414 lines
16 KiB
C++
414 lines
16 KiB
C++
/*
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* Copyright (c) 2017-2018 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_MEMORY_MANAGER
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#define ARM_COMPUTE_TEST_UNIT_MEMORY_MANAGER
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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 "arm_compute/runtime/BlobLifetimeManager.h"
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#include "arm_compute/runtime/MemoryManagerOnDemand.h"
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#include "arm_compute/runtime/PoolManager.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/FullyConnectedLayer.h"
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#include "tests/validation/reference/SoftmaxLayer.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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/** Simple test case to run two fully connected layers using a blob affinity memory manager
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*
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* Runs two fully connected layers back to back
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*/
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template <typename TensorType, typename AccessorType, typename AllocatorType, typename FullyConnectedFunction>
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class BlobMemoryManagerSimpleTestCaseFixture : public framework::Fixture
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{
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using T = float;
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public:
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void setup()
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{
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_target = compute_target();
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_reference = compute_reference();
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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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std::uniform_real_distribution<> distribution(0.5f, 1.f);
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library->fill(tensor, distribution, i);
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}
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TensorType compute_target()
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{
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auto lifetime_mgr = std::make_shared<BlobLifetimeManager>();
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auto pool_mgr = std::make_shared<PoolManager>();
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auto mm = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr);
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// Create tensors
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TensorType w1 = create_tensor<TensorType>(TensorShape(128U, 128U), DataType::F32, 1);
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TensorType b1 = create_tensor<TensorType>(TensorShape(128U), DataType::F32, 1);
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TensorType w2 = create_tensor<TensorType>(TensorShape(128U, 24U), DataType::F32, 1);
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TensorType b2 = create_tensor<TensorType>(TensorShape(24U), DataType::F32, 1);
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TensorType src = create_tensor<TensorType>(TensorShape(128U), DataType::F32, 1);
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TensorType fc1 = create_tensor<TensorType>(TensorShape(128U), DataType::F32, 1);
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TensorType dst = create_tensor<TensorType>(TensorShape(24U), DataType::F32, 1);
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// Create and configure function
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FullyConnectedFunction fc_layer_1(mm);
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FullyConnectedFunction fc_layer_2(mm);
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fc_layer_1.configure(&src, &w1, &b1, &fc1);
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fc_layer_2.configure(&fc1, &w2, &b2, &dst);
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// Allocate tensors
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w1.allocator()->allocate();
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b1.allocator()->allocate();
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w2.allocator()->allocate();
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b2.allocator()->allocate();
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src.allocator()->allocate();
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fc1.allocator()->allocate();
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dst.allocator()->allocate();
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// Finalize memory manager
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mm->populate(_allocator, 1 /* num_pools */);
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ARM_COMPUTE_EXPECT(mm->lifetime_manager()->are_all_finalized(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(mm->pool_manager()->num_pools() == 1, framework::LogLevel::ERRORS);
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// Fill tensors
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fill(AccessorType(src), 0);
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fill(AccessorType(w1), 1);
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fill(AccessorType(b1), 2);
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fill(AccessorType(w2), 3);
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fill(AccessorType(b2), 4);
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// Compute functions
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fc_layer_1.run();
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fc_layer_2.run();
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return dst;
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}
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SimpleTensor<T> compute_reference()
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{
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// Create reference
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SimpleTensor<T> w1{ TensorShape(128U, 128U), DataType::F32 };
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SimpleTensor<T> b1{ TensorShape(128U), DataType::F32 };
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SimpleTensor<T> w2{ TensorShape(128U, 24U), DataType::F32 };
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SimpleTensor<T> b2{ TensorShape(24U), DataType::F32 };
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SimpleTensor<T> src{ TensorShape(128U), DataType::F32 };
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// Fill reference
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fill(src, 0);
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fill(w1, 1);
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fill(b1, 2);
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fill(w2, 3);
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fill(b2, 4);
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auto fc1 = reference::fully_connected_layer(src, w1, b1, TensorShape(128U));
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return reference::fully_connected_layer(fc1, w2, b2, TensorShape(24U));
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}
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protected:
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TensorType _target{};
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SimpleTensor<T> _reference{};
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AllocatorType _allocator{};
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};
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/** Test case to run two fully connected layers using a blob affinity memory manager,
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* reconfigure with different shapes and rerun
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*
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* Runs two fully connected layers back to back then reconfigures with different batch size and reruns
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* Shapes of the reconfigure step are smaller that the initial configured step
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*/
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template <typename TensorType, typename AccessorType, typename AllocatorType, typename FullyConnectedFunction>
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class BlobMemoryManagerReconfigureTestCaseFixture : public framework::Fixture
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{
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using T = float;
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public:
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void setup()
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{
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_max_batches = 8;
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_cur_batches = 6;
