| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include <ResolveType.hpp> |
| #include "WorkloadTestUtils.hpp" |
| |
| #include <armnn/ArmNN.hpp> |
| #include <armnn/Tensor.hpp> |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| #include <backendsCommon/IBackendInternal.hpp> |
| #include <backendsCommon/WorkloadFactory.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| |
| namespace |
| { |
| |
| template<typename T, std::size_t InDim, std::size_t OutDim> |
| LayerTestResult<T, OutDim> StridedSliceTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::TensorInfo& inputTensorInfo, |
| armnn::TensorInfo& outputTensorInfo, |
| std::vector<float>& inputData, |
| std::vector<float>& outputExpectedData, |
| armnn::StridedSliceQueueDescriptor descriptor, |
| const float qScale = 1.0f, |
| const int32_t qOffset = 0) |
| { |
| if(armnn::IsQuantizedType<T>()) |
| { |
| inputTensorInfo.SetQuantizationScale(qScale); |
| inputTensorInfo.SetQuantizationOffset(qOffset); |
| |
| outputTensorInfo.SetQuantizationScale(qScale); |
| outputTensorInfo.SetQuantizationOffset(qOffset); |
| } |
| |
| boost::multi_array<T, InDim> input = |
| MakeTensor<T, InDim>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| |
| LayerTestResult<T, OutDim> ret(outputTensorInfo); |
| ret.outputExpected = |
| MakeTensor<T, OutDim>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); |
| |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = |
| workloadFactory.CreateTensorHandle(inputTensorInfo); |
| |
| std::unique_ptr<armnn::ITensorHandle> outputHandle = |
| workloadFactory.CreateTensorHandle(outputTensorInfo); |
| |
| armnn::WorkloadInfo info; |
| AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateStridedSlice(descriptor, info); |
| |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), input.data()); |
| |
| ExecuteWorkload(*workload, memoryManager); |
| |
| CopyDataFromITensorHandle(ret.output.data(), outputHandle.get()); |
| |
| return ret; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> StridedSlice4DTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 2, 3, 1}; |
| unsigned int outputShape[] = {1, 2, 3, 1}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {1, 0, 0, 0}; |
| desc.m_Parameters.m_End = {2, 2, 3, 1}; |
| desc.m_Parameters.m_Stride = {1, 1, 1, 1}; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| |
| 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| |
| 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 4, 4>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> StridedSlice4DReverseTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 2, 3, 1}; |
| unsigned int outputShape[] = {1, 2, 3, 1}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {1, -1, 0, 0}; |
| desc.m_Parameters.m_End = {2, -3, 3, 1}; |
| desc.m_Parameters.m_Stride = {1, -1, 1, 1}; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| |
| 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| |
| 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 4.0f, 4.0f, 4.0f, 3.0f, 3.0f, 3.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 4, 4>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> StridedSliceSimpleStrideTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 2, 3, 1}; |
| unsigned int outputShape[] = {2, 1, 2, 1}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {0, 0, 0, 0}; |
| desc.m_Parameters.m_End = {3, 2, 3, 1}; |
| desc.m_Parameters.m_Stride = {2, 2, 2, 1}; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| |
| 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| |
| 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 1.0f, 1.0f, |
| |
| 5.0f, 5.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 4, 4>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> StridedSliceSimpleRangeMaskTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 2, 3, 1}; |
| unsigned int outputShape[] = {3, 2, 3, 1}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {1, 1, 1, 1}; |
| desc.m_Parameters.m_End = {1, 1, 1, 1}; |
| desc.m_Parameters.m_Stride = {1, 1, 1, 1}; |
| desc.m_Parameters.m_BeginMask = (1 << 4) - 1; |
| desc.m_Parameters.m_EndMask = (1 << 4) - 1; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| |
| 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| |
| 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| |
| 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| |
| 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 4, 4>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 2> StridedSliceShrinkAxisMaskTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 2, 3, 1}; |
| unsigned int outputShape[] = {3, 1}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {0, 0, 1, 0}; |
| desc.m_Parameters.m_End = {1, 1, 1, 1}; |
| desc.m_Parameters.m_Stride = {1, 1, 1, 1}; |
| desc.m_Parameters.m_EndMask = (1 << 4) - 1; |
| desc.m_Parameters.m_ShrinkAxisMask = (1 << 1) | (1 << 2); |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(2, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, |
| |
| 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, |
| |
| 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 2.0f, 8.0f, 14.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 4, 2>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 3> StridedSlice3DTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 3, 3}; |
| unsigned int outputShape[] = {2, 2, 2}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {0, 0, 0}; |
| desc.m_Parameters.m_End = {1, 1, 1}; |
| desc.m_Parameters.m_Stride = {2, 2, 2}; |
| desc.m_Parameters.m_EndMask = (1 << 3) - 1; |
| |
| inputTensorInfo = armnn::TensorInfo(3, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(3, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
| |
| 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, |
| |
| 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 1.0f, 3.0f, 7.0f, 9.0f, |
| |
| 19.0f, 21.0f, 25.0f, 27.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 3, 3>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 3> StridedSlice3DReverseTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 3, 3}; |
| unsigned int outputShape[] = {2, 2, 2}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {-1, -1, -1}; |
| desc.m_Parameters.m_End = {-4, -4, -4}; |
| desc.m_Parameters.m_Stride = {-2, -2, -2}; |
| |
| inputTensorInfo = armnn::TensorInfo(3, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(3, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
| |
| 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, |
| |
| 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 27.0f, 25.0f, 21.0f, 19.0f, |
| |
| 9.0f, 7.0f, 3.0f, 1.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 3, 3>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 2> StridedSlice2DTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 3}; |
| unsigned int outputShape[] = {2, 2}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {0, 0}; |
| desc.m_Parameters.m_End = {1, 1}; |
| desc.m_Parameters.m_Stride = {2, 2}; |
| desc.m_Parameters.m_EndMask = (1 << 2) - 1; |
| |
| inputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(2, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 2.0f, 3.0f, |
| |
| 4.0f, 5.0f, 6.0f, |
| |
| 7.0f, 8.0f, 9.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 1.0f, 3.0f, |
| |
| 7.0f, 9.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 2, 2>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 2> StridedSlice2DReverseTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {3, 3}; |
| unsigned int outputShape[] = {2, 2}; |
| |
| armnn::StridedSliceQueueDescriptor desc; |
| desc.m_Parameters.m_Begin = {0, 0}; |
| desc.m_Parameters.m_End = {1, 1}; |
| desc.m_Parameters.m_Stride = {-2, -2}; |
| desc.m_Parameters.m_BeginMask = (1 << 2) - 1; |
| desc.m_Parameters.m_EndMask = (1 << 2) - 1; |
| |
| inputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(2, outputShape, ArmnnType); |
| |
| std::vector<float> input = std::vector<float>( |
| { |
| 1.0f, 2.0f, 3.0f, |
| |
| 4.0f, 5.0f, 6.0f, |
| |
| 7.0f, 8.0f, 9.0f |
| }); |
| |
| std::vector<float> outputExpected = std::vector<float>( |
| { |
| 9.0f, 7.0f, |
| |
| 3.0f, 1.0f |
| }); |
| |
| return StridedSliceTestImpl<T, 2, 2>( |
| workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| } |
| |
| } // anonymous namespace |