inline void PadImpl()

in tensorflow/lite/kernels/internal/reference/pad.h [38:115]
70 lines of code
20 McCabe index (conditional complexity)

inline void PadImpl(const tflite::PadParams& op_params,
                    const RuntimeShape& input_shape, const T* input_data,
                    const P* pad_value_ptr, const RuntimeShape& output_shape,
                    T* output_data) {
  const RuntimeShape ext_input_shape =
      RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), input_shape);
  const RuntimeShape ext_output_shape =
      RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), output_shape);
  TFLITE_DCHECK_LE(op_params.left_padding_count, PadKernelMaxDimensionCount());
  TFLITE_DCHECK_LE(op_params.right_padding_count, PadKernelMaxDimensionCount());

  // Runtime calls are currently fixed at 5 dimensions. Copy inputs so we can
  // pad them to 5 dims (yes, we are "padding the padding").
  int left_padding_copy[PadKernelMaxDimensionCount()];
  for (int i = 0; i < PadKernelMaxDimensionCount(); i++) {
    left_padding_copy[i] = 0;
  }
  for (int i = 0; i < op_params.left_padding_count; ++i) {
    left_padding_copy[i + PadKernelMaxDimensionCount() -
                      op_params.left_padding_count] = op_params.left_padding[i];
  }
  int right_padding_copy[PadKernelMaxDimensionCount()];
  for (int i = 0; i < PadKernelMaxDimensionCount(); i++) {
    right_padding_copy[i] = 0;
  }
  for (int i = 0; i < op_params.right_padding_count; ++i) {
    right_padding_copy[i + PadKernelMaxDimensionCount() -
                       op_params.right_padding_count] =
        op_params.right_padding[i];
  }

  const int output_batch = ext_output_shape.Dims(0);
  const int output_plane = ext_output_shape.Dims(1);
  const int output_height = ext_output_shape.Dims(2);
  const int output_width = ext_output_shape.Dims(3);
  const int output_depth = ext_output_shape.Dims(4);

  const int left_b_padding = left_padding_copy[0];
  const int left_p_padding = left_padding_copy[1];
  const int left_h_padding = left_padding_copy[2];
  const int left_w_padding = left_padding_copy[3];
  const int left_d_padding = left_padding_copy[4];

  const int right_b_padding = right_padding_copy[0];
  const int right_p_padding = right_padding_copy[1];
  const int right_h_padding = right_padding_copy[2];
  const int right_w_padding = right_padding_copy[3];
  const int right_d_padding = right_padding_copy[4];

  const T pad_value = *pad_value_ptr;

  const T* in_ptr = input_data;
  T* out_ptr = output_data;
  for (int out_b = 0; out_b < output_batch; ++out_b) {
    for (int out_p = 0; out_p < output_plane; ++out_p) {
      for (int out_h = 0; out_h < output_height; ++out_h) {
        for (int out_w = 0; out_w < output_width; ++out_w) {
          for (int out_d = 0; out_d < output_depth; ++out_d) {
            if (out_b < left_b_padding ||
                out_b >= output_batch - right_b_padding ||
                out_p < left_p_padding ||
                out_p >= output_plane - right_p_padding ||
                out_h < left_h_padding ||
                out_h >= output_height - right_h_padding ||
                out_w < left_w_padding ||
                out_w >= output_width - right_w_padding ||
                out_d < left_d_padding ||
                out_d >= output_depth - right_d_padding) {
              *out_ptr++ = pad_value;
            } else {
              *out_ptr++ = *in_ptr++;
            }
          }
        }
      }
    }
  }
}