| /* |
| * Copyright (c) 2019-2020 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/runtime/Allocator.h" |
| #include "arm_compute/runtime/MemoryManagerOnDemand.h" |
| #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" |
| #include "arm_compute/runtime/NEON/functions/NENormalizationLayer.h" |
| #include "arm_compute/runtime/OffsetLifetimeManager.h" |
| #include "arm_compute/runtime/PoolManager.h" |
| #include "tests/AssetsLibrary.h" |
| #include "tests/NEON/Accessor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/framework/datasets/Datasets.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/fixtures/UNIT/DynamicTensorFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| constexpr AbsoluteTolerance<float> absolute_tolerance_float(0.0001f); /**< Absolute Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ |
| RelativeTolerance<float> tolerance_f32(0.1f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ |
| constexpr float tolerance_num = 0.07f; /**< Tolerance number */ |
| } // namespace |
| #ifndef DOXYGEN_SKIP_THIS |
| using NENormLayerWrapper = SimpleFunctionWrapper<MemoryManagerOnDemand, NENormalizationLayer, ITensor>; |
| template <> |
| void NENormLayerWrapper::configure(arm_compute::ITensor *src, arm_compute::ITensor *dst) |
| { |
| _func.configure(src, dst, NormalizationLayerInfo(NormType::CROSS_MAP, 3)); |
| } |
| #endif // DOXYGEN_SKIP_THIS |
| TEST_SUITE(NEON) |
| TEST_SUITE(UNIT) |
| TEST_SUITE(DynamicTensor) |
| |
| using OffsetMemoryManagementService = MemoryManagementService<Allocator, OffsetLifetimeManager, PoolManager, MemoryManagerOnDemand>; |
| using NEDynamicTensorType3SingleFunction = DynamicTensorType3SingleFunction<Tensor, Accessor, OffsetMemoryManagementService, NENormLayerWrapper>; |
| |
| /** Tests the memory manager with dynamic input and output tensors. |
| * |
| * Create and manage the tensors needed to run a simple function. After the function is executed, |
| * change the input and output size requesting more memory and go through the manage/allocate process. |
| * The memory manager should be able to update the inner structures and allocate the requested memory |
| * */ |
| FIXTURE_DATA_TEST_CASE(DynamicTensorType3Single, NEDynamicTensorType3SingleFunction, framework::DatasetMode::ALL, |
| framework::dataset::zip(framework::dataset::make("Level0Shape", { TensorShape(12U, 11U, 3U), TensorShape(256U, 8U, 12U) }), |
| framework::dataset::make("Level1Shape", { TensorShape(67U, 31U, 15U), TensorShape(11U, 2U, 3U) }))) |
| { |
| if(input_l0.total_size() < input_l1.total_size()) |
| { |
| ARM_COMPUTE_EXPECT(internal_l0.size < internal_l1.size, framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(cross_l0.size < cross_l1.size, framework::LogLevel::ERRORS); |
| } |
| else |
| { |
| ARM_COMPUTE_EXPECT(internal_l0.size == internal_l1.size, framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(cross_l0.size == cross_l1.size, framework::LogLevel::ERRORS); |
| } |
| } |
| |
| using NEDynamicTensorType3ComplexFunction = DynamicTensorType3ComplexFunction<Tensor, Accessor, OffsetMemoryManagementService, NEConvolutionLayer>; |
| /** Tests the memory manager with dynamic input and output tensors. |
| * |
| * Create and manage the tensors needed to run a complex function. After the function is executed, |
| * change the input and output size requesting more memory and go through the manage/allocate process. |
| * The memory manager should be able to update the inner structures and allocate the requested memory |
| * */ |
| FIXTURE_DATA_TEST_CASE(DynamicTensorType3Complex, NEDynamicTensorType3ComplexFunction, framework::DatasetMode::ALL, |
| framework::dataset::zip(framework::dataset::zip(framework::dataset::zip(framework::dataset::zip( |
| framework::dataset::make("InputShape", { std::vector<TensorShape>{ TensorShape(12U, 12U, 6U), TensorShape(128U, 128U, 6U) } }), |
| framework::dataset::make("WeightsManager", { TensorShape(3U, 3U, 6U, 3U) })), |
| framework::dataset::make("BiasShape", { TensorShape(3U) })), |
| framework::dataset::make("OutputShape", { std::vector<TensorShape>{ TensorShape(12U, 12U, 3U), TensorShape(128U, 128U, 3U) } })), |
| framework::dataset::make("PadStrideInfo", { PadStrideInfo(1U, 1U, 1U, 1U) }))) |
| { |
| for(unsigned int i = 0; i < num_iterations; ++i) |
| { |
| run_iteration(i); |
| validate(Accessor(dst_target), dst_ref, tolerance_f32, tolerance_num, absolute_tolerance_float); |
| } |
| } |
| |
| using NEDynamicTensorType2PipelineFunction = DynamicTensorType2PipelineFunction<Tensor, Accessor, OffsetMemoryManagementService, NEConvolutionLayer>; |
| /** Tests the memory manager with dynamic input and output tensors. |
| * |
| * Create and manage the tensors needed to run a pipeline. After the function is executed, resize the input size and rerun. |
| */ |
| FIXTURE_DATA_TEST_CASE(DynamicTensorType2Pipeline, NEDynamicTensorType2PipelineFunction, framework::DatasetMode::ALL, |
| framework::dataset::make("InputShape", { std::vector<TensorShape>{ TensorShape(12U, 12U, 6U), TensorShape(128U, 128U, 6U) } })) |
| { |
| } |
| TEST_SUITE_END() // DynamicTensor |
| TEST_SUITE_END() // UNIT |
| TEST_SUITE_END() // NEON |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |