134 lines
6.4 KiB
C++
134 lines
6.4 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 saudet
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// @author raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <system/platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include "mkldnnUtils.h"
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#include <ops/declarable/helpers/convolutions.h>
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using namespace dnnl;
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using namespace samediff;
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namespace sd {
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namespace ops {
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namespace platforms {
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(avgpool2d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
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const auto kH = INT_ARG(0);
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const auto kW = INT_ARG(1);
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const auto sH = INT_ARG(2);
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const auto sW = INT_ARG(3);
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auto pH = INT_ARG(4);
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auto pW = INT_ARG(5);
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const auto dH = INT_ARG(6);
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const auto dW = INT_ARG(7);
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const auto paddingMode = INT_ARG(8);
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const auto extraParam0 = INT_ARG(9);
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const int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
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REQUIRE_TRUE(input->rankOf() == 4, 0, "AVGPOOL2D MKLDNN op: input should have rank of 4, but got %i instead", input->rankOf());
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REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D MKLDNN op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
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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, 0, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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if (paddingMode)
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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auto mode = (extraParam0 == 0) ? algorithm::pooling_avg_exclude_padding : algorithm::pooling_avg_include_padding;
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mkldnnUtils::poolingMKLDNN(input, output, 0,kH,kW, 0,sH,sW, 0,pH,pW, isNCHW, mode);
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_CHECK(avgpool2d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output});
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(avgpool2d_bp, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto gradO = INPUT_VARIABLE(1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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int kH = INT_ARG(0); // filter(kernel) height
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int kW = INT_ARG(1); // filter(kernel) width
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int sH = INT_ARG(2); // strides height
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int sW = INT_ARG(3); // strides width
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int pH = INT_ARG(4); // paddings height
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int pW = INT_ARG(5); // paddings width
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int dH = INT_ARG(6); // dilations height
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int dW = INT_ARG(7); // dilations width
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int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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int extraParam0 = INT_ARG(9);
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int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
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REQUIRE_TRUE(input->rankOf() == 4, 0, "AVGPOOL2D_BP MKLDNN op: input should have rank of 4, but got %i instead", input->rankOf());
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REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D_BP MKLDNN op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
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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, 0, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1});
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "AVGPOOL2D_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());
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if(paddingMode) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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auto mode = (extraParam0 == 0) ? algorithm::pooling_avg_exclude_padding : algorithm::pooling_avg_include_padding;
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mkldnnUtils::poolingBpMKLDNN(input, gradO, gradI, 0,kH,kW, 0,sH,sW, 0,pH,pW, isNCHW, mode);
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_CHECK(avgpool2d_bp, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output});
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}
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}
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}
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} |