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

151 lines
4.6 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_split)
#include <ops/declarable/headers/parity_ops.h>
#include<ops/declarable/helpers/transforms.h>
#include <array>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(split, 1, -1, false, 0, 1) {
NDArray *input = nullptr;
int num_splits = INT_ARG(0);
// axis is 0 by default
int axis = 0;
if (block.width() == 1) {
input = INPUT_VARIABLE(0);
} else {
auto a = INPUT_VARIABLE(0);
auto b = INPUT_VARIABLE(1);
if (a->isScalar()) {
// axis goes first
axis = a->e<int>(0);
input = b;
} else if (b->isScalar()) {
axis = b->e<int>(0);
input = a;
}
}
//Edge case: splitting empty array (mainly for TF import compatibility) -> return N empty arrays
if(input->isEmpty()){
for( int i=0; i< num_splits; i++ ){
REQUIRE_TRUE(OUTPUT_VARIABLE(i)->isEmpty(), 0, "Split: When input array is empty, all output arrays must be empty");
}
//No op
return Status::OK();
}
if (block.numI() == 2)
axis = INT_ARG(1);
if(axis < 0) axis += input->rankOf();
REQUIRE_TRUE(input->sizeAt(axis) % num_splits == 0, 0, "Split: num_splits has wrong value, remainder of division should be 0, but it's %i", input->sizeAt(axis) % num_splits);
std::vector<NDArray*> outArrs(num_splits);
for (int e = 0; e < num_splits; e++) {
outArrs[e] = OUTPUT_VARIABLE(e);
}
helpers::split(block.launchContext(), *input, outArrs, axis);
return Status::OK();
}
DECLARE_TYPES(split) {
getOpDescriptor()
->setAllowedInputTypes({ALL_INTS, ALL_FLOATS})
->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS});
}
DECLARE_SHAPE_FN(split) {
int num_splits = INT_ARG(0);
Nd4jLong *input = nullptr;
nd4j::DataType dataType;
// axis is 0 by default
int axis = 0;
int inputVar = 0;
if (inputShape->size() == 1) {
input = inputShape->at(0);
dataType = ArrayOptions::dataType(input);
} else {
auto shape0 = inputShape->at(0);
auto shape1 = inputShape->at(1);
if (shape::isScalar(shape0)) {
input = shape1;
auto _a = INPUT_VARIABLE(0);
axis = _a->e<int>(0);
dataType = ArrayOptions::dataType(shape1);
inputVar = 1;
} else if (shape::isScalar(shape1)) {
input = shape0;
auto _a = INPUT_VARIABLE(1);
axis = _a->e<int>(0);
dataType = ArrayOptions::dataType(shape0);
inputVar = 0;
}
}
auto shapes = SHAPELIST();
//Edge case: splitting empty array (mainly for TF import compatibility) -> return N empty arrays
if(INPUT_VARIABLE(inputVar)->isEmpty()){
for (int e = 0; e < num_splits; e++) {
auto empty = ConstantShapeHelper::getInstance()->emptyShapeInfo(dataType);
shapes->push_back(empty);
}
return shapes;
}
if (block.numI() == 2)
axis = INT_ARG(1);
if (axis < 0)
axis += shape::rank(input);
std::vector<Nd4jLong> shape(shape::rank(input));
for (int e = 0; e < shape::rank(input); e++)
if (e == axis)
shape[e] = shape::sizeAt(input, e) / num_splits;
else
shape[e] = shape::sizeAt(input, e);
for (int e = 0; e < num_splits; e++) {
auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(dataType, shape::order(input), shape);
shapes->push_back(newShape);
}
return shapes;
}
}
}
#endif