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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (C) 2017 The Android Open Source Project
*
* 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 "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
#include "kernel/cpu/OperationUtils.h"
namespace neurun
{
namespace kernel
{
namespace cpu
{
#define AVGPOOLING_PARAMETERS \
tflite::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()
: _inputData(nullptr), _outputData(nullptr), _inputShape(), _outputShape(), _paddingLeft(0),
_paddingTop(0), _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0),
_kernelWidth(0), _kernelHeight(0), _activation(ANEURALNETWORKS_FUSED_NONE),
_inputType(OperandType::SCALAR_FLOAT32)
{
// DO NOTHING
}
bool 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;
::tflite::optimized_ops::AveragePool(op_params, convertShapeToTFLiteShape(_inputShape),
reinterpret_cast<const float *>(_inputData),
convertShapeToTFLiteShape(_outputShape),
reinterpret_cast<float *>(_outputData));
return true;
}
bool AvgPoolLayer::averagePoolQuant8()
{
AVGPOOLING_PARAMETERS
int32_t output_activation_min = 0;
int32_t output_activation_max = 0;
CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min,
&output_activation_max);
op_params.quantized_activation_min = output_activation_min;
op_params.quantized_activation_max = output_activation_max;
::tflite::optimized_ops::AveragePool(op_params, convertShapeToTFLiteShape(_inputShape),
_inputData, convertShapeToTFLiteShape(_outputShape),
_outputData);
return true;
}
void AvgPoolLayer::configure(uint8_t *inputData, const Shape inputShape, 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 FuseCode activation,
uint8_t *outputData, const Shape outputShape)
{
_inputData = inputData;
_inputShape = inputShape;
_inputType = inputShape.type;
_paddingLeft = paddingLeft;
_paddingRight = paddingRight;
_paddingTop = paddingTop;
_paddingBottom = paddingBottom;
_strideWidth = strideWidth;
_strideHeight = strideHeight;
_kernelWidth = kernelWidth;
_kernelHeight = kernelHeight;
_activation = activation;
_outputData = outputData;
_outputShape = outputShape;
}
void AvgPoolLayer::run()
{
if (_inputType == OperandType::TENSOR_FLOAT32)
{
averagePoolFloat32();
}
else if (_inputType == OperandType::TENSOR_QUANT8_ASYMM)
{
throw std::runtime_error{"AvgPoolLayer : Not tested for TENSOR_QUANT8_ASYMM"};
// averagePoolQuant8();
}
}
#undef AVGPOOLING_PARAMETERS
} // namespace cpu
} // namespace kernel
} // namespace neurun
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