98e2814879
* platform helpers draft Signed-off-by: raver119 <raver119@gmail.com> * typo Signed-off-by: raver119 <raver119@gmail.com> * disable platform cmake Signed-off-by: raver119 <raver119@gmail.com> * another draft Signed-off-by: raver119 <raver119@gmail.com> * mkldnn convolution refactored Signed-off-by: raver119 <raver119@gmail.com> * minor tweaks Signed-off-by: raver119 <raver119@gmail.com> * one more safety check Signed-off-by: raver119 <raver119@gmail.com> * prototype works Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * force static library mode for mkldnn Signed-off-by: raver119 <raver119@gmail.com> * - ismax fix - experimental arg fix - don't enforce openblas on Apple hardware Signed-off-by: raver119 <raver119@gmail.com> * bunch of small fixes Signed-off-by: raver119@gmail.com <raver119@gmail.com> * declare concurrent Signed-off-by: raver119@gmail.com <raver119@gmail.com> * - MKLDNN version upgrade to 1.0.2 - avgpool2d/maxpool2d APIs update Signed-off-by: raver119 <raver119@gmail.com> * - avgpool2d_bp/maxpool2d_bp APIs update Signed-off-by: raver119 <raver119@gmail.com> * - conv2d/batchnorm APIs update Signed-off-by: raver119 <raver119@gmail.com> * - lrn/conv2d_bp/conv3d/conv3d_bp APIs update Signed-off-by: raver119 <raver119@gmail.com> * all ops converted to MKLDNN 1.x Signed-off-by: raver119 <raver119@gmail.com> * bunch of tweaks Signed-off-by: raver119 <raver119@gmail.com> * namespace for platform helpers Signed-off-by: raver119 <raver119@gmail.com> * make sure platform helpers aren't opimized out Signed-off-by: raver119 <raver119@gmail.com> * build cpu_features on x86 systems Signed-off-by: raver119 <raver119@gmail.com> * build cpu_features on x86 systems Signed-off-by: raver119 <raver119@gmail.com> * more of cpu_features Signed-off-by: raver119 <raver119@gmail.com> * - mkldnn removed from java - cpu_features checks in CpuNDArrayFactory Signed-off-by: raver119 <raver119@gmail.com> * F16C definition renamed Signed-off-by: raver119 <raver119@gmail.com> * some mkldnn rearrangements Signed-off-by: raver119 <raver119@gmail.com> * check supported instructions before doing anything Signed-off-by: raver119 <raver119@gmail.com> * typo Signed-off-by: raver119 <raver119@gmail.com> * missied impl Signed-off-by: raver119 <raver119@gmail.com> * BUILD_PIC option Signed-off-by: raver119 <raver119@gmail.com> * conv2d fix Signed-off-by: raver119 <raver119@gmail.com> * avgpool3d fix Signed-off-by: raver119 <raver119@gmail.com> * avgpool3d_bp fix Signed-off-by: raver119 <raver119@gmail.com> * avgpool2d_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * avgpool3d_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * maxpool bp leaks fixed Signed-off-by: raver119 <raver119@gmail.com> * printf removed Signed-off-by: raver119 <raver119@gmail.com> * batchnorm fix Signed-off-by: raver119 <raver119@gmail.com> * AVX warning/error polishing Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * More polish Signed-off-by: AlexDBlack <blacka101@gmail.com> * Polish Signed-off-by: AlexDBlack <blacka101@gmail.com> * remove previous MKL-DNN support layer Signed-off-by: raver119 <raver119@gmail.com> * avx2 tweak Signed-off-by: raver119 <raver119@gmail.com> * allow static for apple Signed-off-by: raver119@gmail.com <raver119@gmail.com> * exclude mkldnn in one more place Signed-off-by: raver119 <raver119@gmail.com> * exclude mkldnn in one more place Signed-off-by: raver119 <raver119@gmail.com> * restore OPENBLAS_PATH use Signed-off-by: raver119 <raver119@gmail.com> * add runtime check for avx/avx2 support Signed-off-by: raver119 <raver119@gmail.com> * convolution_auto Signed-off-by: raver119 <raver119@gmail.com> * Add logic for helper argument * minor test fix Signed-off-by: raver119 <raver119@gmail.com> * few tweaks Signed-off-by: raver119 <raver119@gmail.com> * few tweaks Signed-off-by: raver119 <raver119@gmail.com> * skip OpTracker props for non-x86 builds Signed-off-by: raver119 <raver119@gmail.com> * linux arm isn't x86 :) Signed-off-by: raver119 <raver119@gmail.com> * avx-512 Signed-off-by: raver119 <raver119@gmail.com> * CUDA presets fix Signed-off-by: raver119 <raver119@gmail.com> * BUILD_PIC Signed-off-by: raver119 <raver119@gmail.com> * prefetchw for avx2 Signed-off-by: raver119 <raver119@gmail.com> * BUILD_PIC again Signed-off-by: raver119 <raver119@gmail.com>
1706 lines
97 KiB
C++
1706 lines
97 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 Yurii Shyrma (iuriish@yahoo.com), created on 18.09.2018
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//
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#include <ops/declarable/helpers/convolutions.h>
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#include<ops/declarable/helpers/addBias.h>
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#include <ops/declarable/helpers/im2col.h>
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#include <ops/declarable/helpers/col2im.h>
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#include <NDArrayFactory.h>
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#include <MmulHelper.h>
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namespace nd4j {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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// [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
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template <typename T>
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static void vol2col_(const NDArray& volume, NDArray& columns, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
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const int bS = volume.sizeAt(0);
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const int iC = volume.sizeAt(1);
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const int iD = volume.sizeAt(2);
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const int iH = volume.sizeAt(3);
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const int iW = volume.sizeAt(4);
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const int kD = columns.sizeAt(2);
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const int kH = columns.sizeAt(3);
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const int kW = columns.sizeAt(4);
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const int oD = columns.sizeAt(5);
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const int oH = columns.sizeAt(6);
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const int oW = columns.sizeAt(7);
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const Nd4jLong colStride0 = columns.stridesOf()[0];
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const Nd4jLong colStride1 = columns.stridesOf()[1];
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const Nd4jLong colStride2 = columns.stridesOf()[2];
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const Nd4jLong colStride3 = columns.stridesOf()[3];
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const Nd4jLong colStride4 = columns.stridesOf()[4];
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const Nd4jLong colStride5 = columns.stridesOf()[5];
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const Nd4jLong colStride6 = columns.stridesOf()[6];
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const Nd4jLong colStride7 = columns.stridesOf()[7];
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const Nd4jLong volStride0 = volume.stridesOf()[0];
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const Nd4jLong volStride1 = volume.stridesOf()[1];
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const Nd4jLong volStride2 = volume.stridesOf()[2];
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const Nd4jLong volStride3 = volume.stridesOf()[3];
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const Nd4jLong volStride4 = volume.stridesOf()[4];
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T* colBuff = columns.bufferAsT<T>();
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T* volBuff = const_cast<NDArray&>(volume).bufferAsT<T>();
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T *col, *vol;
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int volDep, volRow, volCol;
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if (volume.ordering() == 'c' && columns.ordering() == 'c' && shape::strideDescendingCAscendingF(volume.getShapeInfo()) && shape::strideDescendingCAscendingF(columns.getShapeInfo()))
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PRAGMA_OMP_PARALLEL_FOR_ARGS(private(col, vol, volDep, volRow, volCol) collapse(2))
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for (int b = 0; b < bS; ++b) {
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for (int c = 0; c < iC; ++c) {
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for (int kDep = 0; kDep < kD; ++kDep) {
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for (int kRow = 0; kRow < kH; ++kRow) {
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for (int kCol = 0; kCol < kW; ++kCol) {
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for (int colD = 0; colD < oD; ++colD) {
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for (int colH = 0; colH < oH; ++colH) {
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for (int colW = 0; colW < oW; ++colW) {
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volDep = (-pD + kDep * dD) + colD*sD;
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volRow = (-pH + kRow * dH) + colH*sH;
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volCol = (-pW + kCol * dW) + colW*sW;
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col = colBuff + b*colStride0 + c*colStride1 + kDep*colStride2 + kRow*colStride3 + kCol*colStride4 + colD*colStride5 + colH*colStride6 + colW*colStride7;
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if (static_cast<unsigned>(volDep) >= static_cast<unsigned>(iD) || static_cast<unsigned>(volRow) >= static_cast<unsigned>(iH) || static_cast<unsigned>(volCol) >= static_cast<unsigned>(iW))
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*col = static_cast<T>(0.);
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else {
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vol = volBuff + b*volStride0 + c*volStride1 + volDep*volStride2 + volRow*volStride3 + volCol*volStride4;
