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Legacy API changes (#441)
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* one more initial commit

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* Refactored buffer() and shapeInfo() methods usage with NDArray class.

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* Adopt Graph class methods to use const shapes.

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* Adopt choose op to use constant shapes.

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* Adopt where op shape method to use constant shapes.

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* Adopt lstsq op to use constant empty shapes.

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* Adopt matrix_diag_part op shape routine to use constant shapes.

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* Adopt determinant ops to use constant shapes.

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* Adopt mean_pairwssqerr_loss ops to use constant shapes.

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* Adopt ops shape methods.

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* Adopt shape methods for loss ops.

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* Adopt log_loss op shape method.

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* Adopt shape methods for ops.

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* Adopt dilation2d ops shape methods.

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* Adopted deconv2d ops shape methods.

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* Adopted dynamicRNN op shape method.

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* Adopted shape methods for ops.

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* Adopted shape methods for lstm layer ops.

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* few updates

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* first cuda tweak

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* Adopt constant shapes for sconv2d ops.

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* Adopt constant shapes for gru ops.

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* Adopt constant shapes with shape methods for segment ops and so on.

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* Adopted constant shapes with unsorted_segment_* ops.

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* Adopted constant shapes with gamma op shape method.

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* Adopted shape methods of reduce_stddev ops.

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* Adopted shape methods for reduce_* ops.

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* Adopt shape method for squeeze op.

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* Adopt strided_slice shape method.

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* Refactored concat op shape method to adopt constant shapes.

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* Adopted shape method for mirror_pad op.

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* Adopted split op shape method.

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* Adopted tile ops shape methods.

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* Added const cast for mkldnn routines handles.

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* Refactored logSoftMaxForVector_ routine to conform with proper data and shape pointer casts.

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* Cosmetic changes to proper usage of constant pointers.

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* Refactored a couple shape comparators for strides and addBias helpers to proper use data pointers with inplace option.

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* Refactored depthToSpace helpers.

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* Refactored histogram helpers.

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* Refactored im2col helpers.

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* Refactored gather and gatherND helpers.

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* Fixed buffer usage on percentile helper.

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* Fixed gather shape with helpers and range buffer usage.

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* Fixed buffer usage with space to depth helpers.

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* Fixed buffer usage and constant shapes.

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* Fixed buffer usage with LUP decomposition>

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* Refactored onehot_ helper.

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* Refactored pad and prefix to use constant shapes.

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* Refactoed softmax helpers.

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* Fixed space to batch helpers to use buffers properly.

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* Fixed stack and split helpers.

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* Fixed buffer usage with sparse to dense helpers.

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* Fixed buffer usage with mindistance_ helpers.

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* Fixed buffer usage with tile helper.

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* Fixed constant shape usage.

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* Fixed constant shape usage with legacy pairwise bool ops.

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* Refactored a couple of methods to adopt constant shape usage.

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* Fixed broadcasting with constant shape."

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* Fixed const usage with inplace reverse and constant shapes with legacy reduction.

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* Refactored legacy ops with const shapes.

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* Refactored sort to adopt constant shapes.

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* Corrected sort for constant shape usage.

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* Fixed constant shape usage with special methods.

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* Refactored Context to conform with constant shape usage.

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* CUDA broadcasting headers

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* pairwise/indexreduce/random headers

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* Refactored native ops to adopt constant shapes.

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* legacy reduce3/scalar headers

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* Corrected pullRow signature and tests.

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* Corrected routines to proper use of constant shapes.

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* Refactored tests to use constant shapes properly.

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* Refactored legacy ops tests to use constant shapes properly.

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* Refactored buffer usage with NDArray tests.

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* Fixed native ops tests.

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* Fixed special concat routine.

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* Fixed buffer usage with test.

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* Fixed buffer usage with a test.

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* Refactored TAD.h and tests.

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* Refactored calcStrides* routines to use constant shapes.

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* Fixed miscelaneous errors with constant shapes.

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* NativeOps const changes

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* Corrected definitions for declared functions.

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* NativeOps const changes

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* few more const changes

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* Fixed const shapes with shape routines.

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* few more const changes

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* Fixed shape method for broadcastable case.

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* few more const changes

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* xw_plus_b BP shape fn restored

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* Fixed signatures with broadcasting.

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* Repaired backprops shape methods for a set of operations.

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* Refactored broadcast bool for cuda.

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* Refactored methods for 3 args with const qualifier.

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* Fixed a couple of kernel signatures for broadcasting.

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* Fixed kernels signatures for const buffers and shapes.

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* Refactored pairwise methods to persistent buffers and shapes usage.

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* Adopt const to buffers and shapes with kernels.

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* Adopt const to buffers and shapes with scalar kernels.

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* Refactored indexreduce kernels signatures to use const buffers and shapes.

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* Refactored pairwise kernels to adopt cons shapes and buffers.

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* Refactored pairwise bool kernels to adopt cons shapes and buffers.

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* Refactored random special ops to conform with const shapes and buffers.

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* Refactored native ops to conform with const shapes and buffers under cuda platform.

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* Cosmetical changes only.

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* Fixed const shapes and buffers error.

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* Corrected start pos routine.

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* Refactored methods to conform with const shapes and buffers.

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* Refactored helpers to use proper methods instead.

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* bunch of changes

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* next bunch of changes

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* next bunch of changes

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* Fixed execScalar declaration.

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* Fixed execScalar declaration.

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* Corrected const shape cases with sort and so on.

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* Fixed const shapes for sort.

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* Refactored kernel declarations to adopt const shapes.

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* Fixed kernels declarations to adopt const shapes.

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* Corrected kernel declarations to adopt const shapes and buffers.

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* Fixed kernels declarations to adopt const shapes.

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* Fixed segment helpers kernels declarations and so on to adopt const shapes.

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* Fixed const shape usage with segment and solve helpers.

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* Fixed kernel declaration with adjustWeight helper.

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* Fixed cuda implementations for constant shape helpers.

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* Adopted const shape usage with kernels.

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* Adopted top_k kernels to use const shapes and buffers.

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* Corrected kernels declarations to adopt const shapes with helpers.

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* Refactored NDArray definitions to adopt const shapes and buffers.

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* Fixed const shapes with image suppression helpers.

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* Slight improvement with buffers.

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* Refactored buffer usage.

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* Refactored buffer usage with tests.

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* Fixed const shape usage with definitions.

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* minor updates on cpu side

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* Refactored const shape usage with ConstantDescritor and native ops with cuda platform.

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* Refactored tear and tile kernels to adopt with const shapes.

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* softmax_loop fix

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* update missing signature

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* softmax again

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* few more missing consts

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* new methods updated

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Co-authored-by: shugeo <sgazeos@gmail.com>
2020-05-09 08:06:14 +03:00

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 sd {
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 = 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];
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(sd::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_(sd::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_(sd::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_, (sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC);, FLOAT_TYPES);
*/
}
}
}