Pad.cpp

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/*
 * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * 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.
 */
 
#include "kernels/Pad.h"
 
#include "kernels/Utils.h"
 
#include <tensorflow/lite/kernels/internal/reference/pad.h>
 
#include <limits>
 
namespace luci_interpreter
{
namespace kernels
{
 
Pad::Pad(const Tensor *input, const Tensor *paddings, Tensor *output)
  : Kernel({input, paddings}, {output})
{
}
 
void Pad::configure()
{
  const Shape &input_shape = input()->shape();
  const int num_dims = input_shape.num_dims();
 
  if (num_dims > 4)
    throw std::runtime_error("Unsupported number of dimensions.");
 
  assert(output()->element_type() == input()->element_type());
  assert(paddings()->element_type() == DataType::S32);
  // Paddings shape should be [N, 2].
  assert(paddings()->shape().num_dims() == 2);
  assert(paddings()->shape().dim(0) == num_dims);
  assert(paddings()->shape().dim(1) == 2);
 
  Shape output_shape(num_dims);
  const auto *paddings_data = getTensorData<int32_t>(paddings());
  for (int i = 0; i < num_dims; ++i)
  {
    const int32_t padding_before = paddings_data[i * 2];
    const int32_t padding_after = paddings_data[i * 2 + 1];
    assert(padding_before >= 0 && padding_after >= 0);
    output_shape.dim(i) = input_shape.dim(i) + padding_before + padding_after;
  }
 
  output()->resize(output_shape);
}
 
void Pad::execute() const
{
  const int num_dims = input()->shape().num_dims();
 
  tflite::PadParams params{};
  params.left_padding_count = num_dims;
  params.right_padding_count = num_dims;
 
  const auto *paddings_data = getTensorData<int32_t>(paddings());
  for (int i = num_dims - 1; i >= 0; --i)
  {
    params.left_padding[i] = paddings_data[i * 2];
    params.right_padding[i] = paddings_data[i * 2 + 1];
  }
 
  switch (input()->element_type())
  {
    case DataType::FLOAT32:
    {
      const float pad_value = 0.0f;
      tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData<float>(input()),
                                 &pad_value, getTensorShape(output()),
                                 getTensorData<float>(output()));
      break;
    }
    case DataType::U8:
    {
      assert(output()->zero_point() >= std::numeric_limits<uint8_t>::min());
      assert(output()->zero_point() <= std::numeric_limits<uint8_t>::max());
      const auto pad_value = static_cast<uint8_t>(output()->zero_point());
      tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData<uint8_t>(input()),
                                 &pad_value, getTensorShape(output()),
                                 getTensorData<uint8_t>(output()));
      break;
    }
    case DataType::S8:
    {
      assert(output()->zero_point() >= std::numeric_limits<int8_t>::min());
      assert(output()->zero_point() <= std::numeric_limits<int8_t>::max());
      const auto pad_value = static_cast<int8_t>(output()->zero_point());
      tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData<int8_t>(input()),
                                 &pad_value, getTensorShape(output()),
                                 getTensorData<int8_t>(output()));
      break;
    }
    default:
      throw std::runtime_error("luci-intp Pad Unsupported type.");
  }
}
 
} // namespace kernels
} // namespace luci_interpreter