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Diffstat (limited to 'src/armnn/backends/test/SplitterTestImpl.hpp')
| -rw-r--r-- | src/armnn/backends/test/SplitterTestImpl.hpp | 328 |
1 files changed, 328 insertions, 0 deletions
diff --git a/src/armnn/backends/test/SplitterTestImpl.hpp b/src/armnn/backends/test/SplitterTestImpl.hpp new file mode 100644 index 0000000000..b72046e4bc --- /dev/null +++ b/src/armnn/backends/test/SplitterTestImpl.hpp @@ -0,0 +1,328 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// +#pragma once + +#include <armnn/ArmNN.hpp> +#include <armnn/Tensor.hpp> +#include <backends/WorkloadInfo.hpp> + +#include "test/TensorHelpers.hpp" + +#include "backends/CpuTensorHandle.hpp" +#include "backends/WorkloadFactory.hpp" + +#include "backends/test/QuantizeHelper.hpp" + + +template<typename T> +std::vector<LayerTestResult<T,3>> SplitterTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 0.0f, + int32_t qOffset = 0) +{ + unsigned int inputWidth = 5; + unsigned int inputHeight = 6; + unsigned int inputChannels = 3; + + unsigned int outputWidth1 = 2; + unsigned int outputHeight1 = 2; + unsigned int outputChannels1 = 3; + + unsigned int outputWidth2 = 2; + unsigned int outputHeight2 = 4; + unsigned int outputChannels2 = 3; + + unsigned int outputWidth3 = 3; + unsigned int outputHeight3 = 6; + unsigned int outputChannels3 = 2; + + unsigned int outputWidth4 = 3; + unsigned int outputHeight4 = 6; + unsigned int outputChannels4 = 1; + + + // Define the tensor descriptors + armnn::TensorInfo inputTensorInfo({ inputChannels, inputHeight, inputWidth }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo1({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo2({ outputChannels2, outputHeight2, outputWidth2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo3({ outputChannels3, outputHeight3, outputWidth3 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo4({ outputChannels4, outputHeight4, outputWidth4 }, armnn::GetDataType<T>()); + // note that output 5 should match output 2 + armnn::TensorInfo outputTensorInfo5({ outputChannels2, outputHeight2, outputWidth2 }, armnn::GetDataType<T>()); + + // 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); + outputTensorInfo5.SetQuantizationScale(qScale); + outputTensorInfo5.SetQuantizationOffset(qOffset); + } + + LayerTestResult<T,3> ret1(outputTensorInfo1); + LayerTestResult<T,3> ret2(outputTensorInfo2); + LayerTestResult<T,3> ret3(outputTensorInfo3); + LayerTestResult<T,3> ret4(outputTensorInfo4); + LayerTestResult<T,3> ret5(outputTensorInfo5); + + auto input = MakeTensor<T, 3>(inputTensorInfo, std::vector<T>( + QuantizedVector<T>(qScale, qOffset, { + 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, + }) + )); + + + ret1.outputExpected = MakeTensor<T, 3>(outputTensorInfo1, std::vector<T>( + QuantizedVector<T>(qScale, qOffset, { + 1.0f, 2.0f, + 6.0f, 7.0f, + + 31.0f, 32.0f, + 36.0f, 37.0f, + + 61.0f, 62.0f, + 66.0f, 67.0f, + }) + )); + + ret2.outputExpected = MakeTensor<T, 3>(outputTensorInfo2, std::vector<T>( + QuantizedVector<T>(qScale, qOffset, { + 11.0f, 12.0f, + 16.0f, 17.0f, + 21.0f, 22.0f, + 26.0f, 27.0f, + + 41.0f, 42.0f, + 46.0f, 47.0f, + 51.0f, 52.0f, + 56.0f, 57.0f, + + 71.0f, 72.0f, + 76.0f, 77.0f, + 81.0f, 82.0f, + 86.0f, 87.0f, + }) + )); + + ret3.outputExpected = MakeTensor<T, 3>(outputTensorInfo3, std::vector<T>( + QuantizedVector<T>(qScale, qOffset, { + 3.0f, 4.0f, 5.0f, + 8.0f, 9.0f, 10.0f, + 13.0f, 14.0f, 15.0f, + 18.0f, 19.0f, 20.0f, + 23.0f, 24.0f, 25.0f, + 28.0f, 29.0f, 30.0f, + + 33.0f, 34.0f, 35.0f, + 38.0f, 39.0f, 40.0f, + 43.0f, 44.0f, 45.0f, + 48.0f, 49.0f, 50.0f, + 53.0f, 54.0f, 55.0f, + 58.0f, 59.0f, 60.0f, + }) + )); + + ret4.outputExpected = MakeTensor<T, 3>(outputTensorInfo4, std::vector<T>( + QuantizedVector<T>(qScale, qOffset, { + 63.0f, 64.0f, 65.0f, + 68.0f, 69.0f, 70.0f, + 73.0f, 74.0f, 75.0f, + 78.0f, 79.0f, 80.0f, + 83.0f, 84.0f, 85.0f, + 88.0f, 89.0f, 90.0f, + }) + )); + + + ret5.outputExpected = MakeTensor<T, 3>(outputTensorInfo5, std::vector<T>( + QuantizedVector<T>(qScale, qOffset, { + 11.0f, 12.0f, + 16.0f, 17.0f, + 21.0f, 22.0f, + 26.0f, 27.0f, + + 41.0f, 42.0f, + 46.0f, 47.0f, + 