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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/Relu.h"
#include "kernels/Utils.h"
#include "PALRelu.h"
#include <stdexcept>
namespace luci_interpreter
{
namespace kernels
{
Relu::Relu(const Tensor *input, Tensor *output) : Kernel({input}, {output}) {}
void Relu::configure()
{
LUCI_INTERPRETER_CHECK(input()->element_type() == output()->element_type());
if (input()->element_type() == DataType::S16)
{
LUCI_INTERPRETER_CHECK(input()->zero_point() == 0 && output()->zero_point() == 0);
}
if (input()->element_type() == DataType::U8 || input()->element_type() == DataType::S16)
{
double multiplier = input()->scale() / output()->scale();
quantizeMultiplier(multiplier, &_output_multiplier, &_output_shift);
}
output()->resize(input()->shape());
}
void Relu::execute() const
{
switch (input()->element_type())
{
case DataType::FLOAT32:
evalFloat();
break;
case DataType::U8:
evalQuantized();
break;
case DataType::S16:
evalQuantizedS16();
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
void Relu::evalFloat() const
{
const auto input_data = getTensorData<float>(input());
const auto input_shape = getTensorShape(input());
auto output_data = getTensorData<float>(output());
auto output_shape = getTensorShape(output());
luci_interpreter_pal::Relu(input_shape, input_data, output_shape, output_data);
}
void Relu::evalQuantized() const
{
tflite::ReluParams params;
params.input_offset = input()->zero_point();
params.output_offset = output()->zero_point();
params.output_multiplier = _output_multiplier;
params.output_shift = _output_shift;
params.quantized_activation_min =
std::max(static_cast<int32_t>(std::numeric_limits<uint8_t>::min()), params.output_offset);
params.quantized_activation_max = static_cast<int32_t>(std::numeric_limits<uint8_t>::max());
luci_interpreter_pal::ReluX(params, getTensorShape(input()), getTensorData<uint8_t>(input()),
getTensorShape(output()), getTensorData<uint8_t>(output()));
}
void Relu::evalQuantizedS16() const
{
const auto *input_data = getTensorData<int16_t>(input());
auto *output_data = getTensorData<int16_t>(output());
constexpr int32_t output_min = 0;
constexpr int32_t output_max = std::numeric_limits<int16_t>::max();
const int32_t num_elements = input()->shape().num_elements();
for (int32_t i = 0; i < num_elements; ++i)
{
const int32_t input_val = input_data[i];
int32_t output_val =
tflite::MultiplyByQuantizedMultiplier(input_val, _output_multiplier, _output_shift);
output_val = std::max(output_val, output_min);
output_val = std::min(output_val, output_max);
output_data[i] = static_cast<int16_t>(output_val);
}
}
} // namespace kernels
} // namespace luci_interpreter
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