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/*
* Copyright (c) 2020 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 "kernels/Tanh.h"
#include "kernels/Utils.h"
#include <tensorflow/lite/kernels/internal/reference/reference_ops.h>
namespace luci_interpreter
{
namespace kernels
{
Tanh::Tanh(const Tensor *input, Tensor *output) : Kernel({input}, {output}) {}
void Tanh::configure()
{
assert(input()->element_type() == output()->element_type());
if (input()->element_type() == DataType::U8)
{
populateLookupTable();
}
output()->resize(input()->shape());
}
void Tanh::execute() const
{
switch (input()->element_type())
{
case DataType::FLOAT32:
evalFloat();
break;
case DataType::U8:
evalQuantized();
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
void Tanh::evalFloat() const
{
tflite::reference_ops::Tanh(getTensorShape(input()), getTensorData<float>(input()),
getTensorShape(output()), getTensorData<float>(output()));
}
void Tanh::evalQuantized() const
{
const int size = tflite::MatchingFlatSize(getTensorShape(input()), getTensorShape(output()));
uint8_t *output_data = getTensorData<uint8_t>(output());
const uint8_t *input_data = getTensorData<uint8_t>(input());
for (int i = 0; i < size; ++i)
{
output_data[i] = getTableValue(input_data[i]);
}
}
void Tanh::populateLookupTable()
{
const auto input_scale = static_cast<double>(input()->scale());
const auto input_zero_point = static_cast<int32_t>(input()->zero_point());
const auto output_scale = static_cast<double>(output()->scale());
const auto output_zero_point = static_cast<int32_t>(output()->zero_point());
const float inverse_scale = 1 / output_scale;
int32_t maxval = std::numeric_limits<uint8_t>::max();
int32_t minval = std::numeric_limits<uint8_t>::min();
for (int32_t val = minval; val <= maxval; ++val)
{
const float dequantized = input_scale * (val - input_zero_point);
const float transformed = std::tanh(dequantized);
const float rescaled = std::round(transformed * inverse_scale);
const int32_t quantized = static_cast<int32_t>(rescaled + output_zero_point);
setTableValue(static_cast<uint8_t>(std::max(std::min(maxval, quantized), minval)),
static_cast<uint8_t>(val));
}
}
} // namespace kernels
} // namespace luci_interpreter
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