90 lines
3.9 KiB
C++
90 lines
3.9 KiB
C++
/*******************************************************************************
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* Copyright (c) 2019 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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#include <ops/declarable/helpers/adjust_hue.h>
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#include <ops/declarable/helpers/color_models_conv.h>
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#include <helpers/ConstantTadHelper.h>
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#include <execution/Threads.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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//local
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template <typename T, typename Op>
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FORCEINLINE static void triple_transformer(const NDArray* input, NDArray* output, const int dimC, Op op) {
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const int rank = input->rankOf();
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const T* x = input->bufferAsT<T>();
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T* z = output->bufferAsT<T>();
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if (dimC == rank - 1 && input->ews() == 1 && output->ews() == 1 && input->ordering() == 'c' && output->ordering() == 'c') {
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auto func = PRAGMA_THREADS_FOR{
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for (auto i = start; i < stop; i += increment) {
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op(x[i], x[i + 1], x[i + 2], z[i], z[i + 1], z[i + 2]);
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}
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};
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samediff::Threads::parallel_for(func, 0, input->lengthOf(), 3);
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}
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else {
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auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC);
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auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC);
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const Nd4jLong numOfTads = packX.numberOfTads();
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const Nd4jLong xDimCstride = input->stridesOf()[dimC];
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const Nd4jLong zDimCstride = output->stridesOf()[dimC];
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auto func = PRAGMA_THREADS_FOR{
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for (auto i = start; i < stop; i += increment) {
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const T* xTad = x + packX.platformOffsets()[i];
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T* zTad = z + packZ.platformOffsets()[i];
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op(xTad[0], xTad[xDimCstride], xTad[2 * xDimCstride], zTad[0], zTad[zDimCstride], zTad[2 * zDimCstride]);
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}
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};
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samediff::Threads::parallel_tad(func, 0, numOfTads);
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}
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}
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template <typename T>
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FORCEINLINE static void hsv_rgb(const NDArray* input, NDArray* output, const int dimC) {
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auto op = nd4j::ops::helpers::hsvToRgb<T>;
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return triple_transformer<T>(input, output, dimC, op);
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}
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template <typename T>
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FORCEINLINE static void rgb_hsv(const NDArray* input, NDArray* output, const int dimC) {
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auto op = nd4j::ops::helpers::rgbToHsv<T>;
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return triple_transformer<T>(input, output, dimC, op);
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}
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void transform_hsv_rgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) {
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BUILD_SINGLE_SELECTOR(input->dataType(), hsv_rgb, (input, output, dimC), FLOAT_TYPES);
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}
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void transform_rgb_hsv(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) {
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BUILD_SINGLE_SELECTOR(input->dataType(), rgb_hsv, (input, output, dimC), FLOAT_TYPES);
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}
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}
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}
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} |