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/*
* Copyright (c) 2019 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 "Convert.h"
#include <cassert>
#include <cpp14/memory.h>
#include <ir/DataType.h>
#include "Swizzle.h"
#include <vector>
namespace neurun
{
namespace backend
{
namespace srcn
{
ir::Shape asKernelShape(const ir::Shape &shape, kernel::FilterLayout frontend_layout,
kernel::FilterLayout backend_layout)
{
assert(shape.rank() == 4);
if (frontend_layout == backend_layout)
{
return ir::Shape{shape.dim(0), shape.dim(1), shape.dim(2), shape.dim(3)};
}
const auto permutation = getFilterPermutation(frontend_layout, backend_layout);
if (permutation.size() == 0)
{
throw std::runtime_error("Not supported FilterLayout");
}
return ir::Shape{shape.dim(permutation[0]), shape.dim(permutation[1]), shape.dim(permutation[2]),
shape.dim(permutation[3])};
}
ir::Shape asTensorShape(const ir::Shape &shape, ir::Layout frontend_layout,
ir::Layout backend_layout)
{
const uint32_t rank = shape.rank();
ir::Shape ret(rank);
for (uint32_t axis = 0; axis < rank; ++axis)
{
const auto ncnn_axis = ToNCNNAxis(rank, axis, frontend_layout, backend_layout);
ret.dim(ncnn_axis) = shape.dim(axis);
}
return ret;
}
ir::OperandInfo asTensorInfo(const ir::Shape &shape, const ir::TypeInfo &typeInfo,
ir::Layout frontend_layout, ir::Layout backend_layout)
{
ir::OperandInfo info(asTensorShape(shape, frontend_layout, backend_layout), typeInfo);
return info;
}
} // namespace srcn
} // namespace backend
} // namespace neurun
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