| // |
| // Copyright © 2017, 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "SplitterTestImpl.hpp" |
| |
| #include <armnnUtils/QuantizeHelper.hpp> |
| #include <ResolveType.hpp> |
| |
| |
| #include <armnnTestUtils/TensorCopyUtils.hpp> |
| #include <armnnTestUtils/WorkloadTestUtils.hpp> |
| |
| #include <armnnTestUtils/TensorHelpers.hpp> |
| |
| namespace |
| { |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| std::vector<LayerTestResult<T,3>> SplitterTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| IgnoreUnused(memoryManager); |
| unsigned int inputWidth = 5; |
| unsigned int inputHeight = 6; |
| unsigned int inputChannels = 3; |
| |
| // NOTE: Compute Library imposes a restriction that the x and y dimension (input height and width) |
| // cannot be split. |
| // For the reasons for this, see first comment on https://jira.arm.com/browse/IVGCVSW-1239 |
| // |
| // This test has therefore been recast to split the channels, then split the resulting subtensor. |
| |
| // To take channel 0 of original output |
| // and channel 0 and channel 1 of the split subtensor. |
| unsigned int outputWidth1 = inputWidth; |
| unsigned int outputHeight1 = inputHeight; |
| unsigned int outputChannels1 = 1; |
| |
| // To take channel 1 and 2 of the original output. |
| unsigned int outputWidth2 = inputWidth; |
| unsigned int outputHeight2 = inputHeight; |
| unsigned int outputChannels2 = 2; |
| |
| // Define the tensor descriptors. |
| armnn::TensorInfo inputTensorInfo({ inputChannels, inputHeight, inputWidth }, ArmnnType, qScale, qOffset); |
| |
| // Outputs of the original split. |
| armnn::TensorInfo outputTensorInfo1({ outputChannels1, outputHeight1, outputWidth1 }, ArmnnType, qScale, qOffset); |
| armnn::TensorInfo outputTensorInfo2({ outputChannels2, outputHeight2, outputWidth2 }, ArmnnType, qScale, qOffset); |
| |
| // Outputs of the subsequent subtensor split. |
| armnn::TensorInfo outputTensorInfo3({ outputChannels1, outputHeight1, outputWidth1 }, ArmnnType, qScale, qOffset); |
| armnn::TensorInfo outputTensorInfo4({ outputChannels1, outputHeight1, outputWidth1 }, ArmnnType, qScale, qOffset); |
| |
| // Set quantization parameters if the requested type is a quantized type. |
| // The quantization doesn't really matter as the splitter operator doesn't dequantize/quantize. |
| if(armnn::IsQuantizedType<T>()) |
| { |
| inputTensorInfo.SetQuantizationScale(qScale); |
| inputTensorInfo.SetQuantizationOffset(qOffset); |
| outputTensorInfo1.SetQuantizationScale(qScale); |
| outputTensorInfo1.SetQuantizationOffset(qOffset); |
| outputTensorInfo2.SetQuantizationScale(qScale); |
| outputTensorInfo2.SetQuantizationOffset(qOffset); |
| outputTensorInfo3.SetQuantizationScale(qScale); |
| outputTensorInfo3.SetQuantizationOffset(qOffset); |
| outputTensorInfo4.SetQuantizationScale(qScale); |
| outputTensorInfo4.SetQuantizationOffset(qOffset); |
| } |
| |
| auto input = armnnUtils::QuantizedVector<T>( |
| { |
| 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, 28.0f, 29.0f, 30.0f, |
| |
| 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, |
| 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, |
| 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, |
| 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, |
| 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, |
| 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, |
| |
| 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, |
| 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, |
| 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, |
| 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, |
| 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, |
| 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, |
| }, |
| qScale, qOffset); |
| |
| // Channel 0 of the original input. |
| auto expectedData1 = armnnUtils::QuantizedVector<T>( |
| { |
| 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, 28.0f, 29.0f, 30.0f, |
| }, |
| qScale, qOffset); |
| |
| // Channel 1 & 2 of the original input. |
| auto expectedData2 = armnnUtils::QuantizedVector<T>( |
| { |
| 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, |
| 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, |
| 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, |
| 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, |
| 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, |
| 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, |
| |
| 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, |
| 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, |
| 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, |
| 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, |
| 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, |
| 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, |
| }, |
| qScale, qOffset); |
| |
| // Channel 0 of return 2 (i.e. channels 1 and 2 of the original input). |
| auto expectedData3 = armnnUtils::QuantizedVector<T>( |
| { |
| 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, |
| 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, |
| 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, |
| 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, |
| 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, |
| 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, |
| }, |
| qScale, qOffset); |
| |
| // Channel 1 of return 2. |
| auto expectedData4 = armnnUtils::QuantizedVector<T>( |
| { |
| 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, |
| 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, |
| 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, |
| 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, |
| 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, |
| 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, |
| }, |
| qScale, qOffset); |
| |
| std::vector<T> actualData1(outputTensorInfo1.GetNumElements()); |
| std::vector<T> actualData2(outputTensorInfo2.GetNumElements()); |
| std::vector<T> actualData3(outputTensorInfo3.GetNumElements()); |
| std::vector<T> actualData4(outputTensorInfo4.GetNumElements()); |
| |
| // NOTE: as a corollary of the splitting of x and y restriction the x and y values of the view origins |
| // have to be zero, the co-ordinates are as per the tensor info above channels, height/y, width/x |
| // note that under the hood the compute engine reverses these i.e. its coordinate system is x, y, channels. |
| std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of output[0]. |
| armnn::SplitterQueueDescriptor::ViewOrigin window1(wOrigin1); |
| |
| std::vector<unsigned int> wOrigin2 = {1, 0, 0}; //Extent of the window is defined by size of output[1]. |
| armnn::SplitterQueueDescriptor::ViewOrigin window2(wOrigin2); |
| |
| std::vector<unsigned int> wOrigin3 = {0, 0, 0}; //Extent of the window is defined by size of output[2]. |
| armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3); |
| |
| std::vector<unsigned int> wOrigin4 = {1, 0, 0}; //Extent of the window is defined by size of output[3]. |
| armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4); |
| |
| bool subTensorsSupported = tensorHandleFactory.SupportsSubTensors(); |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| |
| std::unique_ptr<armnn::ITensorHandle> outputHandle1 = |
| subTensorsSupported ? |
| tensorHandleFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo1.GetShape(), wOrigin1.data()) : |
| tensorHandleFactory.CreateTensorHandle(outputTensorInfo1); |
| |
| std::unique_ptr<armnn::ITensorHandle> outputHandle2 = |
| subTensorsSupported ? |
| tensorHandleFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo2.GetShape(), wOrigin2.data()) : |
| tensorHandleFactory.CreateTensorHandle(outputTensorInfo2); |
| |
| std::unique_ptr<armnn::ITensorHandle> outputHandle3 = |
| subTensorsSupported ? |
| tensorHandleFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo3.GetShape(), wOrigin3.data()) : |
| tensorHandleFactory.CreateTensorHandle(outputTensorInfo3); |
| |
| std::unique_ptr<armnn::ITensorHandle> outputHandle4 = |
| subTensorsSupported ? |
| tensorHandleFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo4.GetShape(), wOrigin4.data()) : |
| tensorHandleFactory.CreateTensorHandle(outputTensorInfo4); |
| |
| // Do the first split |
| armnn::SplitterQueueDescriptor data; |
| armnn::WorkloadInfo info; |
| AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| AddOutputToWorkload(data, info, outputTensorInfo1, outputHandle1.get()); |
| AddOutputToWorkload(data, info, outputTensorInfo2, outputHandle2.get()); |
| |
| data.m_ViewOrigins.push_back(window1); |
| data.m_ViewOrigins.push_back(window2); |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::Splitter, |
| data, |
| info); |
| |
| inputHandle->Allocate(); |
| outputHandle1->Allocate(); |
| outputHandle2->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), input.data()); |
| |
| workload->Execute(); |
| |
| CopyDataFromITensorHandle(actualData1.data(), outputHandle1.get()); |
| CopyDataFromITensorHandle(actualData2.data(), outputHandle2.get()); |
| |
| // Do the second split. |
| armnn::SplitterQueueDescriptor data2; |
| armnn::WorkloadInfo info2; |
| AddInputToWorkload(data2, info2, outputTensorInfo2, outputHandle2.get()); |
| AddOutputToWorkload(data2, info2, outputTensorInfo3, outputHandle3.get()); |
| AddOutputToWorkload(data2, info2, outputTensorInfo4, outputHandle4.get()); |
| |
| data2.m_ViewOrigins.push_back(window3); |
| data2.m_ViewOrigins.push_back(window4); |
| |
| std::unique_ptr<armnn::IWorkload> workload2 = workloadFactory.CreateWorkload(armnn::LayerType::Splitter, |
| data2, |
| info2); |
| |
| outputHandle3->Allocate(); |
| outputHandle4->Allocate(); |
| |
| ExecuteWorkload(*workload2, memoryManager); |
| |
