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/*
* Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "QuantizationLayer.h"
#include <cmath>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
SimpleTensor<uint8_t> quantization_layer(const SimpleTensor<T> &src)
{
// Create reference
SimpleTensor<uint8_t> dst{ src.shape(), DataType::U8 };
const int width = src.shape().x();
const int height = src.shape().y();
const int depth = src.shape().z();
const int stride_w = width * height * depth;
const int num_batches = src.shape().total_size_upper(3);
for(int k = 0; k < num_batches; ++k)
{
// Compute min and max of the 3D tensor
float min = src[k * stride_w];
float max = src[k * stride_w];
// Look for min and max values
for(int i = 1; i < stride_w; ++i)
{
float val = src[i + k * stride_w];
min = std::min(min, val);
max = std::max(max, val);
}
// Saturate the result in case min = max
if(min == max)
{
min = 0.0f;
max = 1.0f;
}
const float range = max - min;
for(int i = 0; i < stride_w; ++i)
{
// map values to range [0.0, 1.0]
float val = src[i + k * stride_w];
const float normalized = (val - min) / range;
dst[i + k * stride_w] = static_cast<uint8_t>(std::min(255.0f, normalized * 256.0f));
}
}
return dst;
}
template SimpleTensor<uint8_t> quantization_layer(const SimpleTensor<float> &src);
template <typename T>
SimpleTensor<uint8_t> quantization_layer(const SimpleTensor<T> &src, const QuantizationInfo quantization_info)
{
// Create reference
SimpleTensor<uint8_t> dst{ src.shape(), DataType::QASYMM8, 1, quantization_info };
for(int i = 0; i < src.num_elements(); ++i)
{
#ifdef __aarch64__
dst[i] = quantization_info.quantize((src[i]), RoundingPolicy::TO_NEAREST_EVEN);
#else // __aarch64__
dst[i] = quantization_info.quantize((src[i]), RoundingPolicy::TO_ZERO);
#endif // __aarch64__
}
return dst;
}
template SimpleTensor<uint8_t> quantization_layer(const SimpleTensor<half> &src, const QuantizationInfo quantization_info);
template SimpleTensor<uint8_t> quantization_layer(const SimpleTensor<float> &src, const QuantizationInfo quantization_info);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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