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