98 lines
3.8 KiB
Plaintext
98 lines
3.8 KiB
Plaintext
/*******************************************************************************
|
|
* Copyright (c) 2015-2018 Skymind, Inc.
|
|
* Copyright (c) 2019 Konduit K.K.
|
|
*
|
|
* 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 Yurii Shyrma (iuriish@yahoo.com)
|
|
//
|
|
|
|
#include <ops/declarable/helpers/convolutions.h>
|
|
#include <helpers/PointersManager.h>
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
__global__ static void upsampling2dCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int factorH, const int factorW, const bool isNCHW) {
|
|
|
|
// x has shape [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)
|
|
// z has shape [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC)
|
|
|
|
const T* x = reinterpret_cast<const T*>(vx);
|
|
T* z = reinterpret_cast<T*>(vz);
|
|
|
|
__shared__ int rank, dimIH;
|
|
__shared__ Nd4jLong zLen, *sharedMem;
|
|
|
|
if (threadIdx.x == 0) {
|
|
extern __shared__ unsigned char shmem[];
|
|
sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
|
|
|
|
dimIH = isNCHW ? 2 : 1;
|
|
zLen = shape::length(zShapeInfo);
|
|
rank = 4;
|
|
}
|
|
__syncthreads();
|
|
|
|
const auto zInd = threadIdx.x + blockIdx.x * blockDim.x;
|
|
|
|
if(zInd >= zLen)
|
|
return;
|
|
|
|
auto coords = sharedMem + threadIdx.x * rank;
|
|
|
|
shape::index2coords(zInd, zShapeInfo, coords);
|
|
|
|
const auto zOffset = shape::getOffset(zShapeInfo, coords);
|
|
|
|
coords[dimIH] /= factorH;
|
|
coords[dimIH + 1] /= factorW;
|
|
|
|
const auto xOffset = shape::getOffset(xShapeInfo, coords);
|
|
|
|
z[zOffset] = x[xOffset];
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void upsampling2dCudaLauncher(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 factorH, const int factorW, const bool isNCHW) {
|
|
|
|
upsampling2dCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, factorH, factorW, isNCHW);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void ConvolutionUtils::upsampling2d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW) {
|
|
|
|
PointersManager manager(block.launchContext(), "upsampling2d");
|
|
|
|
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
|
const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
|
const int sharedMem = output.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
|
|
|
|
NDArray::prepareSpecialUse({&output}, {&input});
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), upsampling2dCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, block.launchContext()->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), factorH, factorW, isNCHW), FLOAT_TYPES);
|
|
NDArray::registerSpecialUse({&output}, {&input});
|
|
|
|
manager.synchronize();
|
|
}
|
|
|
|
}
|
|
}
|