51.0f, 52.0f, + 56.0f, 57.0f, + + 71.0f, 72.0f, + 76.0f, 77.0f, + 81.0f, 82.0f, + 86.0f, 87.0f, + }) + )); + + 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 = {0, 2, 0}; //extent of the window is defined by size of output[1] + armnn::SplitterQueueDescriptor::ViewOrigin window2(wOrigin2); + + std::vector<unsigned int> wOrigin3 = {0, 0, 2}; //extent of the window is defined by size of output[2] + armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3); + + std::vector<unsigned int> wOrigin4 = {2, 0, 2}; //extent of the window is defined by size of output[3] + armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4); + + bool subTensorsSupported = workloadFactory.SupportsSubTensors(); + + std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> outputHandle1 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo1.GetShape(), wOrigin1.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo1); + + std::unique_ptr<armnn::ITensorHandle> outputHandle2 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo2.GetShape(), wOrigin2.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo2); + + std::unique_ptr<armnn::ITensorHandle> outputHandle3 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo3.GetShape(), wOrigin3.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo3); + + std::unique_ptr<armnn::ITensorHandle> outputHandle4 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo4.GetShape(), wOrigin4.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo4); + + std::unique_ptr<armnn::ITensorHandle> outputHandle5 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo5.GetShape(), wOrigin2.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo5); + + armnn::SplitterQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo1, outputHandle1.get()); + AddOutputToWorkload(data, info, outputTensorInfo2, outputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo3, outputHandle3.get()); + AddOutputToWorkload(data, info, outputTensorInfo4, outputHandle4.get()); + AddOutputToWorkload(data, info, outputTensorInfo5, outputHandle5.get()); + + data.m_ViewOrigins.push_back(window1); + data.m_ViewOrigins.push_back(window2); + data.m_ViewOrigins.push_back(window3); + data.m_ViewOrigins.push_back(window4); + //add window2 again (to have an overlapping split) + data.m_ViewOrigins.push_back(window2); + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSplitter(data, info); + + inputHandle->Allocate(); + outputHandle1->Allocate(); + outputHandle2->Allocate(); + outputHandle3->Allocate(); + outputHandle4->Allocate(); + outputHandle5->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0]); + + workload->Execute(); + + CopyDataFromITensorHandle(&ret1.output[0][0][0], outputHandle1.get()); + CopyDataFromITensorHandle(&ret2.output[0][0][0], outputHandle2.get()); + CopyDataFromITensorHandle(&ret3.output[0][0][0], outputHandle3.get()); + CopyDataFromITensorHandle(&ret4.output[0][0][0], outputHandle4.get()); + CopyDataFromITensorHandle(&ret5.output[0][0][0], outputHandle5.get()); + + std::vector<LayerTestResult<T,3>> ret = {ret1, ret2, ret3, ret4, ret5}; + + return ret; +} + + +template <typename T> +LayerTestResult<T, 3> CopyViaSplitterTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) +{ + const armnn::TensorInfo tensorInfo({ 3, 6, 5 }, armnn::GetDataType<T>()); + auto input = MakeTensor<T, 3>(tensorInfo, QuantizedVector<T>(qScale, qOffset, + { + 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, + })); + + std::vector<unsigned int> origin = { 0, 0, 0 }; + armnn::SplitterQueueDescriptor::ViewOrigin window(origin); + + const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); + + std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(tensorInfo); + + std::unique_ptr<armnn::ITensorHandle> outputHandle = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, tensorInfo.GetShape(), origin.data()) : + workloadFactory.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.CreateSplitter(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0]); + + workload->Execute(); + + LayerTestResult<T, 3> ret(tensorInfo); + CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); + ret.outputExpected = input; + + return ret; +} |