495 lines
17 KiB
C++
495 lines
17 KiB
C++
/*
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* Copyright (c) 2019 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_UNIT_DYNAMIC_TENSOR
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#define ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR
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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 "tests/AssetsLibrary.h"
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#include "tests/Globals.h"
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#include "tests/IAccessor.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Fixture.h"
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#include "tests/validation/Helpers.h"
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#include "tests/validation/reference/ConvolutionLayer.h"
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#include "tests/validation/reference/NormalizationLayer.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 validation
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{
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template <typename AllocatorType,
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typename LifetimeMgrType,
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typename PoolMgrType,
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typename MemoryMgrType>
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struct MemoryManagementService
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{
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public:
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using LftMgrType = LifetimeMgrType;
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public:
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MemoryManagementService()
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: allocator(), lifetime_mgr(nullptr), pool_mgr(nullptr), mm(nullptr), mg(), num_pools(0)
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{
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lifetime_mgr = std::make_shared<LifetimeMgrType>();
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pool_mgr = std::make_shared<PoolMgrType>();
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mm = std::make_shared<MemoryMgrType>(lifetime_mgr, pool_mgr);
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mg = MemoryGroup(mm);
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}
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void populate(size_t pools)
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{
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mm->populate(allocator, pools);
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num_pools = pools;
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}
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void clear()
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{
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mm->clear();
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num_pools = 0;
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}
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void validate(bool validate_finalized) const
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{
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ARM_COMPUTE_EXPECT(mm->pool_manager() != nullptr, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(mm->lifetime_manager() != nullptr, framework::LogLevel::ERRORS);
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if(validate_finalized)
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{
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ARM_COMPUTE_EXPECT(mm->lifetime_manager()->are_all_finalized(), framework::LogLevel::ERRORS);
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}
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ARM_COMPUTE_EXPECT(mm->pool_manager()->num_pools() == num_pools, framework::LogLevel::ERRORS);
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}
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AllocatorType allocator;
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std::shared_ptr<LifetimeMgrType> lifetime_mgr;
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std::shared_ptr<PoolMgrType> pool_mgr;
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std::shared_ptr<MemoryMgrType> mm;
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MemoryGroup mg;
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size_t num_pools;
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};
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template <typename MemoryMgrType, typename FuncType, typename ITensorType>
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class SimpleFunctionWrapper
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{
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public:
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SimpleFunctionWrapper(std::shared_ptr<MemoryMgrType> mm)
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: _func(mm)
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{
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}
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void configure(ITensorType *src, ITensorType *dst)
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{
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ARM_COMPUTE_UNUSED(src, dst);
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}
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void run()
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{
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_func.run();
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}
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private:
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FuncType _func;
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};
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/** Simple test case to run a single function with different shapes twice.
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*
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* Runs a specified function twice, where the second time the size of the input/output is different
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* Internal memory of the function and input/output are managed by different services
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*/
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template <typename TensorType,
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typename AccessorType,
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typename MemoryManagementServiceType,
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typename SimpleFunctionWrapperType>
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class DynamicTensorType3SingleFunction : public framework::Fixture
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{
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using T = float;
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public:
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template <typename...>
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void setup(TensorShape input_level0, TensorShape input_level1)
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{
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input_l0 = input_level0;
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input_l1 = input_level1;
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run();
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}
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protected:
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void run()
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{
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MemoryManagementServiceType serv_internal;
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MemoryManagementServiceType serv_cross;
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const size_t num_pools = 1;
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const bool validate_finalized = true;
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// Create Tensor shapes.
