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 raver119, created on 29/10/17.
// @author Yurii Shyrma (iuriish@yahoo.com), changed on 03.05.2018
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_upsampling2d)

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

namespace sd {
namespace ops {

//////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(upsampling2d, 1, 1, false, 0, 2) {
  auto input = INPUT_VARIABLE(0);  // [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)
  auto output =
      OUTPUT_NULLIFIED(0);  // [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC)

  const int factorH = INT_ARG(0);
  const int factorW = INT_ARG(1);
  const int isNCHW = block.getIArguments()->size() > 2 ? INT_ARG(2) : 0;  // INT_ARG(2): 0-NCHW,  1-NHWC

  REQUIRE_TRUE(input->rankOf() == 4, 0, "UPSAMPLING2D op: input should be 4D, but got %i instead!", input->rankOf());
  REQUIRE_TRUE(output->rankOf() == 4, 0, "UPSAMPLING2D op: output should be 4D, but got %i instead!", output->rankOf());

  ConvolutionUtils::upsampling2d(block, *input, *output, factorH, factorW, (bool)isNCHW);

  return sd::Status::OK;
}
DECLARE_SYN(upsampling, upsampling2d);

DECLARE_TYPES(upsampling2d) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

DECLARE_SHAPE_FN(upsampling2d) {
  auto inputShapeInfo = inputShape->at(0);

  REQUIRE_TRUE(inputShapeInfo[0] == 4, 0, "UPSAMPLING2D op: input should be 4D, but got %i instead!",
               inputShapeInfo[0]);

  const LongType factorH = INT_ARG(0);
  const LongType factorW = INT_ARG(1);
  const int isNCHW = block.getIArguments()->size() > 2 ? INT_ARG(2) : 0;  // INT_ARG(2): 0-NCHW,  1-NHWC

  sd::LongType *outputShapeInfo = nullptr;
  ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo[0]), sd::LongType);

  outputShapeInfo[0] = inputShapeInfo[0];
  outputShapeInfo[1] = inputShapeInfo[1];

  if (isNCHW) {
    outputShapeInfo[2] = inputShapeInfo[2];
    outputShapeInfo[3] = inputShapeInfo[3] * factorH;
    outputShapeInfo[4] = inputShapeInfo[4] * factorW;
  } else {
    outputShapeInfo[2] = inputShapeInfo[2] * factorH;
    outputShapeInfo[3] = inputShapeInfo[3] * factorW;
    outputShapeInfo[4] = inputShapeInfo[4];
  }

  ShapeUtils::updateStridesAndType(outputShapeInfo, inputShapeInfo, shape::order(inputShapeInfo));

  return SHAPELIST(CONSTANT(outputShapeInfo));
}

DECLARE_TYPES(upsampling2d_bp) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

//////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(upsampling2d_bp, 2, 1, false, 0, 0) {
  // NDArray<T>* input = INPUT_VARIABLE(0);             // [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)
  auto gradO =
      INPUT_VARIABLE(1);  // [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC)
  auto gradI = OUTPUT_NULLIFIED(0);  // [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)

  const int isNCHW = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0;  // INT_ARG(0): 0-NCHW,  1-NHWC

  REQUIRE_TRUE(gradO->rankOf() == 4, 0, "UPSAMPLING2D_BP op: output's gradient array must be 4D, but got %i instead!",
               gradO->rankOf());
  REQUIRE_TRUE(gradI->rankOf() == 4, 0, "UPSAMPLING2D_BP op: input's gradient array must be 4D, but got %i instead!",
               gradI->rankOf());

  ConvolutionUtils::upsampling2dBP(block, *gradO, *gradI, (bool)isNCHW);

  return sd::Status::OK;
}
DECLARE_SYN(upsampling_bp, upsampling2d_bp);

DECLARE_SHAPE_FN(upsampling2d_bp) {
  REQUIRE_TRUE(inputShape->at(0)[0] == 4, 0, "UPSAMPLING2D_BP op: input array must be 4D, but got %i instead!",
               inputShape->at(0)[0]);
  REQUIRE_TRUE(inputShape->at(1)[0] == 4, 0,
               "UPSAMPLING2D_BP op: output's gradient array must be 4D, but got %i instead!", inputShape->at(1)[0]);

  auto gradIShapeInfo =
      ShapeBuilders::copyShapeInfoAndType(inputShape->at(0), inputShape->at(1), false, block.getWorkspace());

  return SHAPELIST(CONSTANT(gradIShapeInfo));
}

}  // namespace ops
}  // namespace sd

#endif