cavis/libnd4j/include/ops/declarable/platform/armcompute/maxpooling2d.cpp

107 lines
4.2 KiB
C++

/*******************************************************************************
* 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 <ops/declarable/PlatformHelper.h>
#include <ops/declarable/OpRegistrator.h>
#include <system/platform_boilerplate.h>
#include <ops/declarable/helpers/convolutions.h>
#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 pad_left, pad_top, pad_right, pad_bottom;
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);
}
pad_left = pW;
pad_top = pH;
pad_right = (oW - 1) * sW - iW + kW - pW ;
pad_bottom = (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, pad_left,pad_right, pad_top, pad_bottom, arm_compute::DimensionRoundingType::FLOOR);
auto poolInfo = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::MAX, arm_compute::Size2D(kW, kH), dataLayout, poolPad);
ArmFunction<arm_compute::NEPoolingLayer> 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 is_supported = dH==1 && dW==1 && isArmcomputeFriendly(*input) && isArmcomputeFriendly(*output)
&& (dTypeInput ==Arm_DataType::F32)
&& (dTypeOutput ==Arm_DataType::F32);
return is_supported;
}
}
}
}