| CopyDataFromITensorHandle(actualData3.data(), outputHandle3.get()); |
| CopyDataFromITensorHandle(actualData4.data(), outputHandle4.get()); |
| |
| LayerTestResult<T,3> ret1(actualData1, expectedData1, outputHandle1->GetShape(), outputTensorInfo1.GetShape()); |
| LayerTestResult<T,3> ret2(actualData2, expectedData2, outputHandle2->GetShape(), outputTensorInfo2.GetShape()); |
| LayerTestResult<T,3> ret3(actualData3, expectedData3, outputHandle3->GetShape(), outputTensorInfo3.GetShape()); |
| LayerTestResult<T,3> ret4(actualData4, expectedData4, outputHandle4->GetShape(), outputTensorInfo4.GetShape()); |
| |
| std::vector<LayerTestResult<T,3>> ret = {ret1, ret2, ret3, ret4,}; |
| |
| return ret; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 3> CopyViaSplitterTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| float qScale, int32_t qOffset) |
| { |
| IgnoreUnused(memoryManager); |
| |
| const armnn::TensorInfo tensorInfo({ 3, 6, 5 }, ArmnnType, qScale, qOffset); |
| auto input = armnnUtils::QuantizedVector<T>( |
| { |
| 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, 28.0f, 29.0f, 30.0f, |
| |
| 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, |
| 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, |
| 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, |
| 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, |
| 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, |
| 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, |
| |
| 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, |
| 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, |
| 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, |
| 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, |
| 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, |
| 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, |
| }, |
| qScale, qOffset); |
| |
| std::vector<T> actualOutput(tensorInfo.GetNumElements()); |
| |
| std::vector<unsigned int> origin = { 0, 0, 0 }; |
| armnn::SplitterQueueDescriptor::ViewOrigin window(origin); |
| |
| const bool subTensorsSupported = tensorHandleFactory.SupportsSubTensors(); |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(tensorInfo); |
| |
| std::unique_ptr<armnn::ITensorHandle> outputHandle = |
| subTensorsSupported ? |
| tensorHandleFactory.CreateSubTensorHandle(*inputHandle, tensorInfo.GetShape(), origin.data()) : |
| tensorHandleFactory.CreateTensorHandle(tensorInfo); |
| |
| armnn::SplitterQueueDescriptor data; |
| armnn::WorkloadInfo info; |
| AddInputToWorkload(data, info, tensorInfo, inputHandle.get()); |
| AddOutputToWorkload(data, info, tensorInfo, outputHandle.get()); |
| |
| data.m_ViewOrigins.push_back(window); |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::Splitter, |
| data, |
| info); |
| |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), input.data()); |
| |
| workload->Execute(); |
| |
| CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| |
| return LayerTestResult<T, 3>(actualOutput, |
| input, |
| outputHandle->GetShape(), |
| tensorInfo.GetShape()); |
| } |
| |
| } // anonymous namespace |
| |
| std::vector<LayerTestResult<float,3>> SplitterFloat32Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return SplitterTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| std::vector<LayerTestResult<armnn::Half,3>> SplitterFloat16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return SplitterTestCommon<armnn::DataType::Float16>(workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| std::vector<LayerTestResult<uint8_t,3>> SplitterUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return SplitterTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 1.0f, 0); |
| } |
| |
| std::vector<LayerTestResult<int16_t,3>> SplitterInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return SplitterTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, tensorHandleFactory, 1.0f, 0); |
| } |
| |
| LayerTestResult<float, 3> CopyViaSplitterFloat32Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return CopyViaSplitterTestImpl<armnn::DataType::Float32>(workloadFactory, |
| memoryManager, |
| tensorHandleFactory, |
| 0.0f, |
| 0); |
| } |
| |
| LayerTestResult<armnn::Half, 3> CopyViaSplitterFloat16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return CopyViaSplitterTestImpl<armnn::DataType::Float16>(workloadFactory, |
| memoryManager, |
| tensorHandleFactory, |
| 0.0f, |
| 0); |
| } |
| |
| LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return CopyViaSplitterTestImpl<armnn::DataType::QAsymmU8>(workloadFactory, |
| memoryManager, |
| tensorHandleFactory, |
| 1.0f, |
| 0); |
| } |
| |
| LayerTestResult<int16_t, 3> CopyViaSplitterInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return CopyViaSplitterTestImpl<armnn::DataType::QSymmS16>(workloadFactory, |
| memoryManager, |
| tensorHandleFactory, |
| 1.0f, |
| 0); |
| } |