/******************************************************************************* * 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 Yurii Shyrma (iuriish@yahoo.com), created on 01.03.2018 // #include #if NOT_EXCLUDED(OP_avgpool3dnew) #include #include namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(avgpool3dnew, 1, 1, false, 0, 14) { auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) auto output = OUTPUT_NULLIFIED(0); // [bS, oD, oH, oW, iC] (NDHWC) or [bS, iC, oD, oH, oW] (NCDHW) int kD = INT_ARG(0); // filter(kernel) depth int kH = INT_ARG(1); // filter(kernel) height int kW = INT_ARG(2); // filter(kernel) width int sD = INT_ARG(3); // strides depth int sH = INT_ARG(4); // strides height int sW = INT_ARG(5); // strides width int pD = INT_ARG(6); // paddings depth int pH = INT_ARG(7); // paddings height int pW = INT_ARG(8); // paddings width int dD = INT_ARG(9); // dilations depth int dH = INT_ARG(10); // dilations height int dW = INT_ARG(11); // dilations width int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID int extraParam0 = INT_ARG(13); int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC REQUIRE_TRUE(input->rankOf() == 5, 0, "AVGPOOL3DNEW OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf()); REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0, "AVGPOOL3DNEW OP: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW); int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); std::vector expectedOutputShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); REQUIRE_TRUE(output->isSameShape(expectedOutputShape), 0, "AVGPOOL3DNEW OP: wrong shape of output array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedOutputShape).c_str(), ShapeUtils::shapeAsString(output).c_str()); if(!isNCDHW) { input = new NDArray(input->permute({0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW] output = new NDArray(output->permute({0, 4, 1, 2, 3})); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW] } if(isSameMode) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW); //T extraParams[] = {}; ConvolutionUtils::pooling3d(block, *input, *output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, 1, extraParam0); if(!isNCDHW) { delete input; delete output; } return Status::OK(); } DECLARE_TYPES(avgpool3dnew) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(avgpool3dnew) { int kD = INT_ARG(0); // filter(kernel) depth int kH = INT_ARG(1); // filter(kernel) height int kW = INT_ARG(2); // filter(kernel) width int sD = INT_ARG(3); // strides depth int sH = INT_ARG(4); // strides height int sW = INT_ARG(5); // strides width int pD = INT_ARG(6); // paddings depth int pH = INT_ARG(7); // paddings height int pW = INT_ARG(8); // paddings width int dD = INT_ARG(9); // dilations depth int dH = INT_ARG(10); // dilations height int dW = INT_ARG(11); // dilations width int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0, "AVGPOOL3DNEW op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW); auto inputShapeInfo = inputShape->at(0); int idxID, idxIC; if(isNCDHW) { idxID = 2; idxIC = 1;} else { idxID = 1; idxIC = 4;} int bS = inputShapeInfo[1]; // batch size int iC = inputShapeInfo[idxIC+1]; // input channels int iD = inputShapeInfo[idxID+1]; // input depth int iH = inputShapeInfo[idxID+2]; // input height int iW = inputShapeInfo[idxID+3]; // input width int oD, oH, oW; // output depth, height, width ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode); Nd4jLong outputShape[5]; outputShape[0] = bS; if (isNCDHW) { outputShape[1] = iC; outputShape[2] = oD; outputShape[3] = oH; outputShape[4] = oW; } else { outputShape[1] = oD; outputShape[2] = oH; outputShape[3] = oW; outputShape[4] = iC; } // TF DOC: A Tensor. Has the same type as input. return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inputShapeInfo), shape::order(inputShapeInfo), outputShape, 5))); } DECLARE_TYPES(avgpool3dnew_bp) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(avgpool3dnew_bp, 2, 1, false, 0, 14) { auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) auto gradO = INPUT_VARIABLE(1); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next auto gradI = OUTPUT_NULLIFIED(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon const int kD = INT_ARG(0); // filter(kernel) depth const int kH = INT_ARG(1); // filter(kernel) height const int kW = INT_ARG(2); // filter(kernel) width const int sD = INT_ARG(3); // strides depth const int sH = INT_ARG(4); // strides height const int sW = INT_ARG(5); // strides width int pD = INT_ARG(6); // paddings depth int pH = INT_ARG(7); // paddings height int pW = INT_ARG(8); // paddings width const int dD = INT_ARG(9); // dilations depth const int dH = INT_ARG(10); // dilations height const int dW = INT_ARG(11); // dilations width const int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID const int extraParam0 = INT_ARG(13); // define what divisor to use while averaging const int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC REQUIRE_TRUE(input->rankOf() == 5, 0, "AVGPOOL3DNEW_BP op: input should have rank of 5, but got %i instead", input->rankOf()); REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0, "AVGPOOL3DNEW_BP op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW); int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); std::vector expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iD,iH,iW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "AVGPOOL3DNEW_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, "AVGPOOL3DNEW_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(!isNCDHW) { input = new NDArray(input->permute({0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW] gradI = new NDArray(gradI->permute({0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW] gradO = new NDArray(gradO->permute({0, 4, 1, 2, 3})); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW] } if(isSameMode) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW); // 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::pooling3dBP(block, *input, *gradO, *gradI, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, 1, extraParam0); if(!isNCDHW) { delete input; delete gradI; delete gradO; } return Status::OK(); } DECLARE_SHAPE_FN(avgpool3dnew_bp) { return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(inputShape->at(0), ArrayOptions::dataType(inputShape->at(1))))); } } } #endif