TensorflowLiteMicro/tensorflow/lite/kernels/padding.h

/* Copyright 2017 The TensorFlow Authors. 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_PADDING_H_
#define TENSORFLOW_LITE_KERNELS_PADDING_H_

#include "tensorflow/lite/c/builtin_op_data.h"

namespace tflite {

// TODO(renjieliu): Migrate others to use ComputePaddingWithLeftover.
inline int ComputePadding(int stride, int dilation_rate, int in_size,
                          int filter_size, int out_size) {
  int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
  int padding = ((out_size - 1) * stride + effective_filter_size - in_size) / 2;
  return padding > 0 ? padding : 0;
}

// It's not guaranteed that padding is symmetric. It's important to keep
// offset for algorithms need all paddings.
inline int ComputePaddingWithOffset(int stride, int dilation_rate, int in_size,
                                    int filter_size, int out_size,
                                    int* offset) {
  int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
  int total_padding =
      ((out_size - 1) * stride + effective_filter_size - in_size);
  total_padding = total_padding > 0 ? total_padding : 0;
  *offset = total_padding % 2;
  return total_padding / 2;
}

// Matching GetWindowedOutputSize in TensorFlow.
inline int ComputeOutSize(TfLitePadding padding, int image_size,
                          int filter_size, int stride, int dilation_rate = 1) {
  int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
  switch (padding) {
    case kTfLitePaddingSame:
      return (image_size + stride - 1) / stride;
    case kTfLitePaddingValid:
      return (image_size + stride - effective_filter_size) / stride;
    default:
      return 0;
  }
}

inline TfLitePaddingValues ComputePaddingHeightWidth(
    int stride_height, int stride_width, int dilation_rate_height,
    int dilation_rate_width, int in_height, int in_width, int filter_height,
    int filter_width, TfLitePadding padding, int* out_height, int* out_width) {
  *out_width = ComputeOutSize(padding, in_width, filter_width, stride_width,
                              dilation_rate_width);
  *out_height = ComputeOutSize(padding, in_height, filter_height, stride_height,
                               dilation_rate_height);

  TfLitePaddingValues padding_values;
  int offset = 0;
  padding_values.height =
      ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height,
                               filter_height, *out_height, &offset);
  padding_values.height_offset = offset;
  padding_values.width =
      ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width,
                               filter_width, *out_width, &offset);
  padding_values.width_offset = offset;
  return padding_values;
}
}  // namespace tflite

#endif  // TENSORFLOW_LITE_KERNELS_PADDING_H_