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/*
* Copyright (c) 2018 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.
*/
#include "AvgPoolLayer.h"
#include <cker/operation/AveragePool.h>
namespace onert
{
namespace backend
{
namespace cpu
{
namespace kernel
{
#define AVGPOOLING_PARAMETERS \
nnfw::cker::PoolParams op_params; \
op_params.stride_height = _strideHeight; \
op_params.stride_width = _strideWidth; \
op_params.filter_height = _kernelHeight; \
op_params.filter_width = _kernelWidth; \
op_params.padding_values.height = (int8_t)_paddingTop; \
op_params.padding_values.width = (int8_t)_paddingLeft;
AvgPoolLayer::AvgPoolLayer()
: _input(nullptr), _output(nullptr), _paddingLeft(0), _paddingTop(0), _paddingRight(0),
_paddingBottom(0), _strideWidth(0), _strideHeight(0), _kernelWidth(0), _kernelHeight(0),
_activation(ir::Activation::NONE)
{
// DO NOTHING
}
void AvgPoolLayer::averagePoolFloat32()
{
AVGPOOLING_PARAMETERS
float output_activation_min, output_activation_max;
CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
op_params.float_activation_min = output_activation_min;
op_params.float_activation_max = output_activation_max;
nnfw::cker::AveragePool(op_params, convertTensorToCkerShape(_input),
reinterpret_cast<const float *>(_input->buffer()),
convertTensorToCkerShape(_output),
reinterpret_cast<float *>(_output->buffer()));
}
void AvgPoolLayer::averagePoolQuant8()
{
AVGPOOLING_PARAMETERS
int32_t output_activation_min = 0;
int32_t output_activation_max = 0;
CalculateActivationRangeUint8(_activation, _output, &output_activation_min,
&output_activation_max);
op_params.quantized_activation_min = output_activation_min;
op_params.quantized_activation_max = output_activation_max;
nnfw::cker::AveragePool(op_params, convertTensorToCkerShape(_input),
reinterpret_cast<const uint8_t *>(_input->buffer()),
convertTensorToCkerShape(_output),
reinterpret_cast<uint8_t *>(_output->buffer()));
}
void AvgPoolLayer::configure(const operand::Tensor *input, const uint32_t paddingLeft,
const uint32_t paddingRight, const uint32_t paddingTop,
const uint32_t paddingBottom, const uint32_t strideWidth,
const uint32_t strideHeight, const uint32_t kernelWidth,
const uint32_t kernelHeight, const ir::Activation activation,
operand::Tensor *output)
{
assert(input != nullptr);
assert(output != nullptr);
_input = input;
_paddingLeft = paddingLeft;
_paddingRight = paddingRight;
_paddingTop = paddingTop;
_paddingBottom = paddingBottom;
_strideWidth = strideWidth;
_strideHeight = strideHeight;
_kernelWidth = kernelWidth;
_kernelHeight = kernelHeight;
_activation = activation;
_output = output;
}
void AvgPoolLayer::run()
{
if (_input->data_type() == OperandType::FLOAT32)
{
averagePoolFloat32();
}
else if (_input->data_type() == OperandType::QUANT8_ASYMM)
{
averagePoolQuant8();
}
}
#undef AVGPOOLING_PARAMETERS
} // namespace kernel
} // namespace cpu
} // namespace backend
} // namespace onert
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