/*******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Oleh Semeniv (oleg.semeniv@gmail.com)
// @author AbdelRauf (rauf@konduit.ai)
//
#include <ops/declarable/helpers/adjust_hue.h>
#include <ops/declarable/helpers/imagesHelpers.h>
#include <helpers/ConstantTadHelper.h>
#include <execution/Threads.h>
namespace sd {
namespace ops {
namespace helpers {
template <typename T>
static void rgbToGrs_(const NDArray& input, NDArray& output, const int dimC) {
const T* x = input.bufferAsT<T>();
T* z = output.bufferAsT<T>();
const int rank = input.rankOf();
if(dimC == rank - 1 && 'c' == input.ordering() && 1 == input.ews() &&
'c' == output.ordering() && 1 == output.ews()){
auto func = PRAGMA_THREADS_FOR{
for (auto i = start; i < stop; i++) {
const auto xStep = i*3;
z[i] = 0.2989f*x[xStep] + 0.5870f*x[xStep + 1] + 0.1140f*x[xStep + 2];
}
};
samediff::Threads::parallel_for(func, 0, output.lengthOf(), 1);
return;
}
auto func = PRAGMA_THREADS_FOR{
int coords[MAX_RANK];
for (auto i = start; i < stop; i++) {
shape::index2coordsCPU(start, i, output.shapeInfo(), coords);
const auto zOffset = shape::getOffset(output.shapeInfo(), coords);
const auto xOffset0 = shape::getOffset(input.shapeInfo(), coords);
const auto xOffset1 = xOffset0 + input.strideAt(dimC);
const auto xOffset2 = xOffset1 + input.strideAt(dimC);
z[zOffset] = 0.2989f*x[xOffset0] + 0.5870f*x[xOffset1] + 0.1140f*x[xOffset2];
}
};
samediff::Threads::parallel_for(func, 0, output.lengthOf(), 1);
return;
}
void transformRgbGrs(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) {
BUILD_SINGLE_SELECTOR(input.dataType(), rgbToGrs_, (input, output, dimC), NUMERIC_TYPES);
}
template <typename T, typename Op>
FORCEINLINE static void rgbToFromYuv_(const NDArray& input, NDArray& output, const int dimC, Op op) {
const T* x = input.bufferAsT<T>();
T* z = output.bufferAsT<T>();
const int rank = input.rankOf();
bool bSimple = (dimC == rank - 1 && 'c' == input.ordering() && 1 == input.ews() &&
'c' == output.ordering() && 1 == output.ews());
if (bSimple) {
auto func = PRAGMA_THREADS_FOR{
for (auto i = start; i < stop; i += increment) {
op(x[i], x[i + 1], x[i + 2], z[i], z[i + 1], z[i + 2]);
}
};
samediff::Threads::parallel_for(func, 0, input.lengthOf(), 3);
return;
}
auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(input.shapeInfo(), dimC);
auto packZ = sd::ConstantTadHelper::getInstance().tadForDimensions(output.shapeInfo(), dimC);
const Nd4jLong numOfTads = packX.numberOfTads();
const Nd4jLong xDimCstride = input.stridesOf()[dimC];
const Nd4jLong zDimCstride = output.stridesOf()[dimC];
auto func = PRAGMA_THREADS_FOR{
for (auto i = start; i < stop; i++) {
const T* xTad = x + packX.platformOffsets()[i];
T* zTad = z + packZ.platformOffsets()[i];
op(xTad[0], xTad[xDimCstride], xTad[2 * xDimCstride], zTad[0], zTad[zDimCstride], zTad[2 * zDimCstride]);
}
};
samediff::Threads::parallel_tad(func, 0, numOfTads);
return;
}
template <typename T>
FORCEINLINE static void rgbYuv_(const NDArray& input, NDArray& output, const int dimC) {
auto op = sd::ops::helpers::rgbYuv<T>;
return rgbToFromYuv_<T>(input, output, dimC, op);
}
void transformRgbYuv(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) {
BUILD_SINGLE_SELECTOR(input.dataType(), rgbYuv_, (input, output, dimC), FLOAT_TYPES);
}
template <typename T>
FORCEINLINE static void yuvRgb_(const NDArray& input, NDArray& output, const int dimC) {
auto op = sd::ops::helpers::yuvRgb<T>;
return rgbToFromYuv_<T>(input, output, dimC, op);
}
void transformYuvRgb(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) {
BUILD_SINGLE_SELECTOR(input.dataType(), yuvRgb_, (input, output, dimC), FLOAT_TYPES);
}
template <typename T, typename Op>
FORCEINLINE static void tripleTransformer(const NDArray* input, NDArray* output, const int dimC, Op op) {
const int rank = input->rankOf();
const T* x = input->bufferAsT<T>();
T* z = output->bufferAsT<T>();
if (dimC == rank - 1 && input->ews() == 1 && output->ews() == 1 && input->ordering() == 'c' && output->ordering() == 'c') {
auto func = PRAGMA_THREADS_FOR{
for (auto i = start; i < stop; i += increment) {
op(x[i], x[i + 1], x[i + 2], z[i], z[i + 1], z[i + 2]);
}
};
samediff::Threads::parallel_for(func, 0, input->lengthOf(), 3);
}
else {
auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimC);
auto packZ = sd::ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimC);
const Nd4jLong numOfTads = packX.numberOfTads();
const Nd4jLong xDimCstride = input->stridesOf()[dimC];
const Nd4jLong zDimCstride = output->stridesOf()[dimC];
auto func = PRAGMA_THREADS_FOR{
for (auto i = start; i < stop; i++) {
const T* xTad = x + packX.platformOffsets()[i];
T* zTad = z + packZ.platformOffsets()[i];
op(xTad[0], xTad[xDimCstride], xTad[2 * xDimCstride], zTad[0], zTad[zDimCstride], zTad[2 * zDimCstride]);
}
};
samediff::Threads::parallel_tad(func, 0, numOfTads);
}
}
template <typename T>
FORCEINLINE static void tripleTransformer(const NDArray* input, NDArray* output, const int dimC , T (&tr)[3][3] ) {
const int rank = input->rankOf();
const T* x = input->bufferAsT<T>();
T* z = output->bufferAsT<T>();
// TODO: Use tensordot or other optimizied helpers to see if we can get better performance.