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_target = compute_target();
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_reference = compute_reference();
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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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std::uniform_real_distribution<> distribution(0.5f, 1.f);
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library->fill(tensor, distribution, i);
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}
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TensorType compute_target()
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{
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AllocatorType allocator{};
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auto lifetime_mgr = std::make_shared<BlobLifetimeManager>();
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auto pool_mgr = std::make_shared<PoolManager>();
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auto mm = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr);
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// Create tensors
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TensorType w1 = create_tensor<TensorType>(TensorShape(128U, 128U), DataType::F32, 1);
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TensorType b1 = create_tensor<TensorType>(TensorShape(128U), DataType::F32, 1);
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TensorType w2 = create_tensor<TensorType>(TensorShape(128U, 24U), DataType::F32, 1);
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TensorType b2 = create_tensor<TensorType>(TensorShape(24U), DataType::F32, 1);
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TensorType src = create_tensor<TensorType>(TensorShape(128U, _max_batches), DataType::F32, 1);
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TensorType fc1 = create_tensor<TensorType>(TensorShape(128U, _max_batches), DataType::F32, 1);
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TensorType dst = create_tensor<TensorType>(TensorShape(24U, _max_batches), DataType::F32, 1);
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// Create and configure function
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FullyConnectedFunction fc_layer_1(mm);
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FullyConnectedFunction fc_layer_2(mm);
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fc_layer_1.configure(&src, &w1, &b1, &fc1);
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fc_layer_2.configure(&fc1, &w2, &b2, &dst);
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// Allocate persistent tensors
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w1.allocator()->allocate();
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b1.allocator()->allocate();
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w2.allocator()->allocate();
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b2.allocator()->allocate();
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// Allocate tensors (1st iteration)
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src.allocator()->allocate();
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fc1.allocator()->allocate();
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dst.allocator()->allocate();
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// Finalize memory manager
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mm->populate(_allocator, 1 /* num_pools */);
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ARM_COMPUTE_EXPECT(mm->lifetime_manager()->are_all_finalized(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(mm->pool_manager()->num_pools() == 1, framework::LogLevel::ERRORS);
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// Fill tensors (1st iteration)
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fill(AccessorType(src), 0);
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fill(AccessorType(w1), 1);
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fill(AccessorType(b1), 2);
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fill(AccessorType(w2), 3);
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fill(AccessorType(b2), 4);
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// Compute functions (1st iteration)
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fc_layer_1.run();
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fc_layer_2.run();
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// Update tensor shapes (2nd iteration)
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auto src_padding = src.allocator()->info().padding();
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auto fc1_padding = fc1.allocator()->info().padding();
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auto dst_padding = dst.allocator()->info().padding();
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int diff = _max_batches - _cur_batches;
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auto new_src_padding = PaddingSize(src_padding.top, src_padding.right, src_padding.bottom + diff, src_padding.left);
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auto new_fc1_padding = PaddingSize(fc1_padding.top, fc1_padding.right, fc1_padding.bottom + diff, fc1_padding.left);
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auto new_dst_padding = PaddingSize(dst_padding.top, dst_padding.right, dst_padding.bottom + diff, dst_padding.left);
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src.allocator()->info().set_tensor_shape(TensorShape(128U, _cur_batches)).set_is_resizable(true).extend_padding(new_src_padding);
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src.allocator()->info().set_is_resizable(false);
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fc1.allocator()->info().set_tensor_shape(TensorShape(128U, _cur_batches)).set_is_resizable(true).extend_padding(new_fc1_padding);
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fc1.allocator()->info().set_is_resizable(false);
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dst.allocator()->info().set_tensor_shape(TensorShape(24U, _cur_batches)).set_is_resizable(true).extend_padding(new_dst_padding);
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dst.allocator()->info().set_is_resizable(false);
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// Configure FC info
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FullyConnectedLayerInfo fc_info;
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fc_info.retain_internal_weights = true;
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// Configure functions (2nd iteration)
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fc_layer_1.configure(&src, &w1, &b1, &fc1, fc_info);
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fc_layer_2.configure(&fc1, &w2, &b2, &dst, fc_info);
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// Fill tensors (2nd iteration)
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fill(AccessorType(src), 5);
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// Compute functions (2nd iteration)
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fc_layer_1.run();
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fc_layer_2.run();
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return dst;
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}
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SimpleTensor<T> compute_reference()
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{
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// Create reference
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SimpleTensor<T> w1{ TensorShape(128U, 128U), DataType::F32 };
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SimpleTensor<T> b1{ TensorShape(128U), DataType::F32 };
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SimpleTensor<T> w2{ TensorShape(128U, 24U), DataType::F32 };
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SimpleTensor<T> b2{ TensorShape(24U), DataType::F32 };
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SimpleTensor<T> src{ TensorShape(128U, _cur_batches), DataType::F32 };
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// Fill reference
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fill(src, 5);
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fill(w1, 1);
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fill(b1, 2);
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fill(w2, 3);
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fill(b2, 4);
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auto fc1 = reference::fully_connected_layer(src, w1, b1, TensorShape(128U, _cur_batches));
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return reference::fully_connected_layer(fc1, w2, b2, TensorShape(24U, _cur_batches));
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}
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protected:
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TensorType _target{};
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SimpleTensor<T> _reference{};
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AllocatorType _allocator{};
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unsigned int _max_batches{};
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unsigned int _cur_batches{};
