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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NetworkQuantizerUtils.hpp"
#include <algorithm>
#include <cmath>
#include <stdint.h>
namespace armnn
{
std::pair<float, int> ComputeQAsymmParams(int numBits, double min, double max)
{
BOOST_ASSERT_MSG(min < max, "min >= max will result in invalid quantization.");
double highest = (1 << numBits) - 1;
min = std::min(0.0, min); // min <= 0.0
max = std::max(0.0, max); // max >= 0.0
// Assumes quantization range [0-highest]
double scale = (max-min) / highest;
double offset = -min / scale;
// Clamp offset [0-highest]
offset = std::max(0.0, std::min(highest, offset));
return std::make_pair(static_cast<float>(scale), static_cast<int>(std::round(offset)));
}
ConstTensor CreateQuantizedConst(const ConstTensor& tensor, std::vector<uint8_t>& backing)
{
float scale = 0.0f;
int offset = 0;
// Reserve the backing memory
backing.resize(tensor.GetInfo().GetNumElements());
DataType type = tensor.GetInfo().GetDataType();
switch(type)
{
case DataType::Float32:
{
Quantize(static_cast<const float*>(tensor.GetMemoryArea()),
backing.data(),
backing.size(),
scale,
offset);
}
break;
default:
BOOST_ASSERT_MSG(false, "Can't quantize unsupported data type");
}
TensorInfo qInfo(tensor.GetInfo().GetShape(), DataType::QuantisedAsymm8, scale, offset);
return ConstTensor(qInfo, backing);
}
} // namespace armnn
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