106 lines
4.1 KiB
C++
106 lines
4.1 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 raver119@gmail.com
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_norm)
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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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REDUCTION_OP_IMPL(norm, 1, 1, false, 1, -2) {
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auto input = INPUT_VARIABLE(0);
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NDArray *output = OUTPUT_VARIABLE(0);
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auto mode = (int) T_ARG(0);
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std::vector<int> dims = *block.getIArguments();
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bool overwrite = false;
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if (block.width() == 1) {
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output = OUTPUT_VARIABLE(0);
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} else {
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auto axisVector = INPUT_VARIABLE(1);
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dims.resize(axisVector->lengthOf());
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helpers::adjustAxis(input->rankOf(), axisVector, dims);
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axisVector->printIndexedBuffer("AXIS");
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auto shape = ShapeUtils::evalReduceShapeInfo(input->ordering(), dims, *input, false, false);
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if (!shape::equalsStrict(shape, output->shapeInfo())) {
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output = new NDArray(shape, false, block.launchContext());
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overwrite = true;
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}
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}
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output->printShapeInfo("Output Shape Info");
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switch(mode) {
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case 0: {
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REQUIRE_TRUE(dims.size() == 2 || (input->rankOf() == 2 && dims.size() == 0), 0, "Norm: Frobenius is defined for 2D matrices or TADS only");
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// fro
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input->reduceAlongDimension(reduce::NormFrobenius, output, dims, false, output->rankOf() == 2);
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}
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break;
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case 1: {
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// euclidean
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if ((input->rankOf() == 2 && dims.size() == 0) || dims.size() == 2) {
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input->reduceAlongDimension(reduce::NormFrobenius, output, dims, false, output->rankOf() == 2);
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} else {
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input->reduceAlongDimension(reduce::Norm2, output, dims, false, output->rankOf() == 2);
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}
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}
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break;
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case 2: {
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// 1
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input->reduceAlongDimension(reduce::Norm1, output, dims, false, output->rankOf() == 2);
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}
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break;
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case 3: {
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// 2
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input->reduceAlongDimension(reduce::Norm2, output, dims, false, output->rankOf() == 2);
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}
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break;
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case 4: {
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// inf-norm
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input->reduceAlongDimension(reduce::NormMax, output, dims, false, output->rankOf() == 2);
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}
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break;
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default: {
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// p-norm
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REQUIRE_TRUE(block.getIArguments()->size() > 1, 0, "P-Norm reductions requires 2 TArguments, but only 1 was provided");
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// FIXME: p is required here
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//T p = T_ARG(1);
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input->reduceAlongDimension(reduce::NormP, output, dims, false, output->rankOf() == 2);
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}
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}
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if (overwrite) {
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OVERWRITE_RESULT(output);
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}
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return ND4J_STATUS_OK;
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};
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DECLARE_TYPES(norm) {
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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 |