/******************************************************************************* * Copyright (c) 2019 Konduit K.K. * 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 ******************************************************************************/ // Created by Abdelrauf 2020 #include #include #include #include #include "armcomputeUtils.h" namespace sd { namespace ops { namespace platforms { ////////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(maxpool2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); REQUIRE_TRUE(input->rankOf() == 4, 0, "MAXPOOL2D ARMCOMPUTE OP: input array should have rank of 4, but got %i instead", input->rankOf()); // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; const int kH = INT_ARG(0); const int kW = INT_ARG(1); const int sH = INT_ARG(2); const int sW = INT_ARG(3); int pH = INT_ARG(4); int pW = INT_ARG(5); const int dH = INT_ARG(6); const int dW = INT_ARG(7); const int paddingMode = INT_ARG(8); // const int extraParam0 = INT_ARG(9); const int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 1-NHWC, 0-NCHW REQUIRE_TRUE(dH != 0 && dW != 0, 0, "MAXPOOL2D MKLDNN op: dilation must not be zero, but got instead {%i, %i}", dH, dW); auto dataLayout = isNCHW ? arm_compute::DataLayout::NCHW : arm_compute::DataLayout::NHWC; // Calculate individual paddings unsigned int padLeft, padTop, padRight, padBottom; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); if(paddingMode){ ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); } padLeft = pW; padTop = pH; padRight = (oW - 1) * sW - iW + kW - pW ; padBottom = (oH - 1) * sH - iH + kH - pH ; #if 0 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 , pW , dH , dW , paddingMode,isNCHW?1:0 ,exclude_padding?1:0); #endif auto poolPad = arm_compute::PadStrideInfo(sW, sH, padLeft,padRight, padTop, padBottom, arm_compute::DimensionRoundingType::FLOOR); auto poolInfo = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::MAX, arm_compute::Size2D(kW, kH), dataLayout, poolPad); ArmFunction pool; pool.configure(input,output, dataLayout, poolInfo); pool.run(); // run function return Status::OK(); } ////////////////////////////////////////////////////////////////////////// PLATFORM_CHECK(maxpool2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); const int dH = INT_ARG(6); const int dW = INT_ARG(7); // Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32 auto dTypeInput = getArmType(input->dataType()); auto dTypeOutput = getArmType(output->dataType()); bool isSupported = dH==1 && dW==1 && isArmcomputeFriendly(*input) && isArmcomputeFriendly(*output) && (dTypeInput ==Arm_DataType::F32) && (dTypeOutput ==Arm_DataType::F32); return isSupported; } } } }