wehub-resource-sync
116 lines
5.1 KiB
C++
116 lines
5.1 KiB
C++
/*
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* ******************************************************************************
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* *
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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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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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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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// Created by Abdelrauf (rauf@konduit.ai) 2020
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#include <ops/declarable/OpRegistrator.h>
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <system/platform_boilerplate.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(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
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// mode;
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const sd::LongType kH = INT_ARG(0);
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const sd::LongType kW = INT_ARG(1);
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const sd::LongType sH = INT_ARG(2);
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const sd::LongType sW = INT_ARG(3);
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sd::LongType pH = INT_ARG(4);
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sd::LongType pW = INT_ARG(5);
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const sd::LongType dH = INT_ARG(6);
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const sd::LongType 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 ARMCOMPUTE op: input should have rank of 4, but got %i instead",
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input->rankOf());
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REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D ARMCOMPUTE op: dilation must not be zero, but got instead {%i, %i}",
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dH, dW);
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bool excludePadding = (extraParam0 == 0) ? true : false;
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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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sd::LongType padLeft, padTop, padRight, padBottom;
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sd::LongType bS, iC, iH, iW, oC, oH,
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oW; // batch size, input channels, input height/width, output channels, output height/width;
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sd::LongType 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,
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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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auto poolPad = arm_compute::PadStrideInfo(sW, sH, padLeft, padRight, padTop, padBottom,
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arm_compute::DimensionRoundingType::FLOOR);
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auto poolInfo = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::AVG, arm_compute::Size2D(kW, kH), dataLayout,
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poolPad, excludePadding);
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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 sd::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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const sd::LongType dH = INT_ARG(6);
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const sd::LongType dW = INT_ARG(7);
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// Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32
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// for now, we will ignore F16 as it shoulde be preconditioned for pool size 2,3 and arm64-v8.2-a architecture
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Requirements req("ARMCOMPUTE AVGPOOL2d OP");
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req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT), DataType::FLOAT32) &&
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req.expectEq(makeInfoVariable(output->dataType(), TYPE_MSG_OUTPUT), DataType::FLOAT32) &&
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req.expectEq(makeInfoVariable(dH, "dilation#H"), 1) && req.expectEq(makeInfoVariable(dW, "dilation#W"), 1) &&
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req.expectLessEq(makeInfoVariable(input->rankOf(), RANK_MSG_INPUT), arm_compute::MAX_DIMS) &&
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req.expectEq(makeInfoVariable(input->ordering(), ORDERING_MSG_INPUT), 'c') &&
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req.expectEq(makeInfoVariable(input->stridesOf()[input->rankOf() - 1], "input#lastStride"), 1) &&
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req.expectLessEq(makeInfoVariable(output->rankOf(), RANK_MSG_OUTPUT), arm_compute::MAX_DIMS) &&
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req.expectEq(makeInfoVariable(output->ordering(), ORDERING_MSG_OUTPUT), 'c') &&
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req.expectEq(makeInfoVariable(output->stridesOf()[output->rankOf() - 1], "output#lastStride"), 1);
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req.logTheSuccess();
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return req;
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}
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} // namespace platforms
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} // namespace ops
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} // namespace sd
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