/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * 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. * * 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 ******************************************************************************/ // // @author raver119@gmail.com, created on 29/10/17. // @author Yurii Shyrma (iuriish@yahoo.com), changed on 14.05.2018 // #include #if NOT_EXCLUDED(OP_pnormpool2d) #include #include namespace sd { namespace ops { CUSTOM_OP_IMPL(pnormpool2d, 1, 1, false, 0, 10) { REQUIRE_OK(this->validateInputLengthMatch(block)); REQUIRE_OK(this->validateInputDimensionsMatch(block)); auto input = INPUT_VARIABLE(0); auto output = OUTPUT_NULLIFIED(0); REQUIRE_TRUE(input->rankOf() == 4, 0, "PNORMPOOL2D op: input should have rank of 4, but got %i instead", input->rankOf()); auto kY = INT_ARG(0); auto kX = INT_ARG(1); auto sY = INT_ARG(2); auto sX = INT_ARG(3); auto pY = INT_ARG(4); auto pX = INT_ARG(5); auto dY = INT_ARG(6); auto dX = INT_ARG(7); bool isSameMode = static_cast(INT_ARG(8)); auto extraParam0 = INT_ARG(9); REQUIRE_TRUE(dY != 0 && dX != 0, 0, "PNORMPOOL2D op: dilation must not be zero, but got instead {%i, %i}", dY, dX); int oY = 0; int oX = 0; int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // 1-NHWC, 0-NCHW if(!isNCHW) { input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] output = new NDArray(output->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW] } const auto inY = static_cast(input->sizeAt(2)); const auto inX = static_cast(input->sizeAt(3)); ConvolutionUtils::calcOutSizePool2D(oY, oX, kY, kX, sY, sX, pY, pX, dY, dX, inY, inX, isSameMode); if (isSameMode) ConvolutionUtils::calcPadding2D(pY, pX, oY, oX, inY, inX, kY, kX, sY, sX, dY, dX); // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor; ConvolutionUtils::pooling2d(block, *input, *output, kY, kX, sY, sX, pY, pX, dY, dX, PoolingType::PNORM_POOL, extraParam0); if(!isNCHW) { delete input; delete output; } return Status::OK(); } DECLARE_SYN(PnormPool2D, pnormpool2d); DECLARE_SYN(PnormPool, pnormpool2d); DECLARE_SYN(pnormpool, pnormpool2d); DECLARE_TYPES(pnormpool2d) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(pnormpool2d) { auto inShape = inputShape->at(0); auto shapeOf = shape::shapeOf(inShape); // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; std::vector argI = *(block.getIArguments()); int kH = INT_ARG(0); int kW = INT_ARG(1); int sH = INT_ARG(2); int sW = INT_ARG(3); int pH = INT_ARG(4); int pW = INT_ARG(5); int dH = INT_ARG(6); int dW = INT_ARG(7); int isSameMode = INT_ARG(8); int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // 1-NHWC, 0-NCHW REQUIRE_TRUE(dH != 0 && dW != 0, 0, "PNORMPOOL2D op: dilation must not be zero, but got instead {%i, %i}", dH, dW); int bS = shapeOf[0]; int iC = isNCHW ? shapeOf[1] : shapeOf[3]; int iH = isNCHW ? shapeOf[2] : shapeOf[1]; int iW = isNCHW ? shapeOf[3] : shapeOf[2]; char order = shape::order(inShape); // output order must be equal to input order // calculate output Height/Width int oH, oW; ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); // allocate memory for new shape Nd4jLong newShape[4]; newShape[0] = bS; if (isNCHW) { newShape[1] = iC; newShape[2] = oH; newShape[3] = oW; } else { newShape[1] = oH; newShape[2] = oW; newShape[3] = iC; } return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inShape), order, newShape, 4))); } DECLARE_TYPES(pnormpool2d_bp) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(pnormpool2d_bp, 2, 1, false, 1, 10) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto gradO = INPUT_VARIABLE(1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next auto gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon int kH = INT_ARG(0); // filter(kernel) height int kW = INT_ARG(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 isSameMode = INT_ARG(8); // 0-VALID, 1-SAME int pnorm = INT_ARG(9); int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // 1-NHWC, 0-NCHW // FIXME: double? double eps = T_ARG(0); REQUIRE_TRUE(input->rankOf() == 4, 0, "PNORMPOOL2D_BP op: input should have rank of 4, but got %i instead", input->rankOf()); REQUIRE_TRUE(dH != 0 && dW != 0, 0, "PNORMPOOL2D_BP op: dilation must not be zero, but got instead {%i, %i}", dH, dW); 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, 0, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1}); std::vector expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1}); REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "PNORMPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "PNORMPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str()); if(!isNCHW) { input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] gradI = new NDArray(gradI->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] gradO = new NDArray(gradO->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW] } // if(isSameMode) // SAME // ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); // NDArray columnsWrongShape(input->ordering(), {bS, iC, oH, oW, kH, kW}, input->getWorkspace()); // NDArray* columns = columnsWrongShape.permute({0, 1, 4, 5, 2, 3}); // [bS, iC, oH, oW, kH, kW] -> [bS, iC, kH, kW, oH, oW] // NDArray* gradOVector = gradO->reshape('c', {(int) gradO->lengthOf(), 1}); // NDArray* columns2d = columnsWrongShape.reshape('c', {bS*iC*oH*oW, kH*kW}); // NDArray pNorm(columns2d->shapeInfo(), block.getWorkspace()); // input->template applyTransform>(columns, std::vector({(T)kH, (T)kW, (T)sH, (T)sW, (T)pH, (T)pW, (T)dH, (T)dW, (T)0.f, (T)0.f}).data()); // columns2d->template applyTransform>(&pNorm); // pNorm.template applyTransform>(&pNorm, std::vector({(T)pnorm}).data()); // NDArray* denomVec = pNorm.sum({1}); // denomVec->template applyTransform>(std::vector({(T)1. - (T)1. / pnorm}).data()); // denomVec->template applyScalar>(eps); // in case of 0 // denomVec->template applyPairwiseTransform>(gradOVector, denomVec, nullptr); // if(pnorm != 2) { // T extraParams[] = {(T)1. - (T)2. / pnorm}; // pNorm.template applyTransform>(std::vector({(T)1. - (T)2. / pnorm}).data()); // *columns2d *= pNorm; // } // columns2d->muliColumnVector(denomVec); // columns->template applyTransform>(gradI, std::vector({(T)sH, (T)sW, (T)pH, (T)pW, (T)iH, (T)iW, (T)dH, (T)dW}).data()); ConvolutionUtils::pooling2dBP(block, *input, *gradO, *gradI, kH, kW, sH, sW, pH, pW, dH, dW, 2, pnorm); if(!isNCHW) { delete input; delete gradI; delete gradO; } return Status::OK(); } DECLARE_SHAPE_FN(pnormpool2d_bp) { REQUIRE_TRUE(inputShape->at(0)[0] == 4, 0, "PNORMPOOL2D_BP op: input array must be 4D, but got %i instead!", inputShape->at(0)[0]); REQUIRE_TRUE(inputShape->at(1)[0] == 4, 0, "PNORMPOOL2D_BP op: output's gradient array (next epsilon) must be 4D, but got %i instead!", inputShape->at(1)[0]); return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(inputShape->at(0), ArrayOptions::dataType(inputShape->at(1))))); } } } #endif