/* * 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; struct iterator { iterator(std::vector::const_iterator src_it, std::vector::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::const_iterator _src_it; std::vector::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 _src_shapes{}; std::vector _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