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TensorShape level_0 = TensorShape(input_l0);
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TensorShape level_1 = TensorShape(input_l1);
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// Level 0
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// Create tensors
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TensorType src = create_tensor<TensorType>(level_0, DataType::F32, 1);
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TensorType dst = create_tensor<TensorType>(level_0, DataType::F32, 1);
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serv_cross.mg.manage(&src);
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serv_cross.mg.manage(&dst);
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// Create and configure function
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SimpleFunctionWrapperType layer(serv_internal.mm);
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layer.configure(&src, &dst);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Populate and validate memory manager
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serv_cross.populate(num_pools);
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serv_internal.populate(num_pools);
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serv_cross.validate(validate_finalized);
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serv_internal.validate(validate_finalized);
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// Extract lifetime manager meta-data information
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internal_l0 = serv_internal.lifetime_mgr->info();
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cross_l0 = serv_cross.lifetime_mgr->info();
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// Acquire memory manager, fill tensors and compute functions
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serv_cross.mg.acquire();
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arm_compute::test::library->fill_tensor_value(AccessorType(src), 12.f);
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layer.run();
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serv_cross.mg.release();
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// Clear manager
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serv_cross.clear();
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serv_internal.clear();
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serv_cross.validate(validate_finalized);
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serv_internal.validate(validate_finalized);
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// Level 1
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// Update the tensor shapes
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src.info()->set_tensor_shape(level_1);
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dst.info()->set_tensor_shape(level_1);
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src.info()->set_is_resizable(true);
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dst.info()->set_is_resizable(true);
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serv_cross.mg.manage(&src);
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serv_cross.mg.manage(&dst);
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// Re-configure the function
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layer.configure(&src, &dst);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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// Populate and validate memory manager
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serv_cross.populate(num_pools);
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serv_internal.populate(num_pools);
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serv_cross.validate(validate_finalized);
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serv_internal.validate(validate_finalized);
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// Extract lifetime manager meta-data information
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internal_l1 = serv_internal.lifetime_mgr->info();
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cross_l1 = serv_cross.lifetime_mgr->info();
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// Compute functions
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serv_cross.mg.acquire();
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arm_compute::test::library->fill_tensor_value(AccessorType(src), 12.f);
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layer.run();
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serv_cross.mg.release();
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// Clear manager
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serv_cross.clear();
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serv_internal.clear();
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serv_cross.validate(validate_finalized);
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serv_internal.validate(validate_finalized);
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}
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public:
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TensorShape input_l0{}, input_l1{};
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typename MemoryManagementServiceType::LftMgrType::info_type internal_l0{}, internal_l1{};
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typename MemoryManagementServiceType::LftMgrType::info_type cross_l0{}, cross_l1{};
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};
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/** Simple test case to run a single function with different shapes twice.
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*
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* Runs a specified function twice, where the second time the size of the input/output is different
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* Internal memory of the function and input/output are managed by different services
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*/
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template <typename TensorType,
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typename AccessorType,
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typename MemoryManagementServiceType,
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typename ComplexFunctionType>
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class DynamicTensorType3ComplexFunction : public framework::Fixture
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{
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using T = float;
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public:
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template <typename...>
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void setup(std::vector<TensorShape> input_shapes, TensorShape weights_shape, TensorShape bias_shape, std::vector<TensorShape> output_shapes, PadStrideInfo info)
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{
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num_iterations = input_shapes.size();
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_data_type = DataType::F32;
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_data_layout = DataLayout::NHWC;
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_input_shapes = input_shapes;
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_output_shapes = output_shapes;
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_weights_shape = weights_shape;
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_bias_shape = bias_shape;
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_info = info;
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// Create function
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_f_target = support::cpp14::make_unique<ComplexFunctionType>(_ms.mm);
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}
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void run_iteration(unsigned int idx)
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{
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auto input_shape = _input_shapes[idx];
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auto output_shape = _output_shapes[idx];
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dst_ref = run_reference(input_shape, _weights_shape, _bias_shape, output_shape, _info);
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dst_target = run_target(input_shape, _weights_shape, _bias_shape, output_shape, _info, WeightsInfo());
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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switch(tensor.data_type())
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{
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case DataType::F32:
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{
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std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
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library->fill(tensor, distribution, i);
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break;
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}
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default:
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library->fill_tensor_uniform(tensor, i);
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}
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}
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TensorType run_target(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape,
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PadStrideInfo info, WeightsInfo weights_info)
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{
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if(_data_layout == DataLayout::NHWC)
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{
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permute(input_shape, PermutationVector(2U, 0U, 1U));
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permute(weights_shape, PermutationVector(2U, 0U, 1U));
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permute(output_shape, PermutationVector(2U, 0U, 1U));
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}
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_weights_target = create_tensor<TensorType>(weights_shape, _data_type, 1, QuantizationInfo(), _data_layout);
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_bias_target = create_tensor<TensorType>(bias_shape, _data_type, 1);
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// Create tensors
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TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, QuantizationInfo(), _data_layout);
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TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, QuantizationInfo(), _data_layout);
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// Create and configure function
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_f_target->configure(&src, &_weights_target, &_bias_target, &dst, info, weights_info);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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_weights_target.allocator()->allocate();
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_bias_target.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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fill(AccessorType(src), 0);
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fill(AccessorType(_weights_target), 1);
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fill(AccessorType(_bias_target), 2);
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// Populate and validate memory manager
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_ms.clear();
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_ms.populate(1);
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_ms.mg.acquire();
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// Compute NEConvolutionLayer function
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_f_target->run();
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_ms.mg.release();
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return dst;
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}
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SimpleTensor<T> run_reference(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info)
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{
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// Create reference
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SimpleTensor<T> src{ input_shape, _data_type, 1 };
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SimpleTensor<T> weights{ weights_shape, _data_type, 1 };
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SimpleTensor<T> bias{ bias_shape, _data_type, 1 };
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// Fill reference
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fill(src, 0);
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fill(weights, 1);
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fill(bias, 2);
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return reference::convolution_layer<T>(src, weights, bias, output_shape, info);
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}
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public:
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unsigned int num_iterations{ 0 };
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SimpleTensor<T> dst_ref{};
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TensorType dst_target{};
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private:
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DataType _data_type{ DataType::UNKNOWN };
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DataLayout _data_layout{ DataLayout::UNKNOWN };
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PadStrideInfo _info{};
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std::vector<TensorShape> _input_shapes{};
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std::vector<TensorShape> _output_shapes{};
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TensorShape _weights_shape{};
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TensorShape _bias_shape{};
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MemoryManagementServiceType _ms{};
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TensorType _weights_target{};
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TensorType _bias_target{};
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std::unique_ptr<ComplexFunctionType> _f_target{};
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};
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/** Fixture that create a pipeline of Convolutions and changes the inputs dynamically
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*
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* Runs a list of convolutions and then resizes the inputs and reruns.
