PALDepthwiseConv2DCommon.h

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/*
 * Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
 * 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 LUCI_INTERPRETER_PAL_DEPTHWISE_CONV2D_COMMON_H
#define LUCI_INTERPRETER_PAL_DEPTHWISE_CONV2D_COMMON_H
#include "Params.h"
#include "PALUtils.h"
 
namespace luci_interpreter_pal
{
static inline void DepthwiseConv2D(const ConvParams &params, const int32_t *input_shape,
                                   const float *input_data, const int32_t *filter_shape,
                                   const float *filter_data, const float *bias_data,
                                   const int32_t *output_shape, float *output_data)
{
  const int stride_width = params.stride_width;
  const int stride_height = params.stride_height;
  const int dilation_width_factor = params.dilation_width_factor;
  const int dilation_height_factor = params.dilation_height_factor;
  const int pad_width = params.padding_values.width;
  const int pad_height = params.padding_values.height;
  const int depth_multiplier = params.depth_multiplier;
  const float output_activation_min = params.float_activation_min;
  const float output_activation_max = params.float_activation_max;
 
  const int batches = input_shape[0];
  const int input_height = input_shape[1];
  const int input_width = input_shape[2];
  const int input_depth = input_shape[3];
  const int filter_height = filter_shape[1];
  const int filter_width = filter_shape[2];
  const int output_height = output_shape[1];
  const int output_width = output_shape[2];
 
  for (int b = 0; b < batches; ++b)
  {
    for (int out_y = 0; out_y < output_height; ++out_y)
    {
      for (int out_x = 0; out_x < output_width; ++out_x)
      {
        for (int ic = 0; ic < input_depth; ++ic)
        {
          for (int m = 0; m < depth_multiplier; m++)
          {
            const int oc = m + ic * depth_multiplier;
            const int in_x_origin = (out_x * stride_width) - pad_width;
            const int in_y_origin = (out_y * stride_height) - pad_height;
            float total = 0.f;
            for (int filter_y = 0; filter_y < filter_height; ++filter_y)
            {
              for (int filter_x = 0; filter_x < filter_width; ++filter_x)
              {
                const int in_x = in_x_origin + dilation_width_factor * filter_x;
                const int in_y = in_y_origin + dilation_height_factor * filter_y;
                // If the location is outside the bounds of the input image,
                // use zero as a default value.
                if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height))
                {
                  float input_value = input_data[offset(input_shape, b, in_y, in_x, ic)];
                  float filter_value = filter_data[offset(filter_shape, 0, filter_y, filter_x, oc)];
                  total += (input_value * filter_value);
                }
              }
            }
            float bias_value = 0.0f;
            if (bias_data)
            {
              bias_value = bias_data[oc];
            }
            output_data[offset(output_shape, b, out_y, out_x, oc)] = activationFunctionWithMinMax(
              total + bias_value, output_activation_min, output_activation_max);
          }
        }
      }
    }
  }
}
 
} // namespace luci_interpreter_pal
 
#endif // LUCI_INTERPRETER_PAL_DEPTHWISE_CONV2D_COMMON_H