133 lines
8.0 KiB
C++
133 lines
8.0 KiB
C++
/*******************************************************************************
|
|
* Copyright (c) 2015-2018 Skymind, Inc.
|
|
*
|
|
* This program and the accompanying materials are made available under the
|
|
* terms of the Apache License, Version 2.0 which is available at
|
|
* https://www.apache.org/licenses/LICENSE-2.0.
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
|
|
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
|
|
* License for the specific language governing permissions and limitations
|
|
* under the License.
|
|
*
|
|
* SPDX-License-Identifier: Apache-2.0
|
|
******************************************************************************/
|
|
|
|
//
|
|
// @author raver119@gmail.com
|
|
// @author Yurii Shyrma (iuriish@yahoo.com)
|
|
//
|
|
|
|
#include <ops/declarable/PlatformHelper.h>
|
|
#include <ops/declarable/OpRegistrator.h>
|
|
#include <system/platform_boilerplate.h>
|
|
|
|
#include <helpers/MKLDNNStream.h>
|
|
#include "mkldnnUtils.h"
|
|
#include <ops/declarable/helpers/convolutions.h>
|
|
|
|
using namespace dnnl;
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
namespace platforms {
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(maxpool3dnew, ENGINE_CPU) {
|
|
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, iC] (NDHWC) or [bS, iC, oD, oH, oW] (NCDHW)
|
|
|
|
int kD = INT_ARG(0); // filter(kernel) depth
|
|
int kH = INT_ARG(1); // filter(kernel) height
|
|
int kW = INT_ARG(2); // filter(kernel) width
|
|
int sD = INT_ARG(3); // strides depth
|
|
int sH = INT_ARG(4); // strides height
|
|
int sW = INT_ARG(5); // strides width
|
|
int pD = INT_ARG(6); // paddings depth
|
|
int pH = INT_ARG(7); // paddings height
|
|
int pW = INT_ARG(8); // paddings width
|
|
int dD = INT_ARG(9); // dilations depth
|
|
int dH = INT_ARG(10); // dilations height
|
|
int dW = INT_ARG(11); // dilations width
|
|
int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
|
|
// int extraParam0 = INT_ARG(13); // unnecessary for max case, required only for avg and pnorm cases
|
|
int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 5, 0, "MAXPOOL3DNEW MKLDNN OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
|
|
REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0, "MAXPOOL3DNEW MKLDNN op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
|
|
|
|
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
|
|
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
|
|
|
|
if(paddingMode) // SAME
|
|
ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
|
|
|
|
mkldnnUtils::poolingMKLDNN(input, output, kD,kH,kW, sD,sH,sW, pD,pH,pW, isNCDHW, algorithm::pooling_max);
|
|
|
|
return Status::OK();
|
|
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_CHECK(maxpool3dnew, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0);
|
|
auto output = OUTPUT_VARIABLE(0);
|
|
|
|
return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(maxpool3dnew_bp, ENGINE_CPU) {
|
|
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto gradO = INPUT_VARIABLE(1); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
|
|
|
|
const int kD = INT_ARG(0); // filter(kernel) depth
|
|
const int kH = INT_ARG(1); // filter(kernel) height
|
|
const int kW = INT_ARG(2); // filter(kernel) width
|
|
const int sD = INT_ARG(3); // strides depth
|
|
const int sH = INT_ARG(4); // strides height
|
|
const int sW = INT_ARG(5); // strides width
|
|
int pD = INT_ARG(6); // paddings depth
|
|
int pH = INT_ARG(7); // paddings height
|
|
int pW = INT_ARG(8); // paddings width
|
|
const int dD = INT_ARG(9); // dilations depth
|
|
const int dH = INT_ARG(10); // dilations height
|
|
const int dW = INT_ARG(11); // dilations width
|
|
const int paddngMode = INT_ARG(12); // 1-SAME, 0-VALID
|
|
// int extraParam0 = INT_ARG(13); // unnecessary for max case, required only for avg and pnorm cases
|
|
int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 5, 0, "MAXPOOL3DNEW_BP MKLDNN op: input should have rank of 5, but got %i instead", input->rankOf());
|
|
REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0, "MAXPOOL3DNEW_BP MKLDNN op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
|
|
|
|
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
|
|
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
|
|
|
|
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2});
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "MAXPOOL3DNEW_BP MKLDNN op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
|
|
|
|
if(paddngMode) // SAME
|
|
ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
|
|
|
|
mkldnnUtils::poolingBpMKLDNN(input, gradO, gradI, kD,kH,kW, sD,sH,sW, pD,pH,pW, isNCDHW, algorithm::pooling_max);
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_CHECK(maxpool3dnew_bp, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0);
|
|
auto output = OUTPUT_VARIABLE(0);
|
|
|
|
return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output});
|
|
}
|
|
|
|
}
|
|
}
|
|
} |