107 lines
4.2 KiB
C++
107 lines
4.2 KiB
C++
/*******************************************************************************
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* Copyright (c) 2019 Konduit K.K.
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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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// Created by Abdelrauf 2020
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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 <ops/declarable/helpers/convolutions.h>
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#include "armcomputeUtils.h"
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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(maxpool2d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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REQUIRE_TRUE(input->rankOf() == 4, 0, "MAXPOOL2D ARMCOMPUTE OP: input array should have rank of 4, but got %i instead", input->rankOf());
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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 int kH = INT_ARG(0);
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const int kW = INT_ARG(1);
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const int sH = INT_ARG(2);
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const int sW = INT_ARG(3);
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int pH = INT_ARG(4);
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int pW = INT_ARG(5);
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const int dH = INT_ARG(6);
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const int dW = INT_ARG(7);
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const int paddingMode = INT_ARG(8);
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// const int extraParam0 = INT_ARG(9);
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const int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 1-NHWC, 0-NCHW
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REQUIRE_TRUE(dH != 0 && dW != 0, 0, "MAXPOOL2D MKLDNN op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
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auto dataLayout = isNCHW ? arm_compute::DataLayout::NCHW : arm_compute::DataLayout::NHWC;
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// Calculate individual paddings
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unsigned int padLeft, padTop, padRight, padBottom;
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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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}
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padLeft = pW;
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padTop = pH;
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padRight = (oW - 1) * sW - iW + kW - pW ;
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padBottom = (oH - 1) * sH - iH + kH - pH ;
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#if 0
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nd4j_printf("avgpool kH = %d, kW = %d, sH = %d, sW = %d , pH = %d , pW = %d, dH = %d, dW = %d, paddingMode = %d , isNCHW %d exclude pad %d \n" , kH , kW , sH , sW , pH
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, pW , dH , dW , paddingMode,isNCHW?1:0 ,exclude_padding?1:0);
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#endif
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auto poolPad = arm_compute::PadStrideInfo(sW, sH, padLeft,padRight, padTop, padBottom, arm_compute::DimensionRoundingType::FLOOR);
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auto poolInfo = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::MAX, arm_compute::Size2D(kW, kH), dataLayout, poolPad);
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ArmFunction<arm_compute::NEPoolingLayer> pool;
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pool.configure(input,output, dataLayout, poolInfo);
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pool.run(); // run function
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_CHECK(maxpool2d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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const int dH = INT_ARG(6);
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const int dW = INT_ARG(7);
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// Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32
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auto dTypeInput = getArmType(input->dataType());
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auto dTypeOutput = getArmType(output->dataType());
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bool isSupported = dH==1 && dW==1 && isArmcomputeFriendly(*input) && isArmcomputeFriendly(*output)
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&& (dTypeInput ==Arm_DataType::F32)
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&& (dTypeOutput ==Arm_DataType::F32);
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return isSupported;
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}
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}
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}
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}
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