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 sgazeos@gmail.com
//

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

//#include <ops/declarable/headers/parity_ops.h>
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/crop_and_resize.h>

namespace sd {
namespace ops {
CUSTOM_OP_IMPL(crop_and_resize, 4, 1, false, 0, 0) {
  auto image = INPUT_VARIABLE(0);
  auto boxes = INPUT_VARIABLE(1);
  auto boxIndexes = INPUT_VARIABLE(2);

  auto output = OUTPUT_VARIABLE(0);
  int width;
  int height;
  int method = 0;  // bilinear
  double extrapolationVal = 0.;

  auto newImageSize = INPUT_VARIABLE(3);
  REQUIRE_TRUE(output->dataType() == image->dataType(), 0,
               "crop_and_resize: Source images and output should have the same data type.");
  REQUIRE_TRUE(newImageSize->lengthOf() == 2, 0, "crop_and_resize: Resize params is a pair of values, not %i.",
               newImageSize->lengthOf());
  // REQUIRE_TRUE(block.numI() <= 1, 0, "crop_and_resize: Resize params already given by the second param. Int params
  // are expensive."); width = int(newImageSize->getScalar(0)); height = int(newImageSize->getScalar(1));
  if (block.numI() == 1) {
    method = INT_ARG(0);
  }

  if (block.numT() == 1) {
    extrapolationVal = T_ARG(0);
  }

  helpers::cropAndResizeFunctor(block.launchContext(), image, boxes, boxIndexes, newImageSize, method, extrapolationVal,
                                output);
  return sd::Status::OK;
}

DECLARE_SHAPE_FN(crop_and_resize) {
  auto in = inputShape->at(0);
  auto boxShape = inputShape->at(1);

  sd::LongType outputShape[4];

  int width;
  int height;
  auto newImageSize = INPUT_VARIABLE(3);
  REQUIRE_TRUE(newImageSize->lengthOf() == 2, 0, "crop_and_resize: Resize params is a pair of values, not %i.",
               newImageSize->lengthOf());
  // REQUIRE_TRUE(block.numI() <= 1, 0, "crop_and_resize: Resize params already given by the second param. Int params
  // are expensive.");
  width = newImageSize->e<int>(0);
  height = newImageSize->e<int>(1);

  outputShape[0] = boxShape[1];
  outputShape[1] = width;
  outputShape[2] = height;
  outputShape[3] = in[4];
  auto desc = new  ShapeDescriptor(ArrayOptions::dataType(in), shape::order(in), outputShape, 4);
  return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(desc));
}

DECLARE_TYPES(crop_and_resize) {
  getOpDescriptor()
      ->setAllowedInputTypes(0, {ALL_INTS, ALL_FLOATS})
          //                    ->setAllowedInputTypes(1, {ALL_FLOATS})
      ->setAllowedInputTypes(1, {ALL_INTS, ALL_FLOATS})
      ->setAllowedInputTypes(2, {ALL_INTS})
      ->setAllowedInputTypes(3, {ALL_INTS})
      ->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS});  // as TF
  //                    ->setAllowedOutputTypes({ALL_FLOATS});
}
}  // namespace ops
}  // namespace sd

#endif