wehub-resource-sync
519 lines
26 KiB
C++
519 lines
26 KiB
C++
/* ******************************************************************************
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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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// @author Yurii Shyrma, created on 06.03.2018
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//
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#ifndef LIBND4J_CONVO_OPS_H
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#define LIBND4J_CONVO_OPS_H
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_conv2d)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <system/op_boilerplate.h>
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#include <memory>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(conv2d, 2, 1, false, 0, 9) {
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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, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_NULLIFIED(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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LongType sH = INT_ARG(2); // strides height
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LongType sW = INT_ARG(3); // strides width
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LongType pH = INT_ARG(4); // paddings height
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LongType pW = INT_ARG(5); // paddings width
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LongType dH = INT_ARG(6); // dilations height
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LongType dW = INT_ARG(7); // dilations width
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int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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int 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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//normally nchw is 0 and 1 being passed in, we're using it as a boolean here
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//so we want it to be whether nchw is 0 or not.
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isNCHW = isNCHW == 0;
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LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) height
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LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) width
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ConvolutionUtils::conv2d(block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW,
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wFormat);
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return Status::OK;
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}
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DECLARE_SHAPE_FN(conv2d) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
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// output [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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LongType sH = INT_ARG(2); // strides height
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LongType sW = INT_ARG(3); // strides width
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LongType pH = INT_ARG(4); // paddings height
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LongType pW = INT_ARG(5); // paddings width
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LongType dH = INT_ARG(6); // dilations height
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LongType dW = INT_ARG(7); // dilations width
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int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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int isNCHW = block.getIArguments()->size() > 9 ? INT_ARG(9) : 0; // INT_ARG(9): 0-NCHW, 1-NHWC
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LongType 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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//normally nchw is 0 and 1 being passed in, we're using it as a boolean here
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//so we want it to be whether nchw is 0 or not.
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isNCHW = isNCHW == 0;
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LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(ConvolutionUtils::sizeOfKh(weightsShapeInfo,wFormat)); // filter(kernel) height
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LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(ConvolutionUtils::sizeOfKw(weightsShapeInfo,wFormat)); // filter(kernel) width
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const int rank = 4; // 4
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0,
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"CUSTOM CONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank,
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inputShapeInfo[0]);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
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"CUSTOM CONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank,
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weightsShapeInfo[0]);
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LongType bS = shape::sizeAt(inputShapeInfo, 0); // batch size
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LongType iC = ConvolutionUtils::inChannels(weightsShapeInfo, wFormat);
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LongType iH = ConvolutionUtils::inputHeight(inputShapeInfo, isNCHW);
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LongType iW = ConvolutionUtils::inputWidth(inputShapeInfo, isNCHW);
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LongType oC = ConvolutionUtils::outChannels(weightsShapeInfo, wFormat);
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std::vector<LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
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if(!ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape)) {
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std::string errorMessage;
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errorMessage += "CUSTOM CONV2D OP: wrong shape of weights array, expected is ";
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errorMessage += ShapeUtils::shapeAsString(expectedWeightsShape);
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errorMessage += ", but got ";
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errorMessage += ShapeUtils::shapeAsString(weightsShapeInfo);
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errorMessage += " instead !";
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THROW_EXCEPTION(errorMessage.c_str());
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}
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if (biasShapeInfo) {
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if(biasShapeInfo[0] > 2 || oC != shape::length(biasShapeInfo)) {
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std::string errorMessage;
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errorMessage += "CUSTOM CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, ";
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errorMessage += std::to_string(oC);
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errorMessage += ", but got ";
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errorMessage += std::to_string(biasShapeInfo[0]);
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errorMessage += ", ";
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errorMessage += std::to_string(shape::length(biasShapeInfo));
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errorMessage += " instead !";
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THROW_EXCEPTION(errorMessage.c_str());
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}
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}
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LongType* outputShapeInfo = new LongType[shape::shapeInfoLength(rank)];
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outputShapeInfo[0] = 4;
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LongType oH = ConvolutionUtils::calcOutDimConv(iH, kH, sH, pH, dH, paddingMode);
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LongType oW = ConvolutionUtils::calcOutDimConv(iW,kW,sW,pW,dW,paddingMode); // batch size, input channels, input height/width, output channels, output height/width;
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/**
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* NOTE: THIS BLOCK OF LOGIC PROBABLY LOOKS STRANGE.
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* THIS IS FOR COMPATIBILITY WITH THE CONV2D implementation in dl4j.
