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Diffstat (limited to 'tests/validation/fixtures/UNIT/DynamicTensorFixture.h')
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diff --git a/tests/validation/fixtures/UNIT/DynamicTensorFixture.h b/tests/validation/fixtures/UNIT/DynamicTensorFixture.h new file mode 100644 index 0000000000..df12a4aa30 --- /dev/null +++ b/tests/validation/fixtures/UNIT/DynamicTensorFixture.h @@ -0,0 +1,224 @@ +/* + * Copyright (c) 2019 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_UNIT_DYNAMIC_TENSOR +#define ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/IAccessor.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Fixture.h" +#include "tests/validation/Helpers.h" +#include "tests/validation/reference/NormalizationLayer.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +template <typename AllocatorType, + typename LifetimeMgrType, + typename PoolMgrType, + typename MemoryMgrType> +struct MemoryManagementService +{ +public: + MemoryManagementService() + : allocator(), lifetime_mgr(nullptr), pool_mgr(nullptr), mm(nullptr), mg(), num_pools(0) + { + lifetime_mgr = std::make_shared<LifetimeMgrType>(); + pool_mgr = std::make_shared<PoolMgrType>(); + mm = std::make_shared<MemoryMgrType>(lifetime_mgr, pool_mgr); + mg = MemoryGroup(mm); + } + + void populate(size_t pools) + { + mm->populate(allocator, pools); + num_pools = pools; + } + + void clear() + { + mm->clear(); + num_pools = 0; + } + + void validate(bool validate_finalized) const + { + ARM_COMPUTE_EXPECT(mm->pool_manager() != nullptr, framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(mm->lifetime_manager() != nullptr, framework::LogLevel::ERRORS); + + if(validate_finalized) + { + ARM_COMPUTE_EXPECT(mm->lifetime_manager()->are_all_finalized(), framework::LogLevel::ERRORS); + } + ARM_COMPUTE_EXPECT(mm->pool_manager()->num_pools() == num_pools, framework::LogLevel::ERRORS); + } + + AllocatorType allocator; + std::shared_ptr<LifetimeMgrType> lifetime_mgr; + std::shared_ptr<PoolMgrType> pool_mgr; + std::shared_ptr<MemoryMgrType> mm; + MemoryGroup mg; + size_t num_pools; +}; +} // namespace + +/** Simple test case to run a single function with different shapes twice. + * + * Runs a specified function twice, where the second time the size of the input/output is different + * Internal memory of the function and input/output are managed by different services + */ +template <typename TensorType, + typename AccessorType, + typename AllocatorType, + typename LifetimeMgrType, + typename PoolMgrType, + typename MemoryManagerType, + typename NormalizationFunctionType> +class DynamicTensorType3SingleFunction : public framework::Fixture +{ + using T = float; + using MemoryManagementServiceType = MemoryManagementService<AllocatorType, LifetimeMgrType, PoolMgrType, MemoryManagerType>; + +public: + template <typename...> + void setup(TensorShape input_level0, TensorShape input_level1) + { + input_l0 = input_level0; + input_l1 = input_level1; + run(); + } + +protected: + void run() + { + MemoryManagementServiceType serv_internal; + MemoryManagementServiceType serv_cross; + const size_t num_pools = 1; + const bool validate_finalized = true; + + // Create Tensor shapes. + TensorShape level_0 = TensorShape(input_l0); + TensorShape level_1 = TensorShape(input_l1); + + // Level 0 + // Create tensors + TensorType src = create_tensor<Tensor>(level_0, DataType::F32, 1); + TensorType dst = create_tensor<Tensor>(level_0, DataType::F32, 1); + + serv_cross.mg.manage(&src); + serv_cross.mg.manage(&dst); + + // Create and configure function + NormalizationFunctionType norm_layer(serv_internal.mm); + norm_layer.configure(&src, &dst, NormalizationLayerInfo(NormType::CROSS_MAP, 3)); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Populate and validate memory manager + serv_cross.populate(num_pools); + serv_internal.populate(num_pools); + serv_cross.validate(validate_finalized); + serv_internal.validate(validate_finalized); + + // Extract lifetime manager meta-data information + internal_l0 = serv_internal.lifetime_mgr->info(); + cross_l0 = serv_cross.lifetime_mgr->info(); + + // Acquire memory manager, fill tensors and compute functions + serv_cross.mg.acquire(); + arm_compute::test::library->fill_tensor_value(Accessor(src), 12.f); + norm_layer.run(); + serv_cross.mg.release(); + + // Clear manager + serv_cross.clear(); + serv_internal.clear(); + serv_cross.validate(validate_finalized); + serv_internal.validate(validate_finalized); + + // Level 1 + // Update the tensor shapes + src.info()->set_tensor_shape(level_1); + dst.info()->set_tensor_shape(level_1); + src.info()->set_is_resizable(true); + dst.info()->set_is_resizable(true); + + serv_cross.mg.manage(&src); + serv_cross.mg.manage(&dst); + + // Re-configure the function + norm_layer.configure(&src, &dst, NormalizationLayerInfo(NormType::CROSS_MAP, 3)); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + // Populate and validate memory manager + serv_cross.populate(num_pools); + serv_internal.populate(num_pools); + serv_cross.validate(validate_finalized); + serv_internal.validate(validate_finalized); + + // Extract lifetime manager meta-data information + internal_l1 = serv_internal.lifetime_mgr->info(); + cross_l1 = serv_cross.lifetime_mgr->info(); + + // Compute functions + serv_cross.mg.acquire(); + arm_compute::test::library->fill_tensor_value(AccessorType(src), 12.f); + norm_layer.run(); + serv_cross.mg.release(); + + // Clear manager + serv_cross.clear(); + serv_internal.clear(); + serv_cross.validate(validate_finalized); + serv_internal.validate(validate_finalized); + } + +public: + TensorShape input_l0{}, input_l1{}; + typename LifetimeMgrType::info_type internal_l0{}, internal_l1{}; + typename LifetimeMgrType::info_type cross_l0{}, cross_l1{}; +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR */ |