From 10b4dfd8e9ccd7a03df7bb053ee1c644cb37f8ab Mon Sep 17 00:00:00 2001 From: David Beck Date: Wed, 19 Sep 2018 12:03:20 +0100 Subject: IVGCVSW-1897 : build infrastructure for the src/backends folder Change-Id: I7ebafb675ccc77ad54d1deb01412a8379a5356bb --- src/armnn/backends/test/ActivationFixture.hpp | 56 - src/armnn/backends/test/ActivationTestImpl.hpp | 560 --- src/armnn/backends/test/ArmComputeCl.cpp | 311 -- src/armnn/backends/test/ArmComputeNeon.cpp | 463 -- src/armnn/backends/test/BatchNormTestImpl.hpp | 112 - .../backends/test/ClContextControlFixture.hpp | 21 - src/armnn/backends/test/Conv2dTestImpl.hpp | 921 ---- .../backends/test/ConvertFp16ToFp32TestImpl.hpp | 55 - .../backends/test/ConvertFp32ToFp16TestImpl.hpp | 55 - src/armnn/backends/test/CreateWorkloadCl.cpp | 564 --- src/armnn/backends/test/CreateWorkloadNeon.cpp | 455 -- src/armnn/backends/test/CreateWorkloadRef.cpp | 478 -- src/armnn/backends/test/FullyConnectedTestImpl.hpp | 287 -- src/armnn/backends/test/IsLayerSupportedTest.cpp | 239 - .../backends/test/IsLayerSupportedTestImpl.hpp | 565 --- .../backends/test/LayerReleaseConstantDataTest.cpp | 212 - src/armnn/backends/test/LayerTests.cpp | 4750 -------------------- src/armnn/backends/test/LayerTests.hpp | 345 -- src/armnn/backends/test/LstmTestImpl.hpp | 1150 ----- src/armnn/backends/test/MemCopyTests.cpp | 180 - src/armnn/backends/test/NormTestImpl.hpp | 241 - src/armnn/backends/test/PermuteTestImpl.hpp | 225 - src/armnn/backends/test/Pooling2dTestImpl.hpp | 1116 ----- src/armnn/backends/test/QuantizeHelper.hpp | 91 - src/armnn/backends/test/Reference.cpp | 253 -- src/armnn/backends/test/ReshapeTestImpl.hpp | 177 - src/armnn/backends/test/SoftmaxTestImpl.hpp | 153 - src/armnn/backends/test/SplitterTestImpl.hpp | 307 -- src/armnn/backends/test/TensorCopyUtils.cpp | 159 - src/armnn/backends/test/TensorCopyUtils.hpp | 14 - src/armnn/backends/test/WorkloadDataValidation.cpp | 471 -- src/armnn/backends/test/WorkloadTestUtils.hpp | 55 - 32 files changed, 15041 deletions(-) delete mode 100644 src/armnn/backends/test/ActivationFixture.hpp delete mode 100644 src/armnn/backends/test/ActivationTestImpl.hpp delete mode 100644 src/armnn/backends/test/ArmComputeCl.cpp delete mode 100644 src/armnn/backends/test/ArmComputeNeon.cpp delete mode 100644 src/armnn/backends/test/BatchNormTestImpl.hpp delete mode 100644 src/armnn/backends/test/ClContextControlFixture.hpp delete mode 100644 src/armnn/backends/test/Conv2dTestImpl.hpp delete mode 100644 src/armnn/backends/test/ConvertFp16ToFp32TestImpl.hpp delete mode 100644 src/armnn/backends/test/ConvertFp32ToFp16TestImpl.hpp delete mode 100644 src/armnn/backends/test/CreateWorkloadCl.cpp delete mode 100644 src/armnn/backends/test/CreateWorkloadNeon.cpp delete mode 100644 src/armnn/backends/test/CreateWorkloadRef.cpp delete mode 100644 src/armnn/backends/test/FullyConnectedTestImpl.hpp delete mode 100644 src/armnn/backends/test/IsLayerSupportedTest.cpp delete mode 100644 src/armnn/backends/test/IsLayerSupportedTestImpl.hpp delete mode 100644 src/armnn/backends/test/LayerReleaseConstantDataTest.cpp delete mode 100644 src/armnn/backends/test/LayerTests.cpp delete mode 100644 src/armnn/backends/test/LayerTests.hpp delete mode 100644 src/armnn/backends/test/LstmTestImpl.hpp delete mode 100644 src/armnn/backends/test/MemCopyTests.cpp delete mode 100644 src/armnn/backends/test/NormTestImpl.hpp delete mode 100644 src/armnn/backends/test/PermuteTestImpl.hpp delete mode 100644 src/armnn/backends/test/Pooling2dTestImpl.hpp delete mode 100644 src/armnn/backends/test/QuantizeHelper.hpp delete mode 100644 src/armnn/backends/test/Reference.cpp delete mode 100644 src/armnn/backends/test/ReshapeTestImpl.hpp delete mode 100644 src/armnn/backends/test/SoftmaxTestImpl.hpp delete mode 100644 src/armnn/backends/test/SplitterTestImpl.hpp delete mode 100644 src/armnn/backends/test/TensorCopyUtils.cpp delete mode 100644 src/armnn/backends/test/TensorCopyUtils.hpp delete mode 100644 src/armnn/backends/test/WorkloadDataValidation.cpp delete mode 100644 src/armnn/backends/test/WorkloadTestUtils.hpp (limited to 'src/armnn/backends/test') diff --git a/src/armnn/backends/test/ActivationFixture.hpp b/src/armnn/backends/test/ActivationFixture.hpp deleted file mode 100644 index d9d4ca7470..0000000000 --- a/src/armnn/backends/test/ActivationFixture.hpp +++ /dev/null @@ -1,56 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include "TensorCopyUtils.hpp" -#include "WorkloadTestUtils.hpp" - -struct ActivationFixture -{ - ActivationFixture() - { - auto boostArrayExtents = boost::extents - [boost::numeric_cast(batchSize)] - [boost::numeric_cast(channels)] - [boost::numeric_cast(height)] - [boost::numeric_cast(width)]; - output.resize(boostArrayExtents); - outputExpected.resize(boostArrayExtents); - input.resize(boostArrayExtents); - - unsigned int inputShape[] = { batchSize, channels, height, width }; - unsigned int outputShape[] = { batchSize, channels, height, width }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - input = MakeRandomTensor(inputTensorInfo, 21453); - } - - unsigned int width = 17; - unsigned int height = 29; - unsigned int channels = 2; - unsigned int batchSize = 5; - - boost::multi_array output; - boost::multi_array outputExpected; - boost::multi_array input; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - // Parameters used by some of the activation functions. - float a = 0.234f; - float b = -12.345f; -}; - - -struct PositiveActivationFixture : public ActivationFixture -{ - PositiveActivationFixture() - { - input = MakeRandomTensor(inputTensorInfo, 2342423, 0.0f, 1.0f); - } -}; \ No newline at end of file diff --git a/src/armnn/backends/test/ActivationTestImpl.hpp b/src/armnn/backends/test/ActivationTestImpl.hpp deleted file mode 100644 index a5d327c287..0000000000 --- a/src/armnn/backends/test/ActivationTestImpl.hpp +++ /dev/null @@ -1,560 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include -#include - -#include "test/TensorHelpers.hpp" -#include "QuantizeHelper.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" -#include "ActivationFixture.hpp" - -#include - -template -LayerTestResult BoundedReLuTestCommon(armnn::IWorkloadFactory& workloadFactory, - float upperBound, float lowerBound, - float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset, - const std::vector& inputData, const std::vector& outputExpectedData, - unsigned int inputWidth, unsigned int inputHeight, - unsigned int inputChannels, unsigned int inputBatchSize) -{ - unsigned int outputWidth = inputWidth; - unsigned int outputHeight = inputHeight; - unsigned int outputChannels = inputChannels; - unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::GetDataType()); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType()); - - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(inputScale); - inputTensorInfo.SetQuantizationOffset(inputOffset); - - outputTensorInfo.SetQuantizationScale(outputScale); - outputTensorInfo.SetQuantizationOffset(outputOffset); - } - - LayerTestResult result(inputTensorInfo); - - auto input = MakeTensor(inputTensorInfo, inputData); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - // Setup bounded ReLu. - armnn::ActivationQueueDescriptor descriptor; - armnn::WorkloadInfo workloadInfo; - AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - - descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu; - descriptor.m_Parameters.m_A = upperBound; - descriptor.m_Parameters.m_B = lowerBound; - - std::unique_ptr workload = workloadFactory.CreateActivation(descriptor, workloadInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - result.outputExpected = MakeTensor(outputTensorInfo, outputExpectedData); - - return result; -} - -LayerTestResult BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int inputWidth = 4u; - unsigned int inputHeight = 5u; - unsigned int inputChannels = 1u; - unsigned int inputBatchSize = 1; - - std::vector input = std::vector{ - -2.0f, 0.1f, 0.5f, 1.25f, - 0.786f, 0.9875f, -1.5f, 0.384f, - 1.0001f, 3.5f, 7.5f, 0.896f, - 2.126f, 2.0f, 0.3f, 0.15f, - 0.999f, 1.2f, 0.89f, 6.1f, - }; - - // Calculated manually. - std::vector output = std::vector{ - -1.0f, 0.1f, 0.5f, 1.0f, - 0.786f, 0.9875f, -1.0f, 0.384f, - 1.0f, 1.0f, 1.0f, 0.896f, - 1.0f, 1.0f, 0.3f, 0.15f, - 0.999f, 1.0f, 0.89f, 1.0f, - }; - - return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output, - inputWidth, inputHeight, inputChannels, inputBatchSize); -} - -LayerTestResult BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int inputWidth = 4u; - unsigned int inputHeight = 5u; - unsigned int inputChannels = 1u; - unsigned int inputBatchSize = 1; - - std::vector input = std::vector{ - -1.0f, 0.1f, 0.5f, 6.25f, - 0.786f, 5.9875f, -0.5f, 0.384f, - 6.0001f, 3.5f, 7.5f, 0.896f, - 2.126f, 12.0f, 0.3f, 0.15f, - 0.999f, 1.2f, 0.89f, 6.1f, - }; - - // Calculated manually. - std::vector output = std::vector{ - 0.0f, 0.1f, 0.5f, 6.0f, - 0.786f, 5.9875f, 0.0f, 0.384f, - 6.0f, 3.5f, 6.0f, 0.896f, - 2.126f, 6.0f, 0.3f, 0.15f, - 0.999f, 1.2f, 0.89f, 6.0f, - }; - - return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output, - inputWidth, inputHeight, inputChannels, inputBatchSize); -} - -LayerTestResult BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int inputWidth = 3u; - unsigned int inputHeight = 2u; - unsigned int inputChannels = 1u; - unsigned int inputBatchSize = 1; - - std::vector input = std::vector{ - 51, 124, 28, - 251, 8, 92 - }; - - // Calculated manually. - std::vector output = std::vector{ - 0, 122, 0, - 255, 0, 58 - }; - - float inputScale = 12.0f / 255.0f; - int32_t inputOffset = 63; - float outputScale = 6.0f / 255.0f; - int32_t outputOffset = 0; - - return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, - inputScale, inputOffset, outputScale, outputOffset, - input, output, - inputWidth, inputHeight, inputChannels, inputBatchSize); -} - -LayerTestResult BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int inputWidth = 3u; - unsigned int inputHeight = 2u; - unsigned int inputChannels = 1u; - unsigned int inputBatchSize = 1; - - std::vector input = std::vector{ - 51, 230, 28, - 251, 8, 92 - }; - - // Calculated manually. - std::vector output = std::vector{ - 51, 192, 32, - 192, 32, 92 - }; - - int32_t inputOffset = 112; - float inputScale = 0.0125f; - - return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, - inputScale, inputOffset, inputScale, inputOffset, // Input/output scale & offset same. - input, output, - inputWidth, inputHeight, inputChannels, inputBatchSize); -} - -namespace -{ - -struct BoundedReLuRandomInputTestTraits -{ - constexpr static unsigned int inputHeight = 31u; - constexpr static unsigned int inputWidth = 19u; - constexpr static unsigned int inputChannels = 4u; - constexpr static unsigned int inputBatchSize = 2; - - constexpr static unsigned int outputHeight = inputHeight; - constexpr static unsigned int outputWidth = inputWidth; - constexpr static unsigned int outputChannels = inputChannels; - constexpr static unsigned int outputBatchSize = inputBatchSize; - - static armnn::TensorInfo GetInputTensorInfo() - { - return armnn::TensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - } - - static armnn::TensorInfo GetOutputTensorInfo() - { - return armnn::TensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - } -}; - -boost::multi_array BoundedReLuRandomInputTest(armnn::IWorkloadFactory& workloadFactory, - float lowerBound, - float upperBound, - const armnn::ActivationDescriptor& activationDescriptor) -{ - const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo(); - const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo(); - - boost::multi_array output(GetTensorShapeAsArray<4>(outputTensorInfo)); - - // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu - // range [lowerBound, upperBound]. - auto input = MakeRandomTensor(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - // Set up bounded ReLu. - armnn::ActivationQueueDescriptor descriptor; - armnn::WorkloadInfo workloadInfo; - AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - descriptor.m_Parameters = activationDescriptor; - - std::unique_ptr workload = workloadFactory.CreateActivation(descriptor, workloadInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get()); - - return output; -} - -} // namespace - -LayerTestResult CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& otherWorkloadFactory, - float upperBound, - float lowerBound) -{ - LayerTestResult result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo()); - - armnn::ActivationDescriptor activationDescriptor; - activationDescriptor.m_Function = armnn::ActivationFunction::BoundedReLu; - activationDescriptor.m_A = upperBound; - activationDescriptor.m_B = lowerBound; - - result.output = BoundedReLuRandomInputTest(workloadFactory, 0.0f, upperBound, activationDescriptor); - result.outputExpected = BoundedReLuRandomInputTest(otherWorkloadFactory, 0.0f, upperBound, activationDescriptor); - - return result; -} - -template -LayerTestResult ConstantLinearActivationTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 0.0f, - int32_t qOffset = 0) -{ - unsigned int inputHeight = 20; - unsigned int inputWidth = 17; - unsigned int inputChannels = 3; - unsigned int batchSize = 5; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape[] = {batchSize, inputChannels, inputHeight, inputWidth}; - - inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType()); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - LayerTestResult ret(outputTensorInfo); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - // Do linear activation that should leave the tensor unchanged. - armnn::ActivationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Parameters.m_A = 1.0f; - data.m_Parameters.m_B = 0.0f; - data.m_Parameters.m_Function = armnn::ActivationFunction::Linear; - - std::unique_ptr workload = workloadFactory.CreateActivation(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - boost::multi_array input = MakeRandomTensor(inputTensorInfo, 7123561); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - // Ensure output equals input. - ret.outputExpected = input; - - return ret; -} - -LayerTestResult ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory) -{ - return ConstantLinearActivationTestCommon(workloadFactory); -} - -LayerTestResult ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return ConstantLinearActivationTestCommon(workloadFactory, 4.0f, 3); -} - -template -LayerTestResult SimpleActivationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::ActivationFunction activationFunction, - float activationParameterA, - float activationParameterB, - float qScale, - int32_t qOffset, - const std::vector& inputData, - const std::vector& outputExpectedData) -{ - constexpr static unsigned int inputWidth = 16u; - constexpr static unsigned int inputHeight = 1u; - constexpr static unsigned int inputChannels = 1u; - constexpr static unsigned int inputBatchSize = 1u; - - constexpr static unsigned int outputWidth = inputWidth; - constexpr static unsigned int outputHeight = inputHeight; - constexpr static unsigned int outputChannels = inputChannels; - constexpr static unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - LayerTestResult result(inputTensorInfo); - - auto input = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, inputData)); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - // Setup bounded ReLu. - armnn::ActivationQueueDescriptor descriptor; - armnn::WorkloadInfo workloadInfo; - AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - - descriptor.m_Parameters.m_Function = activationFunction; - descriptor.m_Parameters.m_A = activationParameterA; - descriptor.m_Parameters.m_B = activationParameterB; - - std::unique_ptr workload = workloadFactory.CreateActivation(descriptor, workloadInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - // Calculated manually. - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, outputExpectedData)); - - return result; -} - -template -LayerTestResult SimpleSigmoidTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) -{ - std::vector inputData = { - -0.1f, -0.2f, -0.3f, -0.4f, - 0.1f, 0.2f, 0.3f, 0.4f, - -1.0f, -2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, 3.0f, 4.0f - }; - - // Calculate output values for input. - auto f = [](float value) - { - return 1.0f / (1.0f + std::exp(-value)); - }; - std::vector outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); - - return SimpleActivationTest(workloadFactory, - armnn::ActivationFunction::Sigmoid, - 0.f, - 0.f, - qScale, - qOffset, - inputData, - outputExpectedData); -} - -LayerTestResult SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleSigmoidTestCommon(workloadFactory, 0.0f, 0); -} - -LayerTestResult SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleSigmoidTestCommon(workloadFactory, 0.1f, 50); -} - -template -LayerTestResult CompareActivationTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::ActivationFunction f, - unsigned int batchSize = 5, - float qScale = 0.0f, - int32_t qOffset = 0) -{ - unsigned int width = 17; - unsigned int height = 29; - unsigned int channels = 2; - - float a = 0.234f; - float b = -12.345f; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape[] = {batchSize, channels, height, width}; - - inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType()); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - float minVal = -10.f; - if (f == armnn::ActivationFunction::Sqrt) - { - minVal = 0.f; - } - - boost::multi_array input = MakeRandomTensor(inputTensorInfo, 21453, minVal, 10.f); - - - LayerTestResult ret(outputTensorInfo); - auto boostArrayExtents = boost::extents - [boost::numeric_cast(batchSize)] - [boost::numeric_cast(channels)] - [boost::numeric_cast(height)] - [boost::numeric_cast(width)]; - ret.output.resize(boostArrayExtents); - ret.outputExpected.resize(boostArrayExtents); - - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ActivationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Parameters.m_A = a; - data.m_Parameters.m_B = b; - data.m_Parameters.m_Function = f; - - armnn::ActivationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr workload = workloadFactory.CreateActivation(data, info); - BOOST_ASSERT(workload != nullptr); - std::unique_ptr workloadRef = refWorkloadFactory.CreateActivation(refData, refInfo); - BOOST_ASSERT(workloadRef != nullptr); - - inputHandle->Allocate(); - outputHandle->Allocate(); - inputHandleRef->Allocate(); - outputHandleRef->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); - - workload->Execute(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} - -LayerTestResult CompareActivationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::ActivationFunction f, - unsigned int batchSize) -{ - return CompareActivationTestImpl(workloadFactory, refWorkloadFactory, f, batchSize); -} - -LayerTestResult CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::ActivationFunction f) -{ - return CompareActivationTestImpl(workloadFactory, refWorkloadFactory, f, 5, 0.1f, 50); -} diff --git a/src/armnn/backends/test/ArmComputeCl.cpp b/src/armnn/backends/test/ArmComputeCl.cpp deleted file mode 100644 index 9a516b6d60..0000000000 --- a/src/armnn/backends/test/ArmComputeCl.cpp +++ /dev/null @@ -1,311 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include -#include "test/TensorHelpers.hpp" -#include "LayerTests.