229 lines
9.4 KiB
Plaintext
229 lines
9.4 KiB
Plaintext
/*******************************************************************************
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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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//
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <op_boilerplate.h>
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#include <ops/declarable/helpers/imagesHelpers.h>
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#include <helpers/ConstantTadHelper.h>
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#include <ops/declarable/helpers/adjust_hue.h>
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#include <PointersManager.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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///////////////////////////////////////////////////////////////////
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// for example xShapeInfo = {2,3,4}, zShapeInfo = {2,1,4}
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template<typename T>
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__global__ void rgbToGrsCuda(const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, const int dimC) {
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const auto x = reinterpret_cast<const T*>(vx);
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auto z = reinterpret_cast<T*>(vz);
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__shared__ Nd4jLong zLen, *sharedMem;
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__shared__ int rank; // xRank == zRank
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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zLen = shape::length(zShapeInfo);
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rank = shape::rank(zShapeInfo);
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}
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__syncthreads();
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Nd4jLong* coords = sharedMem + threadIdx.x * rank;
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for (Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x; i < zLen; i += gridDim.x * blockDim.x) {
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if (dimC == (rank - 1) && 'c' == shape::order(xShapeInfo) && 1 == shape::elementWiseStride(xShapeInfo) && 'c' == shape::order(zShapeInfo) && 1 == shape::elementWiseStride(zShapeInfo)) {
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const auto xStep = i*3;
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z[i] = 0.2989f * x[xStep] + 0.5870f * x[xStep + 1] + 0.1140f * x[xStep + 2];
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}
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else {
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shape::index2coords(i, zShapeInfo, coords);
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const auto zOffset = shape::getOffset(zShapeInfo, coords);
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const auto xOffset0 = shape::getOffset(xShapeInfo, coords);
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const auto xOffset1 = xOffset0 + shape::stride(xShapeInfo)[dimC];
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const auto xOffset2 = xOffset1 + shape::stride(xShapeInfo)[dimC];
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z[zOffset] = 0.2989f * x[xOffset0] + 0.5870f * x[xOffset1] + 0.1140f * x[xOffset2];
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}
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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linkage void rgbToGrsCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, const int dimC) {
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rgbToGrsCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, dimC);
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}
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///////////////////////////////////////////////////////////////////
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void transformRgbGrs(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) {
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PointersManager manager(context, "rgbToGrs");
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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const int sharedMem = input.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
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NDArray::prepareSpecialUse({&output}, {&input});
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BUILD_SINGLE_SELECTOR(input.dataType(), rgbToGrsCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), dimC), NUMERIC_TYPES);
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NDArray::registerSpecialUse({&output}, {&input});
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manager.synchronize();
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}
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///////////////////////////////////////////////////////////////////
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template <typename T>
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static void _CUDA_G rgbToHsvCuda(const void* vx, const Nd4jLong* xShapeInfo, const Nd4jLong* xTadOffsets,
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void* vz, const Nd4jLong *zShapeInfo, const Nd4jLong* zTadOffsets,
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const Nd4jLong numOfTads, const int dimC) {
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const T* x = reinterpret_cast<const T*>(vx);
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T* z = reinterpret_cast<T*>(vz);
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__shared__ int rank;
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__shared__ Nd4jLong xDimCstride, zDimCstride;
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if (threadIdx.x == 0) {
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rank = shape::rank(xShapeInfo);
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xDimCstride = shape::stride(xShapeInfo)[dimC];
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zDimCstride = shape::stride(zShapeInfo)[dimC];
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (Nd4jLong i = tid; i < numOfTads; i += gridDim.x * blockDim.x) {
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const T* xTad = x + xTadOffsets[i];
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T* zTad = z + zTadOffsets[i];
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rgbToHsv<T>(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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///////////////////////////////////////////////////////////////////
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template <typename T>
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static void _CUDA_G hsvToRgbCuda(const void* vx, const Nd4jLong* xShapeInfo, const Nd4jLong* xTadOffsets,
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void* vz, const Nd4jLong *zShapeInfo, const Nd4jLong* zTadOffsets,
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const Nd4jLong numOfTads, const int dimC) {
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const T* x = reinterpret_cast<const T*>(vx);
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T* z = reinterpret_cast<T*>(vz);
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__shared__ int rank;
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__shared__ Nd4jLong xDimCstride, zDimCstride;
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if (threadIdx.x == 0) {
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rank = shape::rank(xShapeInfo);
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xDimCstride = shape::stride(xShapeInfo)[dimC];
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zDimCstride = shape::stride(zShapeInfo)[dimC];
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (Nd4jLong i = tid; i < numOfTads; i += gridDim.x * blockDim.x) {
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const T* xTad = x + xTadOffsets[i];
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T* zTad = z + zTadOffsets[i];
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hsvToRgb<T>(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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///////////////////////////////////////////////////////////////////
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template<typename T>
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static _CUDA_H void hsvToRgbCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
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const void* vx, const Nd4jLong* xShapeInfo, const Nd4jLong* xTadOffsets,
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void* vz, const Nd4jLong* zShapeInfo, const Nd4jLong* zTadOffsets,
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const Nd4jLong numOfTads, const int dimC) {
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hsvToRgbCuda<T><<<blocksPerGrid, threadsPerBlock, 256, *stream>>>(vx, xShapeInfo, xTadOffsets, vz, zShapeInfo, zTadOffsets, numOfTads, dimC);
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}
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template<typename T>
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static _CUDA_H void rgbToHsvCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
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const void* vx, const Nd4jLong* xShapeInfo, const Nd4jLong* xTadOffsets,
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void* vz, const Nd4jLong* zShapeInfo, const Nd4jLong* zTadOffsets,
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const Nd4jLong numOfTads, const int dimC) {
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rgbToHsvCuda<T><<<blocksPerGrid, threadsPerBlock, 256, *stream>>>(vx, xShapeInfo, xTadOffsets, vz, zShapeInfo, zTadOffsets, numOfTads, dimC);
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}
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///////////////////////////////////////////////////////////////////
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void transformHsvRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) {
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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 int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (numOfTads + threadsPerBlock - 1) / threadsPerBlock;
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PointersManager manager(context, "hsv_to_rgb");
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NDArray::prepareSpecialUse({output}, {input});
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BUILD_SINGLE_SELECTOR(input->dataType(), hsvToRgbCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), packX.platformOffsets(), output->specialBuffer(), output->specialShapeInfo(), packZ.platformOffsets(), numOfTads, dimC), FLOAT_TYPES);
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NDArray::registerSpecialUse({output}, {input});
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manager.synchronize();
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}
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///////////////////////////////////////////////////////////////////
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void transformRgbHsv(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) {
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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 int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (numOfTads + threadsPerBlock - 1) / threadsPerBlock;
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PointersManager manager(context, "rgb_to_hsv");
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NDArray::prepareSpecialUse({output}, {input});
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BUILD_SINGLE_SELECTOR(input->dataType(), rgbToHsvCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), packX.platformOffsets(), output->specialBuffer(), output->specialShapeInfo(), packZ.platformOffsets(), numOfTads, dimC), FLOAT_TYPES);
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NDArray::registerSpecialUse({output}, {input});
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manager.synchronize();
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}
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}
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}
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}
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