120 lines
3.9 KiB
C++
120 lines
3.9 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_POOLING_LAYER_DATASET
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#define ARM_COMPUTE_TEST_POOLING_LAYER_DATASET
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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#include "utils/TypePrinter.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace datasets
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{
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class PoolingLayerDataset
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{
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public:
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using type = std::tuple<TensorShape, PoolingLayerInfo>;
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struct iterator
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{
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iterator(std::vector<TensorShape>::const_iterator src_it,
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std::vector<PoolingLayerInfo>::const_iterator infos_it)
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: _src_it{ std::move(src_it) },
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_infos_it{ std::move(infos_it) }
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{
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}
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std::string description() const
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{
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std::stringstream description;
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description << "In=" << *_src_it << ":";
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description << "Info=" << *_infos_it << ":";
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return description.str();
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}
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PoolingLayerDataset::type operator*() const
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{
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return std::make_tuple(*_src_it, *_infos_it);
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}
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iterator &operator++()
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{
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++_src_it;
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++_infos_it;
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return *this;
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}
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private:
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std::vector<TensorShape>::const_iterator _src_it;
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std::vector<PoolingLayerInfo>::const_iterator _infos_it;
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};
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iterator begin() const
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{
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return iterator(_src_shapes.begin(), _infos.begin());
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}
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int size() const
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{
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return std::min(_src_shapes.size(), _infos.size());
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}
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void add_config(TensorShape src, PoolingLayerInfo info)
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{
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_src_shapes.emplace_back(std::move(src));
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_infos.emplace_back(std::move(info));
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}
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protected:
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PoolingLayerDataset() = default;
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PoolingLayerDataset(PoolingLayerDataset &&) = default;
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private:
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std::vector<TensorShape> _src_shapes{};
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std::vector<PoolingLayerInfo> _infos{};
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};
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// Special pooling dataset
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class PoolingLayerDatasetSpecial final : public PoolingLayerDataset
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{
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public:
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PoolingLayerDatasetSpecial()
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{
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// Special cases
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add_config(TensorShape(2U, 3U, 4U, 1U), PoolingLayerInfo(PoolingType::AVG, Size2D(3, 3), DataLayout::NCHW, PadStrideInfo(3, 3, 0, 0), true));
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add_config(TensorShape(60U, 52U, 3U, 2U), PoolingLayerInfo(PoolingType::AVG, Size2D(100, 100), DataLayout::NCHW, PadStrideInfo(5, 5, 50, 50), true));
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// Asymmetric padding
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add_config(TensorShape(112U, 112U, 32U), PoolingLayerInfo(PoolingType::MAX, 3, DataLayout::NCHW, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)));
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add_config(TensorShape(14U, 14U, 832U), PoolingLayerInfo(PoolingType::MAX, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)));
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}
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};
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} // namespace datasets
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_POOLING_LAYER_DATASET */
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