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*/
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sd::LongType strideCalcShape[4];
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strideCalcShape[0] = oW;
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strideCalcShape[1] = oH;
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strideCalcShape[2] = bS;
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strideCalcShape[3] = oC;
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sd::LongType *permute = new sd::LongType[4];
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permute[0] = 2;
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permute[1] = 3;
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permute[2] = 1;
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permute[3] = 0;
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sd::LongType * second = shape::calcStridesFortran(strideCalcShape,shape::rank(outputShapeInfo));
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shape::doPermuteSwap(4, second,permute);
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shape::doPermuteSwap(4, strideCalcShape,permute);
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if(!isNCHW) {
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permute[0] = 0;
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permute[1] = 2;
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permute[2] = 3;
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permute[3] = 1;
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shape::doPermuteSwap(4, strideCalcShape,permute);
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shape::doPermuteSwap(4, second,permute);
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sd::LongType * second2 = shape::calcStridesFortran(strideCalcShape,shape::rank(outputShapeInfo));
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shape::doPermuteSwap(4, second2,permute);
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shape::setShape(outputShapeInfo, strideCalcShape);
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shape::setStride(outputShapeInfo,second);
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shape::setOrder(outputShapeInfo, 'f');
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ArrayOptions::setExtra(outputShapeInfo,ArrayOptions::defaultFlag());
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ArrayOptions::setDataType(outputShapeInfo,ArrayOptions::dataType(inputShapeInfo));
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delete[] second2;
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} else {
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shape::setShape(outputShapeInfo, strideCalcShape);
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shape::setStride(outputShapeInfo,second);
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shape::setOrder(outputShapeInfo, 'f');
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ArrayOptions::setExtra(outputShapeInfo,ArrayOptions::defaultFlag());
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ArrayOptions::setDataType(outputShapeInfo,ArrayOptions::dataType(inputShapeInfo));
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}
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delete[] second;
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delete[] permute;
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auto ret = ConstantShapeHelper::getInstance().createFromExisting(outputShapeInfo);
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return SHAPELIST(ret);
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}
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DECLARE_TYPES(conv2d) {
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getOpDescriptor()
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->setAllowedInputTypes(0, ANY)
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_TYPES(conv2d_bp) {
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getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv2d_bp, 3, 2, false, 0, 9) {
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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, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto gradO = block.width() > 3
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? INPUT_VARIABLE(3)
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: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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LongType kH = INT_ARG(0); // filter(kernel) height
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LongType kW = INT_ARG(1); // filter(kernel) width
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LongType sH = INT_ARG(2); // strides height
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LongType sW = INT_ARG(3); // strides width
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LongType pH = INT_ARG(4); // paddings height
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LongType pW = INT_ARG(5); // paddings width
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LongType dH = INT_ARG(6); // dilations height
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LongType dW = INT_ARG(7); // dilations width
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int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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int 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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isNCHW = isNCHW == 0;
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REQUIRE_TRUE(input->rankOf() == 4, 0,
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"CUSTOM CONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 4, 0,
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"CUSTOM CONV2D_BP OP: rank of weights array must be equal to 4, but got %i instead !",
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weights->rankOf());
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REQUIRE_TRUE(
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gradO->rankOf() == 4, 0,
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"CUSTOM CONV2D_BP OP: rank of output's gradients (next epsilon) array must be equal to 4, but got %i instead !",
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gradO->rankOf());
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int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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ConvolutionUtils::conv2dBP(block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW,
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isSameMode, isNCHW, wFormat);
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return Status::OK;
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}
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DECLARE_SHAPE_FN(conv2d_bp) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
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auto gradOShapeInfo = block.width() > 3
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? inputShape->at(3)
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: inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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LongType sH = INT_ARG(2); // strides height
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LongType sW = INT_ARG(3); // strides width
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LongType pH = INT_ARG(4); // paddings height
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LongType pW = INT_ARG(5); // paddings width
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LongType dH = INT_ARG(6); // dilations height
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LongType dW = INT_ARG(7); // dilations width
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int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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int isNCHW = block.getIArguments()->size() > 9 ? INT_ARG(9) : 0; // INT_ARG(9): 0-NCHW, 1-NHWC
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LongType 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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//normally nchw is 0 and 1 being passed in, we're using it as a boolean here
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//so we want it to be whether nchw is 0 or not.