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/ClWorkloadFactory.hpp" -#include "backends/ClWorkloads/ClWorkloadUtils.hpp" -#include "backends/RefWorkloadFactory.hpp" -#include "backends/ClLayerSupport.hpp" -#include "ActivationFixture.hpp" -#include "ClContextControlFixture.hpp" - -#include -#include -#include -#include - -#include "test/UnitTests.hpp" - -BOOST_FIXTURE_TEST_SUITE(Compute_ArmComputeCl, ClContextControlFixture) -using FactoryType = armnn::ClWorkloadFactory; - -// ============================================================================ -// UNIT tests - -// Activation -ARMNN_AUTO_TEST_CASE(ConstantLinearActivation, ConstantLinearActivationTest) - -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1, SimpleSoftmaxTest, 1.0f) -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2, SimpleSoftmaxTest, 2.0f) -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1Uint8, SimpleSoftmaxUint8Test, 1.0f) -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2Uint8, SimpleSoftmaxUint8Test, 2.0f) - -ARMNN_AUTO_TEST_CASE(ReLu1Uint8, BoundedReLuUint8UpperAndLowerBoundTest) -ARMNN_AUTO_TEST_CASE(ReLu6Uint8, BoundedReLuUint8UpperBoundOnlyTest) - -// Fully Connected -ARMNN_AUTO_TEST_CASE(SimpleFullyConnected, FullyConnectedFloat32Test, false, false) -ARMNN_AUTO_TEST_CASE(SimpleFullyConnectedWithBias, FullyConnectedFloat32Test, true, false) -ARMNN_AUTO_TEST_CASE(SimpleFullyConnectedWithTranspose, FullyConnectedFloat32Test, false, true) -ARMNN_AUTO_TEST_CASE(FullyConnectedUint8, FullyConnectedUint8Test, false) -ARMNN_AUTO_TEST_CASE(FullyConnectedBiasedUint8, FullyConnectedUint8Test, true) - -ARMNN_AUTO_TEST_CASE(FullyConnectedLarge, FullyConnectedLargeTest, false) -ARMNN_AUTO_TEST_CASE(FullyConnectedLargeTransposed, FullyConnectedLargeTest, true) - -// Convolution -ARMNN_AUTO_TEST_CASE(SimpleConvolution1d, Convolution1dTest, true) - -ARMNN_AUTO_TEST_CASE(SimpleConvolution2d, SimpleConvolution2d3x5Test, true) -ARMNN_AUTO_TEST_CASE(SimpleConvolution2dSquare, SimpleConvolution2d3x3Test, true) -ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x3Uint8, SimpleConvolution2d3x3Uint8Test, true) -ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false) -ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2dSquare, SimpleConvolution2d3x3Test, false) -ARMNN_AUTO_TEST_CASE(SimpleConvolution2dAsymmetricPadding, Convolution2dAsymmetricPaddingTest) - -// Depthwise Convolution -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, true) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, false) -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, true) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, false) - -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dAsymmetric, DepthwiseConvolution2dAsymmetricTest, true) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dAsymmetric, DepthwiseConvolution2dAsymmetricTest, false) - -// Softmax -BOOST_AUTO_TEST_CASE(Softmax4dSupport) -{ - const unsigned int numDimensions = 4u; - std::array dimensionSizes; - dimensionSizes.fill(1u); - - const armnn::TensorInfo inputInfo(numDimensions, &dimensionSizes.front(), armnn::DataType::Float32); - const armnn::TensorInfo outputInfo(numDimensions, &dimensionSizes.front(), armnn::DataType::Float32); - - // 4D Softmax should be reported as unsupported on the CL backend - BOOST_TEST(!armnn::IsSoftmaxSupportedCl(inputInfo, outputInfo, armnn::SoftmaxDescriptor())); -} - -// Splitter -ARMNN_AUTO_TEST_CASE(SimpleSplitter, SplitterTest) -ARMNN_AUTO_TEST_CASE(SimpleSplitterUint8, SplitterUint8Test) - -ARMNN_AUTO_TEST_CASE(CopyViaSplitter, CopyViaSplitterTest) -ARMNN_AUTO_TEST_CASE(CopyViaSplitterUint8, CopyViaSplitterUint8Test) - -// Merger -ARMNN_AUTO_TEST_CASE(SimpleMerger, MergerTest) -ARMNN_AUTO_TEST_CASE(MergerUint8, MergerUint8Test) - -// Pooling -ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4, SimpleMaxPooling2dSize3x3Stride2x4Test, true) -ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4Uint8, SimpleMaxPooling2dSize3x3Stride2x4Uint8Test, true) - -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleMaxPooling2d, IgnorePaddingSimpleMaxPooling2dTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleMaxPooling2dUint8, IgnorePaddingSimpleMaxPooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingMaxPooling2dSize3, IgnorePaddingMaxPooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingMaxPooling2dSize3Uint8, IgnorePaddingMaxPooling2dSize3Uint8Test) - -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2d, IgnorePaddingSimpleAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dUint8, IgnorePaddingSimpleAveragePooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPadding, IgnorePaddingSimpleAveragePooling2dNoPaddingTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPaddingUint8, - IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3, IgnorePaddingAveragePooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3Uint8, IgnorePaddingAveragePooling2dSize3Uint8Test) - -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleL2Pooling2d, IgnorePaddingSimpleL2Pooling2dTest) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_IgnorePaddingSimpleL2Pooling2dUint8, IgnorePaddingSimpleL2Pooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingL2Pooling2dSize3, IgnorePaddingL2Pooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_IgnorePaddingL2Pooling2dSize3Uint8, IgnorePaddingL2Pooling2dSize3Uint8Test) - -ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2d, SimpleAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2dUint8, SimpleAveragePooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2, - IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, - false) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2NoPadding, - IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, - true) -ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2d, LargeTensorsAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2dUint8, LargeTensorsAveragePooling2dUint8Test) - -ARMNN_AUTO_TEST_CASE(SimpleL2Pooling2d, SimpleL2Pooling2dTest) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_SimpleL2Pooling2dUint8, SimpleL2Pooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize3Stride1, L2Pooling2dSize3Stride1Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize3Stride1Uint8, L2Pooling2dSize3Stride1Uint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize3Stride3, L2Pooling2dSize3Stride3Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize3Stride3Uint8, L2Pooling2dSize3Stride3Uint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize3Stride4, L2Pooling2dSize3Stride4Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize3Stride4Uint8, L2Pooling2dSize3Stride4Uint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize7, L2Pooling2dSize7Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize7Uint8, L2Pooling2dSize7Uint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize9, L2Pooling2dSize9Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize9Uint8, L2Pooling2dSize9Uint8Test) - -// Add -ARMNN_AUTO_TEST_CASE(SimpleAdd, AdditionTest) -ARMNN_AUTO_TEST_CASE(AddBroadcast1Element, AdditionBroadcast1ElementTest) -ARMNN_AUTO_TEST_CASE(AddBroadcast, AdditionBroadcastTest) - -ARMNN_AUTO_TEST_CASE(AdditionUint8, AdditionUint8Test) -ARMNN_AUTO_TEST_CASE(AddBroadcastUint8, AdditionBroadcastUint8Test) -ARMNN_AUTO_TEST_CASE(AddBroadcast1ElementUint8, AdditionBroadcast1ElementUint8Test) - -// Sub -ARMNN_AUTO_TEST_CASE(SimpleSub, SubtractionTest) - -// Div -ARMNN_AUTO_TEST_CASE(SimpleDivision, DivisionTest) -ARMNN_AUTO_TEST_CASE(DivisionByZero, DivisionByZeroTest) -ARMNN_AUTO_TEST_CASE(DivisionBroadcast1Element, DivisionBroadcast1ElementTest) -ARMNN_AUTO_TEST_CASE(DivisionBroadcast1DVector, DivisionBroadcast1DVectorTest) -// NOTE: quantized division is not supported by CL and not required by the -// android NN api - -// Mul -ARMNN_AUTO_TEST_CASE(SimpleMultiplication, MultiplicationTest) -ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1Element, MultiplicationBroadcast1ElementTest) -ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVector, MultiplicationBroadcast1DVectorTest) - -// Batch Norm -ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest) - -ARMNN_AUTO_TEST_CASE(L2Normalization1d, L2Normalization1dTest) -ARMNN_AUTO_TEST_CASE(L2Normalization2d, L2Normalization2dTest) -ARMNN_AUTO_TEST_CASE(L2Normalization3d, L2Normalization3dTest) -ARMNN_AUTO_TEST_CASE(L2Normalization4d, L2Normalization4dTest) - -// Resize Bilinear -ARMNN_AUTO_TEST_CASE(SimpleResizeBilinear, SimpleResizeBilinearTest) -ARMNN_AUTO_TEST_CASE(ResizeBilinearNop, ResizeBilinearNopTest) -ARMNN_AUTO_TEST_CASE(ResizeBilinearSqMin, ResizeBilinearSqMinTest) -ARMNN_AUTO_TEST_CASE(ResizeBilinearMin, ResizeBilinearMinTest) -ARMNN_AUTO_TEST_CASE(ResizeBilinearMag, ResizeBilinearMagTest) - -// Constant -ARMNN_AUTO_TEST_CASE(Constant, ConstantTest) -ARMNN_AUTO_TEST_CASE(ConstantUint8, ConstantTestUint8) - -// Concat -ARMNN_AUTO_TEST_CASE(Concatenation1d, Concatenation1dTest) -ARMNN_AUTO_TEST_CASE(Concatenation1dUint8, Concatenation1dUint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0, Concatenation2dDim0Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0Uint8, Concatenation2dDim0Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1, Concatenation2dDim1Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1Uint8, Concatenation2dDim1Uint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0DiffInputDims, Concatenation2dDim0DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0DiffInputDimsUint8, Concatenation2dDim0DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1DiffInputDims, Concatenation2dDim1DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1DiffInputDimsUint8, Concatenation2dDim1DiffInputDimsUint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0, Concatenation3dDim0Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0Uint8, Concatenation3dDim0Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1, Concatenation3dDim1Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1Uint8, Concatenation3dDim1Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2, Concatenation3dDim2Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2Uint8, Concatenation3dDim2Uint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0DiffInputDims, Concatenation3dDim0DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0DiffInputDimsUint8, Concatenation3dDim0DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1DiffInputDims, Concatenation3dDim1DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1DiffInputDimsUint8, Concatenation3dDim1DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2DiffInputDims, Concatenation3dDim2DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2DiffInputDimsUint8, Concatenation3dDim2DiffInputDimsUint8Test) - -// Floor -ARMNN_AUTO_TEST_CASE(SimpleFloor, SimpleFloorTest) - -// Reshape -ARMNN_AUTO_TEST_CASE(SimpleReshapeFloat32, SimpleReshapeFloat32Test) -ARMNN_AUTO_TEST_CASE(SimpleReshapeUint8, SimpleReshapeUint8Test) - -// Permute -ARMNN_AUTO_TEST_CASE(SimplePermuteFloat32, SimplePermuteFloat32Test) -ARMNN_AUTO_TEST_CASE(SimplePermuteUint8, SimplePermuteUint8Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet1, PermuteFloat32ValueSet1Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet2, PermuteFloat32ValueSet2Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test) - -// Lstm -ARMNN_AUTO_TEST_CASE(LstmLayerFloat32WithCifgWithPeepholeNoProjection, - LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest) -ARMNN_AUTO_TEST_CASE(LstmLayerFloat32NoCifgNoPeepholeNoProjection, - LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest) -ARMNN_AUTO_TEST_CASE(LstmLayerFloat32NoCifgWithPeepholeWithProjection, - LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest) - -// Convert from Float16 to Float32 -ARMNN_AUTO_TEST_CASE(SimpleConvertFp16ToFp32, SimpleConvertFp16ToFp32Test) -// Convert from Float32 to Float16 -ARMNN_AUTO_TEST_CASE(SimpleConvertFp32ToFp16, SimpleConvertFp32ToFp16Test) - -// ============================================================================ -// COMPARE tests - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareConv2dWithReference, CompareConvolution2dTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareDepthwiseConv2dWithReferenceFloat32, CompareDepthwiseConvolution2dTest) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareDepthwiseConv2dWithReferenceUint8, CompareDepthwiseConvolution2dTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareNormalizationWithinWithReference, CompareNormalizationTest, - armnn::NormalizationAlgorithmChannel::Within, - armnn::NormalizationAlgorithmMethod::LocalBrightness) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareNormalizationAcrossWithReference, CompareNormalizationTest, - armnn::NormalizationAlgorithmChannel::Across, - armnn::NormalizationAlgorithmMethod::LocalBrightness) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxBeta1WithReference, CompareSoftmaxTest, 1.0f) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxBeta2WithReference, CompareSoftmaxTest, 2.0f) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxUint8, CompareSoftmaxUint8Test, 1.0f) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareMaxPooling2dWithRef, ComparePooling2dTest, armnn::PoolingAlgorithm::Max) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareAveragePooling2dWithRef, ComparePooling2dTest, armnn::PoolingAlgorithm::Average) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareAveragePooling2dWithRefUint8, ComparePooling2dUint8Test, - armnn::PoolingAlgorithm::Average) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareL2Pooling2dWithRef, ComparePooling2dTest, armnn::PoolingAlgorithm::L2) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareAddition, CompareAdditionTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareMultiplicationWithRef, CompareMultiplicationTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareBatchNorm, CompareBatchNormTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareReLu1, CompareBoundedReLuTest, 1.0f, -1.0f) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareReLu6, CompareBoundedReLuTest, 6.0f, 0.0f) - -// ============================================================================ -// FIXTURE tests - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareSigmoidActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Sigmoid, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareTanhActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::TanH, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareLinearActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Linear, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareReLuActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::ReLu, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareBoundedReLuActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::BoundedReLu, 5u) -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareBoundedReLuActivationWithReferenceUint8, ActivationFixture, - CompareActivationUint8Test, armnn::ActivationFunction::BoundedReLu) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareSoftReLuActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::SoftReLu, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareLeakyReLuActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::LeakyReLu, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareAbsActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Abs, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareSqrtActivationWithReference, PositiveActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Sqrt, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareSquareActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Square, 5u) - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/ArmComputeNeon.cpp b/src/armnn/backends/test/ArmComputeNeon.cpp deleted file mode 100644 index f1a2cf65bd..0000000000 --- a/src/armnn/backends/test/ArmComputeNeon.cpp +++ /dev/null @@ -1,463 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include - -#include "test/TensorHelpers.hpp" -#include "LayerTests.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/NeonLayerSupport.hpp" -#include "backends/NeonWorkloadFactory.hpp" -#include "backends/RefWorkloadFactory.hpp" -#include "backends/test/TensorCopyUtils.hpp" -#include "ActivationFixture.hpp" - -#include "WorkloadTestUtils.hpp" - -#include "test/UnitTests.hpp" - -BOOST_AUTO_TEST_SUITE(Compute_ArmComputeNeon) -using FactoryType = armnn::NeonWorkloadFactory; - -// ============================================================================ -// UNIT tests - -// Convolution -ARMNN_AUTO_TEST_CASE(SimpleConvolution1d, Convolution1dTest, true) - -ARMNN_AUTO_TEST_CASE(SimpleConvolution2d, SimpleConvolution2d3x5Test, true) -ARMNN_AUTO_TEST_CASE(SimpleConvolution2dSquare, SimpleConvolution2d3x3Test, true) -ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false) -ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2dSquare, SimpleConvolution2d3x3Test, false) -ARMNN_AUTO_TEST_CASE(SimpleConvolution2dAsymmetricPadding, Convolution2dAsymmetricPaddingTest) - -namespace -{ - -armnn::Convolution2dDescriptor MakeConv2dDesc(uint32_t strideX, uint32_t strideY, - uint32_t padLeft = 0, uint32_t padRight = 0, uint32_t padTop = 0, uint32_t padBottom = 0) -{ - armnn::Convolution2dDescriptor result; - result.m_StrideX = strideX; - result.m_StrideY = strideY; - result.m_PadLeft = padLeft; - result.m_PadRight = padRight; - result.m_PadTop = padTop; - result.m_PadBottom = padBottom; - result.m_BiasEnabled = true; - return result; -} - -} - -BOOST_AUTO_TEST_CASE(Conv2dUtils) -{ - // The only preferred Neon convolution is 1x1 with padding=0 and stride size {1,2,3}. - armnn::TensorShape shape1x1({ 1,1,1,1 }); - armnn::TensorInfo info1x1(shape1x1, armnn::DataType::Float32); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(1, 1))); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(1, 2))); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(1, 3))); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(2, 1))); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(2, 2))); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(2, 3))); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(3, 1))); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(3, 2))); - BOOST_TEST(armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(3, 3))); - - BOOST_TEST(!armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(4, 1))); - BOOST_TEST(!armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(4, 5))); - BOOST_TEST(!armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(3, 6))); - - // non zero padding is not preferred for direct convolution - BOOST_TEST(!armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(1, 1, 1, 0))); - BOOST_TEST(!armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(1, 1, 0, 1))); - BOOST_TEST(!armnn::IsNeonDirectConvolutionPreferred(info1x1, MakeConv2dDesc(1, 1, 1, 1))); - - // 2x2 filter not preferred for direct convolution - armnn::TensorShape shape2x2({ 1,1,2,2 }); - armnn::TensorInfo info2x2(shape2x2, armnn::DataType::Float32); - BOOST_TEST(!armnn::IsNeonDirectConvolutionPreferred(info2x2, MakeConv2dDesc(1, 1))); -} - -// Depthwise Convolution -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, true) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, false) -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, true) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, false) - -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dAsymmetric, DepthwiseConvolution2dAsymmetricTest, true) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dAsymmetric, DepthwiseConvolution2dAsymmetricTest, false) - -namespace -{ - -armnn::DepthwiseConvolution2dDescriptor MakeDepthwiseConv2dDesc(uint32_t strideX, uint32_t strideY, - uint32_t depthMultiplier = 1, uint32_t padLeft = 0, uint32_t padRight = 0, - uint32_t padTop = 0, uint32_t padBottom = 0) -{ - boost::ignore_unused(depthMultiplier); - - armnn::DepthwiseConvolution2dDescriptor desc; - - desc.m_PadLeft = padLeft; - desc.m_PadRight = padRight; - - desc.m_PadTop = padTop; - desc.m_PadBottom = padBottom; - desc.m_StrideX = strideX; - desc.m_StrideY = strideY; - desc.m_BiasEnabled = false; - - return desc; -} - -armnn::TensorInfo CreateOutputTensorInfo(const armnn::TensorInfo& inputInfo, - const armnn::TensorInfo& weightsInfo, - const armnn::DepthwiseConvolution2dDescriptor& descriptor, - armnn::DataType dataType) -{ - const armnn::TensorShape& inputShape = inputInfo.GetShape(); - const armnn::TensorShape& filterShape = weightsInfo.GetShape(); - - unsigned