if (dimC == rank - 1 && input->ews() == 1 && output->ews() == 1 && input->ordering() == 'c' && output->ordering() == 'c') {
auto func = PRAGMA_THREADS_FOR{
for (auto i = start; i < stop; i += increment) {
//simple M*v //tr.T*v.T // v * tr //rule: (AB)' =B'A'
// v.shape (1,3) row vector
T x0, x1, x2;
x0 = x[i]; //just additional hint
x1 = x[i + 1];
x2 = x[i + 2];
z[i] = x0 * tr[0][0] + x1 * tr[1][0] + x2 * tr[2][0];
z[i+1] = x0 * tr[0][1] + x1 * tr[1][1] + x2 * tr[2][1];
z[i+2] = x0 * tr[0][2] + x1 * tr[1][2] + x2 * tr[2][2];
}
};
samediff::Threads::parallel_for(func, 0, input->lengthOf(), 3);
}
else {
auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimC);
auto packZ = sd::ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimC);
const Nd4jLong numOfTads = packX.numberOfTads();
const Nd4jLong xDimCstride = input->stridesOf()[dimC];
const Nd4jLong zDimCstride = output->stridesOf()[dimC];
auto func = PRAGMA_THREADS_FOR{
for (auto i = start; i < stop; i++) {
const T* xTad = x + packX.platformOffsets()[i];
T* zTad = z + packZ.platformOffsets()[i];
//simple M*v //tr.T*v
T x0, x1, x2;
x0 = xTad[0];
x1 = xTad[xDimCstride];
x2 = xTad[2 * xDimCstride];
zTad[0] = x0 * tr[0][0] + x1 * tr[1][0] + x2 * tr[2][0];
zTad[zDimCstride] = x0 * tr[0][1] + x1 * tr[1][1] + x2 * tr[2][1];
zTad[2 * zDimCstride] = x0 * tr[0][2] + x1 * tr[1][2] + x2 * tr[2][2];
}
};
samediff::Threads::parallel_tad(func, 0, numOfTads);
}
}
template <typename T>
FORCEINLINE static void hsvRgb(const NDArray* input, NDArray* output, const int dimC) {
auto op = sd::ops::helpers::hsvToRgb<T>;
return tripleTransformer<T>(input, output, dimC, op);
}
template <typename T>
FORCEINLINE static void rgbHsv(const NDArray* input, NDArray* output, const int dimC) {
auto op = sd::ops::helpers::rgbToHsv<T>;
return tripleTransformer<T>(input, output, dimC, op);
}
template <typename T>
FORCEINLINE static void rgbYiq(const NDArray* input, NDArray* output, const int dimC) {
T arr[3][3] = {
{ (T)0.299, (T)0.59590059, (T)0.2115 },
{ (T)0.587, (T)-0.27455667, (T)-0.52273617 },
{ (T)0.114, (T)-0.32134392, (T)0.31119955 }
};
return tripleTransformer<T>(input, output, dimC, arr);
}
template <typename T>
FORCEINLINE static void yiqRgb(const NDArray* input, NDArray* output, const int dimC) {
//TODO: this operation does not use the clamp operation, so there is a possibility being out of range.
//Justify that it will not be out of range for images data
T arr[3][3] = {
{ (T)1, (T)1, (T)1 },
{ (T)0.95598634, (T)-0.27201283, (T)-1.10674021 },
{ (T)0.6208248, (T)-0.64720424, (T)1.70423049 }
};
return tripleTransformer<T>(input, output, dimC, arr);
}
void transformHsvRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) {
BUILD_SINGLE_SELECTOR(input->dataType(), hsvRgb, (input, output, dimC), FLOAT_TYPES);
}
void transformRgbHsv(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) {
BUILD_SINGLE_SELECTOR(input->dataType(), rgbHsv, (input, output, dimC), FLOAT_TYPES);
}
void transformYiqRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) {
BUILD_SINGLE_SELECTOR(input->dataType(), yiqRgb, (input, output, dimC), FLOAT_TYPES);
}
void transformRgbYiq(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) {
BUILD_SINGLE_SELECTOR(input->dataType(), rgbYiq, (input, output, dimC), FLOAT_TYPES);
}
}
}
}