/******************************************************************************* * 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 // #include namespace nd4j { namespace ops { namespace helpers { template 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(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(context, 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(context, 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(context, _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(context, 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(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); } } }