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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 __NEURUN_EXEC_SOURCE_H__
#define __NEURUN_EXEC_SOURCE_H__
#include <cassert>
#include "cpp14/memory.h"
#include "util/feature/nchw/Reader.h"
#include "util/feature/nchw/View.h"
#include "util/feature/nhwc/Reader.h"
#include "util/feature/nhwc/View.h"
#include "util/Utils.h"
#include <misc/feature/IndexIterator.h>
#include <ir/Layout.h>
#include "ir/Shape.h"
namespace neurun
{
namespace exec
{
struct ISource
{
virtual ~ISource() = default;
virtual void push(::neurun::backend::operand::ITensor &tensor) const = 0;
};
// Create second lever inheritance: the first lever is used as a reference type in use-case places
template <typename T> class ITemplSource : public ISource
{
public:
ITemplSource(const void *input_buffer, const size_t &input_size, const ir::Shape &shape,
const bool copy, ir::Layout io_layout)
: _input_buffer{reinterpret_cast<const T *>(input_buffer)}, _input_size{input_size},
_shape{shape}, _copy(copy), _io_layout{io_layout}
{
}
virtual void push(::neurun::backend::operand::ITensor &tensor) const = 0;
protected:
void pushUnif(neurun::backend::operand::ITensor &tensor) const
{
assert(((_io_layout == ir::Layout::NHWC && tensor.layout() == ir::Layout::NCHW) ||
(_io_layout == ir::Layout::NCHW && tensor.layout() == ir::Layout::NHWC)) ||
_copy);
auto output_buffer = tensor.buffer();
auto rank = _shape.rank();
if (!tensor.has_padding() && rank < 4 + _copy)
{
memcpy(output_buffer, _input_buffer, _input_size);
return;
}
switch (rank)
{
case 0:
case 1:
{
memcpy(output_buffer, _input_buffer, _input_size);
break;
}
case 2:
{
const int32_t copy_len = _shape.dim(1);
for (auto i = 0; i < _shape.dim(0); ++i)
{
neurun::util::Coordinates coords{i, 0};
memcpy(output_buffer + tensor.calcOffset(coords), _input_buffer + i * copy_len,
copy_len * sizeof(T));
}
break;
}
case 3:
{
const int32_t dim1 = _shape.dim(1);
const int32_t dim2 = _shape.dim(2);
for (auto i = 0; i < _shape.dim(0); ++i)
{
for (auto j = 0; j < _shape.dim(1); ++j)
{
neurun::util::Coordinates coords{i, j, 0};
memcpy(output_buffer + tensor.calcOffset(coords),
_input_buffer + i * dim1 * dim2 + j * dim2, dim2 * sizeof(T));
}
}
break;
}
case 4:
{
if (_copy)
{
const int32_t dim1 = _shape.dim(1);
const int32_t dim2 = _shape.dim(2);
const int32_t dim3 = _shape.dim(3);
for (auto i = 0; i < _shape.dim(0); ++i)
{
for (auto j = 0; j < _shape.dim(1); ++j)
{
for (auto k = 0; k < _shape.dim(2); ++k)
{
neurun::util::Coordinates coords{i, j, k, 0};
memcpy(output_buffer + tensor.calcOffset(coords),
_input_buffer + i * dim1 * dim2 * dim3 + j * dim2 * dim3 + k * dim3,
dim3 * sizeof(T));
}
}
}
}
else
{
const auto shape = _shape.asFeature(_io_layout);
if (_io_layout == ir::Layout::NCHW)
{
const util::feature::nchw::Reader<T> from(shape, _input_buffer, _input_size);
util::feature::nhwc::View<T> into(&tensor);
::nnfw::misc::feature::iterate(shape)
<< [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) {
const auto value = from.at(batch, ch, row, col);
into.at(batch, row, col, ch) = value;
};
}
else if (_io_layout == ir::Layout::NHWC)
{
const util::feature::nhwc::Reader<T> from(shape, _input_buffer, _input_size);
util::feature::nchw::View<T> into(&tensor);
::nnfw::misc::feature::iterate(shape)
<< [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) {
const auto value = from.at(batch, row, col, ch);
into.at(batch, ch, row, col) = value;
};
}
else
{
throw std::runtime_error("Wrong Layout");
}
}
break;
}
default:
throw std::runtime_error("NYI");
break;
}
}
private:
const T *_input_buffer;
const size_t _input_size;
const ir::Shape _shape;
const bool _copy;
const ir::Layout _io_layout;
};
template <typename T> class PermutateSource final : public ITemplSource<T>
{
public:
PermutateSource(const void *input_buffer, const size_t &input_size, const ir::Shape &shape,
ir::Layout io_layout)
: ITemplSource<T>(input_buffer, input_size, shape, false, io_layout)
{
}
public:
void push(neurun::backend::operand::ITensor &tensor) const override
{
// do NHWC_TO_NCHW or NCHW_TO_NHWC permutation
ITemplSource<T>::pushUnif(tensor);
}
};
template <typename T> class CopySource final : public ITemplSource<T>
{
public:
CopySource(const void *input_buffer, const size_t &input_size, const ir::Shape &shape,
ir::Layout io_layout = ir::Layout::UNKNOWN)
: ITemplSource<T>(input_buffer, input_size, shape, true, io_layout)
{
}
public:
void push(neurun::backend::operand::ITensor &tensor) const override
{
ITemplSource<T>::pushUnif(tensor);
}
};
} // namespace exec
} // namespace neurun
#endif // __NEURUN_EXEC_SOURCE_H__
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