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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 "TanhLayer.h"
#include "OperationUtils.h"
#include <cker/operation/Tanh.h>
namespace onert
{
namespace backend
{
namespace cpu
{
namespace kernel
{
TanhLayer::TanhLayer() : _input(nullptr), _output(nullptr)
{
// DO NOTHING
}
void TanhLayer::tanhFloat32()
{
nnfw::cker::Tanh(convertTensorToCkerShape(_input),
reinterpret_cast<const float *>(_input->buffer()),
convertTensorToCkerShape(_output), reinterpret_cast<float *>(_output->buffer()));
}
void TanhLayer::tanhQuant8()
{
// cker quant8 tanh is not implemented yet
throw std::runtime_error{"NYI"};
}
void TanhLayer::configure(const operand::Tensor *input, operand::Tensor *output)
{
_input = input;
_output = output;
}
void TanhLayer::run()
{
if (_input->data_type() == OperandType::FLOAT32)
{
tanhFloat32();
}
else if (_input->data_type() == OperandType::QUANT8_ASYMM)
{
tanhQuant8();
}
}
} // namespace kernel
} // namespace cpu
} // namespace backend
} // namespace onert
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