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*col = *vol;
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}
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}
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}
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}
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}
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}
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}
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}
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}
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else
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PRAGMA_OMP_PARALLEL_FOR_ARGS(private(vol, col, volDep, volRow, volCol))
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for (int b = 0; b < bS; b++) {
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for (int colD = 0; colD < oD; ++colD) {
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for (int colH = 0; colH < oH; ++colH) {
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for (int colW = 0; colW < oW; ++colW) {
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for (int c = 0; c < iC; ++c) {
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for (int kDep = 0; kDep < kD; ++kDep) {
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for (int kRow = 0; kRow < kH; ++kRow) {
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for (int kCol = 0; kCol < kW; ++kCol) {
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volDep = (-pD + kDep * dD) + colD*sD;
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volRow = (-pH + kRow * dH) + colH*sH;
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volCol = (-pW + kCol * dW) + colW*sW;
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col = colBuff + b*colStride0 + c*colStride1 + kDep*colStride2 + kRow*colStride3 + kCol*colStride4 + colD*colStride5 + colH*colStride6 + colW*colStride7;
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if (static_cast<unsigned>(volDep) >= static_cast<unsigned>(iD) || static_cast<unsigned>(volRow) >= static_cast<unsigned>(iH) || static_cast<unsigned>(volCol) >= static_cast<unsigned>(iW))
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*col = static_cast<T>(0.);
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else {
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vol = volBuff + b*volStride0 + c*volStride1 + volDep*volStride2 + volRow*volStride3 + volCol*volStride4;
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*col = *vol;
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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// [bS, iC, kD, kH, kW, oD, oH, oW] is de-convoluted to [bS, iC, iD, iH, iW]
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template <typename T>
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static void col2vol_(const NDArray& columns, NDArray& volume, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
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// initial zeroing of volume content
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volume.nullify();
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const int bS = volume.sizeAt(0);
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const int iC = volume.sizeAt(1);
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const int iD = volume.sizeAt(2);
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const int iH = volume.sizeAt(3);
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const int iW = volume.sizeAt(4);
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const int kD = columns.sizeAt(2);
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const int kH = columns.sizeAt(3);
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const int kW = columns.sizeAt(4);
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const int oD = columns.sizeAt(5);
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const int oH = columns.sizeAt(6);
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const int oW = columns.sizeAt(7);
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const Nd4jLong colStride0 = columns.stridesOf()[0];
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const Nd4jLong colStride1 = columns.stridesOf()[1];
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const Nd4jLong colStride2 = columns.stridesOf()[2];
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const Nd4jLong colStride3 = columns.stridesOf()[3];
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const Nd4jLong colStride4 = columns.stridesOf()[4];
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const Nd4jLong colStride5 = columns.stridesOf()[5];
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const Nd4jLong colStride6 = columns.stridesOf()[6];
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const Nd4jLong colStride7 = columns.stridesOf()[7];
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const Nd4jLong volStride0 = volume.stridesOf()[0];
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const Nd4jLong volStride1 = volume.stridesOf()[1];
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const Nd4jLong volStride2 = volume.stridesOf()[2];
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const Nd4jLong volStride3 = volume.stridesOf()[3];
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const Nd4jLong volStride4 = volume.stridesOf()[4];
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T* volBuff = volume.bufferAsT<T>();
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T* colBuff = const_cast<NDArray&>(columns).bufferAsT<T>();
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T* col, *vol;
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int volDep, volRow, volCol;
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if (volume.ordering() == 'c' && columns.ordering() == 'c' && shape::strideDescendingCAscendingF(volume.getShapeInfo()) && shape::strideDescendingCAscendingF(columns.getShapeInfo()))
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PRAGMA_OMP_PARALLEL_FOR_ARGS(private(col, vol, volDep, volRow, volCol) collapse(2))
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for (int b = 0; b < bS; b++) {
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for (int c = 0; c < iC; ++c) {
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for (int kDep = 0; kDep < kD; ++kDep) {
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for (int kRow = 0; kRow < kH; ++kRow) {
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for (int kCol = 0; kCol < kW; ++kCol) {
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for (int colD = 0; colD < oD; ++colD) {
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for (int colH = 0; colH < oH; ++colH) {
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for (int colW = 0; colW < oW; ++colW) {
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volDep = -pD + kDep * dD + colD * sD;
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volRow = -pH + kRow * dH + colH * sH;
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volCol = -pW + kCol * dW + colW * sW;
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if (static_cast<unsigned>(volDep) < static_cast<unsigned>(iD) && static_cast<unsigned>(volRow) < static_cast<unsigned>(iH) && static_cast<unsigned>(volCol) < static_cast<unsigned>(iW)) {
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col = colBuff + b*colStride0 + c*colStride1 + kDep*colStride2 + kRow*colStride3 + kCol*colStride4 + colD*colStride5 + colH*colStride6 + colW*colStride7;
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vol = volBuff + b*volStride0 + c*volStride1 + volDep*volStride2 + volRow*volStride3 + volCol*volStride4;
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*vol += *col;
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}
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}
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}
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}
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}
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}
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}
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}
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}
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else
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PRAGMA_OMP_PARALLEL_FOR_ARGS(private(vol, col, volDep, volRow, volCol))
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for (int b = 0; b < bS; b++) {
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for (int colD = 0; colD < oD; ++colD) {
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for (int colH = 0; colH < oH; ++colH) {
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for (int colW = 0; colW < oW; ++colW) {
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for (int c = 0; c < iC; ++c) {
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for (int kDep = 0; kDep < kD; ++kDep) {
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for (int kRow = 0; kRow < kH; ++kRow) {
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for (int kCol = 0; kCol < kW; ++kCol) {
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volDep = (-pD + kDep * dD) + colD*sD;
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volRow = (-pH + kRow * dH) + colH*sH;
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volCol = (-pW + kCol * dW) + colW*sW;
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if (static_cast<unsigned>(volDep) < static_cast<unsigned>(iD) && static_cast<unsigned>(volRow) < static_cast<unsigned>(iH) && static_cast<unsigned>(volCol) < static_cast<unsigned>(iW)) {
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col = colBuff + b*colStride0 + c*colStride1 + kDep*colStride2 + kRow*colStride3 + kCol*colStride4 + colD*colStride5 + colH*colStride6 + colW*colStride7;
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vol = volBuff + b*volStride0 + c*volStride1 + volDep*volStride2 + volRow*volStride3 + volCol*volStride4;
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*vol += *col;
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
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// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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// weights [kH, kW, iC, oC] always
