145 lines
6.2 KiB
Plaintext
145 lines
6.2 KiB
Plaintext
/*******************************************************************************
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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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#include <helpers/ConstantTadHelper.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 _CUDA_G adjustHueSingleNHWCKernel(void *xBuffer, Nd4jLong *xShapeInfo, void *zBuffer, Nd4jLong *zShapeInfo, Nd4jLong tuples, float delta) {
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int numChannels = 3;
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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auto bIn = reinterpret_cast<T*>(xBuffer);
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auto bOut = reinterpret_cast<T*>(zBuffer);
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static const int kChannelRange = 6;
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for (Nd4jLong e = tid; e < tuples; e += blockDim.x * gridDim.x) {
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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(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(h, v_min, v_max, o, o + 1, o + 2);
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}
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}
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template <typename T>
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static void _CUDA_G adjustHueSingleNCHWKernel(void *xBuffer, Nd4jLong *xTadShapeInfo, Nd4jLong *xOffsets, void *zBuffer, Nd4jLong *zTadShapeInfo, Nd4jLong *zOffsets, Nd4jLong tadLength, Nd4jLong tuples, float delta) {
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int numChannels = 3;
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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static const int kChannelRange = 6;
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auto bufferR = reinterpret_cast<T *>(xBuffer) + xOffsets[0];
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auto bufferG = reinterpret_cast<T *>(xBuffer) + xOffsets[1];
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auto bufferB = reinterpret_cast<T *>(xBuffer) + xOffsets[2];
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auto outputR = reinterpret_cast<T *>(zBuffer) + zOffsets[0];
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auto outputG = reinterpret_cast<T *>(zBuffer) + zOffsets[1];
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auto outputB = reinterpret_cast<T *>(zBuffer) + zOffsets[2];
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for (Nd4jLong e = tid; e < tuples; e += blockDim.x * gridDim.x) {
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auto _ri = bufferR + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
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auto _gi = bufferG + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
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auto _bi = bufferB + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
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auto _ro = outputR + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
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auto _go = outputG + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
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auto _bo = outputB + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
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T h, v_min, v_max;
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helpers::rgb_to_hv(_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(h, v_min, v_max, _ro, _go, _bo);
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}
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}
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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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// numChannels is always 3
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auto tuples = array->lengthOf() / 3;
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if (isNHWC) {
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adjustHueSingleNHWCKernel<T><<<256, 256, 1024, *context->getCudaStream()>>>(array->specialBuffer(), array->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), tuples, delta);
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} else {
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// TODO: check this one
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auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {1, 2});
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auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {1, 2});
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auto tadLength = shape::length(packX.primaryShapeInfo());
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adjustHueSingleNCHWKernel<T><<<256, 256, 1024, *context->getCudaStream()>>>(array->specialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), output->specialBuffer(), packZ.platformShapeInfo(), packZ.platformOffsets(), tadLength, tuples, delta);
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}
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}
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template <typename T>
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static void _adjust_hue_batch(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) {
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auto xType = array->dataType();
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// numChannels is always 3
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auto tuples = array->lengthOf() / 3;
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if (isNHWC) {
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// in case of nhwc batch, we don't really care about examples: it's still bunch of RGB values
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BUILD_SINGLE_SELECTOR(xType, _adjust_hue_single, (context, array, output, delta, isNHWC);, FLOAT_TYPES);
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} else {
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// TODO: check this one
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auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {0, 2, 3});
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auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {0, 2, 3});
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auto tadLength = shape::length(packX.primaryShapeInfo());
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adjustHueSingleNCHWKernel<T><<<256, 256, 1024, *context->getCudaStream()>>>(array->specialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), output->specialBuffer(), packZ.platformShapeInfo(), packZ.platformOffsets(), tadLength, tuples, delta);
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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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} 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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}
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}
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}
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