wehub-resource-sync
123 lines
5.6 KiB
Plaintext
123 lines
5.6 KiB
Plaintext
/*
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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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//
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <helpers/MmulHelper.h>
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#include <helpers/PointersManager.h>
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#include <ops/declarable/helpers/addBias.h>
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#include <ops/declarable/helpers/col2im.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <ops/declarable/helpers/im2col.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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static void depthwiseConv2d_(sd::graph::Context& block, NDArray* input, NDArray* weights,
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NDArray* bias, NDArray* output, const LongType kH, const LongType kW, const LongType sH,
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const LongType sW, LongType pH, LongType pW, const LongType dH, const LongType dW, const int paddingMode,
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const int isNCHW, const int wFormat) {
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// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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// weights [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
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// bias [oC] = iC*mC
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// output [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)
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// kH filter(kernel) height
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// kW filter(kernel) width
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// sH strides height
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// sW strides width
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// pH paddings height
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// pW paddings width
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// dH dilations height
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// dW dilations width
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// paddingMode 0-VALID, 1-SAME
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// isNCHW 0-NCHW, 1-NHWC
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LongType bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC =
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// iC*mC), output channels, output height/width
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LongType indIOioC, indIiH, indWmC, 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, indWiC, indWmC, indWkH, indOoH);
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mC = weights->sizeAt(indWmC); // channels multiplier
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std::vector<std::vector<sd::LongType>> modifColumns = {
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{1, 0, 4, 5, 2, 3},
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{iC, bS * oH * oW, kH * kW}}; // [bS,iC,kH,kW,oH,oW] -> [iC,bS,oH,oW,kH,kW] -> [iC,bS*oH*oW,kH*kW]
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std::vector<std::vector<sd::LongType>> modifOutput, modifWeights;
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std::vector<sd::LongType> outReShape;
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if (!isNCHW) {
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outReShape = {bS, oH, oW, iC, mC}; // [bS,oH,oW,iC*mC] -> [bS,oH,oW,iC,mC]
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modifOutput = {{3, 0, 1, 2, 4},
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{iC, bS * oH * oW, mC}}; // [bS,oH,oW,iC,mC] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC]
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std::vector<sd::LongType> permuteVec = {0, 3, 1, 2};
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input = input->permute(permuteVec, false, false); // permute() already returns NDArray*
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} else {
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outReShape = {bS, iC, mC, oH, oW}; // [bS,iC*mC,oH,oW] -> [bS,iC,mC,oH,oW]
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modifOutput = {{1, 0, 3, 4, 2},
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{iC, bS * oH * oW, mC}}; // [bS,iC,mC,oH,oW] -> [iC,bS,oH,oW,mC] -> [iC,bS*oH*oW,mC]
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}
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if (0 == wFormat)
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modifWeights = {{2, 0, 1, 3}, {iC, kH * kW, mC}};
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else if (1 == wFormat)
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modifWeights = {{1, 2, 3, 0}, {iC, kH * kW, mC}};
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else
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modifWeights = {{3, 1, 2, 0}, {iC, kH * kW, mC}};
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if (paddingMode == 1) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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std::vector<sd::LongType> colShape = {bS, iC, kH, kW, oH, oW};
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NDArray columns(input->ordering(),colShape, input->dataType(), input->getContext());
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NDArray outputReshaped = output->reshape(output->ordering(), outReShape, false);
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NDArray zero = NDArrayFactory::create(0.f, input->getContext());
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helpers::im2col(
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*output->getContext(), *input, columns, kH, kW, sH, sW, pH, pW, dH, dW,
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zero); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW]
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MmulHelper::tensorDot(&columns, weights, &outputReshaped, modifColumns, modifWeights,
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modifOutput); // [iC, bS*oH*oW, kW*kH] x [iC, kH*kW, mC] = [iC, bS*oH*oW, mC]
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if (bias)
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helpers::addBias(block, *output, *bias, *output, isNCHW);
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if (!isNCHW) delete input;
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}
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void ConvolutionUtils::depthwiseConv2d(sd::graph::Context& block, NDArray* input, NDArray* weights,
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NDArray* bias, NDArray* output, const LongType kH, const LongType kW, const LongType sH,
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const LongType sW, LongType pH, LongType pW, const LongType dH, const LongType dW, const int paddingMode,
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const int isNCHW, const int wFormat) {
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BUILD_SINGLE_SELECTOR_TWICE(
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input->dataType(), depthwiseConv2d_,
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(block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, wFormat),
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SD_FLOAT_TYPES);
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}
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} // namespace ops
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} // namespace sd
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