int inWidth = inputShape[3]; - unsigned int inHeight = inputShape[2]; - unsigned int inBatchSize = inputShape[0]; - - unsigned int filterWidth = filterShape[3]; - unsigned int readWidth = (inWidth + descriptor.m_PadLeft + descriptor.m_PadRight) - (filterWidth); - unsigned int outWidth = 1u + (readWidth / descriptor.m_StrideX); - - unsigned int filterHeight = filterShape[2]; - unsigned int readHeight = (inHeight + descriptor.m_PadTop + descriptor.m_PadBottom) - (filterHeight); - unsigned int outHeight = 1u + (readHeight / descriptor.m_StrideY); - unsigned int depthMultiplier = filterShape[0]; - - unsigned int outChannels = filterShape[1] * depthMultiplier; - unsigned int outBatchSize = inBatchSize; - - armnn::TensorShape outputShape({outBatchSize, outChannels, outHeight, outWidth}); - return armnn::TensorInfo(outputShape, dataType); -} -} - -BOOST_AUTO_TEST_CASE(DepthwiseConv2dUtils) -{ - const armnn::DataType dataType = armnn::DataType::Float32; - - armnn::TensorInfo inputInfo({1, 1, 10, 10 }, dataType); - armnn::TensorInfo outputInfo; - armnn::TensorInfo weightsInfo3x3({ 1, 1, 3, 3 }, dataType); - armnn::TensorInfo biasesInfo; - - armnn::DepthwiseConvolution2dDescriptor descriptor; - - // Strides supported: 1,2,3 - descriptor = MakeDepthwiseConv2dDesc(1, 1); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - descriptor = MakeDepthwiseConv2dDesc(1, 2); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - descriptor = MakeDepthwiseConv2dDesc(1, 3); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - descriptor = MakeDepthwiseConv2dDesc(2, 1); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - descriptor = MakeDepthwiseConv2dDesc(2, 2); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - descriptor = MakeDepthwiseConv2dDesc(2, 3); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - descriptor = MakeDepthwiseConv2dDesc(3, 1); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - descriptor = MakeDepthwiseConv2dDesc(3, 2); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - descriptor = MakeDepthwiseConv2dDesc(3, 3); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - // Supported stride 4 - descriptor = MakeDepthwiseConv2dDesc(4, 1); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); - - // Supported weights shape 1x1 - armnn::TensorInfo weightsInfo1x1({ 1, 1, 1, 1 }, armnn::DataType::Float32); - descriptor = MakeDepthwiseConv2dDesc(1, 1); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo1x1, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo1x1, biasesInfo)); - - // Supported shape 2x2 - armnn::TensorInfo weightsInfo2x2({ 1, 1, 2, 2 }, armnn::DataType::Float32); - descriptor = MakeDepthwiseConv2dDesc(1, 1); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo2x2, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo2x2, biasesInfo)); - - // Asymmetric padding - descriptor = MakeDepthwiseConv2dDesc(1, 1, 1, 1, 2, 1, 2); - outputInfo = CreateOutputTensorInfo(inputInfo, weightsInfo3x3, descriptor, dataType); - BOOST_TEST(armnn::IsDepthwiseConvolutionSupportedNeon(inputInfo, outputInfo, descriptor, - weightsInfo3x3, biasesInfo)); -} - -// Pooling -ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4, SimpleMaxPooling2dSize3x3Stride2x4Test, true) -ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4Uint8, SimpleMaxPooling2dSize3x3Stride2x4Uint8Test, true) -ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2d, SimpleAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2dUint8, SimpleAveragePooling2dUint8Test) - -ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2d, LargeTensorsAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2dUint8, LargeTensorsAveragePooling2dUint8Test) - -ARMNN_AUTO_TEST_CASE(SimpleL2Pooling2d, SimpleL2Pooling2dTest) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_SimpleL2Pooling2dUint8, SimpleL2Pooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize3Stride1, L2Pooling2dSize3Stride1Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize3Stride1Uint8, L2Pooling2dSize3Stride1Uint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize3Stride3, L2Pooling2dSize3Stride3Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize3Stride3Uint8, L2Pooling2dSize3Stride3Uint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize3Stride4, L2Pooling2dSize3Stride4Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize3Stride4Uint8, L2Pooling2dSize3Stride4Uint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize7, L2Pooling2dSize7Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize7Uint8, L2Pooling2dSize7Uint8Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize9, L2Pooling2dSize9Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_L2Pooling2dSize9Uint8, L2Pooling2dSize9Uint8Test) - -// Ignore padding values for pooling but count padding fields into the divisor -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleMaxPooling2d, IgnorePaddingSimpleMaxPooling2dTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleMaxPooling2dUint8, IgnorePaddingSimpleMaxPooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingMaxPooling2dSize3, IgnorePaddingMaxPooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingMaxPooling2dSize3Uint8, IgnorePaddingMaxPooling2dSize3Uint8Test) - -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2d, IgnorePaddingSimpleAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dUint8, IgnorePaddingSimpleAveragePooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPadding, IgnorePaddingSimpleAveragePooling2dNoPaddingTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPaddingUint8, - IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3, IgnorePaddingAveragePooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3Uint8, IgnorePaddingAveragePooling2dSize3Uint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2, - IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, false) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2NoPadding, - IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, - true) - -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleL2Pooling2d, IgnorePaddingSimpleL2Pooling2dTest) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_IgnorePaddingSimpleL2Pooling2dUint8, IgnorePaddingSimpleL2Pooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingL2Pooling2dSize3, IgnorePaddingL2Pooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(UNSUPPORTED_IgnorePaddingL2Pooling2dSize3Uint8, IgnorePaddingL2Pooling2dSize3Uint8Test) - -// Activation -ARMNN_AUTO_TEST_CASE(ConstantLinearActivation, ConstantLinearActivationTest) - -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1, SimpleSoftmaxTest, 1.0f) -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2, SimpleSoftmaxTest, 2.0f) - -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1Uint8, SimpleSoftmaxUint8Test, 1.0f) -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2Uint8, SimpleSoftmaxUint8Test, 2.0f) - -ARMNN_AUTO_TEST_CASE(ReLu1Uint8, BoundedReLuUint8UpperAndLowerBoundTest) -ARMNN_AUTO_TEST_CASE(ReLu6Uint8, BoundedReLuUint8UpperBoundOnlyTest) - -// Softmax -BOOST_AUTO_TEST_CASE(Softmax4dSupport) -{ - const unsigned int numDimensions = 4u; - std::array dimensionSizes; - dimensionSizes.fill(1u); - - const armnn::TensorInfo inputInfo(numDimensions, &dimensionSizes.front(), armnn::DataType::Float32); - const armnn::TensorInfo outputInfo(numDimensions, &dimensionSizes.front(), armnn::DataType::Float32); - - // 4D Softmax should be reported as unsupported on the NEON backend - BOOST_TEST(!armnn::IsSoftmaxSupportedNeon(inputInfo, outputInfo, armnn::SoftmaxDescriptor())); -} - -// Splitter -ARMNN_AUTO_TEST_CASE(SimpleSplitter, SplitterTest) -ARMNN_AUTO_TEST_CASE(SimpleSplitterUint8, SplitterUint8Test) - -ARMNN_AUTO_TEST_CASE(CopyViaSplitter, CopyViaSplitterTest) -ARMNN_AUTO_TEST_CASE(CopyViaSplitterUint8, CopyViaSplitterUint8Test) - -// Merger -ARMNN_AUTO_TEST_CASE(SimpleMerger, MergerTest) -ARMNN_AUTO_TEST_CASE(MergerUint8, MergerUint8Test) - -// Fully Connected -ARMNN_AUTO_TEST_CASE(SimpleFullyConnected, FullyConnectedFloat32Test, false, false) -ARMNN_AUTO_TEST_CASE(SimpleFullyConnectedWithBias, FullyConnectedFloat32Test, true, false) -ARMNN_AUTO_TEST_CASE(SimpleFullyConnectedWithTranspose, FullyConnectedFloat32Test, false, true) -ARMNN_AUTO_TEST_CASE(FullyConnectedLarge, FullyConnectedLargeTest, false) -ARMNN_AUTO_TEST_CASE(FullyConnectedLargeTransposed, FullyConnectedLargeTest, true) - -// Add -ARMNN_AUTO_TEST_CASE(SimpleAdd, AdditionTest) -ARMNN_AUTO_TEST_CASE(AddBroadcast, AdditionBroadcastTest) -ARMNN_AUTO_TEST_CASE(AddBroadcast1Element, AdditionBroadcast1ElementTest) - -// Sub -ARMNN_AUTO_TEST_CASE(SimpleSub, SubtractionTest) - -// Mul -ARMNN_AUTO_TEST_CASE(SimpleMultiplication, MultiplicationTest) -ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1Element, MultiplicationBroadcast1ElementTest) -ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVector, MultiplicationBroadcast1DVectorTest) - -// Batch Norm -ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest) - -// Constant -ARMNN_AUTO_TEST_CASE(Constant, ConstantTest) -ARMNN_AUTO_TEST_CASE(ConstantUint8, ConstantTestUint8) - -// Concatenation -ARMNN_AUTO_TEST_CASE(Concatenation1d, Concatenation1dTest) -ARMNN_AUTO_TEST_CASE(Concatenation1dUint8, Concatenation1dUint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0, Concatenation2dDim0Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0Uint8, Concatenation2dDim0Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1, Concatenation2dDim1Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1Uint8, Concatenation2dDim1Uint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0DiffInputDims, Concatenation2dDim0DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0DiffInputDimsUint8, Concatenation2dDim0DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1DiffInputDims, Concatenation2dDim1DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1DiffInputDimsUint8, Concatenation2dDim1DiffInputDimsUint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0, Concatenation3dDim0Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0Uint8, Concatenation3dDim0Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1, Concatenation3dDim1Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1Uint8, Concatenation3dDim1Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2, Concatenation3dDim2Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2Uint8, Concatenation3dDim2Uint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0DiffInputDims, Concatenation3dDim0DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0DiffInputDimsUint8, Concatenation3dDim0DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1DiffInputDims, Concatenation3dDim1DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1DiffInputDimsUint8, Concatenation3dDim1DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2DiffInputDims, Concatenation3dDim2DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2DiffInputDimsUint8, Concatenation3dDim2DiffInputDimsUint8Test) - -// L2 Normalization -ARMNN_AUTO_TEST_CASE(L2Normalization1d, L2Normalization1dTest); -ARMNN_AUTO_TEST_CASE(L2Normalization2d, L2Normalization2dTest); -ARMNN_AUTO_TEST_CASE(L2Normalization3d, L2Normalization3dTest); -ARMNN_AUTO_TEST_CASE(L2Normalization4d, L2Normalization4dTest); - -// Floor -ARMNN_AUTO_TEST_CASE(SimpleFloor, SimpleFloorTest) - -// Reshape -ARMNN_AUTO_TEST_CASE(SimpleReshapeFloat32, SimpleReshapeFloat32Test) -ARMNN_AUTO_TEST_CASE(SimpleReshapeUint8, SimpleReshapeUint8Test) - -// Permute -ARMNN_AUTO_TEST_CASE(SimplePermuteFloat32, SimplePermuteFloat32Test) -ARMNN_AUTO_TEST_CASE(SimplePermuteUint8, SimplePermuteUint8Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet1, PermuteFloat32ValueSet1Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet2, PermuteFloat32ValueSet2Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test) - -// ============================================================================ -// COMPARE tests - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareConv2dWithReference, CompareConvolution2dTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareDepthwiseConv2dWithReferenceFloat32, CompareDepthwiseConvolution2dTest) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareDepthwiseConv2dWithReferenceUint8, CompareDepthwiseConvolution2dTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareNormalizationWithinWithReference, CompareNormalizationTest, - armnn::NormalizationAlgorithmChannel::Within, - armnn::NormalizationAlgorithmMethod::LocalBrightness) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareNormalizationAcrossWithReference, CompareNormalizationTest, - armnn::NormalizationAlgorithmChannel::Across, - armnn::NormalizationAlgorithmMethod::LocalBrightness) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareMaxPooling2dWithReference, ComparePooling2dTest, armnn::PoolingAlgorithm::Max) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareMaxPooling2dWithReferenceUint8, ComparePooling2dUint8Test, - armnn::PoolingAlgorithm::Max) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareAveragePooling2dWithReference, ComparePooling2dTest, - armnn::PoolingAlgorithm::Average) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareAveragePooling2dWithReferenceUint8, ComparePooling2dUint8Test, - armnn::PoolingAlgorithm::Average) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareL2Pooling2dWithReference, ComparePooling2dTest, armnn::PoolingAlgorithm::L2) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(UNSUPPORTED_CompareL2Pooling2dWithReferenceUint8, ComparePooling2dUint8Test, - armnn::PoolingAlgorithm::L2) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxBeta1WithReference, CompareSoftmaxTest, 1.0f) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxBeta2WithReference, CompareSoftmaxTest, 2.0f) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxUint8Beta1WithReference, CompareSoftmaxUint8Test, 1.0f) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxUint8Beta2WithReference, CompareSoftmaxUint8Test, 2.0f) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareAddition, CompareAdditionTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareMultiplicationWithReference, CompareMultiplicationTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareBatchNorm, CompareBatchNormTest) - -ARMNN_COMPARE_REF_AUTO_TEST_CASE(ReLu1, CompareBoundedReLuTest, 1.0f, -1.0f) -ARMNN_COMPARE_REF_AUTO_TEST_CASE(ReLu6, CompareBoundedReLuTest, 6.0f, 0.0f) - -// ============================================================================ -// FIXTURE tests - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareSigmoidActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Sigmoid, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareTanhActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::TanH, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareLinearActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Linear, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareReLuActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::ReLu, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareBoundedReLuActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::BoundedReLu, 5u) -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareBoundedReLuActivationWithReferenceUint8, ActivationFixture, - CompareActivationUint8Test, armnn::ActivationFunction::BoundedReLu) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareSoftReLuActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::SoftReLu, 1u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareLeakyReLuActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::LeakyReLu, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareAbsActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Abs, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareSqrtActivationWithReference, PositiveActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Sqrt, 5u) - -ARMNN_COMPARE_REF_FIXTURE_TEST_CASE(CompareSquareActivationWithReference, ActivationFixture, - CompareActivationTest, armnn::ActivationFunction::Square, 5u) -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/BatchNormTestImpl.hpp b/src/armnn/backends/test/BatchNormTestImpl.hpp deleted file mode 100644 index 7126db9074..0000000000 --- a/src/armnn/backends/test/BatchNormTestImpl.hpp +++ /dev/null @@ -1,112 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include - -#include "test/TensorHelpers.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -#include "backends/test/QuantizeHelper.hpp" - - -template -LayerTestResult BatchNormTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - const unsigned int width = 2; - const unsigned int height = 3; - const unsigned int channels = 2; - const unsigned int num = 1; - - armnn::TensorInfo inputTensorInfo({num, channels, height, width}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({num, channels, height, width}, armnn::GetDataType()); - armnn::TensorInfo tensorInfo({channels}, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - tensorInfo.SetQuantizationScale(qScale); - tensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, - { - 1.f, 4.f, - 4.f, 2.f, - 1.f, 6.f, - - 1.f, 1.f, - 4.f, 1.f, - -2.f, 4.f - })); - // These values are per-channel of the input. - auto mean = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {3, -2})); - auto variance = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {4, 9})); - auto beta = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {3, 2})); - auto gamma = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {2, 1})); - LayerTestResult ret(outputTensorInfo); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::BatchNormalizationQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle meanTensor(tensorInfo); - armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo); - armnn::ScopedCpuTensorHandle betaTensor(tensorInfo); - armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo); - - AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]); - AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]); - AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]); - AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Mean = &meanTensor; - data.m_Variance = &varianceTensor; - data.m_Beta = &betaTensor; - data.m_Gamma = &gammaTensor; - data.m_Parameters.m_Eps = 0.0f; - - // For each channel: - // substract mean, divide by standard deviation (with an epsilon to avoid div by 0), - // multiply by gamma and add beta - ret.outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, - { - 1.f, 4.f, - 4.f, 2.f, - 1.f, 6.f, - - 3.f, 3.f, - 4.f, 3.f, - 2.f, 4.f - })); - - std::unique_ptr workload = workloadFactory.CreateBatchNormalization(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} \ No newline at end of file diff --git a/src/armnn/backends/test/ClContextControlFixture.hpp b/src/armnn/backends/test/ClContextControlFixture.hpp deleted file mode 100644 index 54c5a4f505..0000000000 --- a/src/armnn/backends/test/ClContextControlFixture.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/ClContextControl.hpp" - -template -struct ClContextControlFixtureBase -{ - // Initialising ClContextControl to ensure OpenCL is loaded correctly for each test case - ClContextControlFixtureBase() : m_ClContextControl(nullptr, ProfilingEnabled) {} - ~ClContextControlFixtureBase() {} - - armnn::ClContextControl m_ClContextControl; -}; - -using ClContextControlFixture = ClContextControlFixtureBase; -using ClProfilingContextControlFixture = ClContextControlFixtureBase; diff --git a/src/armnn/backends/test/Conv2dTestImpl.hpp b/src/armnn/backends/test/Conv2dTestImpl.hpp deleted file mode 100644 index eb7165bf09..0000000000 --- a/src/armnn/backends/test/Conv2dTestImpl.hpp +++ /dev/null @@ -1,921 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include -#include - -#include "test/TensorHelpers.hpp" -#include "QuantizeHelper.