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// bias [oC]
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// output [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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// kH filter(kernel) height
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// kW filter(kernel) width
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// sH strides height
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// sW strides width
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// pH paddings height
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// pW paddings width
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// dH dilations height
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// dW dilations width
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// isSameMode 0-VALID, 1-SAME
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// isNCHW 1-NCHW, 0-NHWC
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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nd4j_debug("MKL-DNN is not used for conv2d!\n", 0);
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std::vector<int> permutForOutput;
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if(isNCHW)
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permutForOutput = {0, 3, 1, 2}; // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
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else
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input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] if NHWC
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NDArray col('c', {bS, oH, oW, kH, kW, iC}, input->dataType(), input->getContext());
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NDArray colP = col.permute({0, 5, 3, 4, 1, 2}); // {bS, iC, kH, kW, oH, oW}
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NDArray mmulResult('f', {bS*oH*oW, oC}, output->dataType(), output->getContext());
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//----- calculation of output -----//
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auto ctx = block.launchContext();
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helpers::im2col(*ctx, *input, colP, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW]
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MmulHelper::tensorDot(&col, weights, &mmulResult, {3,4,5}, {0,1,2}, {}); // [bS, oH, oW, kH, kW, iC] x [kH, kW, iC, oC] = [bS, oH, oW, oC]
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//----- assign outTemp to output -----//
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if(isNCHW) {
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mmulResult.reshapei({bS, oH, oW, oC});
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mmulResult.permutei(permutForOutput);
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}
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output->assign(mmulResult);
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//----- add biases if required -----//
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if(bias)
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// output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
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helpers::addBias(*output, *bias, isNCHW);
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if(!isNCHW)
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delete input;
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
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// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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// weights [kH, kW, iC, oC] always
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// bias [oC]
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// gradO [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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// gradI [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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// gradW [kH, kW, iC, oC] always
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// gradB [oC]
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// kH filter(kernel) height
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// kW filter(kernel) width
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// sH strides height
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// sW strides width
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// pH paddings height
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// pW paddings width
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// dH dilations height
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// dW dilations width
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// isSameMode 0-VALID, 1-SAME
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// isNCHW 0-NHWC, 1-NCHW
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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nd4j_debug("MKL-DNN is not used for conv2d_bp!\n", 0);
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std::vector<int> gradOaxesForDot;
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if(!isNCHW) {
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gradOaxesForDot = {0, 1, 2}; // bS, oH, oW
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input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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gradI = new NDArray(gradI->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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} else {
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gradOaxesForDot = {0, 2, 3}; // bS, oH, oW
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}
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NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext());
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// ----- calculation of gradW ----- //
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if(gradW) {
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auto ctx = block.launchContext();
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helpers::im2col(*ctx, *input, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW]
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nd4j::MmulHelper::tensorDot(&columns, gradO, gradW, {0,4,5}, gradOaxesForDot, {2, 0, 1, 3}); // [bS, iC, kH, kW, oH, oW] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [iC, kH, kW, oC]
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}
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// ----- calculation of gradB ----- //
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if(gradB) {
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NDArray* gradBR = gradB;
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if(gradB->rankOf() == 2)
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gradBR = new NDArray(gradB->reshape(gradB->ordering(), {(int)gradB->lengthOf()}));
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gradO->reduceAlongDimension(reduce::Sum, gradBR, gradOaxesForDot); // sum over bS, oH, oW
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if(gradBR != gradB)
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delete gradBR;
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}
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//----- calculation of gradI -----//
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nd4j::MmulHelper::tensorDot(weights, gradO, &columns, {indWoC}, {indIOioC}, {2, 3, 1, 0, 4, 5}); // [kH, kW, iC, oC]/[oC, iC, kH, kW]] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [kH, kW, iC, bS, oH, oW]
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helpers::col2im(*block.launchContext(), columns, *gradI, sH, sW, pH, pW, iH, iW, dH, dW); // [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW]
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if(!isNCHW) {
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delete input;
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delete gradI;
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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static void depthwiseConv2d_(const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
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// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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// weights [kH, kW, iC, mC] always
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// bias [oC] = iC*mC
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// output [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)
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// kH filter(kernel) height
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// kW filter(kernel) width
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// sH strides height
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// sW strides width
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// pH paddings height
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// pW paddings width
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// dH dilations height
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// dW dilations width
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// isSameMode 0-VALID, 1-SAME
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// isNCHW 0-NCHW, 1-NHWC
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int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
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int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
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mC = weights->sizeAt(indWmC); // channels multiplier
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std::vector<std::vector<Nd4jLong>> modifColumns = {{1,0,4,5,2,3}, {iC,bS*oH*oW,kH*kW}}; // [bS,iC,kH,kW,oH,oW] -> [iC,bS,oH,oW,kH,kW] -> [iC,bS*oH*oW,kH*kW]
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std::vector<std::vector<Nd4jLong>> modifOutput;
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std::vector<Nd4jLong> outReShape;