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isNCHW = isNCHW == 0;
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// output [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(ConvolutionUtils::sizeOfKh(weightsShapeInfo,wFormat)); // filter(kernel) height
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LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(ConvolutionUtils::sizeOfKw(weightsShapeInfo,wFormat)); // filter(kernel) width
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const LongType rank = 4;
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LongType bS = shape::sizeAt(inputShapeInfo, 0); // batch size
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LongType iC = ConvolutionUtils::inChannels(weightsShapeInfo, wFormat);
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LongType iH = ConvolutionUtils::inputHeight(inputShapeInfo, isNCHW);
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LongType iW = ConvolutionUtils::inputWidth(inputShapeInfo, isNCHW);
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LongType oC = ConvolutionUtils::outChannels(weightsShapeInfo, wFormat);
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std::vector<LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
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if(!ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape)) {
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std::string errorMessage;
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errorMessage += "CUSTOM CONV2D OP: wrong shape of weights array, expected is ";
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errorMessage += ShapeUtils::shapeAsString(expectedWeightsShape);
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errorMessage += ", but got ";
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errorMessage += ShapeUtils::shapeAsString(weightsShapeInfo);
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errorMessage += " instead !";
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THROW_EXCEPTION(errorMessage.c_str());
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}
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if (biasShapeInfo) {
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if(biasShapeInfo[0] > 2 || oC != shape::length(biasShapeInfo)) {
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std::string errorMessage;
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errorMessage += "CUSTOM CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, ";
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errorMessage += std::to_string(oC);
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errorMessage += ", but got ";
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errorMessage += std::to_string(biasShapeInfo[0]);
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errorMessage += ", ";
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errorMessage += std::to_string(shape::length(biasShapeInfo));
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errorMessage += " instead !";
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THROW_EXCEPTION(errorMessage.c_str());
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}
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}
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sd::LongType * strideCalcShapeGradI = new sd::LongType[shape::rank(inputShapeInfo)];
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strideCalcShapeGradI[0] = iC;
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strideCalcShapeGradI[1] = bS;
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strideCalcShapeGradI[2] = iH;
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strideCalcShapeGradI[3] = iW;
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sd::LongType *strides = new sd::LongType[4];
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sd::LongType *permute = new sd::LongType[4];
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permute[0] = 1;
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permute[1] = isNCHW ? 0 : 2;
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permute[2] = isNCHW ? 2 : 3;
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permute[3] = isNCHW ? 3 : 0;
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shape::calcStrides(strideCalcShapeGradI,shape::rank(inputShapeInfo),strides);
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shape::doPermuteSwap(4, strideCalcShapeGradI, permute);
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shape::doPermuteSwap(4, strides, permute);
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auto shapeDesc = ShapeBuilders::createShapeInfo(ArrayOptions::dataType(inputShapeInfo),
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'c',
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4,
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strideCalcShapeGradI,
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block.getWorkspace(),
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false);
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shape::setStride(shapeDesc,strides);
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auto gradIshapeInfo = ConstantShapeHelper::getInstance().createFromExisting(shapeDesc);
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RELEASE(strides,block.getWorkspace());
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RELEASE(strideCalcShapeGradI,block.getWorkspace());
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RELEASE(permute,block.getWorkspace());
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auto gradWshapeInfo =
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ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
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if (biasShapeInfo) {
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auto gradBshapeInfo =
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ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
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return SHAPELIST(gradIshapeInfo, CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
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}
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return SHAPELIST(gradIshapeInfo, CONSTANT(gradWshapeInfo));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv2d_input_bp, 3, 1, false, 0, 9) {
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auto gradIShape = INPUT_VARIABLE(0); // [4]
