deeplearning4j/deeplearning4j

/* ******************************************************************************
 *
 *
 * 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.
 *
 *  See the NOTICE file distributed with this work for additional
 *  information regarding copyright ownership.
 * 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 GS <sgazeos@gmail.com>
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_dynamic_stitch)

#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/dynamic.h>

namespace sd {
namespace ops {
CUSTOM_OP_IMPL(dynamic_stitch, 2, 1, false, 0, 0) {
  int numOfData = block.width();
  //        int k = 0;
  // checking input data size
  REQUIRE_TRUE(numOfData % 2 == 0, 0,
               "dynamic_stitch: The input params should contains"
               " both indeces and data lists with same length.");
  // split input data list on two equal parts
  numOfData /= 2;

  // form input lists to use with helpers - both indices and float data inputs
  auto output = OUTPUT_VARIABLE(0);
  std::vector<NDArray*> inputs(numOfData);
  std::vector<NDArray*> indices(numOfData);

  for (int e = 0; e < numOfData; e++) {
    auto data = INPUT_VARIABLE(numOfData + e);
    auto index = INPUT_VARIABLE(e);

    inputs[e] = data;
    indices[e] = index;
  }
  // run helper
  return helpers::dynamicStitchFunctor(block.launchContext(), inputs, indices, output);
}

DECLARE_TYPES(dynamic_stitch) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS});
}

DECLARE_SHAPE_FN(dynamic_stitch) {
  sd::LongType maxValue = 0;
  auto numOfData = block.width();
  numOfData /= 2;  // only index part it's needed to review
  auto restShape = inputShape->at(numOfData);
  auto firstShape = inputShape->at(0);
  // check up inputs to avoid non-int indices and calculate max value from indices to output shape length
  for (int i = 0; i < numOfData; i++) {
    auto input = INPUT_VARIABLE(i);
    REQUIRE_TRUE(input->isZ(), 0, "dynamic_stitch: Indices should be integer, but %d type given.",
                 (int)input->dataType());
    auto maxV = input->reduceNumber(reduce::Max);
    if (maxV.e<sd::LongType>(0) > maxValue) maxValue = maxV.e<sd::LongType>(0);
  }
  // calculate output rank - difference between indices shape and data shape
  int outRank = shape::rank(restShape) - shape::rank(firstShape) + 1;  // at least 1D tensor
  std::vector<sd::LongType> outShape(outRank);
  // fill up output shape template: the first to max index, and rests - to vals from the first data input
  outShape[0] = maxValue + 1;
  for (sd::LongType i = 1; i < outRank; ++i) outShape[i] = shape::sizeAt(restShape, i);

  auto desc = new ShapeDescriptor(ArrayOptions::dataType(restShape), shape::order(firstShape), outShape);
  auto ret =  SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(desc));
  delete desc;
  return ret;
}
}  // namespace ops
}  // namespace sd

#endif