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if(!isNCHW) {
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outReShape = {bS, oH, oW, iC, mC}; // [bS,oH,oW,iC*mC] -> [bS,oH,oW,iC,mC]
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modifOutput = {{3,0,1,2,4},{iC, bS*oH*oW, mC}}; // [bS,oH,oW,iC,mC] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC]
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input = new NDArray(input->permute({0, 3, 1, 2})); // [bS,iH,iW,iC] -> [bS,iC,iH,iW]
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}
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else {
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outReShape = {bS, iC, mC, oH, oW}; // [bS,iC*mC,oH,oW] -> [bS,iC,mC,oH,oW]
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modifOutput = {{1,0,3,4,2},{iC, bS*oH*oW, mC}}; // [bS,iC,mC,oH,oW] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC]
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}
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext());
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NDArray outputReshaped = output->reshape(output->ordering(), outReShape);
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helpers::im2col(*output->getContext(), *input, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW]
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MmulHelper::tensorDot(&columns, weights, &outputReshaped, modifColumns, {{2,0,1,3},{iC,kH*kW,mC}}, modifOutput); // [iC, bS*oH*oW, kW*kH] x [iC, kH*kW, mC] = [iC, bS*oH*oW, mC]
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if(bias)
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output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
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if(!isNCHW)
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delete input;
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
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// input [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
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// weights [kH, kW, iC, mC] always
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// bias [oC] = [iC*mC]
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// gradO [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
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// gradI [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW), epsilon
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// gradW [kH, kW, iC, mC] always
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// gradB [oC]
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// kH filter(kernel) height
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// kW filter(kernel) width
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// sH strides height
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// sW strides width
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// pH paddings height
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// pW paddings width
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// dH dilations height
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// dW dilations width
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// isSameMode 0-VALID, 1-SAME
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// isNCHW 0-NHWC, 1-NCHW
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int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
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int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
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mC = weights->sizeAt(indWmC); // channels multiplier
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std::vector<std::vector<Nd4jLong>> modifColumns = {{1,2,3,0,4,5}, {iC, kH*kW, bS*oH*oW}}; // [bS,iC,kH,kW,oH,oW] -> [iC, kH*kW, bS*oH*oW]
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std::vector<std::vector<Nd4jLong>> modifGradO1, modifGradO2;
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std::vector<Nd4jLong> gradOreShape;
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if(!isNCHW) {
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gradOreShape = {bS, oH, oW, iC, mC}; // [bS,oH,oW,iC*mC] -> [bS,oH,oW,iC,mC]
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modifGradO1 = {{3,0,1,2,4},{iC, bS*oH*oW, mC}}; // [bS,oH,oW,iC,mC] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC]
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modifGradO2 = {{3,0,1,2},{iC, mC, bS*oH*oW}}; // [bS,oH,oW,iC*mC] -> [iC*mC,bS,oH,oW] -> [iC,mC,bS*oH*oW]
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input = new NDArray(input->permute({0, 3, 1, 2})); // [bS,iH,iW,iC] -> [bS,iC,iH,iW]
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gradI = new NDArray(gradI->permute({0, 3, 1, 2})); // [bS,iH,iW,iC] -> [bS,iC,iH,iW]
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}
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else {
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gradOreShape = {bS, iC, mC, oH, oW}; // [bS,iC*mC,oH,oW] -> [bS,iC,mC,oH,oW]
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modifGradO1 = {{1,0,3,4,2},{iC, bS*oH*oW, mC}}; // [bS,iC,mC,oH,oW] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC]
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modifGradO2 = {{1,0,2,3},{iC, mC, bS*oH*oW}}; // [bS,iC*mC,oH,oW] -> [iC*mC,bS,oH,oW] -> [iC,mC,bS*oH*oW]
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}
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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NDArray columns(input->ordering(), {bS, iC, kH, kW, oH, oW}, input->dataType(), input->getContext());
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NDArray gradOreshaped = gradO->reshape(gradO->ordering(), gradOreShape);
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// ----- calculation of gradW and gradB ----- //
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helpers::im2col(*input->getContext(), *input, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW]
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nd4j::MmulHelper::tensorDot(&columns, &gradOreshaped, gradW, modifColumns, modifGradO1, {{2,0,1,3},{iC,kH*kW,mC}}); // [iC, kW*kH, bS*oH*oW] x [iC, bS*oH*oW, mC] = [iC, kH*kW, mC]
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// ----- calculation of gradB ----- //
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if(gradB) {
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NDArray* gradBR = gradB;
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if(gradB->rankOf() == 2)
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gradBR = new NDArray(gradB->reshape(gradB->ordering(), {(int)gradB->lengthOf()}));
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gradO->reduceAlongDimension(reduce::Sum, gradBR, {0,indOoH,indOoH+1}); // sum over bS, oH, oW
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if(gradBR != gradB)
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delete gradBR;
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}
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//----- calculation of gradI -----//
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nd4j::MmulHelper::tensorDot(weights, gradO, &columns, {{2,0,1,3},{iC,kH*kW,mC}}, modifGradO2, modifColumns); // [iC, kH*kW, mC] x [iC, mC, bS*oH*oW] = [iC, kW*kH, bS*oH*oW]
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helpers::col2im(*input->getContext(), columns, *gradI, sH, sW, pH, pW, iH, iW, dH, dW); // [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW]
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if(!isNCHW) {
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delete input;
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delete gradI;
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
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// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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// weightsDepth [kH, kW, iC, mC] always
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// weightsPoint [1, 1, iC*mC, oC] always
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// bias [oC], oC = iC*mC if weightsPoint=nullptr
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// output is [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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// kH filter(kernel) height
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// kW filter(kernel) width
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// sH strides height
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// sW strides width
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// pH paddings height
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// pW paddings width
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// dH dilations height
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// dW dilations width
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// isSameMode 0-VALID, 1-SAME
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// isNCHW 1-NCHW, 0-NHWC
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int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier, output channels, output height/width
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int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
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mC = weightsDepth->sizeAt(indWmC); // channels multiplier
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NDArray* outputDepth = output;
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if(weightsPoint) // if pointwise convolution is expected
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outputDepth = new NDArray(output->ordering(), !isNCHW ? std::vector<Nd4jLong>({bS, oH, oW, iC*mC}) : std::vector<Nd4jLong>({bS, iC*mC, oH, oW}), input->dataType(), input->getContext());
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// ----- perform depthwise convolution (if weightsPoint is absent then oC = iC*mC) ----- //
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ConvolutionUtils::depthwiseConv2d(block, input, weightsDepth, weightsPoint ? nullptr : bias, outputDepth, kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, isNCHW);