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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LongType kH = INT_ARG(0); // filter(kernel) height
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LongType kW = INT_ARG(1); // filter(kernel) width
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LongType sH = INT_ARG(2); // strides height
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LongType sW = INT_ARG(3); // strides width
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LongType pH = INT_ARG(4); // paddings height
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LongType pW = INT_ARG(5); // paddings width
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LongType dH = INT_ARG(6); // dilations height
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LongType dW = INT_ARG(7); // dilations width
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int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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int 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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const int rank = gradO->rankOf();
|
|
|
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REQUIRE_TRUE(weights->rankOf() == rank, 0,
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|
"CUSTOM CONV2D_INPUT_BP OP: rank of weights array must be equal to 4, but got %i instead !",
|
|
weights->rankOf());
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|
REQUIRE_TRUE(gradIShape->rankOf() == 1, 0,
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|
"CUSTOM CONV2D_INPUT_BP OP: rank of array with output shape must be equal to 1, but got %i instead !",
|
|
gradIShape->rankOf());
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|
REQUIRE_TRUE(gradIShape->lengthOf() == rank, 0,
|
|
"CUSTOM CONV2D_INPUT_BP OP: length of array with output shape must be equal to 4, but got %i instead !",
|
|
gradIShape->lengthOf());
|
|
|
|
// create empty conv2d input array
|
|
std::vector<LongType> gradIShapeAsVector(rank);
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for (int i = 0; i < rank; ++i) gradIShapeAsVector[i] = gradIShape->e<LongType>(i);
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|
NDArray input(gradO->ordering(), gradIShapeAsVector, gradO->dataType(), block.launchContext());
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|
|
|
LongType bS, iC, iH, iW, oC, oH,
|
|
oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
|
|
indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
LongType trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
|
|
|
|
std::vector<LongType> expectedGradOShape =
|
|
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
|
|
std::vector<LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
|
|
"CUSTOM CONV2D_INPUT_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but "
|
|
"got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
|
|
"CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
|
|
ConvolutionUtils::conv2dBP(block, &input, weights, nullptr, gradO, gradI, nullptr, nullptr, kH, kW, sH, sW, pH, pW,
|
|
dH, dW, isSameMode, isNCHW, wFormat);
|
|
|
|
return Status::OK;
|
|
}
|
|
|
|
DECLARE_TYPES(conv2d_input_bp) {
|
|
getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
DECLARE_SHAPE_FN(conv2d_input_bp) {
|
|
auto gradIShapeShapeInfo = inputShape->at(0); // [4]
|
|
auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
|
|
auto gradOShapeInfo = inputShape->at(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
|
|
|
|
const LongType rank = 4;
|
|
|
|
REQUIRE_TRUE(gradIShapeShapeInfo[0] == 1, 0,
|
|
"CUSTOM CONV2D_INPUT_BP OP: rank of array with output shape must be equal to %i, but got %i instead !",
|
|
1, gradIShapeShapeInfo[0]);
|
|
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
|
|
"CUSTOM CONV2D_INPUT_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank,
|
|
weightsShapeInfo[0]);
|
|
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0,
|
|
"CUSTOM CONV2D_INPUT_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got "
|
|
"%i instead !",
|
|
rank, gradOShapeInfo[0]);
|
|
|
|
const LongType kH = INT_ARG(0); // filter(kernel) height
|
|
const LongType kW = INT_ARG(1); // filter(kernel) width
|
|
const LongType sH = INT_ARG(2); // strides height
|
|
const LongType sW = INT_ARG(3); // strides width
|
|
const LongType pH = INT_ARG(4); // paddings height
|
|
const LongType pW = INT_ARG(5); // paddings width
|
|
const LongType dH = INT_ARG(6); // dilations height
|
|
const LongType dW = INT_ARG(7); // dilations width
|
|
const int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
|
const 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]
|
|
|
|
int indIOioC, indIiH, indWoC(0 == wFormat ? 3 : 0), indOoH;
|
|
if (!isNCHW) {
|
|
indIOioC = 3;
|
|
indIiH = 1;
|
|
indOoH = 1;
|
|
} else {
|
|
indIOioC = 1;
|
|
indIiH = 2;
|
|
indOoH = 2;
|
|
}
|
|
|
|
std::vector<LongType> gradIShape = INPUT_VARIABLE(0)->template asVectorT<LongType>();
|
|
|
|
const LongType bS = gradIShape[0]; // batch size
|
|
const LongType iH = gradIShape[indIiH]; // input height
|
|
const LongType iW = gradIShape[indIiH + 1]; // input width
|
|
const LongType iC = gradIShape[indIOioC]; // input channels
|
|
const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
|
|
|
|
LongType trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
|
|
|
|
std::vector<LongType> expectedGradOShape =
|
|
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
|
|
std::vector<LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0,
|
|
"CUSTOM CONV2D_INPUT_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but "
|
|
"got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedGradOShape).c_str(),
|
|
ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
|
|
"CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
|
|
ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
|
|
|
|
LongType* gradIshapeInfo(nullptr);
|
|
ALLOCATE(gradIshapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), sd::LongType);
|
|
|
|
gradIshapeInfo[0] = rank;
|
|
gradIshapeInfo[1] = bS;
|
|
|
|
if (isNCHW) {
|
|
gradIshapeInfo[2] = iC;
|
|
gradIshapeInfo[3] = iH;
|
|
gradIshapeInfo[4] = iW;
|
|
} else {
|
|
gradIshapeInfo[2] = iH;
|
|
gradIshapeInfo[3] = iW;
|
|
gradIshapeInfo[4] = iC;
|
|
}
|
|
|
|
ShapeUtils::updateStridesAndType(gradIshapeInfo, gradOShapeInfo, shape::order(gradOShapeInfo));
|
|
|
|
return SHAPELIST(CONSTANT(gradIshapeInfo));
|
|
}
|
|
|
|
} // namespace ops
|
|
} // namespace sd
|
|
|
|
#endif
|
|
|
|
#endif // LIBND4J_CONVO_OPS_H
|