(OPERATION::FMOD, Mod, name);
#undef BUILD_CANN_ELTWISE_OP
default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
}
return Ptr<BackendNode>(new CannBackendNode(eltwise_operator));
}
#endif // HAVE_CANN
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
{
return false;
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_Assert(inputs.size());
return inputs.size() * total(outputs[0]);
}
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(inputs.size() == 2);
auto& inp0 = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto& inp1 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
if (inp0.get_element_type() != inp1.get_element_type()) {
auto dtype = preferableTarget == DNN_TARGET_OPENCL_FP16 || preferableTarget == DNN_TARGET_MYRIAD ?
ov::element::f16 : ov::element::f32;
if (inp0.get_element_type() != dtype)
inp0 = std::make_shared<ov::op::v0::Convert>(inp0, dtype);
if (inp1.get_element_type() != dtype)
inp1 = std::make_shared<ov::op::v0::Convert>(inp1, dtype);
}
std::shared_ptr<ov::Node> node;
if (op == OPERATION::ADD)
node = std::make_shared<ov::op::v1::Add>(inp0, inp1);
else if (op == OPERATION::PROD)
node = std::make_shared<ov::op::v1::Multiply>(inp0, inp1);
else if (op == OPERATION::GREATER_EQUAL)
node = std::make_shared<ov::op::v1::GreaterEqual>(inp0, inp1);
else if (op == OPERATION::LESS_EQUAL)
node = std::make_shared<ov::op::v1::LessEqual>(inp0, inp1);
// Ideally we should do this but int32 internal blobs are converted to float32 data type in inference.
// TODO: Remove data type convertion when we have type inference.
else if (op == OPERATION::MOD) {
auto inp0_i64 = std::make_shared<ov::op::v0::Convert>(inp0, ov::element::i64);
auto inp1_i64 = std::make_shared<ov::op::v0::Convert>(inp1, ov::element::i64);
auto mod = std::make_shared<ov::op::v1::FloorMod>(inp0_i64, inp1_i64);
node = std::make_shared<ov::op::v0::Convert>(mod, ov::element::f32);
}
else if (op == OPERATION::FMOD)
node = std::make_shared<ov::op::v1::Mod>(inp0, inp1);
else
CV_Error(Error::StsNotImplemented, "Operation is not implemented for nGraph backend");
return Ptr<BackendNode>(new InfEngineNgraphNode(node));
}
#endif
#ifdef HAVE_VULKAN
virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> > &inputs,
std::vector<Ptr<BackendWrapper> > &outputs) CV_OVERRIDE
{
Ptr<vkcom::OpBase> op = makePtr<vkcom::OpNary>((vkcom::OpNary::OPERATION) this->op, helper.ninputs, helper.max_ndims, helper.shapes, helper.steps);
return Ptr<BackendNode>(makePtr<VkComBackendNode>(inputs, op, outputs));
}
#endif
};
Ptr<NaryEltwiseLayer> NaryEltwiseLayer::create(const LayerParams& params)
{
return Ptr<NaryEltwiseLayer>(new NaryEltwiseLayerImpl(params));
}
}
}