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* Updates the memory manager and allocated memory.
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*/
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template <typename TensorType,
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typename AccessorType,
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typename MemoryManagementServiceType,
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typename ComplexFunctionType>
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class DynamicTensorType2PipelineFunction : public framework::Fixture
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{
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using T = float;
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public:
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template <typename...>
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void setup(std::vector<TensorShape> input_shapes)
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{
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_data_type = DataType::F32;
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_data_layout = DataLayout::NHWC;
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_input_shapes = input_shapes;
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run();
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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switch(tensor.data_type())
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{
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case DataType::F32:
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{
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std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
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library->fill(tensor, distribution, i);
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break;
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}
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default:
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library->fill_tensor_uniform(tensor, i);
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}
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}
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void run()
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{
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const unsigned int num_functions = 5;
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const unsigned int num_tensors = num_functions + 1;
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const unsigned int num_resizes = _input_shapes.size();
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for(unsigned int i = 0; i < num_functions; ++i)
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{
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_functions.emplace_back(support::cpp14::make_unique<ComplexFunctionType>(_ms.mm));
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}
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for(unsigned int i = 0; i < num_resizes; ++i)
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{
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TensorShape input_shape = _input_shapes[i];
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TensorShape weights_shape = TensorShape(3U, 3U, input_shape[2], input_shape[2]);
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TensorShape output_shape = input_shape;
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PadStrideInfo info(1U, 1U, 1U, 1U);
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if(_data_layout == DataLayout::NHWC)
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{
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permute(input_shape, PermutationVector(2U, 0U, 1U));
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permute(weights_shape, PermutationVector(2U, 0U, 1U));
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permute(output_shape, PermutationVector(2U, 0U, 1U));
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}
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std::vector<TensorType> tensors(num_tensors);
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std::vector<TensorType> ws(num_functions);
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std::vector<TensorType> bs(num_functions);
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auto tensor_info = TensorInfo(input_shape, 1, _data_type);
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auto weights_info = TensorInfo(weights_shape, 1, _data_type);
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tensor_info.set_data_layout(_data_layout);
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weights_info.set_data_layout(_data_layout);
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tensors[0].allocator()->init(tensor_info);
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for(unsigned int f = 0; f < num_functions; ++f)
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{
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tensors[f + 1].allocator()->init(tensor_info);
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ws[f].allocator()->init(weights_info);
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_functions[f]->configure(&tensors[f], &ws[f], nullptr, &tensors[f + 1], info);
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// Allocate tensors
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tensors[f].allocator()->allocate();
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ws[f].allocator()->allocate();
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}
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tensors[num_functions].allocator()->allocate();
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// Populate and validate memory manager
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_ms.clear();
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_ms.populate(1);
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_ms.mg.acquire();
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// Run pipeline
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for(unsigned int f = 0; f < num_functions; ++f)
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{
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_functions[f]->run();
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}
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// Release memory group
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_ms.mg.release();
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}
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}
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private:
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DataType _data_type{ DataType::UNKNOWN };
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DataLayout _data_layout{ DataLayout::UNKNOWN };
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std::vector<TensorShape> _input_shapes{};
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MemoryManagementServiceType _ms{};
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std::vector<std::unique_ptr<ComplexFunctionType>> _functions{};
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};
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR */
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