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// ----- perform pointwise convolution (oH = iH, oW = iW) ----- //
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if (weightsPoint) {
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ConvolutionUtils::conv2d(block, outputDepth, weightsPoint, bias, output, 1,1, 1,1, 0,0, 1,1, isSameMode, isNCHW); // in this case oH=iH, oW=iW
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delete outputDepth;
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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 upsampling2d_(const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW) {
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// input has shape [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)
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// output has shape [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC)
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const T* x = input.bufferAsT<T>();
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T* z = output.bufferAsT<T>();
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const uint dimIH = isNCHW ? 2 : 1;
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const uint dimIC = isNCHW ? 1 : 3;
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const uint bS = input.sizeAt(0);
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const uint iC = input.sizeAt(dimIC);
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const uint oH = output.sizeAt(dimIH);
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const uint oW = output.sizeAt(dimIH + 1);
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const Nd4jLong xStride0 = input.stridesOf()[0];
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const Nd4jLong xStride1 = input.stridesOf()[dimIC];
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const Nd4jLong xStride2 = input.stridesOf()[dimIH];
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const Nd4jLong xStride3 = input.stridesOf()[dimIH + 1];
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const Nd4jLong zStride0 = output.stridesOf()[0];
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const Nd4jLong zStride1 = output.stridesOf()[dimIC];
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const Nd4jLong zStride2 = output.stridesOf()[dimIH];
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const Nd4jLong zStride3 = output.stridesOf()[dimIH + 1];
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uint xCoord2, xCoord3;
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// loop through output array
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PRAGMA_OMP_PARALLEL_FOR_ARGS(collapse(4) private(xCoord2, xCoord3))
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for(uint b = 0; b < bS; ++b) {
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for(uint c = 0; c < iC; ++c) {
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for(uint h = 0; h < oH ; ++h) {
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for(uint w = 0; w < oW ; ++w) {
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xCoord2 = h / factorH;
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xCoord3 = w / factorW;
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z[b*zStride0 + c*zStride1 + h*zStride2 + w*zStride3] = x[b*xStride0 + c*xStride1 + xCoord2*xStride2 + xCoord3*xStride3];
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}
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}
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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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static void upsampling3d_(const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) {
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// input has shape [bS, iC, iD, iH, iW] (NCDHW) or [bS, iD, iH, iW, iC] (NDHWC)
|
|
// output has shape [bS, iC, factorD*iD, factorH*iH, factorW*iW ] (NCDHW) or [bS, factorD*iD, factorH*iH, factorW*iW, iC] (NDHWC)
|
|
|
|
const T* x = input.bufferAsT<T>();
|
|
T* z = output.bufferAsT<T>();
|
|
|
|
const uint dimID = isNCDHW ? 2 : 1;
|
|
const uint dimIC = isNCDHW ? 1 : 4;
|
|
|
|
const uint bS = input.sizeAt(0);
|
|
const uint iC = input.sizeAt(dimIC);
|
|
const uint oD = output.sizeAt(dimID);
|
|
const uint oH = output.sizeAt(dimID + 1);
|
|
const uint oW = output.sizeAt(dimID + 2);
|
|
|
|
const Nd4jLong xStride0 = input.stridesOf()[0];
|
|
const Nd4jLong xStride1 = input.stridesOf()[dimIC];
|
|
const Nd4jLong xStride2 = input.stridesOf()[dimID];
|
|
const Nd4jLong xStride3 = input.stridesOf()[dimID + 1];
|
|
const Nd4jLong xStride4 = input.stridesOf()[dimID + 2];
|
|
|
|
const Nd4jLong zStride0 = output.stridesOf()[0];
|
|
const Nd4jLong zStride1 = output.stridesOf()[dimIC];
|
|
const Nd4jLong zStride2 = output.stridesOf()[dimID];
|
|
const Nd4jLong zStride3 = output.stridesOf()[dimID + 1];
|
|
const Nd4jLong zStride4 = output.stridesOf()[dimID + 2];
|
|
|
|
uint xCoord2, xCoord3, xCoord4;
|
|
// loop through output array
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(collapse(5) private(xCoord2, xCoord3, xCoord4))
|
|
for(uint b = 0; b < bS; ++b) {
|
|
for(uint c = 0; c < iC; ++c) {
|
|
for(uint d = 0; d < oD ; ++d) {
|
|
for(uint h = 0; h < oH ; ++h) {
|
|
for(uint w = 0; w < oW ; ++w) {
|
|
|
|
xCoord2 = d / factorD;
|
|
xCoord3 = h / factorH;
|
|
xCoord4 = w / factorW;
|
|
|
|
z[b*zStride0 + c*zStride1 + d*zStride2 + h*zStride3 + w*zStride4] = x[b*xStride0 + c*xStride1 + xCoord2*xStride2 + xCoord3*xStride3 + xCoord4*xStride4];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void upsampling2dBP_(const NDArray& gradO, NDArray& gradI, const bool isNCHW) {
|
|
// gradO has shape [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC)
|
|
// gradI has shape [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)
|
|
|
|
const T* x = gradO.bufferAsT<T>();
|
|
T* z = gradI.bufferAsT<T>();
|
|
|
|
const uint dimIH = isNCHW ? 2 : 1;
|
|
const uint dimIC = isNCHW ? 1 : 3;
|
|
|
|
const uint bS = gradI.sizeAt(0);
|
|
const uint iC = gradI.sizeAt(dimIC);
|
|
const uint iH = gradI.sizeAt(dimIH);
|
|
const uint iW = gradI.sizeAt(dimIH + 1);
|
|
|
|
const uint factorH = gradO.sizeAt(dimIH) / iH;
|
|
const uint factorW = gradO.sizeAt(dimIH + 1) / iW;
|
|
|
|
const Nd4jLong xStride0 = gradO.stridesOf()[0];
|
|
const Nd4jLong xStride1 = gradO.stridesOf()[dimIC];
|
|
const Nd4jLong xStride2 = gradO.stridesOf()[dimIH];
|
|
const Nd4jLong xStride3 = gradO.stridesOf()[dimIH + 1];
|
|
|
|
const Nd4jLong zStride0 = gradI.stridesOf()[0];
|
|
const Nd4jLong zStride1 = gradI.stridesOf()[dimIC];
|
|
const Nd4jLong zStride2 = gradI.stridesOf()[dimIH];
|
|
const Nd4jLong zStride3 = gradI.stridesOf()[dimIH + 1];
|
|
|
|
// loop through output array
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(collapse(4))
|
|
for(uint b = 0; b < bS; ++b) {
|
|
for(uint c = 0; c < iC; ++c) {
|
|
for(uint h = 0; h < iH; ++h) {
|
|
for(uint w = 0; w < iW; ++w) {
|
|
|
|
const auto zOffset = b*zStride0 + c*zStride1 + h*zStride2 + w*zStride3;
|
|
|
|
z[zOffset] = 0;
|
|
|
|
for(uint xh = h * factorH; xh < h * factorH + factorH; ++xh)
|
|
for(uint xw = w * factorW; xw < w * factorW + factorW; ++xw)
|
|
z[zOffset] += x[b*xStride0 + c*xStride1 + xh*xStride2 + xw*xStride3];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void upsampling3dBP_(const NDArray& gradO, NDArray& gradI, const bool isNCDHW) {
|
|
|
|
// input has shape [bS, iC, iD, iH, iW] (NCDHW) or [bS, iD, iH, iW, iC] (NDHWC)
|
|
// output has shape [bS, iC, factorD*iD, factorH*iH, factorW*iW ] (NCDHW) or [bS, factorD*iD, factorH*iH, factorW*iW, iC] (NDHWC)
|
|
|
|
const T* x = gradO.bufferAsT<T>();
|
|
T* z = gradI.bufferAsT<T>();
|
|
|
|
const uint dimID = isNCDHW ? 2 : 1;
|
|
const uint dimIC = isNCDHW ? 1 : 4;
|
|
|
|
const uint bS = gradI.sizeAt(0);
|
|
const uint iC = gradI.sizeAt(dimIC);
|
|
const uint iD = gradI.sizeAt(dimID);
|
|
const uint iH = gradI.sizeAt(dimID + 1);
|
|
const uint iW = gradI.sizeAt(dimID + 2);
|
|
|
|
const uint factorD = gradO.sizeAt(dimID) / iD;
|
|
const uint factorH = gradO.sizeAt(dimID + 1) / iH;
|
|
const uint factorW = gradO.sizeAt(dimID + 2) / iW;
|
|
|
|
const Nd4jLong xStride0 = gradO.stridesOf()[0];
|
|
const Nd4jLong xStride1 = gradO.stridesOf()[dimIC];
|
|
const Nd4jLong xStride2 = gradO.stridesOf()[dimID];
|
|
const Nd4jLong xStride3 = gradO.stridesOf()[dimID + 1];
|
|
const Nd4jLong xStride4 = gradO.stridesOf()[dimID + 2];
|
|
|
|
const Nd4jLong zStride0 = gradI.stridesOf()[0];
|
|
const Nd4jLong zStride1 = gradI.stridesOf()[dimIC];
|
|
const Nd4jLong zStride2 = gradI.stridesOf()[dimID];
|
|
const Nd4jLong zStride3 = gradI.stridesOf()[dimID + 1];
|
|
const Nd4jLong zStride4 = gradI.stridesOf()[dimID + 2];
|
|
|
|
// loop through output array
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(collapse(5))
|
|
for(uint b = 0; b < bS; ++b) {
|
|
for(uint c = 0; c < iC; ++c) {
|
|
for(uint d = 0; d < iD; ++d) {
|
|
for(uint h = 0; h < iH; ++h) {
|
|
for(uint w = 0; w < iW; ++w) {
|
|
|
|
const auto zOffset = b*zStride0 + c*zStride1 + d*zStride2 + h*zStride3 + w*zStride4;
|
|
|
|
z[zOffset] = 0;
|
|
|
|
for(uint xd = d * factorD; xd < d * factorD + factorD; ++xd)
|
|
for(uint xh = h * factorH; xh < h * factorH + factorH; ++xh)
|
|
for(uint xw = w * factorW; xw < w * factorW + factorW; ++xw)
|
|
z[zOffset] += x[b*xStride0 + c*xStride1 + xd*xStride2 + xh*xStride3 + xw*xStride4];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void pooling2d_(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) {
|
|
// input is [bS, iC, iH, iW]
|
|
// output is [bS, iC, oH, oW]
|
|
T* out = output.bufferAsT<T>();
|
|
T* in = const_cast<NDArray&>(input).bufferAsT<T>();
|
|
|
|
const int kHEff = kH + (kH-1)*(dH-1);
|
|
const int kWEff = kW + (kW-1)*(dW-1);
|
|
|
|
const int bS = input.sizeAt(0);
|
|
const int iC = input.sizeAt(1);
|
|
const int iH = input.sizeAt(2);
|
|
const int iW = input.sizeAt(3);
|
|
const int oC = output.sizeAt(1);
|
|
const int oH = output.sizeAt(2);
|
|
const int oW = output.sizeAt(3);
|
|
|
|
nd4j_debug("MKL-DNN is not used for pooling2d!\n", 0);
|
|
|
|
const Nd4jLong iStride0 = input.stridesOf()[0];
|
|
const Nd4jLong iStride1 = input.stridesOf()[1];
|
|
const Nd4jLong iStride2 = input.stridesOf()[2];
|
|
const Nd4jLong iStride3 = input.stridesOf()[3];
