152 lines
7.8 KiB
C++
152 lines
7.8 KiB
C++
/*
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* Copyright (c) 2019-2020 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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#include "arm_compute/runtime/BlobLifetimeManager.h"
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#include "arm_compute/runtime/CL/CLBufferAllocator.h"
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#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
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#include "arm_compute/runtime/CL/functions/CLL2NormalizeLayer.h"
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#include "arm_compute/runtime/MemoryGroup.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 "src/core/CL/kernels/CLFillBorderKernel.h"
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#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
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#include "src/core/CL/kernels/CLIm2ColKernel.h"
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#include "src/core/CL/kernels/CLL2NormalizeLayerKernel.h"
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#include "src/core/CL/kernels/CLReductionOperationKernel.h"
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#include "src/core/CL/kernels/CLWeightsReshapeKernel.h"
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#include "tests/AssetsLibrary.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/Globals.h"
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#include "tests/Utils.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/fixtures/UNIT/DynamicTensorFixture.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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namespace
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{
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constexpr AbsoluteTolerance<float> absolute_tolerance_float(0.0001f); /**< Absolute Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
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RelativeTolerance<float> tolerance_f32(0.1f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
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constexpr float tolerance_num = 0.07f; /**< Tolerance number */
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} // namespace
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#ifndef DOXYGEN_SKIP_THIS
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using CLL2NormLayerWrapper = SimpleFunctionWrapper<MemoryManagerOnDemand, CLL2NormalizeLayer, ICLTensor>;
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template <>
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void CLL2NormLayerWrapper::configure(ICLTensor *src, ICLTensor *dst)
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{
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_func.configure(src, dst, 0, 0.0001f);
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}
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#endif // DOXYGEN_SKIP_THIS
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TEST_SUITE(CL)
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TEST_SUITE(UNIT)
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TEST_SUITE(DynamicTensor)
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using BlobMemoryManagementService = MemoryManagementService<CLBufferAllocator, BlobLifetimeManager, PoolManager, MemoryManagerOnDemand>;
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using CLDynamicTensorType3SingleFunction = DynamicTensorType3SingleFunction<CLTensor, CLAccessor, BlobMemoryManagementService, CLL2NormLayerWrapper>;
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/** Tests the memory manager with dynamic input and output tensors.
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*
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* Create and manage the tensors needed to run a simple function. After the function is executed,
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* change the input and output size requesting more memory and go through the manage/allocate process.
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* The memory manager should be able to update the inner structures and allocate the requested memory
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* */
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FIXTURE_DATA_TEST_CASE(DynamicTensorType3Single, CLDynamicTensorType3SingleFunction, framework::DatasetMode::ALL,
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framework::dataset::zip(framework::dataset::make("Level0Shape", { TensorShape(12U, 11U, 3U), TensorShape(256U, 8U, 12U) }),
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framework::dataset::make("Level1Shape", { TensorShape(67U, 31U, 15U), TensorShape(11U, 2U, 3U) })))
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{
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ARM_COMPUTE_EXPECT(internal_l0.size() == internal_l1.size(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(cross_l0.size() == cross_l1.size(), framework::LogLevel::ERRORS);
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const unsigned int internal_size = internal_l0.size();
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const unsigned int cross_size = cross_l0.size();
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if(input_l0.total_size() < input_l1.total_size())
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{
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for(unsigned int i = 0; i < internal_size; ++i)
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{
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ARM_COMPUTE_EXPECT(internal_l0[i].size < internal_l1[i].size, framework::LogLevel::ERRORS);
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}
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for(unsigned int i = 0; i < cross_size; ++i)
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{
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ARM_COMPUTE_EXPECT(cross_l0[i].size < cross_l1[i].size, framework::LogLevel::ERRORS);
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}
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}
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else
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{
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for(unsigned int i = 0; i < internal_size; ++i)
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{
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ARM_COMPUTE_EXPECT(internal_l0[i].size == internal_l1[i].size, framework::LogLevel::ERRORS);
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}
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for(unsigned int i = 0; i < cross_size; ++i)
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{
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ARM_COMPUTE_EXPECT(cross_l0[i].size == cross_l1[i].size, framework::LogLevel::ERRORS);
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}
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}
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}
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using CLDynamicTensorType3ComplexFunction = DynamicTensorType3ComplexFunction<CLTensor, CLAccessor, BlobMemoryManagementService, CLConvolutionLayer>;
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/** Tests the memory manager with dynamic input and output tensors.
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*
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* Create and manage the tensors needed to run a complex function. After the function is executed,
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* change the input and output size requesting more memory and go through the manage/allocate process.
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* The memory manager should be able to update the inner structures and allocate the requested memory
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* */
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FIXTURE_DATA_TEST_CASE(DynamicTensorType3Complex, CLDynamicTensorType3ComplexFunction, framework::DatasetMode::ALL,
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framework::dataset::zip(framework::dataset::zip(framework::dataset::zip(framework::dataset::zip(
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framework::dataset::make("InputShape", { std::vector<TensorShape>{ TensorShape(12U, 12U, 16U), TensorShape(64U, 64U, 16U) } }),
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framework::dataset::make("WeightsManager", { TensorShape(3U, 3U, 16U, 5U) })),
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framework::dataset::make("BiasShape", { TensorShape(5U) })),
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framework::dataset::make("OutputShape", { std::vector<TensorShape>{ TensorShape(12U, 12U, 5U), TensorShape(64U, 64U, 5U) } })),
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framework::dataset::make("PadStrideInfo", { PadStrideInfo(1U, 1U, 1U, 1U) })))
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{
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for(unsigned int i = 0; i < num_iterations; ++i)
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{
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run_iteration(i);
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validate(CLAccessor(dst_target), dst_ref, tolerance_f32, tolerance_num, absolute_tolerance_float);
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}
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}
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using CLDynamicTensorType2PipelineFunction = DynamicTensorType2PipelineFunction<CLTensor, CLAccessor, BlobMemoryManagementService, CLConvolutionLayer>;
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/** Tests the memory manager with dynamic input and output tensors.
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*
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* Create and manage the tensors needed to run a pipeline. After the function is executed, resize the input size and rerun.
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*/
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FIXTURE_DATA_TEST_CASE(DynamicTensorType2Pipeline, CLDynamicTensorType2PipelineFunction, framework::DatasetMode::ALL,
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framework::dataset::make("InputShape", { std::vector<TensorShape>{ TensorShape(12U, 12U, 6U), TensorShape(128U, 128U, 6U) } }))
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{
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}
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TEST_SUITE_END() // DynamicTensor
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TEST_SUITE_END() // UNIT
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TEST_SUITE_END() // CL
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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