/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.
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* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
// Created by Abdelrauf (rauf@konduit.ai) 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(deconv2d, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weights = INPUT_VARIABLE(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DECONV2D ARMCOMPUTE OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D ARMCOMPUTE OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) height
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) width
int sH = INT_ARG(2); // strides height
int sW = INT_ARG(3); // strides width
int pH = INT_ARG(4); // paddings height
int pW = INT_ARG(5); // paddings width
int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
// 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, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH);
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV2D ARMCOMPUTE OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D ARMCOMPUTE OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
if(paddingMode){
//Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
}
padLeft = pW;
padTop = pH;
padRight = (iW - 1) * sW - oW + kW - pW;
padBottom = (iH - 1) * sH - oH + kH - pH;
//deconv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, wFormat);
#if 0
nd4j_printf("deconv2d bS = %d, iH =%d, iW = %d, oH=%d, oW=%d kH=%d, kW=%d wformat=%d, iC =%d, , oC=%d\n",
bS, iH, iW, oH, oW, kH, kW, wFormat, iC, oC
);
nd4j_printf("deconv2d kH = %d, kW = %d, sH = %d, sW = %d , pH = %d , pW = %d, dH = %d, dW = %d, paddingMode = %d , isNCHW %d \n" , kH , kW , sH , sW , pH
, pW , dH , dW , paddingMode,isNCHW?1:0 );
#endif
auto dataLayout = isNCHW ? arm_compute::DataLayout::NCHW : arm_compute::DataLayout::NHWC;
//check weight input datalayout match
bool dataLayoutMatch = (isNCHW && wFormat == 1) || (!isNCHW && wFormat == 2);
arm_compute::PermutationVector permuteVector;
//unlike in cov2d for weights iC and oC permutted : for example {oC, iC, kH, kW}, {iC, oC, kH, kW}
//but we need it normal way for arm
if (!dataLayoutMatch) {
//lets premute
if (wFormat == 0) {
if (isNCHW) {
#if 0
nd4j_printf("perm choise %d\n", 0);
#endif
//reshape
permuteVector = arm_compute::PermutationVector(2U, 3U, 0U, 1U);
}
else {
#if 0
nd4j_printf("perm choise %d\n", 1);
#endif
//reshape
permuteVector = arm_compute::PermutationVector(0U, 2U, 3U, 1U);
}
}
else if (wFormat == 1) {
#if 0
nd4j_printf("perm choise %d\n", 2);
#endif
permuteVector = arm_compute::PermutationVector(3U, 0U, 1U, 2U);
}
else {
#if 0
nd4j_printf("perm choise %d\n", 3);
#endif
permuteVector = arm_compute::PermutationVector(1U, 2U, 3U, 0U);
}
}
else {
//fix weight
if(isNCHW){
#if 0
nd4j_printf("perm choise %d\n", 4);
#endif
permuteVector = arm_compute::PermutationVector(0U, 1U, 3U, 2U);
}else{
#if 0
nd4j_printf("perm choise %d\n", 5);
#endif
permuteVector = arm_compute::PermutationVector(3U, 1U, 2U, 0U);
}
}
Arm_WeightsInfo wInfo(false, kW, kH, 1);
arm_compute::PadStrideInfo pad(sW, sH, padLeft,padRight, padTop, padBottom, arm_compute::DimensionRoundingType::FLOOR);
ArmFunctionWeighted<arm_compute::NEDeconvolutionLayer> deconv;
deconv.configure( input, weights, bias, output, dataLayout, permuteVector, pad);
deconv.run(); // run function
return Status::OK();
}
PLATFORM_CHECK(deconv2d, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto weights = INPUT_VARIABLE(1);
auto output = OUTPUT_VARIABLE(0);
int dH = INT_ARG(6);
int dW = INT_ARG(7);
// Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
auto dTypeInput = getArmType(input->dataType());
auto dTypeWeight = getArmType(weights->dataType());
auto dTypeOutput = getArmType(output->dataType());
bool isSupported = dW==1 && dH==1
&& isArmcomputeFriendly(*input)
&& isArmcomputeFriendly(*weights)
&& isArmcomputeFriendly(*output)
&& (dTypeInput == Arm_DataType::F32 /*|| dTypeInput == Arm_DataType::F16*/)
&& (dTypeWeight == dTypeInput)
&& (dTypeOutput == dTypeInput);
#if 0
nd4j_printf("deconv2d isSupported %d : isArmcomputeFriendly(*input) = %d , isArmcomputeFriendly(*weights) = %d, isArmcomputeFriendly(*output) %d\n",
isSupported, isArmcomputeFriendly(*input),isArmcomputeFriendly(*weights),isArmcomputeFriendly(*output));
#endif
return isSupported;
}
}
}
}