libnd4j/include/ops/declarable/platform/armcompute/deconv2d.cpp
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
// Created by Abdelrauf (rauf@konduit.ai) 2020
#include <ops/declarable/OpRegistrator.h>
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/helpers/convolutions.h>
#include <system/platform_boilerplate.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());
sd::LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(weights->sizeAt(0)); // filter(kernel) height
sd::LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(weights->sizeAt(1)); // filter(kernel) width
sd::LongType sH = INT_ARG(2); // strides height
sd::LongType sW = INT_ARG(3); // strides width
sd::LongType pH = INT_ARG(4); // paddings height
sd::LongType pW = INT_ARG(5); // paddings width
sd::LongType dH = INT_ARG(6); // dilations height
sd::LongType 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
sd::LongType padLeft, padTop, padRight, padBottom;
sd::LongType bS, iC, iH, iW, oC, oH,
oW; // batch size, input channels, input height/width, output channels, output height/width;
sd::LongType 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<sd::LongType> 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;
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) {
// reshape
permuteVector = arm_compute::PermutationVector(2U, 3U, 0U, 1U);
} else {
// reshape
permuteVector = arm_compute::PermutationVector(0U, 2U, 3U, 1U);
}
} else if (wFormat == 1) {
permuteVector = arm_compute::PermutationVector(3U, 0U, 1U, 2U);
} else {
permuteVector = arm_compute::PermutationVector(1U, 2U, 3U, 0U);
}
} else {
// fix weight
if (isNCHW) {
permuteVector = arm_compute::PermutationVector(0U, 1U, 3U, 2U);
} else {
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 sd::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.
Requirements req("ARMCOMPUTE DECONV2d OP");
req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT0), DataType::FLOAT32) &&
req.expectEq(makeInfoVariable(weights->dataType(), TYPE_MSG_INPUT1), DataType::FLOAT32) &&
req.expectEq(makeInfoVariable(output->dataType(), TYPE_MSG_OUTPUT), DataType::FLOAT32) &&
req.expectEq(makeInfoVariable(dH, "dilation#H"), 1) && req.expectEq(makeInfoVariable(dW, "dilation#W"), 1) &&
req.expectLessEq(makeInfoVariable(input->rankOf(), RANK_MSG_INPUT0), arm_compute::MAX_DIMS) &&
req.expectEq(makeInfoVariable(input->ordering(), ORDERING_MSG_INPUT0), 'c') &&
req.expectEq(makeInfoVariable(input->stridesOf()[input->rankOf() - 1], "input0#lastStride"), 1) &&
req.expectLessEq(makeInfoVariable(weights->rankOf(), RANK_MSG_INPUT1), arm_compute::MAX_DIMS) &&
req.expectEq(makeInfoVariable(weights->ordering(), ORDERING_MSG_INPUT1), 'c') &&
req.expectEq(makeInfoVariable(weights->stridesOf()[weights->rankOf() - 1], "input1#lastStride"), 1) &&
req.expectLessEq(makeInfoVariable(output->rankOf(), RANK_MSG_OUTPUT), arm_compute::MAX_DIMS) &&
req.expectEq(makeInfoVariable(output->ordering(), ORDERING_MSG_OUTPUT), 'c') &&
req.expectEq(makeInfoVariable(output->stridesOf()[output->rankOf() - 1], "output#lastStride"), 1);
req.logTheSuccess();
return req;
}
} // namespace platforms
} // namespace ops
} // namespace sd