214 lines
13 KiB
C++
214 lines
13 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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* 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 (iuriish@yahoo.com), created on 01.03.2018
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_avgpool3dnew)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/convolutions.h>
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namespace nd4j {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(avgpool3dnew, 1, 1, false, 0, 14) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, iC] (NDHWC) or [bS, iC, oD, oH, oW] (NCDHW)
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int kD = INT_ARG(0); // filter(kernel) depth
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int kH = INT_ARG(1); // filter(kernel) height
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int kW = INT_ARG(2); // filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
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int extraParam0 = INT_ARG(13);
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int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC
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REQUIRE_TRUE(input->rankOf() == 5, 0, "AVGPOOL3DNEW OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
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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);
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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std::string expectedOutputShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}));
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REQUIRE_TRUE(expectedOutputShape == ShapeUtils::shapeAsString(output), 0, "AVGPOOL3D op: wrong shape of output array, expected is %s, but got %s instead !", expectedOutputShape.c_str(), ShapeUtils::shapeAsString(output).c_str());
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if(!isNCDHW) {
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input = input->permute({0, 4, 1, 2, 3}); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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output = output->permute({0, 4, 1, 2, 3}); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW]
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}
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
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//T extraParams[] = {};
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ConvolutionUtils::pooling3d(block, *input, *output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, 1, extraParam0);
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if(!isNCDHW) {
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delete input;
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delete output;
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}
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return Status::OK();
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}
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DECLARE_TYPES(avgpool3dnew) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(avgpool3dnew) {
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int kD = INT_ARG(0); // filter(kernel) depth
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int kH = INT_ARG(1); // filter(kernel) height
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int kW = INT_ARG(2); // filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
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int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC
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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);
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auto inputShapeInfo = inputShape->at(0);
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int idxID, idxIC;
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if(isNCDHW) { idxID = 2; idxIC = 1;}
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else { idxID = 1; idxIC = 4;}
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int bS = inputShapeInfo[1]; // batch size
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int iC = inputShapeInfo[idxIC+1]; // input channels
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int iD = inputShapeInfo[idxID+1]; // input depth
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int iH = inputShapeInfo[idxID+2]; // input height
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int iW = inputShapeInfo[idxID+3]; // input width
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int oD, oH, oW; // output depth, height, width
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ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);
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Nd4jLong outputShape[5];
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outputShape[0] = bS;
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if (isNCDHW) {
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outputShape[1] = iC;
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outputShape[2] = oD;
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outputShape[3] = oH;
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outputShape[4] = oW;
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} else {
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outputShape[1] = oD;
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outputShape[2] = oH;
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outputShape[3] = oW;
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outputShape[4] = iC;
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}
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// TF DOC: A Tensor. Has the same type as input.
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return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inputShapeInfo), shape::order(inputShapeInfo), outputShape, 5)));
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}
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DECLARE_TYPES(avgpool3dnew_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(avgpool3dnew_bp, 2, 1, false, 0, 14) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto gradO = INPUT_VARIABLE(1); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
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const int kD = INT_ARG(0); // filter(kernel) depth
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const int kH = INT_ARG(1); // filter(kernel) height
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const int kW = INT_ARG(2); // filter(kernel) width
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const int sD = INT_ARG(3); // strides depth
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const int sH = INT_ARG(4); // strides height
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const int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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const int dD = INT_ARG(9); // dilations depth
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const int dH = INT_ARG(10); // dilations height
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const int dW = INT_ARG(11); // dilations width
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const int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
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const int extraParam0 = INT_ARG(13); // define what divisor to use while averaging
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const int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC
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REQUIRE_TRUE(input->rankOf() == 5, 0, "AVGPOOL3DNEW_BP op: input should have rank of 5, but got %i instead", input->rankOf());
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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);
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}));
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std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iD,iH,iW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}));
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REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "AVGPOOL3D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0, "AVGPOOL3D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str());
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if(!isNCDHW) {
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input = input->permute({0, 4, 1, 2, 3}); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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gradI = gradI->permute({0, 4, 1, 2, 3}); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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gradO = gradO->permute({0, 4, 1, 2, 3}); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW]
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}
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
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// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor;
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ConvolutionUtils::pooling3dBP(block, *input, *gradO, *gradI, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, 1, extraParam0);
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if(!isNCDHW) {
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delete input;
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delete gradI;
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delete gradO;
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}
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return Status::OK();
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}
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DECLARE_SHAPE_FN(avgpool3dnew_bp) {
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return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(inputShape->at(0), ArrayOptions::dataType(inputShape->at(1)))));
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}
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}
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}
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#endif
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