92 lines
3.3 KiB
C++
92 lines
3.3 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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// Created by sgazeos@gmail.com on 26.01.2018.
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_moments)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/axis.h>
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namespace nd4j {
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namespace ops {
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CUSTOM_OP_IMPL(moments, 1, 2, false, 0, -2) {
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auto input = INPUT_VARIABLE(0);
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auto means = OUTPUT_VARIABLE(0);
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auto variances = OUTPUT_VARIABLE(1);
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std::vector<int> axis = *block.getIArguments();
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const bool keepDims = block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
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// axis might be dynamic (i.e. tf mode)
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if (block.width() > 1 && axis.size() == 0) {
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auto axisVector = INPUT_VARIABLE(1);
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helpers::adjustAxis(input->rankOf(), axisVector, axis);
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// for (int e = 0; e < axisVector->lengthOf(); e++) {
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// int ca = (int) axisVector->e(e);
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// if (ca < 0)
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// ca += input->rankOf();
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//
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// axis.emplace_back(ca);
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// }
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}
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std::vector<int>& dims = axis;
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input->varianceAlongDimension(variance::SummaryStatsVariance, variances, false, axis);
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input->reduceAlongDimension(reduce::Mean, means, axis, keepDims);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(moments) {
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auto axis = *block.getIArguments();
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auto input = INPUT_VARIABLE(0);
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// axis might be dynamic (i.e. tf mode)
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if (block.width() > 1 && axis.size() == 0) {
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auto axisVector = INPUT_VARIABLE(1);
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for (int e = 0; e < axisVector->lengthOf(); e++) {
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int ca = axisVector->e<int>(e);
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if (ca < 0)
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ca += input->rankOf();
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axis.emplace_back(ca);
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}
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}
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//std::vector<int> dims = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
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const bool keepDims = block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
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auto meanShape = ShapeUtils::evalReduceShapeInfo('c', axis, *input, keepDims, false, block.workspace());
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auto varianceShape = ShapeUtils::evalReduceShapeInfo('c', axis, *input, keepDims, false, block.workspace());
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return SHAPELIST(meanShape, varianceShape);
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}
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DECLARE_TYPES(moments) {
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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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}
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#endif |