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/*
* Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2020 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_AVERAGE_POOL_2D_COMMON_H
#define LUCI_INTERPRETER_PAL_AVERAGE_POOL_2D_COMMON_H
#include "Params.h"
#include "PALUtils.h"
namespace luci_interpreter_pal
{
// TODO: reduce code duplication with MaxPool
inline void AveragePool(const PoolParams ¶ms, const luci_interpreter::RuntimeShape &input_shape,
const float *input_data, const luci_interpreter::RuntimeShape &output_shape,
float *output_data)
{
const int batches = input_shape.dims(0);
const int depth = output_shape.dims(3);
const int input_height = input_shape.dims(1);
const int input_width = input_shape.dims(2);
const int output_height = output_shape.dims(1);
const int output_width = output_shape.dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch)
{
for (int out_y = 0; out_y < output_height; ++out_y)
{
for (int out_x = 0; out_x < output_width; ++out_x)
{
for (int channel = 0; channel < depth; ++channel)
{
const int in_x_origin = (out_x * stride_width) - params.padding_values.width;
const int in_y_origin = (out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end = std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end = std::min(params.filter_height, input_height - in_y_origin);
float total = 0.f;
float filter_count = 0;
for (int filter_y = filter_y_start; filter_y < filter_y_end; ++filter_y)
{
for (int filter_x = filter_x_start; filter_x < filter_x_end; ++filter_x)
{
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
const int input_data_offset =
((batch * input_shape.dims(1) + in_y) * input_shape.dims(2) + in_x) *
input_shape.dims(3) +
channel;
total += input_data[input_data_offset];
filter_count++;
}
}
const int output_data_offset =
((batch * output_shape.dims(1) + out_y) * output_shape.dims(2) + out_x) *
output_shape.dims(3) +
channel;
assert(filter_count != 0);
const float average = total / filter_count;
output_data[output_data_offset] =
std::min(std::max(average, params.float_activation_min), params.float_activation_max);
}
}
}
}
}
} // namespace luci_interpreter_pal
#endif // LUCI_INTERPRETER_PAL_AVERAGE_POOL_2D_COMMON_H
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