cavis/libnd4j/include/ops/declarable/helpers/cpu/adjust_hue.cpp

197 lines
6.6 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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 raver119@gmail.com
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <ops/declarable/helpers/adjust_hue.h>
#include <helpers/ConstantTadHelper.h>
#include <execution/Threads.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
static void adjustHue_(const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC) {
const T delta = deltaScalarArr->e<T>(0);
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) {
T h, s, v;
rgbToHsv<T>(x[i], x[i + 1], x[i + 2], h, s, v);
h += delta ;
if (h > (T)1)
h -= (T)1;
else if (h < 0)
h += (T)1;
hsvToRgb<T>(h, s, v, z[i], z[i + 1], z[i + 2]);
}
};
samediff::Threads::parallel_for(func, 0, input->lengthOf(), 3);
}
else {
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), 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];
T h, s, v;
rgbToHsv<T>(xTad[0], xTad[xDimCstride], xTad[2 * xDimCstride], h, s, v);
h += delta ;
if (h > (T)1)
h -= (T)1;
else if (h < 0)
h += (T)1;
hsvToRgb<T>(h, s, v, zTad[0], zTad[zDimCstride], zTad[2 * zDimCstride]);
}
};
samediff::Threads::parallel_tad(func, 0, numOfTads);
}
}
void adjustHue(nd4j::LaunchContext* context, const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC) {
BUILD_SINGLE_SELECTOR(input->dataType(), adjustHue_, (input, deltaScalarArr, output, dimC), FLOAT_TYPES);
}
/*
template <typename T>
static void adjust_hue_single_(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) {
// we're 100% sure it's 3
const int numChannels = 3;
int tuples = array->lengthOf() / numChannels;
auto bIn = reinterpret_cast<T *>(array->buffer());
auto bOut = reinterpret_cast<T *>(output->buffer());
static const int kChannelRange = 6;
int stridesDim = isNHWC ? 2 : 0;
if (isNHWC) {
// for NHWC our rgb values are stored one by one
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < tuples; e++) {
auto i = bIn + e * numChannels;
auto o = bOut + e * numChannels;
T h, v_min, v_max;
helpers::rgb_to_hv(i[0], i[1], i[2], &h, &v_min, &v_max);
h += delta * kChannelRange;
while (h < (T) 0.)
h += (T) kChannelRange;
while (h >= (T) kChannelRange)
h -= (T) kChannelRange;
helpers::hv_to_rgb(h, v_min, v_max, o, o + 1, o + 2);
}
} else {
auto tadsChannelsIn = array->allTensorsAlongDimension({0});
auto tadsChannelsOut = output->allTensorsAlongDimension( {0});
auto bufferR = reinterpret_cast<T *>(tadsChannelsIn->at(0)->buffer());
auto bufferG = reinterpret_cast<T *>(tadsChannelsIn->at(1)->buffer());
auto bufferB = reinterpret_cast<T *>(tadsChannelsIn->at(2)->buffer());
auto outputR = reinterpret_cast<T *>(tadsChannelsOut->at(0)->buffer());
auto outputG = reinterpret_cast<T *>(tadsChannelsOut->at(1)->buffer());
auto outputB = reinterpret_cast<T *>(tadsChannelsOut->at(2)->buffer());
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < tuples; e++) {
auto _ri = bufferR + e;
auto _gi = bufferG + e;
auto _bi = bufferB + e;
auto _ro = outputR + e;
auto _go = outputG + e;
auto _bo = outputB + e;
T h, v_min, v_max;
helpers::rgb_to_hv(_ri[0], _gi[0], _bi[0], &h, &v_min, &v_max);
h += delta * kChannelRange;
while (h < (T) 0)
h += (T) kChannelRange;
while (h >= (T) kChannelRange)
h -= (T) kChannelRange;
helpers::hv_to_rgb(h, v_min, v_max, _ro, _go, _bo);
}
delete tadsChannelsIn;
delete tadsChannelsOut;
}
}
void adjust_hue_(nd4j::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) {
auto xType = array->dataType();
float d = delta->e<float>(0);
if (array->rankOf() == 4) {
auto tadsIn = array->allTensorsAlongDimension({0});
auto tadsOut = output->allTensorsAlongDimension({0});
int tSize = tadsIn->size();
// FIXME: template selector should be moved out of loop
PRAGMA_OMP_PARALLEL_FOR
for (int e = 0; e < tSize; e++) {
BUILD_SINGLE_SELECTOR(xType, adjust_hue_single_, (context, tadsIn->at(e), tadsOut->at(e), d, isNHWC);, FLOAT_TYPES);
}
delete tadsIn;
delete tadsOut;
} else {
BUILD_SINGLE_SELECTOR(xType, adjust_hue_single_, (context, array, output, d, isNHWC);, FLOAT_TYPES);
}
}
BUILD_SINGLE_TEMPLATE(template void adjust_hue_single_, (nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC);, FLOAT_TYPES);
*/
}
}
}