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/*
* Copyright (c) 2023 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.
*/
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "utils/TypePrinter.h"
#include "arm_compute/dynamic_fusion/sketch/attributes/Pool2dAttributes.h"
using Pool2dAttributes = arm_compute::experimental::dynamic_fusion::Pool2dAttributes;
namespace arm_compute
{
namespace test
{
namespace datasets
{
class DynamicFusionPoolingLayerDataset
{
public:
using type = std::tuple<TensorShape, Pool2dAttributes>;
struct iterator
{
iterator(std::vector<TensorShape>::const_iterator src_it,
std::vector<Pool2dAttributes>::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();
}
DynamicFusionPoolingLayerDataset::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<Pool2dAttributes>::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, Pool2dAttributes info)
{
_src_shapes.emplace_back(std::move(src));
_infos.emplace_back(std::move(info));
}
protected:
DynamicFusionPoolingLayerDataset() = default;
DynamicFusionPoolingLayerDataset(DynamicFusionPoolingLayerDataset &&) = default;
private:
std::vector<TensorShape> _src_shapes{};
std::vector<Pool2dAttributes> _infos{};
};
// Special pooling dataset
class PoolingLayerDatasetSpecialDynamicFusion final : public DynamicFusionPoolingLayerDataset
{
public:
PoolingLayerDatasetSpecialDynamicFusion()
{
// NCHW DataLayout
// Special cases
add_config(TensorShape(2U, 3U, 4U, 1U), Pool2dAttributes().pool_type(PoolingType::AVG).pool_size(Size2D(2,2)).stride(Size2D(3,3)));
add_config(TensorShape(60U, 52U, 3U, 2U), Pool2dAttributes().pool_type(PoolingType::AVG).pool_size(Size2D(100,100)).stride(Size2D(5,5)).pad(Padding2D(50,50,50,50)));
// Asymmetric padding
add_config(TensorShape(112U, 112U, 32U), Pool2dAttributes().pool_type(PoolingType::MAX).pool_size(Size2D(3,3)).pad(Padding2D(0,1,0,1)).stride(Size2D(2,2)));
add_config(TensorShape(14U, 14U, 832U), Pool2dAttributes().pool_type(PoolingType::MAX).pool_size(Size2D(2,2)).stride(Size2D(1,1)).pad(Padding2D(0,0,0,0)));
}
};
} // namespace datasets
} // namespace test
} // namespace arm_compute
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