122 lines
4.6 KiB
C++
122 lines
4.6 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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//
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// @author raver119@gmail.com
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//
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#include <ops/declarable/helpers/adjust_hue.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static void _adjust_hue_single(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) {
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// we're 100% sure it's 3
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const int numChannels = 3;
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int tuples = array->lengthOf() / numChannels;
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auto bIn = reinterpret_cast<T *>(array->buffer());
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auto bOut = reinterpret_cast<T *>(output->buffer());
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static const int kChannelRange = 6;
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int stridesDim = isNHWC ? 2 : 0;
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if (isNHWC) {
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// for NHWC our rgb values are stored one by one
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PRAGMA_OMP_PARALLEL_FOR_SIMD
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for (int e = 0; e < tuples; e++) {
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auto i = bIn + e * numChannels;
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auto o = bOut + e * numChannels;
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T h, v_min, v_max;
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helpers::rgb_to_hv(context, i[0], i[1], i[2], &h, &v_min, &v_max);
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h += delta * kChannelRange;
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while (h < (T) 0.)
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h += (T) kChannelRange;
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while (h >= (T) kChannelRange)
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h -= (T) kChannelRange;
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helpers::hv_to_rgb(context, h, v_min, v_max, o, o + 1, o + 2);
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}
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} else {
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auto tadsChannelsIn = array->allTensorsAlongDimension({0});
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auto tadsChannelsOut = output->allTensorsAlongDimension( {0});
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auto bufferR = reinterpret_cast<T *>(tadsChannelsIn->at(0)->buffer());
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auto bufferG = reinterpret_cast<T *>(tadsChannelsIn->at(1)->buffer());
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auto bufferB = reinterpret_cast<T *>(tadsChannelsIn->at(2)->buffer());
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auto outputR = reinterpret_cast<T *>(tadsChannelsOut->at(0)->buffer());
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auto outputG = reinterpret_cast<T *>(tadsChannelsOut->at(1)->buffer());
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auto outputB = reinterpret_cast<T *>(tadsChannelsOut->at(2)->buffer());
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PRAGMA_OMP_PARALLEL_FOR_SIMD
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for (int e = 0; e < tuples; e++) {
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auto _ri = bufferR + e;
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auto _gi = bufferG + e;
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auto _bi = bufferB + e;
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auto _ro = outputR + e;
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auto _go = outputG + e;
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auto _bo = outputB + e;
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T h, v_min, v_max;
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helpers::rgb_to_hv(context, _ri[0], _gi[0], _bi[0], &h, &v_min, &v_max);
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h += delta * kChannelRange;
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while (h < (T) 0)
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h += (T) kChannelRange;
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while (h >= (T) kChannelRange)
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h -= (T) kChannelRange;
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helpers::hv_to_rgb(context, h, v_min, v_max, _ro, _go, _bo);
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}
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delete tadsChannelsIn;
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delete tadsChannelsOut;
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}
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}
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void _adjust_hue(nd4j::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) {
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auto xType = array->dataType();
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float d = delta->e<float>(0);
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if (array->rankOf() == 4) {
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auto tadsIn = array->allTensorsAlongDimension({0});
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auto tadsOut = output->allTensorsAlongDimension({0});
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int tSize = tadsIn->size();
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// FIXME: template selector should be moved out of loop
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PRAGMA_OMP_PARALLEL_FOR
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for (int e = 0; e < tSize; e++) {
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BUILD_SINGLE_SELECTOR(xType, _adjust_hue_single, (context, tadsIn->at(e), tadsOut->at(e), d, isNHWC);, FLOAT_TYPES);
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}
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delete tadsIn;
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delete tadsOut;
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} else {
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BUILD_SINGLE_SELECTOR(xType, _adjust_hue_single, (context, array, output, d, isNHWC);, FLOAT_TYPES);
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}
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}
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BUILD_SINGLE_TEMPLATE(template void _adjust_hue_single, (nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC);, FLOAT_TYPES);
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}
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}
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} |