144 lines
5.2 KiB
C++
144 lines
5.2 KiB
C++
/*
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* ******************************************************************************
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* *
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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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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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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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//
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// @author Paul Dubs
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_standardize)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/reverse.h>
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namespace sd {
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namespace ops {
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CONFIGURABLE_OP_IMPL(standardize, 1, 1, true, 0, -2) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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std::vector<int> axis;
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if (block.width() > 1)
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axis = INPUT_VARIABLE(1)->template asVectorT<int>();
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else if (block.numI() > 0)
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axis = *block.getIArguments();
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REQUIRE_TRUE(!axis.empty(), 0, "STANDARDIZE OP: axis has to be non-empty")
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shape::checkDimensions(input->rankOf(), axis);
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auto means = input->reduceAlongDimension(reduce::Mean, axis, true);
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auto stdev = input->varianceAlongDimension(variance::SummaryStatsStandardDeviation, false, axis);
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stdev.reshapei(means.getShapeAsVector());
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input->applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), means, *output, false);
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output->applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), stdev, *output, false);
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output->applyScalar(sd::scalar::ReplaceNans, 0, *output);
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return Status::OK();
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}
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DECLARE_TYPES(standardize) {
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getOpDescriptor()->setAllowedInputTypes(0, {ALL_FLOATS});
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getOpDescriptor()->setAllowedInputTypes(1, {DataType::INT32, DataType::INT64});
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getOpDescriptor()->setAllowedOutputTypes(0, DataType::INHERIT);
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}
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CUSTOM_OP_IMPL(standardize_bp, 2, 1, false, 0, -2) {
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auto input = INPUT_VARIABLE(0);
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auto eps = block.width() == 3 ? INPUT_VARIABLE(2) : INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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std::vector<int> axis;
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if (block.width() == 3)
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axis = INPUT_VARIABLE(1)->template asVectorT<int>();
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else if (block.numI() > 0)
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axis = *block.getIArguments();
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REQUIRE_TRUE(!axis.empty(), 0, "STANDARDIZE OP: axis has to be non-empty")
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shape::checkDimensions(input->rankOf(), axis);
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auto longAxis = ArrayUtils::toLongVector(axis);
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auto means = input->reduceAlongDimension(reduce::Mean, axis, true);
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auto stdev = input->varianceAlongDimension(variance::SummaryStatsStandardDeviation, false, axis);
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stdev.reshapei(means.getShapeAsVector());
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eps->applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), stdev, *output, false);
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NDArray dldu_sum = -output->reduceAlongDimension(reduce::Sum, axis, true);
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NDArray dldx_u(input->shapeInfo(), false, block.launchContext());
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std::vector<NDArray*> meanBpArgs = {input, &dldu_sum};
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std::vector<NDArray*> meanBpOutput = {&dldx_u};
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std::vector<double> meanBpTArgs = {};
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std::vector<bool> meanBpBArgs = {};
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sd::ops::reduce_mean_bp meanBp;
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meanBp.execute(meanBpArgs, meanBpOutput, meanBpTArgs, longAxis, meanBpBArgs);
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*output += dldx_u;
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// (eps * (means - input) / (stdev * stdev))
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NDArray tmp(eps->shapeInfo(), false, block.launchContext());
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means.applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), *input, tmp, false);
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tmp.applyPairwiseTransform(sd::pairwise::Multiply, *eps, tmp);
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stdev.applyPairwiseTransform(sd::pairwise::Multiply, stdev, stdev);
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tmp.applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), stdev, tmp, false);
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auto dlds_sum = tmp.reduceAlongDimension(reduce::Sum, axis, true);
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NDArray dldx_s(input->shapeInfo(), false, block.launchContext());
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std::vector<NDArray*> stdevBpArgs = {input, &dlds_sum};
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std::vector<NDArray*> stdevBpOutput = {&dldx_s};
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std::vector<double> stdevBpTArgs = {};
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std::vector<bool> stdevBpBArgs = {};
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sd::ops::reduce_stdev_bp stdevBp;
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stdevBp.execute(stdevBpArgs, stdevBpOutput, stdevBpTArgs, longAxis, stdevBpBArgs);
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*output += dldx_s;
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output->applyScalar(sd::scalar::ReplaceNans, 0, *output);
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return Status::OK();
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}
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DECLARE_TYPES(standardize_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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DECLARE_SHAPE_FN(standardize_bp) {
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auto in = inputShape->at(0);
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Nd4jLong *out;
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COPY_SHAPE(in, out);
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return SHAPELIST(CONSTANT(out));
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}
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}
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}
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#endif |