89 lines
3.6 KiB
C++
89 lines
3.6 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 GS <sgazeos@gmail.com>
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_dynamic_stitch)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/dynamic.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(dynamic_stitch, 2, 1, false, 0, 0) {
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int numOfData = block.width();
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// int k = 0;
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// checking input data size
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REQUIRE_TRUE(numOfData % 2 == 0, 0,
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"dynamic_stitch: The input params should contains"
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" both indeces and data lists with same length.");
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// split input data list on two equal parts
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numOfData /= 2;
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// form input lists to use with helpers - both indices and float data inputs
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auto output = OUTPUT_VARIABLE(0);
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std::vector<NDArray*> inputs(numOfData);
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std::vector<NDArray*> indices(numOfData);
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for (int e = 0; e < numOfData; e++) {
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auto data = INPUT_VARIABLE(numOfData + e);
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auto index = INPUT_VARIABLE(e);
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inputs[e] = data;
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indices[e] = index;
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}
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// run helper
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return helpers::dynamicStitchFunctor(block.launchContext(), inputs, indices, output);
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}
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DECLARE_TYPES(dynamic_stitch) {
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getOpDescriptor()
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->setAllowedInputTypes(sd::DataType::ANY)
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->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(dynamic_stitch) {
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Nd4jLong maxValue = 0;
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auto numOfData = block.width();
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numOfData /= 2; // only index part it's needed to review
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auto restShape = inputShape->at(numOfData);
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auto firstShape = inputShape->at(0);
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// check up inputs to avoid non-int indices and calculate max value from indices to output shape length
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for(int i = 0; i < numOfData; i++) {
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auto input = INPUT_VARIABLE(i);
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REQUIRE_TRUE(input->isZ(), 0, "dynamic_stitch: Indices should be integer, but %d type given.", (int)input->dataType() );
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auto maxV = input->reduceNumber(reduce::Max);
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if (maxV.e<Nd4jLong>(0) > maxValue) maxValue = maxV.e<Nd4jLong>(0);
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}
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// calculate output rank - difference between indices shape and data shape
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int outRank = shape::rank(restShape) - shape::rank(firstShape) + 1; // at least 1D tensor
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std::vector<Nd4jLong> outShape(outRank);
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// fill up output shape template: the first to max index, and rests - to vals from the first data input
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outShape[0] = maxValue + 1;
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for(int i = 1; i < outRank; ++i)
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outShape[i] = shape::sizeAt(restShape, i);
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return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(restShape), shape::order(firstShape), outShape)));
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}
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}
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}
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#endif |