cavis/libnd4j/include/ops/declarable/generic/parity_ops/reduceVariance.cpp

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2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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 04.06.2018
//
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/axis.h>
namespace nd4j {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_variance, 1, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
bool keepDims = false;//block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
bool biasCorrected = false;//block.getTArguments()->size() > 1 ? (bool)T_ARG(1) : false;
auto dimensions = *block.getIArguments();
if (block.width() > 1) {
auto axesVector = INPUT_VARIABLE(1);
helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
}
if (block.getBArguments()->size()) {
keepDims = B_ARG(0);
if (block.getBArguments()->size() > 1)
biasCorrected = B_ARG(1);
}
else if (block.getTArguments()->size()) {
keepDims = (bool)T_ARG(0);
if (block.getTArguments()->size() > 1)
biasCorrected = (bool)T_ARG(1);
}
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());
for(const auto& item : dimensions)
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);
input->varianceAlongDimension(variance::SummaryStatsVariance, output, biasCorrected, dimensions);
return Status::OK();
}
DECLARE_SHAPE_FN(reduce_variance) {
bool keepDims = false;//block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
auto dimensions = *block.getIArguments();
if (block.width() > 1) {
auto axesVector = INPUT_VARIABLE(1);
helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions);
}
if (block.getBArguments()->size()) {
keepDims = B_ARG(0);
}
else if (block.getTArguments()->size()) {
keepDims = (bool)T_ARG(0);
}
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());
for(const auto& item : dimensions)
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);
auto outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(inputShape->at(0)), dimensions, inputShape->at(0), keepDims, false, block.getWorkspace());
return SHAPELIST(outShapeInfo);
}
DECLARE_TYPES(reduce_variance) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_variance_bp, 2, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto gradO = INPUT_VARIABLE(1);
auto gradI = OUTPUT_VARIABLE(0);
bool keepDims = false;//block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
bool biasCorrected = false;//block.getTArguments()->size() > 1 ? (bool)T_ARG(1) : false;
auto dimensions = *block.getIArguments();
if (block.width() > 2) {
auto axesVector = INPUT_VARIABLE(2);
helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
}
// else if (block.getIArguments()->size())
if (block.getBArguments()->size()) {
keepDims = B_ARG(0);
if (block.getBArguments()->size() > 1)
biasCorrected = B_ARG(1);
}
else if (block.getTArguments()->size()) {
keepDims = (bool)T_ARG(0);
if (block.getTArguments()->size() > 1)
biasCorrected = (bool)T_ARG(1);
}
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());
for(const auto& item : dimensions)
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);
const Nd4jLong N = input->lengthOf() / gradO->lengthOf();
const Nd4jLong NminusOne = biasCorrected ? N - 1 : N;
const double factor1 = 2.0 / NminusOne;
const double factor2 = 2.0 / (N * NminusOne);
auto mean = input->reduceAlongDims(reduce::Mean, dimensions, true);
gradI->assign( (*input - mean) * (2.0f / NminusOne)); // automatic broadcasting happens here
if(!keepDims) {
auto gradOShapeKeepDims = ShapeUtils::evalReduceShapeInfo(gradO->ordering(), dimensions, *input, true, false, block.getWorkspace());
gradO = gradO->reshape(gradO->ordering(), ShapeUtils::pullShapeFromShapeInfo(gradOShapeKeepDims)); // for example could be something like [a,b] -> [1,a,1,b]
}
*gradI *= *gradO; // automatic broadcasting happens here
if(!keepDims)
delete gradO;
return Status::OK();
}
DECLARE_SHAPE_FN(reduce_variance_bp) {
auto in = inputShape->at(0);
auto rank = shape::rank(in);
auto dimensions = *block.getIArguments();
if (block.width() > 2) {
auto axesVector = INPUT_VARIABLE(2);
helpers::adjustAxis(rank, axesVector, dimensions);
}
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());
for(const auto& item : dimensions)
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);
Nd4jLong* gradIshapeInfo(nullptr);
COPY_SHAPE(in, gradIshapeInfo);
return SHAPELIST(CONSTANT(gradIshapeInfo));
}
DECLARE_TYPES(reduce_variance_bp) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
}
}