InstanceNorm.h

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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.
 */
 
#ifndef __NNFW_CKER_INSTANCE_NORM_H__
#define __NNFW_CKER_INSTANCE_NORM_H__
 
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
 
#include <cmath>
 
namespace nnfw
{
namespace cker
{
 
inline void InstanceNorm(const InstanceNormParams &params, const Shape &input_shape,
                         const float *input_data, [[maybe_unused]] const Shape &gamma_shape,
                         const float *gamma_data, [[maybe_unused]] const Shape &beta_shape,
                         const float *beta_data, const Shape &output_shape, float *output_data)
{
  const int32_t batches = MatchingDim(input_shape, 0, output_shape, 0);
  const int32_t heights = MatchingDim(input_shape, 1, output_shape, 1);
  const int32_t widths = MatchingDim(input_shape, 2, output_shape, 2);
  const int32_t channels = MatchingDim(input_shape, 3, output_shape, 3);
  const float output_activation_min = params.float_activation_min;
  const float output_activation_max = params.float_activation_max;
 
  assert(output_activation_min <= output_activation_max);
 
  for (int32_t batch = 0; batch < batches; batch++)
  {
    for (int32_t channel = 0; channel < channels; channel++)
    {
      double sum = 0.0f;
      double square_sum = 0.0f;
      int32_t size = heights * widths;
 
      for (int32_t height = 0; height < heights; height++)
      {
        for (int32_t width = 0; width < widths; width++)
        {
          double input_val = input_data[Offset(input_shape, batch, height, width, channel)];
          sum += input_val;
          square_sum += (input_val * input_val);
        }
      }
 
      double mean = sum / size;
      double var = square_sum / size - mean * mean;
 
      double gamma = gamma_data[channel];
      double beta = beta_data[channel];
 
      double a = gamma / (std::sqrt(var + params.epsilon));
      double b = -mean * a + beta;
 
      for (int32_t height = 0; height < heights; height++)
      {
        for (int32_t width = 0; width < widths; width++)
        {
          double input_value = input_data[Offset(output_shape, batch, height, width, channel)];
          double output_value = input_value * a + b;
          output_data[Offset(output_shape, batch, height, width, channel)] =
            ActivationFunctionWithMinMax((float)output_value, output_activation_min,
                                         output_activation_max);
        }
      }
    }
  }
}
 
} // namespace cker
} // namespace nnfw
 
#endif // __NNFW_CKER_INSTANCE_NORM_H__