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/*
* Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ARM_COMPUTE_TEST_POOLING_LAYER_DATASET
#define ARM_COMPUTE_TEST_POOLING_LAYER_DATASET
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "utils/TypePrinter.h"
namespace arm_compute
{
namespace test
{
namespace datasets
{
class PoolingLayerDataset
{
public:
using type = std::tuple<TensorShape, PoolingLayerInfo>;
struct iterator
{
iterator(std::vector<TensorShape>::const_iterator src_it,
std::vector<PoolingLayerInfo>::const_iterator infos_it)
: _src_it{ std::move(src_it) },
_infos_it{ std::move(infos_it) }
{
}
std::string description() const
{
std::stringstream description;
description << "In=" << *_src_it << ":";
description << "Info=" << *_infos_it << ":";
return description.str();
}
PoolingLayerDataset::type operator*() const
{
return std::make_tuple(*_src_it, *_infos_it);
}
iterator &operator++()
{
++_src_it;
++_infos_it;
return *this;
}
private:
std::vector<TensorShape>::const_iterator _src_it;
std::vector<PoolingLayerInfo>::const_iterator _infos_it;
};
iterator begin() const
{
return iterator(_src_shapes.begin(), _infos.begin());
}
int size() const
{
return std::min(_src_shapes.size(), _infos.size());
}
void add_config(TensorShape src, PoolingLayerInfo info)
{
_src_shapes.emplace_back(std::move(src));
_infos.emplace_back(std::move(info));
}
protected:
PoolingLayerDataset() = default;
PoolingLayerDataset(PoolingLayerDataset &&) = default;
private:
std::vector<TensorShape> _src_shapes{};
std::vector<PoolingLayerInfo> _infos{};
};
// Special pooling dataset
class PoolingLayerDatasetSpecial final : public PoolingLayerDataset
{
public:
PoolingLayerDatasetSpecial()
{
// Special cases
add_config(TensorShape(2U, 3U, 4U, 1U), PoolingLayerInfo(PoolingType::AVG, Size2D(2, 2), DataLayout::NCHW, PadStrideInfo(3, 3, 0, 0), true));
add_config(TensorShape(60U, 52U, 3U, 2U), PoolingLayerInfo(PoolingType::AVG, Size2D(100, 100), DataLayout::NCHW, PadStrideInfo(5, 5, 50, 50), true));
// Asymmetric padding
add_config(TensorShape(112U, 112U, 32U), PoolingLayerInfo(PoolingType::MAX, 3, DataLayout::NCHW, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)));
add_config(TensorShape(14U, 14U, 832U), PoolingLayerInfo(PoolingType::MAX, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)));
}
};
} // namespace datasets
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_POOLING_LAYER_DATASET */
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