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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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 __NNFW_CKER_CONCATENATION_H__
#define __NNFW_CKER_CONCATENATION_H__
#include <cstdint>
#include "cker/Shape.h"
namespace nnfw
{
namespace cker
{
struct ConcatenationParams
{
int8_t axis;
const int32_t *input_zeropoint;
const float *input_scale;
uint16_t inputs_count;
int32_t output_zeropoint;
float output_scale;
};
template <typename Scalar>
inline void Concatenation(const ConcatenationParams ¶ms, const Shape *const *input_shapes,
const Scalar *const *input_data, const Shape &output_shape,
Scalar *output_data)
{
int axis = params.axis;
int inputs_count = params.inputs_count;
const int concat_dimensions = output_shape.DimensionsCount();
assert(axis < concat_dimensions);
int64_t concat_size = 0;
for (int i = 0; i < inputs_count; i++)
{
assert(input_shapes[i]->DimensionsCount() == concat_dimensions);
for (int j = 0; j < concat_dimensions; j++)
{
if (j != axis)
{
auto dim_checked = MatchingDim(*input_shapes[i], j, output_shape, j);
UNUSED_RELEASE(dim_checked);
}
}
concat_size += input_shapes[i]->Dims(axis);
}
assert(concat_size == output_shape.Dims(axis));
int64_t outer_size = 1;
for (int i = 0; i < axis; ++i)
{
outer_size *= output_shape.Dims(i);
}
// For all input arrays,
// FlatSize() = outer_size * Dims(axis) * base_inner_size;
int64_t base_inner_size = 1;
for (int i = axis + 1; i < concat_dimensions; ++i)
{
base_inner_size *= output_shape.Dims(i);
}
Scalar *output_ptr = output_data;
for (int k = 0; k < outer_size; k++)
{
for (int i = 0; i < inputs_count; ++i)
{
const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size;
memcpy(output_ptr, input_data[i] + k * copy_size, copy_size * sizeof(Scalar));
output_ptr += copy_size;
}
}
}
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_CONCATENATION_H__
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