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/*
* Copyright (c) 2018 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.
*/
#ifndef __CONVERT_H__
#define __CONVERT_H__
#include <mio/tflite/schema_generated.h>
#include <tflchef.pb.h>
namespace tflchef
{
tflchef::TensorType as_tflchef_type(const tflite::TensorType type);
tflchef::Activation as_tflchef_activation(const tflite::ActivationFunctionType type);
tflchef::Padding as_tflchef_padding(const tflite::Padding padding);
/**
* @brief extract buffer data to std::vector<DT>
*/
template <typename DT> std::vector<DT> extract_buffer(const tflite::Buffer *buffer)
{
auto buffer_length = buffer->data()->size();
auto num_elements = buffer_length / sizeof(DT);
std::vector<DT> result(num_elements);
std::memcpy(result.data(), buffer->data()->data(), buffer_length);
return result;
}
template <typename T> std::vector<T> as_index_vector(const flatbuffers::Vector<T> *flat_array)
{
std::vector<T> ret(flat_array->Length());
for (uint32_t i = 0; i < flat_array->Length(); i++)
{
ret[i] = flat_array->Get(i);
}
return ret;
}
} // namespace tflchef
#endif // __CONVERT_H__
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