|
|
const Nd4jLong oStride0 = output.stridesOf()[0];
|
|
const Nd4jLong oStride1 = output.stridesOf()[1];
|
|
const Nd4jLong oStride2 = output.stridesOf()[2];
|
|
const Nd4jLong oStride3 = output.stridesOf()[3];
|
|
|
|
const Nd4jLong iStep2 = dH*iStride2;
|
|
const Nd4jLong iStep3 = dW*iStride3;
|
|
const int kProd = kH*kW;
|
|
|
|
Nd4jLong hstart, wstart, hend, wend;
|
|
T *pIn;
|
|
|
|
if(poolingMode == 0) { // max
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, hstart, wstart, hend, wend) collapse(2))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-hstart) / static_cast<T>(dH));
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW -1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-wstart) / static_cast<T>(dW));
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(hend-iH) / static_cast<T>(dH));
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(wend-iW) / static_cast<T>(dW));
|
|
|
|
hstart *= iStride2;
|
|
hend *= iStride2;
|
|
wstart *= iStride3;
|
|
wend *= iStride3;
|
|
|
|
T max = -DataTypeUtils::max<T>();
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep3) {
|
|
T val = pIn[kh + kw];
|
|
if (val > max)
|
|
max = val;
|
|
}
|
|
out[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3] = max;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*************************************************************************/
|
|
else if(poolingMode == 1) { // avg
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, hstart, wstart, hend, wend) collapse(2))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-hstart) / static_cast<T>(dH));
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW -1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-wstart) / static_cast<T>(dW));
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(hend-iH) / static_cast<T>(dH));
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(wend-iW) / static_cast<T>(dW));
|
|
|
|
hstart *= iStride2;
|
|
hend *= iStride2;
|
|
wstart *= iStride3;
|
|
wend *= iStride3;
|
|
|
|
T sum = static_cast<T>(0.f);
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep3)
|
|
sum += pIn[kh + kw];
|
|
|
|
if (extraParam0 == 0) { //Exclude padding
|
|
int a = (hend-hstart)/iStep2 + ((hend-hstart) % iStep2 == 0 ? 0 : 1);
|
|
int b = (wend-wstart)/iStep3 + ((wend-wstart) % iStep3 == 0 ? 0 : 1);
|
|
sum /= static_cast<T>(a * b); // Accounts for dilation
|
|
}
|
|
else if (extraParam0 == 1) //Include padding
|
|
sum /= kProd;
|
|
|
|
out[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3] = sum;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*************************************************************************/
|
|
else if(poolingMode == 2) { // pnorm
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, hstart, wstart, hend, wend) collapse(2))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-hstart) / static_cast<T>(dH));
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW -1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-wstart) / static_cast<T>(dW));
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(hend-iH) / static_cast<T>(dH));
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(wend-iW) / static_cast<T>(dW));
|
|
|
|
hstart *= iStride2;
|
|
hend *= iStride2;
|
|
wstart *= iStride3;
|
|
wend *= iStride3;
|
|
|
|
T sum = static_cast<T>(0.f);
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep3)
|
|
sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(pIn[kh + kw]), extraParam0);
|
|
|
|
sum = nd4j::math::nd4j_pow<T,T,T>(sum, static_cast<T>((T)1.f) / extraParam0);
|
|
|
|
out[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3] = sum;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
nd4j_printf("ConvolutionUtils::pooling2d: pooling mode argument can take three values only: 0, 1, 2, but got %i instead !\n", poolingMode);
|
|
throw "";
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void pooling3d_(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) {
|
|
// input is [bS, iC, iD, iH, iW]
|
|
// output is [bS, iC, oD, oH, oW]
|
|
T* out = output.bufferAsT<T>();
|
|
T* in = const_cast<NDArray&>(input).bufferAsT<T>();
|
|
|
|
const int kDEff = kD + (kD-1)*(dD-1);
|
|
const int kHEff = kH + (kH-1)*(dH-1);
|
|
const int kWEff = kW + (kW-1)*(dW-1);
|
|
|
|
const int bS = input.sizeAt(0);
|
|
const int iC = input.sizeAt(1);
|
|
const int iD = input.sizeAt(2);
|
|
const int iH = input.sizeAt(3);
|
|
const int iW = input.sizeAt(4);
|
|
const int oC = output.sizeAt(1);
|
|
const int oD = output.sizeAt(2);
|
|
const int oH = output.sizeAt(3);
|
|
const int oW = output.sizeAt(4);
|
|
|
|
nd4j_debug("MKL-DNN is not used for pooling3d!\n", 0);
|
|
|
|
const Nd4jLong iStride0 = input.stridesOf()[0];
|
|
const Nd4jLong iStride1 = input.stridesOf()[1];
|
|
const Nd4jLong iStride2 = input.stridesOf()[2];
|
|
const Nd4jLong iStride3 = input.stridesOf()[3];
|
|
const Nd4jLong iStride4 = input.stridesOf()[4];
|
|
const Nd4jLong oStride0 = output.stridesOf()[0];
|
|
const Nd4jLong oStride1 = output.stridesOf()[1];
|
|
const Nd4jLong oStride2 = output.stridesOf()[2];
|
|
const Nd4jLong oStride3 = output.stridesOf()[3];
|
|
const Nd4jLong oStride4 = output.stridesOf()[4];
|
|
const Nd4jLong iStep2 = dD*iStride2;
|
|
const Nd4jLong iStep3 = dH*iStride3;
|
|
const Nd4jLong iStep4 = dW*iStride4;
|
|
const int kProd = kD*kH*kW;
|
|
|
|
Nd4jLong dstart, hstart, wstart, dend, hend, wend;
|
|
T sum, *pIn;
|
|
|
|
if(poolingMode == 0) { // max
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, sum, dstart, hstart, wstart, dend, hend, wend))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int od = 0; od < oD; ++od) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
|
|
dstart = od * sD - pD;
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
dend = dstart + kDEff;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(dstart < 0)
|
|
dstart += dD * ((-dstart + dD - 1) / dD);
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH);
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW - 1) / dW);
|
|
if(dend > iD)
|
|
dend -= dD * ((dend-iD + dD - 1) / dD);
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH);
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW);
|
|
|
|
dstart *= iStride2;
|
|
dend *= iStride2;
|
|
hstart *= iStride3;
|
|
hend *= iStride3;
|
|
wstart *= iStride4;
|
|
wend *= iStride4;
|
|
|
|
sum = -DataTypeUtils::max<T>();
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += iStep2)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep3)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep4) {
|
|
T val = pIn[kd + kh + kw];
|
|
if (val > sum)
|
|
sum = val;
|
|
}
|
|
out[b * oStride0 + c * oStride1 + od * oStride2 + oh * oStride3 + ow * oStride4] = sum;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*************************************************************************/
|
|
else if(poolingMode == 1) { // avg
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, sum, dstart, hstart, wstart, dend, hend, wend))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int od = 0; od < oD; ++od) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
|
|
dstart = od * sD - pD;
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
dend = dstart + kDEff;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(dstart < 0)
|
|
dstart += dD * ((-dstart + dD - 1) / dD);
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH);
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW - 1) / dW);
|
|
if(dend > iD)
|
|
dend -= dD * ((dend-iD + dD - 1) / dD);
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH);
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW);
|
|
|
|
dstart *= iStride2;
|
|
dend *= iStride2;
|
|
hstart *= iStride3;
|
|
hend *= iStride3;
|
|
wstart *= iStride4;
|
|
wend *= iStride4;
|
|
|
|
sum = static_cast<T>(0.);
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += iStep2)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep3)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep4)
|
|
sum += pIn[kd + kh + kw];
|
|
|
|
if (extraParam0 == 0) //Exclude padding
|
|
sum /= nd4j::math::nd4j_ceil<double,T>(static_cast<double>(dend-dstart) / static_cast<double>(iStep2)) * nd4j::math::nd4j_ceil<double,T>(static_cast<double>(hend-hstart) / static_cast<double>(iStep3)) * nd4j::math::nd4j_ceil<double,T>(static_cast<double>(wend-wstart) / static_cast<double>(iStep4)); //Accounts for dilation
|
|
else if (extraParam0 == 1) //Include padding
|
|
sum /= kProd;
|
|
|
|
out[b * oStride0 + c * oStride1 + od * oStride2 + oh * oStride3 + ow * oStride4] = sum;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*************************************************************************/
|
|
else if(poolingMode == 2) { // pnorm
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, sum, dstart, hstart, wstart, dend, hend, wend))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int od = 0; od < oD; ++od) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
|
|
dstart = od * sD - pD;
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
dend = dstart + kDEff;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(dstart < 0)
|
|
dstart += dD * ((-dstart + dD - 1) / dD);
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH);
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW - 1) / dW);
|
|
if(dend > iD)
|
|
dend -= dD * ((dend-iD + dD - 1) / dD);
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH);
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW);
|
|
|
|
dstart *= iStride2;
|
|
dend *= iStride2;
|
|
hstart *= iStride3;
|
|
hend *= iStride3;
|
|
wstart *= iStride4;
|
|
wend *= iStride4;
|
|
|
|
sum = static_cast<T>(0.);
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += iStep2)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep3)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep4)
|
|
sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(pIn[kd + kh + kw]), extraParam0);
|
|
|
|
sum = nd4j::math::nd4j_pow<T,T,T>(sum, (T) 1.f / extraParam0);
|
|
|
|
out[b * oStride0 + c * oStride1 + od * oStride2 + oh * oStride3 + ow * oStride4] = sum;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
nd4j_printf("ConvolutionUtils::pooling3d: pooling mode argument can take three values only: 0, 1, 2, but got %i instead !\n", poolingMode);
|
|
throw "";
|
|
}
