106 lines
4.1 KiB
C++
106 lines
4.1 KiB
C++
|
/*******************************************************************************
|
||
|
|
* 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
|