wehub-resource-sync
160 lines
8.0 KiB
C++
160 lines
8.0 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(deconv2d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weights = INPUT_VARIABLE(1); // [kH, kW, oC, iC], [iC, oC, kH, kW], [iC, kH, kW, oC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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REQUIRE_TRUE(input->rankOf() == 4, 0,
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"CUSTOM DECONV2D ARMCOMPUTE OP: rank of input array must be equal to 4, but got %i instead !",
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input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 4, 0,
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"CUSTOM DECONV2D ARMCOMPUTE OP: rank of weights array must be equal to 4, but got %i instead !",
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weights->rankOf());
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sd::LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(weights->sizeAt(0)); // filter(kernel) height
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sd::LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(weights->sizeAt(1)); // filter(kernel) width
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sd::LongType sH = INT_ARG(2); // strides height
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sd::LongType sW = INT_ARG(3); // strides width
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sd::LongType pH = INT_ARG(4); // paddings height
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sd::LongType pW = INT_ARG(5); // paddings width
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sd::LongType dH = INT_ARG(6); // dilations height
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sd::LongType dW = INT_ARG(7); // dilations width
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int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
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int wFormat = block.getIArguments()->size() > 10
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? INT_ARG(10)
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: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
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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, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
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indIiH, indWoC, indWiC, indWkH, indOoH);
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std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, oC, iC);
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"CUSTOM DECONV2D ARMCOMPUTE OP: wrong shape of weights array, expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
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if (bias)
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REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
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"CUSTOM DECONV2D ARMCOMPUTE OP: wrong shape of array with biases, expected rank, length: <=2, %i, but "
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"got %i, %i instead !",
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oC, bias->rankOf(), bias->lengthOf());
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if (paddingMode) {
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// Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward
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// pass
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ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, 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 = (iW - 1) * sW - oW + kW - pW;
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padBottom = (iH - 1) * sH - oH + kH - pH;
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auto dataLayout = isNCHW ? arm_compute::DataLayout::NCHW : arm_compute::DataLayout::NHWC;
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// check weight input datalayout match
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bool dataLayoutMatch = (isNCHW && wFormat == 1) || (!isNCHW && wFormat == 2);
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arm_compute::PermutationVector permuteVector;
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// unlike in cov2d for weights iC and oC permutted : for example {oC, iC, kH, kW}, {iC, oC, kH, kW}
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// but we need it normal way for arm
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if (!dataLayoutMatch) {
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// lets premute
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if (wFormat == 0) {
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if (isNCHW) {
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// reshape
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permuteVector = arm_compute::PermutationVector(2U, 3U, 0U, 1U);
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} else {
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// reshape
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permuteVector = arm_compute::PermutationVector(0U, 2U, 3U, 1U);
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}
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} else if (wFormat == 1) {
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permuteVector = arm_compute::PermutationVector(3U, 0U, 1U, 2U);
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} else {
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permuteVector = arm_compute::PermutationVector(1U, 2U, 3U, 0U);
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}
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} else {
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// fix weight
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if (isNCHW) {
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permuteVector = arm_compute::PermutationVector(0U, 1U, 3U, 2U);
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} else {
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permuteVector = arm_compute::PermutationVector(3U, 1U, 2U, 0U);
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}
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}
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Arm_WeightsInfo wInfo(false, kW, kH, 1);
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arm_compute::PadStrideInfo pad(sW, sH, padLeft, padRight, padTop, padBottom,
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arm_compute::DimensionRoundingType::FLOOR);
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ArmFunctionWeighted<arm_compute::NEDeconvolutionLayer> deconv;
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deconv.configure(input, weights, bias, output, dataLayout, permuteVector, pad);
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deconv.run(); // run function
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return sd::Status::OK;
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}
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PLATFORM_CHECK(deconv2d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto weights = INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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int dH = INT_ARG(6);
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int dW = INT_ARG(7);
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// Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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Requirements req("ARMCOMPUTE DECONV2d OP");
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req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT0), DataType::FLOAT32) &&
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req.expectEq(makeInfoVariable(weights->dataType(), TYPE_MSG_INPUT1), 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_INPUT0), arm_compute::MAX_DIMS) &&
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req.expectEq(makeInfoVariable(input->ordering(), ORDERING_MSG_INPUT0), 'c') &&
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req.expectEq(makeInfoVariable(input->stridesOf()[input->rankOf() - 1], "input0#lastStride"), 1) &&
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req.expectLessEq(makeInfoVariable(weights->rankOf(), RANK_MSG_INPUT1), arm_compute::MAX_DIMS) &&
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req.expectEq(makeInfoVariable(weights->ordering(), ORDERING_MSG_INPUT1), 'c') &&
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req.expectEq(makeInfoVariable(weights->stridesOf()[weights->rankOf() - 1], "input1#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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