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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.
*/
#include <cassert>
#include "Shape.h"
namespace neurun
{
namespace graph
{
namespace operand
{
Shape::Shape(uint32_t rank) { _dims.resize(rank); }
int32_t Shape::asVector(void) const
{
assert(rank() == 1);
return dim(0);
}
nnfw::util::feature::Shape Shape::asFeature(void) const
{
assert(rank() == 4);
// Feature Map in NNAPI
// - Dimension(0) -> Batch
// - Dimension(1) -> Height
// - Dimension(2) -> Width
// - Dimension(3) -> Depth
const auto batch = dim(0);
const auto depth = dim(3);
const auto height = dim(1);
const auto width = dim(2);
return nnfw::util::feature::Shape(batch, depth, height, width);
}
nnfw::util::kernel::Shape Shape::asKernel(void) const
{
assert(rank() == 4);
// Convolution Kernel in NNAPI
// - Dimension(0) -> Count
// - Dimension(1) -> Height
// - Dimension(2) -> Width
// - Dimension(3) -> Depth
const auto count = dim(0);
const auto depth = dim(3);
const auto height = dim(1);
const auto width = dim(2);
return nnfw::util::kernel::Shape(count, depth, height, width);
}
} // namespace operand
} // namespace graph
} // namespace neurun
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