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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/Equal.h"
#include "kernels/Utils.h"
#include <tensorflow/lite/kernels/internal/reference/comparisons.h>
#include <stdexcept>
namespace luci_interpreter
{
namespace kernels
{
Equal::Equal(const Tensor *x, const Tensor *y, Tensor *output) : Kernel({x, y}, {output}) {}
void Equal::configure()
{
LUCI_INTERPRETER_CHECK(x()->element_type() == y()->element_type());
LUCI_INTERPRETER_CHECK(output()->element_type() == DataType::BOOL);
if (x()->element_type() == DataType::U8)
{
quantizeMultiplierSmallerThanOneExp(x()->scale(), &_x_multiplier, &_x_shift);
quantizeMultiplierSmallerThanOneExp(y()->scale(), &_y_multiplier, &_y_shift);
}
output()->resize(calculateShapeForBroadcast(x()->shape(), y()->shape()));
}
void Equal::execute() const
{
switch (x()->element_type())
{
case DataType::FLOAT32:
evalFloat();
break;
case DataType::U8:
evalQuantized();
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
void Equal::evalFloat() const
{
const auto x_data = getTensorData<float>(x());
const auto y_data = getTensorData<float>(y());
auto output_data = getTensorData<bool>(output());
tflite::ComparisonParams op_params;
op_params.is_broadcast = x()->shape() != y()->shape();
if (op_params.is_broadcast)
{
tflite::reference_ops::Broadcast4DSlowEqual(op_params, getTensorShape(x()), x_data,
getTensorShape(y()), y_data,
getTensorShape(output()), output_data);
}
else
{
tflite::reference_ops::Equal(op_params, getTensorShape(x()), x_data, getTensorShape(y()),
y_data, getTensorShape(output()), output_data);
}
}
void Equal::evalQuantized() const
{
const auto x_data = getTensorData<uint8_t>(x());
const auto y_data = getTensorData<uint8_t>(y());
auto output_data = getTensorData<bool>(output());
tflite::ComparisonParams op_params;
op_params.left_shift = 8;
op_params.input1_offset = -x()->zero_point(); // Note the '-'
op_params.input1_shift = _x_shift;
op_params.input1_multiplier = _x_multiplier;
op_params.input2_offset = -y()->zero_point(); // Note the '-'
op_params.input2_shift = _y_shift;
op_params.input2_multiplier = _y_multiplier;
op_params.is_broadcast = x()->shape() != y()->shape();
if (op_params.is_broadcast)
{
tflite::reference_ops::Broadcast4DSlowEqualWithScaling(op_params, getTensorShape(x()), x_data,
getTensorShape(y()), y_data,
getTensorShape(output()), output_data);
}
else
{
tflite::reference_ops::EqualWithScaling(op_params, getTensorShape(x()), x_data,
getTensorShape(y()), y_data, getTensorShape(output()),
output_data);
}
}
} // namespace kernels
} // namespace luci_interpreter
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