cavis/libnd4j/include/ops/declarable/generic/parity_ops/norm.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 raver119@gmail.com
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_norm)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/axis.h>
namespace nd4j {
namespace ops {
REDUCTION_OP_IMPL(norm, 1, 1, false, 1, -2) {
auto input = INPUT_VARIABLE(0);
NDArray *output = OUTPUT_VARIABLE(0);
auto mode = (int) T_ARG(0);
std::vector<int> dims = *block.getIArguments();
bool overwrite = false;
if (block.width() == 1) {
output = OUTPUT_VARIABLE(0);
} else {
auto axisVector = INPUT_VARIABLE(1);
dims.resize(axisVector->lengthOf());
helpers::adjustAxis(input->rankOf(), axisVector, dims);
axisVector->printIndexedBuffer("AXIS");
auto shape = ShapeUtils::evalReduceShapeInfo(input->ordering(), dims, *input, false, false);
if (!shape::equalsStrict(shape, output->shapeInfo())) {
output = new NDArray(shape, false, block.launchContext());
overwrite = true;
}
}
output->printShapeInfo("Output Shape Info");
switch(mode) {
case 0: {
REQUIRE_TRUE(dims.size() == 2 || (input->rankOf() == 2 && dims.size() == 0), 0, "Norm: Frobenius is defined for 2D matrices or TADS only");
// fro
input->reduceAlongDimension(reduce::NormFrobenius, output, dims, false, output->rankOf() == 2);
}
break;
case 1: {
// euclidean
if ((input->rankOf() == 2 && dims.size() == 0) || dims.size() == 2) {
input->reduceAlongDimension(reduce::NormFrobenius, output, dims, false, output->rankOf() == 2);
} else {
input->reduceAlongDimension(reduce::Norm2, output, dims, false, output->rankOf() == 2);
}
}
break;
case 2: {
// 1
input->reduceAlongDimension(reduce::Norm1, output, dims, false, output->rankOf() == 2);
}
break;
case 3: {
// 2
input->reduceAlongDimension(reduce::Norm2, output, dims, false, output->rankOf() == 2);
}
break;
case 4: {
// inf-norm
input->reduceAlongDimension(reduce::NormMax, output, dims, false, output->rankOf() == 2);
}
break;
default: {
// p-norm
REQUIRE_TRUE(block.getIArguments()->size() > 1, 0, "P-Norm reductions requires 2 TArguments, but only 1 was provided");
// FIXME: p is required here
//T p = T_ARG(1);
input->reduceAlongDimension(reduce::NormP, output, dims, false, output->rankOf() == 2);
}
}
if (overwrite) {
OVERWRITE_RESULT(output);
}
return ND4J_STATUS_OK;
};
DECLARE_TYPES(norm) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
}
}
#endif