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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.
*/
#ifndef __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__
#define __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__
#include <iostream>
#include <limits>
#include <vector>
#include <cker/Shape.h>
#include "model/Operand.h"
#include "model/DataType.h"
#include <model/InternalType.h>
using OperandType = neurun::model::DataType;
namespace neurun
{
namespace backend
{
namespace cpu
{
namespace kernel
{
struct Shape
{
OperandType type;
std::vector<uint32_t> dimensions;
float scale;
int32_t offset;
};
union DataPtr {
uint8_t *u8;
int8_t *i8;
int32_t *i32;
float *f;
void *v;
};
uint32_t getNumberOfDimensions(const Shape &shape);
uint32_t getNumberOfElements(const Shape &shape);
uint32_t getSizeOfDimension(const Shape &shape, uint32_t dimensionIdx);
inline nnfw::cker::Shape convertToExtendedCkerShape(const Shape &shape)
{
std::vector<int32_t> raw_shape;
raw_shape.resize(4);
uint32_t src = 4 - shape.dimensions.size();
for (uint32_t i = 0; i < 4; ++i)
{
if (i < src)
{
raw_shape[i] = 1;
}
else
{
raw_shape[i] = shape.dimensions[i - src];
}
}
return nnfw::cker::GetShape(raw_shape);
}
inline nnfw::cker::Shape convertShapeToCkerShape(const Shape &shape)
{
std::vector<int32_t> raw_shape;
raw_shape.resize(4);
for (uint32_t i = 0; i < 4; ++i)
{
if (i >= shape.dimensions.size())
{
raw_shape[i] = 1;
}
else
{
raw_shape[i] = shape.dimensions[i];
}
}
return nnfw::cker::GetShape(raw_shape);
}
inline int32_t getAxis(uint32_t rank, int32_t axis, ::neurun::model::Layout frontend_layout)
{
auto ret = axis;
if (axis < 0)
{
ret += rank;
}
// NCHW -> NHWC
if (frontend_layout == ::neurun::model::Layout::NCHW)
{
int32_t permutation[4] = {0, 3, 1, 2};
ret = permutation[ret];
}
return ret;
}
void QuantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift);
void GetQuantizedConvolutionMultiplier(const Shape &inputShape, const Shape &filterShape,
const Shape &biasShape, const Shape &outputShape,
float *multiplier);
void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier,
int *left_shift);
void CalculateActivationRangeFloat(model::Activation activation, float *activation_min,
float *activation_max);
void CalculateActivationRangeUint8(model::Activation activation, const Shape &outputShape,
int32_t *act_min, int32_t *act_max);
int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift);
Shape getShape(const ::neurun::model::Operand &o, ::neurun::model::Layout frontend_layout);
uint32_t sizeOfData(OperandType type, const std::vector<uint32_t> &dimensions);
} // namespace kernel
} // namespace cpu
} // namespace backend
} // namespace neurun
#endif // __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__
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