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

91 lines
3.1 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
******************************************************************************/
//
// Created by raver119 on 01.11.2017.
// Modified by GS <sgazeos@gmail.com> 4/5/2018.
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_argmin)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/axis.h>
namespace nd4j {
namespace ops {
DECLARE_TYPES(argmin) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_INTS});
}
CUSTOM_OP_IMPL(argmin, 1, 1, false, 0, -2) {
auto input = INPUT_VARIABLE(0);
auto axis = *block.getIArguments();
auto output = OUTPUT_VARIABLE(0);
// axis might be dynamic (i.e. tf mode)
if (block.width() > 1 && axis.size() == 0) {
auto axisVector = INPUT_VARIABLE(1);
helpers::adjustAxis(input->rankOf(), axisVector, axis);
input->applyIndexReduce(indexreduce::IndexMin, *output, axis);
} else {
helpers::adjustAxis(input->rankOf(), axis);
input->applyIndexReduce(indexreduce::IndexMin, *output, axis);
}
STORE_RESULT(output);
return ND4J_STATUS_OK;
}
DECLARE_SHAPE_FN(argmin) {
std::vector<int> dims;
auto in = inputShape->at(0);
if (block.width() == 1) {
dims = *block.getIArguments();
} else {
auto y = INPUT_VARIABLE(1);
dims = y->template asVectorT<int>();
}
// we're resolving negative axis here
helpers::adjustAxis(shape::rank(in), dims);
if (dims.size() > 1)
std::sort(dims.begin(), dims.end());
for (auto d:dims) {
REQUIRE_TRUE(inputShape->at(0)[d+1] != 0, 0, "ArgMin: you can't reduce along axis with 0 in shape");
}
// special case - output is scalar
if (dims.size() == 0 || (dims.size() == 1 && dims.at(0) == nd4j::DataTypeUtils::max<int>())) {
return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(DataType::INT64));
}
auto newShape = ShapeUtils::evalReduceShapeInfo('c', dims, in, DataType::INT64, false, false, block.getWorkspace());
return SHAPELIST(newShape);
}
}
}
#endif