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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/Div.h"
#include "kernels/Utils.h"
#include <tensorflow/lite/kernels/internal/reference/reference_ops.h>
namespace luci_interpreter
{
namespace kernels
{
Div::Div(const Tensor *input1, const Tensor *input2, Tensor *output, const DivParams ¶ms)
: KernelWithParams<DivParams>({input1, input2}, {output}, params)
{
}
void Div::configure()
{
LUCI_INTERPRETER_CHECK(input1()->element_type() == input2()->element_type());
LUCI_INTERPRETER_CHECK(input1()->element_type() == output()->element_type());
output()->resize(calculateShapeForBroadcast(input1()->shape(), input2()->shape()));
}
void Div::execute() const
{
switch (input1()->element_type())
{
case DataType::FLOAT32:
evalFloat();
break;
case DataType::U8:
evalQuantized();
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
void Div::evalFloat() const
{
float activation_min{};
float activation_max{};
calculateActivationRange(_params.activation, &activation_min, &activation_max);
tflite::ArithmeticParams params{};
params.float_activation_min = activation_min;
params.float_activation_max = activation_max;
const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
getTensorShape(input1()), getTensorShape(input2()), ¶ms);
if (need_broadcast)
{
tflite::reference_ops::BroadcastDivSlow(
params, getTensorShape(input1()), getTensorData<float>(input1()), getTensorShape(input2()),
getTensorData<float>(input2()), getTensorShape(output()), getTensorData<float>(output()));
}
else
{
tflite::reference_ops::Div(params, getTensorShape(input1()), getTensorData<float>(input1()),
getTensorShape(input2()), getTensorData<float>(input2()),
getTensorShape(output()), getTensorData<float>(output()));
}
}
void Div::evalQuantized() const
{
const auto input1_scale = static_cast<double>(input1()->scale());
const auto input2_scale = static_cast<double>(input2()->scale());
const auto output_scale = static_cast<double>(output()->scale());
const double real_output_multiplier = input1_scale / (input2_scale * output_scale);
int32_t output_multiplier{};
int output_shift{};
quantizeMultiplier(real_output_multiplier, &output_multiplier, &output_shift);
int32_t activation_min{};
int32_t activation_max{};
calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max);
tflite::ArithmeticParams params{};
params.input1_offset = -input1()->zero_point(); // Note the '-'.
params.input2_offset = -input2()->zero_point(); // Note the '-'.
params.output_offset = output()->zero_point();
params.output_multiplier = output_multiplier;
params.output_shift = output_shift;
params.quantized_activation_min = activation_min;
params.quantized_activation_max = activation_max;
const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
getTensorShape(input1()), getTensorShape(input2()), ¶ms);
if (need_broadcast)
{
tflite::reference_ops::BroadcastDivSlow(
params, getTensorShape(input1()), getTensorData<uint8_t>(input1()),
getTensorShape(input2()), getTensorData<uint8_t>(input2()), getTensorShape(output()),
getTensorData<uint8_t>(output()));
}
else
{
tflite::reference_ops::Div(params, getTensorShape(input1()), getTensorData<uint8_t>(input1()),
getTensorShape(input2()), getTensorData<uint8_t>(input2()),
getTensorShape(output()), getTensorData<uint8_t>(output()));
}
}
} // namespace kernels
} // namespace luci_interpreter
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