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -// Mapping from input type to bias type for fully connected layers. -// float => float, uint8_t => int32_t -template -struct FullyConnectedBiasTypeForInputType; - -template<> -struct FullyConnectedBiasTypeForInputType -{ - using Type = float; -}; - -template<> -struct FullyConnectedBiasTypeForInputType -{ - using Type = int32_t; -}; - -// Modifies a std::vector in-place using a specified bias. -template -void ApplyBias(std::vector& v, float vScale, int32_t vOffset, - const std::vector& bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h) -{ - BOOST_ASSERT_MSG((armnn::IsQuantizedType() && vScale != 0.0f) || (!armnn::IsQuantizedType()), - "Invalid type and parameter combination."); - BOOST_ASSERT_MSG((armnn::IsQuantizedType() && bScale != 0.0f) || (!armnn::IsQuantizedType()), - "Invalid type and parameter combination."); - - // Note we need to dequantize and re-quantize the image value and the bias. - for (uint32_t i = 0; i < bias.size(); ++i) - { - float dBias = SelectiveDequantize(bias[i], bScale, bOffset); - for (uint32_t y = 0; y < h; ++y) - { - for (uint32_t x = 0; x < w; ++x) - { - uint32_t offset = (i * h + y) * w + x; - BOOST_ASSERT(offset < v.size()); - T& outRef = v[offset]; - float dOutput = SelectiveDequantize(outRef, vScale, vOffset); - outRef = SelectiveQuantize(dOutput + dBias, vScale, vOffset); - } - } - } -} - -template -LayerTestResult SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - const boost::multi_array& input, - const boost::multi_array& kernel, - const boost::multi_array& bias, - const boost::multi_array& outputExpected, - float qScale, - int32_t qOffset, - uint32_t padLeft = 0, - uint32_t padTop = 0, - uint32_t padRight = 0, - uint32_t padBottom = 0) -{ - unsigned int inputHeight = boost::numeric_cast(input.shape()[2]); - unsigned int inputWidth = boost::numeric_cast(input.shape()[3]); - unsigned int inputChannels = boost::numeric_cast(input.shape()[1]); - unsigned int inputNum = boost::numeric_cast(input.shape()[0]); - - unsigned int outputHeight = boost::numeric_cast(outputExpected.shape()[2]); - unsigned int outputWidth = boost::numeric_cast(outputExpected.shape()[3]); - unsigned int outputChannels = boost::numeric_cast(outputExpected.shape()[1]); - unsigned int outputNum = boost::numeric_cast(outputExpected.shape()[0]); - - unsigned int kernelHeight = boost::numeric_cast(kernel.shape()[2]); - unsigned int kernelWidth = boost::numeric_cast(kernel.shape()[3]); - unsigned int kernelChannels = boost::numeric_cast(kernel.shape()[1]); - unsigned int kernelDepthMul = boost::numeric_cast(kernel.shape()[0]); - - bool biasEnabled = bias.size() > 0; - - // This function currently assumes 1 batch of input/output (and duplicates this into 2 batches). - BOOST_ASSERT(inputNum == 1); - BOOST_ASSERT(outputNum == 1); - - // If a bias is used, its size must equal the number of output channels. - BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels); - - - // Note these tensors will use two (identical) batches. - armnn::TensorInfo inputTensorInfo({2*inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({2*outputNum, outputChannels, outputHeight, outputWidth}, - armnn::GetDataType()); - armnn::TensorInfo kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType()); - armnn::TensorInfo biasDesc({static_cast(bias.size())}, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - kernelDesc.SetQuantizationScale(qScale); - kernelDesc.SetQuantizationOffset(qOffset); - biasDesc.SetQuantizationScale(qScale*qScale); - biasDesc.SetQuantizationOffset(0); - } - - LayerTestResult ret(outputTensorInfo); - - // Construct input data - two batches of the same input image. - std::vector inputImage; - inputImage.assign(input.data(), input.data() + 1*inputChannels*inputHeight*inputWidth); - std::vector inputData; - inputData.insert(inputData.end(), inputImage.begin(), inputImage.end()); - inputData.insert(inputData.end(), inputImage.begin(), inputImage.end()); - auto batchedInput = MakeTensor(inputTensorInfo, inputData); - - std::vector outputImage; - outputImage.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth); - - // Apply bias to output image if it is enabled. - if(biasEnabled) - { - std::vector biasV; - biasV.assign(bias.data(), bias.data() + outputChannels); - ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - outputWidth, outputHeight); - } - - // Construct expected output data - two identical images. - std::vector outputData; - outputData.insert(outputData.end(), outputImage.begin(), outputImage.end()); - outputData.insert(outputData.end(), outputImage.begin(), outputImage.end()); - - ret.outputExpected = MakeTensor(outputTensorInfo, outputData); - - // Todo: nontrivial padding and strides. - uint32_t strideX = 1; - uint32_t strideY = 1; - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::Convolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); - armnn::ScopedCpuTensorHandle biasTensor(biasDesc); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - - if(biasEnabled) - { - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - } - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs. - data.m_Parameters.m_StrideX = strideX; - data.m_Parameters.m_StrideY = strideY; - data.m_Parameters.m_PadLeft = padLeft; - data.m_Parameters.m_PadRight = padRight; - data.m_Parameters.m_PadTop = padTop; - data.m_Parameters.m_PadBottom = padBottom; - data.m_Parameters.m_BiasEnabled = biasEnabled; - - std::unique_ptr workload = workloadFactory.CreateConvolution2d(data, info); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template -LayerTestResult DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadFactory& workloadFactory, - const boost::multi_array& input, - const boost::multi_array& kernel, - const boost::multi_array& bias, - const boost::multi_array& outputExpected, - float qScale, - int32_t qOffset, - uint32_t padLeft = 0, - uint32_t padTop = 0, - uint32_t padRight = 0, - uint32_t padBottom = 0, - uint32_t strideX = 1, - uint32_t strideY = 1) -{ - unsigned int inputNum = boost::numeric_cast(input.shape()[0]); - unsigned int inputChannels = boost::numeric_cast(input.shape()[1]); - unsigned int inputHeight = boost::numeric_cast(input.shape()[2]); - unsigned int inputWidth = boost::numeric_cast(input.shape()[3]); - unsigned int kernelChanMul = boost::numeric_cast(kernel.shape()[0]); - unsigned int kernelChannels = boost::numeric_cast(kernel.shape()[1]); - unsigned int kernelHeight = boost::numeric_cast(kernel.shape()[2]); - unsigned int kernelWidth = boost::numeric_cast(kernel.shape()[3]); - unsigned int outputNum = boost::numeric_cast(outputExpected.shape()[0]); - unsigned int outputChannels = boost::numeric_cast(outputExpected.shape()[1]); - unsigned int outputHeight = boost::numeric_cast(outputExpected.shape()[2]); - unsigned int outputWidth = boost::numeric_cast(outputExpected.shape()[3]); - - // If a bias is used, its size must equal the number of output channels. - bool biasEnabled = bias.size() > 0; - BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels); - - // Creates the tensors. - armnn::TensorInfo inputTensorInfo({inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({outputNum, outputChannels, outputHeight, outputWidth}, - armnn::GetDataType()); - armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType()); - armnn::TensorInfo biasDesc({static_cast(bias.size())}, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if (armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - kernelDesc.SetQuantizationScale(qScale); - kernelDesc.SetQuantizationOffset(qOffset); - biasDesc.SetQuantizationScale(qScale*qScale); - biasDesc.SetQuantizationOffset(0); - } - - // Construct the input data. - std::vector inputData; - inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth); - auto batchedInput = MakeTensor(inputTensorInfo, inputData); - - // Construct the output data, with bias applied, as appropriate. - std::vector outputData; - outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth); - if (biasEnabled) - { - std::vector biasV; - biasV.assign(bias.data(), bias.data() + outputChannels); - ApplyBias(outputData, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - outputWidth, outputHeight); - } - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, outputData); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - - armnn::ScopedCpuTensorHandle biasTensor(biasDesc); - if (biasEnabled) - { - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - } - - armnn::DepthwiseConvolution2dQueueDescriptor data; - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs. - data.m_Parameters.m_StrideX = strideX; - data.m_Parameters.m_StrideY = strideY; - data.m_Parameters.m_PadLeft = padLeft; - data.m_Parameters.m_PadRight = padRight; - data.m_Parameters.m_PadTop = padTop; - data.m_Parameters.m_PadBottom = padBottom; - data.m_Parameters.m_BiasEnabled = biasEnabled; - - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template -LayerTestResult DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - unsigned int inputHeight = 3; - unsigned int inputWidth = 3; - unsigned int inputChannels = 2; - unsigned int inputNum = 1; - - unsigned int kernelHeight = 3; - unsigned int kernelWidth = 3; - unsigned int kernelChannels = inputChannels; - - unsigned int outputHeight = 1; - unsigned int outputWidth = 1; - unsigned int outputChannels = kernelChannels; - unsigned int outputNum = inputNum; - - armnn::TensorInfo inputTensorInfo({ inputNum, inputChannels, inputHeight, inputWidth }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ outputNum, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType()); - armnn::TensorInfo kernelDesc({ 1, outputChannels, kernelHeight, kernelWidth }, armnn::GetDataType()); - armnn::TensorInfo biasDesc({ outputChannels }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - kernelDesc.SetQuantizationScale(qScale); - kernelDesc.SetQuantizationOffset(qOffset); - biasDesc.SetQuantizationScale(qScale*qScale); - biasDesc.SetQuantizationOffset(0); - } - - auto input = MakeTensor(inputTensorInfo, std::vector( - QuantizedVector(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 