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void pooling2dBP_(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) {
|
|
// input [bS, iC, iH, iW]
|
|
// gradI [bS, iC, iH, iW] -> gradI is output in this function
|
|
// gradO [bS, iC, oH, oW]
|
|
|
|
// initial zeroing of gradI
|
|
gradI.nullify();
|
|
|
|
T* in = const_cast<NDArray&>(input).bufferAsT<T>();
|
|
T* gO = const_cast<NDArray&>(gradO).bufferAsT<T>();
|
|
T* gI = gradI.bufferAsT<T>();
|
|
|
|
const int kHEff = kH + (kH-1)*(dH-1);
|
|
const int kWEff = kW + (kW-1)*(dW-1);
|
|
|
|
const int bS = gradI.sizeAt(0);
|
|
const int iC = gradI.sizeAt(1);
|
|
const int iH = gradI.sizeAt(2);
|
|
const int iW = gradI.sizeAt(3);
|
|
const int oC = gradO.sizeAt(1);
|
|
const int oH = gradO.sizeAt(2);
|
|
const int oW = gradO.sizeAt(3);
|
|
|
|
nd4j_debug("MKL-DNN is not used for pooling2d_bp!\n", 0);
|
|
|
|
const Nd4jLong iStride0 = input.stridesOf()[0];
|
|
const Nd4jLong iStride1 = input.stridesOf()[1];
|
|
const Nd4jLong iStride2 = input.stridesOf()[2];
|
|
const Nd4jLong iStride3 = input.stridesOf()[3];
|
|
const Nd4jLong gIStride0 = gradI.stridesOf()[0];
|
|
const Nd4jLong gIStride1 = gradI.stridesOf()[1];
|
|
const Nd4jLong gIStride2 = gradI.stridesOf()[2];
|
|
const Nd4jLong gIStride3 = gradI.stridesOf()[3];
|
|
const Nd4jLong oStride0 = gradO.stridesOf()[0];
|
|
const Nd4jLong oStride1 = gradO.stridesOf()[1];
|
|
const Nd4jLong oStride2 = gradO.stridesOf()[2];
|
|
const Nd4jLong oStride3 = gradO.stridesOf()[3];
|
|
const Nd4jLong iStep2 = dH*iStride2;
|
|
const Nd4jLong iStep3 = dW*iStride3;
|
|
const Nd4jLong gIStep2 = dH*gIStride2;
|
|
const Nd4jLong gIStep3 = dW*gIStride3;
|
|
const int kProd = kH*kW;
|
|
|
|
const bool sameStrides = iStride0 == gIStride0 && iStride1 == gIStride1 && iStride2 == gIStride2 && iStride3 == gIStride3;
|
|
|
|
Nd4jLong hstart, wstart,hend, wend, maxKH, maxKW;
|
|
T sum, valO, *pIn, *pgI;
|
|
|
|
if(poolingMode == 0) { // max
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, valO, sum, hstart, wstart, hend, wend, maxKH, maxKW))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-hstart) / static_cast<T>(dH));
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW -1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-wstart) / static_cast<T>(dW));
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(hend-iH) / static_cast<T>(dH));
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(wend-iW) / static_cast<T>(dW));
|
|
|
|
sum = -DataTypeUtils::max<T>();
|
|
valO = gO[b*oStride0 + c*oStride1 + oh*oStride2 + ow*oStride3];
|
|
|
|
if(sameStrides) {
|
|
|
|
hstart *= iStride2;
|
|
hend *= iStride2;
|
|
wstart *= iStride3;
|
|
wend *= iStride3;
|
|
|
|
// we set these to default values
|
|
maxKH = hstart;
|
|
maxKW = wstart;
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep3) {
|
|
T valIn = pIn[kh + kw];
|
|
if (valIn > sum) {
|
|
sum = valIn;
|
|
maxKH = kh;
|
|
maxKW = kw;
|
|
}
|
|
}
|
|
gI[pIn - in + maxKH + maxKW] += valO;
|
|
}
|
|
else {
|
|
|
|
// we set these to default values
|
|
maxKH = hstart;
|
|
maxKW = wstart;
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += dH)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += dW) {
|
|
T valIn = pIn[kh * iStride2 + kw * iStride3];
|
|
if (valIn > sum) {
|
|
sum = valIn;
|
|
maxKH = kh;
|
|
maxKW = kw;
|
|
}
|
|
}
|
|
gI[b * gIStride0 + c * gIStride1 + maxKH * gIStride2 + maxKW * gIStride3] += valO;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*************************************************************************/
|
|
else if(poolingMode == 1) { // avg
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pgI, valO, hstart, wstart, hend, wend))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pgI = gI + b * gIStride0 + c * gIStride1;
|
|
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-hstart) / static_cast<T>(dH));
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW -1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-wstart) / static_cast<T>(dW));
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(hend-iH) / static_cast<T>(dH));
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(wend-iW) / static_cast<T>(dW));
|
|
|
|
hstart *= gIStride2;
|
|
hend *= gIStride2;
|
|
wstart *= gIStride3;
|
|
wend *= gIStride3;
|
|
|
|
valO = gO[b*oStride0 + c*oStride1 + oh*oStride2 + ow*oStride3];
|
|
|
|
if ((int) extraParam0 == 0) //Exclude padding
|
|
valO /= static_cast<T>(nd4j::math::nd4j_ceil<double,T>(static_cast<double>(hend-hstart) / static_cast<double>(gIStep2))) * static_cast<T>(nd4j::math::nd4j_ceil<double,T>(static_cast<double>(wend-wstart) / static_cast<double>(gIStep3))); //Accounts for dilation
|
|
else if ((int) extraParam0 == 1) //Include padding
|
|
valO /= kProd;
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += gIStep2)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += gIStep3)
|
|
pgI[kh + kw] += valO;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*************************************************************************/
|
|
else if(poolingMode == 2) { // pnorm
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, valO, pgI, sum, hstart, wstart, hend, wend))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
pgI = sameStrides ? gI + (pIn - in) : gI + b * gIStride0 + c * gIStride1;
|
|
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-hstart) / static_cast<T>(dH));
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW -1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(-wstart) / static_cast<T>(dW));
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(hend-iH) / static_cast<T>(dH));
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil<T,T>(static_cast<T>(wend-iW) / static_cast<T>(dW));
|
|
|
|
sum = static_cast<T>(0.f);
|
|
valO = gO[b*oStride0 + c*oStride1 + oh*oStride2 + ow*oStride3];
|
|
|
|
if(sameStrides) {
|
|
|
|
hstart *= iStride2;
|
|
hend *= iStride2;
|
|
wstart *= iStride3;
|
|
wend *= iStride3;
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep3)
|
|
sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(pIn[kh + kw]), extraParam0);
|
|
|
|
valO *= nd4j::math::nd4j_pow<T,T,T>(sum, ((T)1. - extraParam0) / extraParam0);
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep3)
|
|
pgI[kh + kw] += valO * nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(pIn[kh + kw]), extraParam0 - 1.f) * nd4j::math::nd4j_sgn<T,T>(pIn[kh + kw]);
|
|
}
|
|
else {
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += dH)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += dW)
|
|
sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(pIn[kh * iStride2 + kw * iStride3]), extraParam0);
|
|
|
|
valO *= nd4j::math::nd4j_pow<T,T,T>(sum, ((T)1. - extraParam0) / extraParam0);
|
|
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += dH) {
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += dW) {
|
|
const auto inVal = pIn[kh * iStride2 + kw * iStride3];
|
|
pgI[kh * gIStride2 + kw * gIStride3] += valO * nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(inVal), extraParam0 - 1.f) * nd4j::math::nd4j_sgn<T,T>(inVal);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
nd4j_printf("ConvolutionUtils::pooling2dBP: pooling mode argument can take three values only: 0, 1, 2, but got %i instead !\n", poolingMode);
|
|
throw "";
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void pooling3dBP_(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) {
|
|
// input [bS, iC, iD, iH, iW]
|
|
// gradI [bS, iC, iD, iH, iW] -> gradI is output in this function
|
|
// gradO [bS, iC, oD, oH, oW]
|
|
|
|
// initial zeroing of gradI
|
|
gradI.nullify();
|
|
|
|
T* in = const_cast<NDArray&>(input).bufferAsT<T>();
|
|
T* gO = const_cast<NDArray&>(gradO).bufferAsT<T>();
|
|
T* gI = gradI.bufferAsT<T>();
|
|
|
|
const int kDEff = kD + (kD-1)*(dD-1);
|
|
const int kHEff = kH + (kH-1)*(dH-1);
|
|
const int kWEff = kW + (kW-1)*(dW-1);
|
|
|
|
const int bS = gradI.sizeAt(0);
|
|
const int iC = gradI.sizeAt(1);
|
|
const int iD = gradI.sizeAt(2);
|
|
const int iH = gradI.sizeAt(3);
|
|
const int iW = gradI.sizeAt(4);
|
|
const int oC = gradO.sizeAt(1);
|
|
const int oD = gradO.sizeAt(2);
|
|
const int oH = gradO.sizeAt(3);
|
|
const int oW = gradO.sizeAt(4);
|
|
|
|
nd4j_debug("MKL-DNN is not used for pooling3d_bp!\n", 0);
|
|
|
|
const Nd4jLong iStride0 = input.stridesOf()[0];
|
|
const Nd4jLong iStride1 = input.stridesOf()[1];
|
|
const Nd4jLong iStride2 = input.stridesOf()[2];
|
|
const Nd4jLong iStride3 = input.stridesOf()[3];
|
|
const Nd4jLong iStride4 = input.stridesOf()[4];
|
|
const Nd4jLong gIStride0 = gradI.stridesOf()[0];
|
|
const Nd4jLong gIStride1 = gradI.stridesOf()[1];
|
|
const Nd4jLong gIStride2 = gradI.stridesOf()[2];
|
|
const Nd4jLong gIStride3 = gradI.stridesOf()[3];
|
|
const Nd4jLong gIStride4 = gradI.stridesOf()[4];
|
|
const Nd4jLong oStride0 = gradO.stridesOf()[0];
|
|
const Nd4jLong oStride1 = gradO.stridesOf()[1];
|
|
const Nd4jLong oStride2 = gradO.stridesOf()[2];
|
|
const Nd4jLong oStride3 = gradO.stridesOf()[3];
|
|
const Nd4jLong oStride4 = gradO.stridesOf()[4];
|
|
const Nd4jLong iStep2 = dD*iStride2;
|
|
const Nd4jLong iStep3 = dH*iStride3;
|
|
const Nd4jLong iStep4 = dW*iStride4;
|
|
const Nd4jLong gIStep2 = dD*gIStride2;
|
|
const Nd4jLong gIStep3 = dH*gIStride3;
|
|
const Nd4jLong gIStep4 = dW*gIStride4;
|
|
const int kProd = kD*kH*kW;
|
|
|
|
const bool sameStrides = iStride0 == gIStride0 && iStride1 == gIStride1 && iStride2 == gIStride2 && iStride3 == gIStride3 && iStride4 == gIStride4;
|
|
|
|
Nd4jLong dstart, hstart, wstart, dend, hend, wend, maxKD, maxKH, maxKW;
|
|
T sum, valO, *pIn, *pgI;
|
|
|
|
if(poolingMode == 0) { // max
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, valO, sum, dstart, hstart, wstart, dend, hend, wend, maxKD, maxKH, maxKW))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int od = 0; od < oD; ++od) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
|
|
dstart = od * sD - pD;
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
dend = dstart + kDEff;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(dstart < 0)
|
|
dstart += dD * ((-dstart + dD - 1) / dD);
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH);
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW - 1) / dW);
|
|
if(dend > iD)
|
|
dend -= dD * ((dend-iD + dD - 1) / dD);
