/******************************************************************************* * 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 #include #include namespace sd { namespace ops { namespace helpers { template static void adjustHue_(const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC) { const T delta = deltaScalarArr->e(0); const int rank = input->rankOf(); const T* x = input->bufferAsT(); T* z = output->bufferAsT(); 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(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(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(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(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 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(array->buffer()); auto bOut = reinterpret_cast(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(tadsChannelsIn->at(0)->buffer()); auto bufferG = reinterpret_cast(tadsChannelsIn->at(1)->buffer()); auto bufferB = reinterpret_cast(tadsChannelsIn->at(2)->buffer()); auto outputR = reinterpret_cast(tadsChannelsOut->at(0)->buffer()); auto outputG = reinterpret_cast(tadsChannelsOut->at(1)->buffer()); auto outputB = reinterpret_cast(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(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); */ } } }