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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/L2Normalize.h"
#include "kernels/Utils.h"
#include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h>
#include <stdexcept>
namespace luci_interpreter
{
namespace kernels
{
L2Normalize::L2Normalize(const Tensor *input, Tensor *output, const L2NormParams ¶ms)
: KernelWithParams<L2NormParams>({input}, {output}, params)
{
}
void L2Normalize::configure()
{
assert(input()->shape().num_dims() <= 4);
assert(output()->element_type() == DataType::FLOAT32 || output()->element_type() == DataType::U8);
assert(input()->element_type() == output()->element_type());
if (output()->element_type() == DataType::U8)
{
assert(output()->scale() == (1. / 128.));
assert(output()->zero_point() == 128);
}
assert(params().activation == Activation::NONE);
output()->resize(input()->shape());
}
void L2Normalize::execute() const
{
switch (output()->element_type())
{
case DataType::FLOAT32:
eval<float>(0);
break;
case DataType::U8:
eval<uint8_t>(input()->zero_point());
break;
default:
throw std::runtime_error("Unsupported type.");
}
}
template <typename T> void L2Normalize::eval(int32_t zero_point) const
{
tflite::L2NormalizationParams op_params{};
op_params.input_zero_point = zero_point;
tflite::optimized_ops::L2Normalization(op_params, getTensorShape(input()),
getTensorData<T>(input()), getTensorShape(output()),
getTensorData<T>(output()));
}
} // namespace kernels
} // namespace luci_interpreter
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