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH);
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW);
|
|
|
|
sum = -DataTypeUtils::max<T>();
|
|
valO = gO[b*oStride0 + c*oStride1+ od*oStride2 + oh*oStride3 + ow*oStride4];
|
|
|
|
if(sameStrides) {
|
|
|
|
dstart *= iStride2;
|
|
dend *= iStride2;
|
|
hstart *= iStride3;
|
|
hend *= iStride3;
|
|
wstart *= iStride4;
|
|
wend *= iStride4;
|
|
|
|
maxKD = dstart;
|
|
maxKH = hstart;
|
|
maxKW = wstart;
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += iStep2)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep3)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep4) {
|
|
T valIn = pIn[kd + kh + kw];
|
|
if (valIn > sum) {
|
|
sum = valIn;
|
|
maxKD = kd;
|
|
maxKH = kh;
|
|
maxKW = kw;
|
|
}
|
|
}
|
|
gI[pIn - in + maxKD + maxKH + maxKW] += valO;
|
|
}
|
|
else {
|
|
|
|
// we set these to default values
|
|
maxKH = hstart;
|
|
maxKW = wstart;
|
|
maxKD = dstart;
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += dD)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += dH)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += dW) {
|
|
T valIn = pIn[kd * iStride2 + kh * iStride3 + kw * iStride4];
|
|
if (valIn > sum) {
|
|
sum = valIn;
|
|
maxKD = kd;
|
|
maxKH = kh;
|
|
maxKW = kw;
|
|
}
|
|
}
|
|
gI[b * gIStride0 + c * gIStride1 + maxKD * gIStride2 + maxKH * gIStride3 + maxKW * gIStride4] += valO;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*************************************************************************/
|
|
else if(poolingMode == 1) { // avg
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pgI, valO, dstart, hstart, wstart, dend, hend, wend))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int od = 0; od < oD; ++od) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pgI = gI + b * gIStride0 + c * gIStride1;
|
|
|
|
dstart = od * sD - pD;
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
dend = dstart + kDEff;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(dstart < 0)
|
|
dstart += dD * ((-dstart + dD - 1) / dD);
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH);
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW - 1) / dW);
|
|
if(dend > iD)
|
|
dend -= dD * ((dend-iD + dD - 1) / dD);
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH);
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW);
|
|
|
|
dstart *= gIStride2;
|
|
dend *= gIStride2;
|
|
hstart *= gIStride3;
|
|
hend *= gIStride3;
|
|
wstart *= gIStride4;
|
|
wend *= gIStride4;
|
|
|
|
valO = gO[b*oStride0 + c*oStride1+ od*oStride2 + oh*oStride3 + ow*oStride4];
|
|
|
|
if (extraParam0 == 0) //Exclude padding
|
|
valO /= nd4j::math::nd4j_ceil<double,T>(static_cast<double>(dend-dstart) / static_cast<double>(gIStep2)) * nd4j::math::nd4j_ceil<double,T>(static_cast<double>(hend-hstart) / static_cast<double>(gIStep3)) * nd4j::math::nd4j_ceil<double,T>(static_cast<double>(wend-wstart) / static_cast<double>(gIStep4)); //Accounts for dilation
|
|
else if (extraParam0 == 1) //Include padding
|
|
valO /= kProd;
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += gIStep2)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += gIStep3)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += gIStep4)
|
|
pgI[kd + kh + kw] += valO;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*************************************************************************/
|
|
else if(poolingMode == 2) { // pnorm
|
|
PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, pgI, valO, sum, dstart, hstart, wstart, dend, hend, wend))
|
|
for(int b = 0; b < bS; ++b) {
|
|
for(int c = 0; c < iC; ++c) {
|
|
for(int od = 0; od < oD; ++od) {
|
|
for(int oh = 0; oh < oH; ++oh) {
|
|
for(int ow = 0; ow < oW; ++ow) {
|
|
|
|
pIn = in + b * iStride0 + c * iStride1;
|
|
pgI = gI + (pIn - in);
|
|
|
|
dstart = od * sD - pD;
|
|
hstart = oh * sH - pH;
|
|
wstart = ow * sW - pW;
|
|
dend = dstart + kDEff;
|
|
hend = hstart + kHEff;
|
|
wend = wstart + kWEff;
|
|
|
|
if(dstart < 0)
|
|
dstart += dD * ((-dstart + dD - 1) / dD);
|
|
if(hstart < 0)
|
|
hstart += dH * ((-hstart + dH - 1) / dH);
|
|
if(wstart < 0)
|
|
wstart += dW * ((-wstart + dW - 1) / dW);
|
|
if(dend > iD)
|
|
dend -= dD * ((dend-iD + dD - 1) / dD);
|
|
if(hend > iH)
|
|
hend -= dH * ((hend-iH + dH - 1) / dH);
|
|
if(wend > iW)
|
|
wend -= dW * ((wend-iW + dW - 1) / dW);
|
|
|
|
sum = static_cast<T>(0.);
|
|
valO = gO[b*oStride0 + c*oStride1+ od*oStride2 + oh*oStride3 + ow*oStride4];
|
|
|
|
if(sameStrides) {
|
|
|
|
dstart *= iStride2;
|
|
dend *= iStride2;
|
|
hstart *= iStride3;
|
|
hend *= iStride3;
|
|
wstart *= iStride4;
|
|
wend *= iStride4;
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += iStep2)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep3)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep4)
|
|
sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(pIn[kd + kh + kw]), extraParam0);
|
|
|
|
valO *= nd4j::math::nd4j_pow<T,T,T>(sum, ((T)1.f - extraParam0) / extraParam0);
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += iStep2)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += iStep3)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += iStep4)
|
|
pgI[kd + kh + kw] += valO * nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(pIn[kd + kh + kw]), extraParam0 - (T)1.f) * nd4j::math::nd4j_sgn<T,T>(pIn[kd + kh + kw]);
|
|
}
|
|
else {
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += dD)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += dH)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += dW)
|
|
sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(pIn[kd * iStride2 + kh * iStride3 + kw * iStride4]), extraParam0);
|
|
|
|
valO *= nd4j::math::nd4j_pow<T,T,T>(sum, ((T)1.f - extraParam0) / extraParam0);
|
|
|
|
for (Nd4jLong kd = dstart; kd < dend; kd += dD)
|
|
for (Nd4jLong kh = hstart; kh < hend; kh += dH)
|
|
for (Nd4jLong kw = wstart; kw < wend; kw += dW) {
|
|
const auto inVal = pIn[kD * iStride2 + kh * iStride3 + kw * iStride4];
|
|
pgI[kd * gIStride2 + kh * gIStride3 + kw * gIStride4] += valO * nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(inVal), extraParam0 - 1.f) * nd4j::math::nd4j_sgn<T,T>(inVal);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
nd4j_printf("ConvolutionUtils::pooling3dBP: pooling mode argument can take three values only: 0, 1, 2, but got %i instead !\n", poolingMode);
|
|
throw "";
|
|
}
|
|
}
|
|
|
|
|
|
|
|
|
|
void ConvolutionUtils::conv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
|
|
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::conv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
|
|
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_, (block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
|
|
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::depthwiseConv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
|
|
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2dBP_, (input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::sconv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
|
|
BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), sconv2d_, (block, input, weightsDepth, weightsPoint, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::vol2col(nd4j::graph::Context& block, const NDArray& volume, NDArray& columns, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
|
|
BUILD_SINGLE_SELECTOR(volume.dataType(), vol2col_, (volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::col2vol(nd4j::graph::Context& block, const NDArray& columns, NDArray& volume, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
|
|
BUILD_SINGLE_SELECTOR(volume.dataType(), col2vol_, (columns, volume, sD, sH, sW, pD, pH, pW, dD, dH, dW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::upsampling2d(nd4j::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);
|
|
}
|
|
void ConvolutionUtils::upsampling3d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), upsampling3d_, (input, output, factorD, factorH, factorW, isNCDHW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::upsampling2dBP(nd4j::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) {
|
|
BUILD_SINGLE_SELECTOR(gradO.dataType(), upsampling2dBP_, (gradO, gradI, isNCHW), FLOAT_TYPES);
|
|
}
|
|
void ConvolutionUtils::upsampling3dBP(nd4j::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) {
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BUILD_SINGLE_SELECTOR(gradO.dataType(), upsampling3dBP_, (gradO, gradI, isNCHW), FLOAT_TYPES);
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}
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void ConvolutionUtils::pooling2d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const PoolingType poolingMode, const int extraParam0) {
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BUILD_SINGLE_SELECTOR(input.dataType(), pooling2d_, (block, input, output, kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0), FLOAT_TYPES);
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}
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void ConvolutionUtils::pooling3d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) {
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BUILD_SINGLE_SELECTOR(input.dataType(), pooling3d_, (block, input, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, poolingMode, extraParam0), FLOAT_TYPES);
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}
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void ConvolutionUtils::pooling2dBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) {
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BUILD_SINGLE_SELECTOR(input.dataType(), pooling2dBP_, (block, input, gradO, gradI, kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0), FLOAT_TYPES);
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}
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void ConvolutionUtils::pooling3dBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) {
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BUILD_SINGLE_SELECTOR(input.dataType(), pooling3dBP_, (block, input, gradO, gradI, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, poolingMode, extraParam0), FLOAT_TYPES);
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}
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}
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} |