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};
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/** Test case to run a fully connected layer followed by a softmax layer using a blob affinity memory manager,
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* reconfigure with different shapes and rerun
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*
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* Runs a fully connected convolution layer followed by a softmax layer then reconfigures with different batch size and reruns
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* Shapes of the reconfigure step are smaller that the initial configured step
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*/
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template <typename TensorType, typename AccessorType, typename AllocatorType, typename FullyConnectedFunction, typename SoftmaxFunction>
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class BlobMemoryManagerReconfigure2TestCaseFixture : public framework::Fixture
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{
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using T = float;
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public:
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void setup()
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{
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_max_batches = 30;
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_cur_batches = 3;
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_target = compute_target();
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_reference = compute_reference();
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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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std::uniform_real_distribution<> distribution(0.5f, 1.f);
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library->fill(tensor, distribution, i);
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}
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TensorType compute_target()
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{
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AllocatorType allocator{};
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auto lifetime_mgr = std::make_shared<BlobLifetimeManager>();
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auto pool_mgr = std::make_shared<PoolManager>();
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auto mm = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr);
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// Create tensors
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TensorType w = create_tensor<TensorType>(TensorShape(112U, 8U), DataType::F32, 1);
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TensorType b = create_tensor<TensorType>(TensorShape(8U), DataType::F32, 1);
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TensorType src = create_tensor<TensorType>(TensorShape(1U, 1U, 112U, _max_batches), DataType::F32, 1);
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TensorType fc = create_tensor<TensorType>(TensorShape(8U, _max_batches), DataType::F32, 1);
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TensorType dst = create_tensor<TensorType>(TensorShape(8U, _max_batches), DataType::F32, 1);
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// Create and configure function
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FullyConnectedFunction fc_layer(mm);
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SoftmaxFunction smx_layer(mm);
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fc_layer.configure(&src, &w, &b, &fc);
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smx_layer.configure(&fc, &dst);
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// Allocate persistent tensors
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w.allocator()->allocate();
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b.allocator()->allocate();
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// Allocate tensors (1st iteration)
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src.allocator()->allocate();
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fc.allocator()->allocate();
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dst.allocator()->allocate();
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// Finalize memory manager
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mm->populate(_allocator, 1 /* num_pools */);
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ARM_COMPUTE_EXPECT(mm->lifetime_manager()->are_all_finalized(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(mm->pool_manager()->num_pools() == 1, framework::LogLevel::ERRORS);
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// Fill tensors (1st iteration)
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fill(AccessorType(src), 0);
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fill(AccessorType(w), 1);
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fill(AccessorType(b), 2);
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// Compute functions (1st iteration)
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fc_layer.run();
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smx_layer.run();
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// Get padding requirements
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auto fc_padding = fc.allocator()->info().padding();
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// Configure FC info
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FullyConnectedLayerInfo fc_info;
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fc_info.retain_internal_weights = true;
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// Run rest iterations
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for(int i = _max_batches; i >= static_cast<int>(_cur_batches); --i)
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{
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int diff = _max_batches - i;
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auto new_fc_padding = PaddingSize(fc_padding.top, fc_padding.right, fc_padding.bottom + diff, fc_padding.left);
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src.allocator()->info().set_tensor_shape(TensorShape(1U, 1U, 112U, i));
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fc.allocator()->info().set_tensor_shape(TensorShape(8U, i)).set_is_resizable(true).extend_padding(new_fc_padding);
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fc.allocator()->info().set_is_resizable(false);
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dst.allocator()->info().set_tensor_shape(TensorShape(8U, i));
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// Configure functions
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fc_layer.configure(&src, &w, &b, &fc, fc_info);
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smx_layer.configure(&fc, &dst);
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// Fill tensors
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fill(AccessorType(src), 3);
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// Compute functions
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fc_layer.run();
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smx_layer.run();
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}
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return dst;
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}
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SimpleTensor<T> compute_reference()
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{
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// Create reference
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SimpleTensor<T> w{ TensorShape(112U, 8U), DataType::F32 };
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SimpleTensor<T> b{ TensorShape(8U), DataType::F32 };
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SimpleTensor<T> src{ TensorShape(1U, 1U, 112U, _cur_batches), DataType::F32 };
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// Fill reference
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fill(src, 3);
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fill(w, 1);
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fill(b, 2);
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auto fc = reference::fully_connected_layer(src, w, b, TensorShape(8U, _cur_batches));
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return reference::softmax_layer(fc, 1.f);
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}
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protected:
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TensorType _target{};
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SimpleTensor<T> _reference{};
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AllocatorType _allocator{};
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unsigned int _max_batches{};
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unsigned int _cur_batches{};
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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_MEMORY_MANAGER */
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