cavis/libnd4j/include/ops/declarable/helpers/cpu/convolutions_upsampling2d.cpp

81 lines
3.4 KiB
C++

/*******************************************************************************
* 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 Yurii Shyrma (iuriish@yahoo.com), created on 18.09.2018
//
#include <ops/declarable/helpers/convolutions.h>
#include <execution/Threads.h>
namespace sd {
namespace ops {
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void upsampling2d_(const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW) {
// input has shape [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)
// output has shape [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC)
const T* x = input.bufferAsT<T>();
T* z = output.bufferAsT<T>();
const uint dimIH = isNCHW ? 2 : 1;
const uint dimIC = isNCHW ? 1 : 3;
const uint bS = input.sizeAt(0);
const uint iC = input.sizeAt(dimIC);
const uint oH = output.sizeAt(dimIH);
const uint oW = output.sizeAt(dimIH + 1);
const Nd4jLong xStride0 = input.stridesOf()[0];
const Nd4jLong xStride1 = input.stridesOf()[dimIC];
const Nd4jLong xStride2 = input.stridesOf()[dimIH];
const Nd4jLong xStride3 = input.stridesOf()[dimIH + 1];
const Nd4jLong zStride0 = output.stridesOf()[0];
const Nd4jLong zStride1 = output.stridesOf()[dimIC];
const Nd4jLong zStride2 = output.stridesOf()[dimIH];
const Nd4jLong zStride3 = output.stridesOf()[dimIH + 1];
// loop through output array
auto func = PRAGMA_THREADS_FOR_3D {
uint xCoord2, xCoord3;
for (uint b = start_x; b < stop_x; b += inc_x) {
for (uint c = start_y; c < stop_y; c += inc_y) {
for (uint h = start_z; h < stop_z; h += inc_z) {
for (uint w = 0; w < oW; ++w) {
xCoord2 = h / factorH;
xCoord3 = w / factorW;
z[b * zStride0 + c * zStride1 + h * zStride2 + w * zStride3] = x[b * xStride0 + c * xStride1 + xCoord2 * xStride2 + xCoord3 * xStride3];
}
}
}
}
};
samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1, 0, oH, 1);
}
void ConvolutionUtils::upsampling2d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW) {
BUILD_SINGLE_SELECTOR(input.dataType(), upsampling2d_, (input, output, factorH, factorW, isNCHW), FLOAT_TYPES);
}
}
}