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Diffstat (limited to 'src/backends/test/SplitterTestImpl.hpp')
-rw-r--r-- | src/backends/test/SplitterTestImpl.hpp | 306 |
1 files changed, 0 insertions, 306 deletions
diff --git a/src/backends/test/SplitterTestImpl.hpp b/src/backends/test/SplitterTestImpl.hpp deleted file mode 100644 index 396cc1bcb2..0000000000 --- a/src/backends/test/SplitterTestImpl.hpp +++ /dev/null @@ -1,306 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <armnn/ArmNN.hpp> -#include <armnn/Tensor.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; - - // 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 }, armnn::GetDataType<T>()); - - // Outputs of the original split. - armnn::TensorInfo outputTensorInfo1({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo2({ outputChannels2, outputHeight2, outputWidth2 }, armnn::GetDataType<T>()); - - // Outputs of the subsequent subtensor split. - armnn::TensorInfo outputTensorInfo3({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo4({ outputChannels1, outputHeight1, outputWidth1 }, 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); - } - - LayerTestResult<T,3> ret1(outputTensorInfo1); - LayerTestResult<T,3> ret2(outputTensorInfo2); - LayerTestResult<T,3> ret3(outputTensorInfo3); - LayerTestResult<T,3> ret4(outputTensorInfo4); - - 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, - }) - )); - - // Channel 0 of the original input. - ret1.outputExpected = MakeTensor<T, 3>(outputTensorInfo1, 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, - }) - )); - - // Channel 1 & 2 of the original input. - ret2.outputExpected = MakeTensor<T, 3>(outputTensorInfo2, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 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, - }) - )); - - // Channel 0 of return 2 (i.e. channels 1 and 2 of the original input). - ret3.outputExpected = MakeTensor<T, 3>(outputTensorInfo3, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 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, - }) - )); - - // Channel 1 of return 2. - ret4.outputExpected = MakeTensor<T, 3>(outputTensorInfo4, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 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, - }) - )); - - // 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 = 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(*outputHandle2, outputTensorInfo3.GetShape(), wOrigin3.data()) : - workloadFactory.CreateTensorHandle(outputTensorInfo3); - - std::unique_ptr<armnn::ITensorHandle> outputHandle4 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo4.GetShape(), wOrigin4.data()) : - workloadFactory.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.CreateSplitter(data, info); - - inputHandle->Allocate(); - outputHandle1->Allocate(); - outputHandle2->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()); - -// // 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.CreateSplitter(data2, info2); - - outputHandle3->Allocate(); - outputHandle4->Allocate(); - - workload2->Execute(); - - CopyDataFromITensorHandle(&ret3.output[0][0][0], outputHandle3.get()); - CopyDataFromITensorHandle(&ret4.output[0][0][0], outputHandle4.get()); - - std::vector<LayerTestResult<T,3>> ret = {ret1, ret2, ret3, ret4,}; - - 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; -} |