175 lines
7.1 KiB
C++
175 lines
7.1 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 04.06.2018
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//
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/axis.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(reduce_variance, 1, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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bool keepDims = false;//block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
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bool biasCorrected = false;//block.getTArguments()->size() > 1 ? (bool)T_ARG(1) : false;
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auto dimensions = *block.getIArguments();
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if (block.width() > 1) {
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auto axesVector = INPUT_VARIABLE(1);
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helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
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}
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if (block.getBArguments()->size()) {
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keepDims = B_ARG(0);
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if (block.getBArguments()->size() > 1)
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biasCorrected = B_ARG(1);
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}
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else if (block.getTArguments()->size()) {
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keepDims = (bool)T_ARG(0);
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if (block.getTArguments()->size() > 1)
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biasCorrected = (bool)T_ARG(1);
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}
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REQUIRE_TRUE(dimensions.size() <= input->rankOf(), 0, "REDUCE_VARIANCE OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());
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for(const auto& item : dimensions)
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REQUIRE_TRUE(item >= -input->rankOf() && item < input->rankOf(), 0, "REDUCE_VARIANCE OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , input->rankOf(), input->rankOf(), item);
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input->varianceAlongDimension(variance::SummaryStatsVariance, *output, biasCorrected, dimensions);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(reduce_variance) {
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bool keepDims = false;//block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
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auto dimensions = *block.getIArguments();
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if (block.width() > 1) {
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auto axesVector = INPUT_VARIABLE(1);
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helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions);
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}
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if (block.getBArguments()->size()) {
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keepDims = B_ARG(0);
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}
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else if (block.getTArguments()->size()) {
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keepDims = (bool)T_ARG(0);
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}
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REQUIRE_TRUE(dimensions.size() <= INPUT_VARIABLE(0)->rankOf(), 0, "REDUCE_VARIANCE OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());
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for(const auto& item : dimensions)
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REQUIRE_TRUE(item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0, "REDUCE_VARIANCE OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , inputShape->at(0)[0], inputShape->at(0)[0], item);
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auto outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(inputShape->at(0)), dimensions, inputShape->at(0), keepDims, false, block.getWorkspace());
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return SHAPELIST(outShapeInfo);
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}
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DECLARE_TYPES(reduce_variance) {
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getOpDescriptor()
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->setAllowedInputTypes(sd::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(reduce_variance_bp, 2, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto gradO = INPUT_VARIABLE(1);
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auto gradI = OUTPUT_VARIABLE(0);
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bool keepDims = false;//block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
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bool biasCorrected = false;//block.getTArguments()->size() > 1 ? (bool)T_ARG(1) : false;
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auto dimensions = *block.getIArguments();
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if (block.width() > 2) {
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auto axesVector = INPUT_VARIABLE(2);
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helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
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}
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// else if (block.getIArguments()->size())
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if (block.getBArguments()->size()) {
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keepDims = B_ARG(0);
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if (block.getBArguments()->size() > 1)
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biasCorrected = B_ARG(1);
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}
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else if (block.getTArguments()->size()) {
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keepDims = (bool)T_ARG(0);
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if (block.getTArguments()->size() > 1)
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biasCorrected = (bool)T_ARG(1);
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}
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REQUIRE_TRUE(dimensions.size() <= input->rankOf(), 0, "REDUCE_VARIANCE_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());
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for(const auto& item : dimensions)
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REQUIRE_TRUE(item >= -input->rankOf() && item < input->rankOf(), 0, "REDUCE_VARIANCE_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , input->rankOf(), input->rankOf(), item);
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const Nd4jLong N = input->lengthOf() / gradO->lengthOf();
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const Nd4jLong NminusOne = biasCorrected ? N - 1 : N;
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const double factor1 = 2.0 / NminusOne;
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const double factor2 = 2.0 / (N * NminusOne);
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auto mean = input->reduceAlongDimension(reduce::Mean, dimensions, true);
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gradI->assign( (*input - mean) * (2.0f / NminusOne)); // automatic broadcasting happens here
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if(!keepDims) {
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auto gradOShapeKeepDims = ShapeUtils::evalReduceShapeInfo(gradO->ordering(), dimensions, *input, true, false, block.getWorkspace());
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*gradI *= gradO->reshape(gradO->ordering(), ShapeUtils::pullShapeFromShapeInfo(gradOShapeKeepDims)); // for example could be something like [a,b] -> [1,a,1,b]
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} else
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*gradI *= *gradO; // automatic broadcasting happens here
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return Status::OK();
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}
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DECLARE_SHAPE_FN(reduce_variance_bp) {
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auto in = inputShape->at(0);
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auto rank = shape::rank(in);
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auto dimensions = *block.getIArguments();
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if (block.width() > 2) {
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auto axesVector = INPUT_VARIABLE(2);
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helpers::adjustAxis(rank, axesVector, dimensions);
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}
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REQUIRE_TRUE(dimensions.size() <= rank, 0, "REDUCE_VARIANCE_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());
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for(const auto& item : dimensions)
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REQUIRE_TRUE(item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0, "REDUCE_VARIANCE_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , inputShape->at(0)[0], inputShape->at(0)[0], item);
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Nd4jLong* gradIshapeInfo(nullptr);
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COPY_SHAPE(in, gradIshapeInfo);
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return SHAPELIST(CONSTANT(gradIshapeInfo));
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}
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DECLARE_TYPES(reduce_variance_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(sd::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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}
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}
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