1.f, 2.f, 1.f, - 2.f, 1.f, 2.f, - 1.f, 2.f, 1.f, - - 1.f, 2.f, 1.f, - 2.f, 1.f, 2.f, - 1.f, 2.f, 1.f, - }))); - - std::vector biasV(QuantizedVector(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - {0, 2})); - auto bias = MakeTensor(biasDesc, biasV); - - auto kernel = MakeTensor(kernelDesc, std::vector( - QuantizedVector(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { - 1.f, 0.f, 1.f, - 0.f, 0.f, 0.f, - -1.f, 0.f, -1.f, - - 1.f, 0.f, 1.f, - 0.f, 0.f, 0.f, - -1.f, 0.f, -1.f, - }))); - - // Manually calculated. - std::vector outputImage( - QuantizedVector(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - {0.f, 0.f}) - ); - - // Optionally apply bias to output image. - if(biasEnabled) - { - ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - outputWidth, outputHeight); - } - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, outputImage); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::DepthwiseConvolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); - armnn::ScopedCpuTensorHandle biasTensor(biasDesc); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled. - data.m_Parameters.m_StrideX = 1; - data.m_Parameters.m_StrideY = 1; - data.m_Parameters.m_PadLeft = 0; - data.m_Parameters.m_PadRight = 0; - data.m_Parameters.m_PadTop = 0; - data.m_Parameters.m_PadBottom = 0; - data.m_Parameters.m_BiasEnabled = biasEnabled; - - std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template -LayerTestResult DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - unsigned int depthMultiplier = 2; - - unsigned int inputHeight = 8; - unsigned int inputWidth = 16; - unsigned int inputChannels = 2; - unsigned int inputBatchSize = 1; - - unsigned int kernelHeight = 5; - unsigned int kernelWidth = 3; - - unsigned int outputHeight = inputHeight - kernelHeight + 1 + 2; - unsigned int outputWidth = (inputWidth - kernelWidth + 1)/2; - unsigned int outputChannels = inputChannels * depthMultiplier; - unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({inputBatchSize, inputChannels, inputHeight, inputWidth}, - armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({outputBatchSize, outputChannels, outputHeight, outputWidth}, - armnn::GetDataType()); - armnn::TensorInfo kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth}, armnn::GetDataType()); - armnn::TensorInfo biasDesc({outputChannels}, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - kernelDesc.SetQuantizationScale(qScale); - kernelDesc.SetQuantizationOffset(qOffset); - biasDesc.SetQuantizationScale(qScale*qScale); - biasDesc.SetQuantizationOffset(0); - } - - auto input = MakeTensor(inputTensorInfo, std::vector( - QuantizedVector(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 - }))); - - std::vector biasV(QuantizedVector(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - {0, 2, 1, -1})); - auto bias = MakeTensor(biasDesc, biasV); - - auto kernel = MakeTensor(kernelDesc, std::vector( - QuantizedVector(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { - 1, 1, 1, - 1, -1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - - 0, 0, 0, - 0, -1, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 0, 0, 0, - 0, 0, 0, - 0, 1, 0, - 0, 0, 0, - 0, 0, 0 - }))); - - // Manually calculated. - std::vector outputImage = std::vector( - QuantizedVector(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { - 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, - 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, - 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, - 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, - 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, - 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, - - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - - 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f - })); - - // Optionally apply bias to output image. - if(biasEnabled) - { - ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - outputWidth, outputHeight); - } - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, outputImage); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::DepthwiseConvolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); - armnn::ScopedCpuTensorHandle biasTensor(biasDesc); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled. - data.m_Parameters.m_StrideX = 2; - data.m_Parameters.m_StrideY = 1; - data.m_Parameters.m_PadLeft = 0; - data.m_Parameters.m_PadRight = 0; - data.m_Parameters.m_PadTop = 1; - data.m_Parameters.m_PadBottom = 1; - data.m_Parameters.m_BiasEnabled = biasEnabled; - - std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template -LayerTestResult Convolution1dTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - using B = typename FullyConnectedBiasTypeForInputType::Type; - - // Until we have a specialist 1D convolution layer, we can fake one using - // 2D convolution with the final dimension set to 1. - // I don't anticipate this being particularly slow, given that convolution is implemented - // as a matrix multiplication, at which point dimension doesn't matter. - - unsigned int batchSize = 1; - unsigned int inputChannels = 2; - unsigned int outputChannels = 3; - unsigned int inputSize = 5; // The 1D size (could view as 'width' or 'height'). - unsigned int kernelSize = 3; - unsigned int padSize = 2; - unsigned int stride = 1; - unsigned int outputSize = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride. - - armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, armnn::GetDataType()); - armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, armnn::GetDataType()); - armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, armnn::GetDataType()); - armnn::TensorInfo biasInfo({outputChannels}, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputInfo.SetQuantizationScale(qScale); - inputInfo.SetQuantizationOffset(qOffset); - outputInfo.SetQuantizationScale(qScale); - outputInfo.SetQuantizationOffset(qOffset); - kernelInfo.SetQuantizationScale(qScale); - kernelInfo.SetQuantizationOffset(qOffset); - biasInfo.SetQuantizationScale(inputInfo.GetQuantizationScale()*kernelInfo.GetQuantizationScale()); - biasInfo.SetQuantizationOffset(0); - } - - std::vector inputData( - QuantizedVector(inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), { - 5.0f, -2.0f, 2.5f, 0.0f, 1.0f, - -3.0f, 3.2f, 5.0f, 2.0f, 3.0f, - })); - - std::vector kernelData( - QuantizedVector(kernelInfo.GetQuantizationScale(), kernelInfo.GetQuantizationOffset(), { - 1.0f, 0.0f, 0.0f, - 0.0f, 2.0f, -1.5f, - - 0.0f, 0.0f, 0.0f, - 0.2f, 0.2f, 0.2f, - - 0.5f, 0.0f, 0.5f, - 0.0f, -1.0f, 0.0f - })); - - std::vector biasData( - QuantizedVector(biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), { - 1.0f, 0.0f, 0.0f - })); - - std::vector outputData( - QuantizedVector(outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), { - 4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f, - -0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f, - 2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f - })); - - // Optionally apply bias to output image. - if(biasEnabled) - { - ApplyBias(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), - biasData, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), - 1, outputSize); - } - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputInfo); - - armnn::Convolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelInfo); - armnn::ScopedCpuTensorHandle biasTensor(biasInfo); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data()); - AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data()); - - AddInputToWorkload(data, info, inputInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputInfo, outputHandle.get()); - - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; - data.m_Parameters.m_StrideX = 1; - data.m_Parameters.m_StrideY = stride; - data.m_Parameters.m_PadLeft = 0; - data.m_Parameters.m_PadRight = 0; - data.m_Parameters.m_PadTop = padSize; - data.m_Parameters.m_PadBottom = padSize; - data.m_Parameters.m_BiasEnabled = biasEnabled; - - std::unique_ptr workload = workloadFactory.CreateConvolution2d(data, info); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), inputData.data()); - - workloadFactory.Finalize(); - workload->Execute(); - - // Output - LayerTestResult ret(outputInfo); - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - ret.outputExpected = MakeTensor(outputInfo, outputData); - return ret; -} - - - -template -LayerTestResult CompareConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - unsigned int inputHeight = 8; - unsigned int inputWidth = 16; - unsigned int inputChannels = 3; - unsigned int inputNum = 5; - - unsigned int kernelHeight = 3; - unsigned int kernelWidth = 3; - - unsigned int strideX = 2; - unsigned int strideY = 3; - unsigned int padX = 1; - unsigned int padY = 1; - - unsigned int outputNum = inputNum; - unsigned int outputChannels = 2; - unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY; - unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo kernelDesc; - armnn::TensorInfo biasDesc; - - unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; - unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; - unsigned int kernelShape[] = {outputChannels, inputChannels, kernelHeight, kernelWidth}; - unsigned int biasShape[] = {outputChannels}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType()); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType()); - kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType()); - biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType()); - - LayerTestResult ret(outputTensorInfo); - - auto input = MakeRandomTensor(inputTensorInfo, 124908); - auto kernel = MakeRandomTensor(kernelDesc, 891234); - auto bias = MakeRandomTensor(biasDesc, 1028); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::Convolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); - armnn::ScopedCpuTensorHandle biasTensor(biasDesc); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; - data.m_Parameters.m_StrideX = strideX; - data.m_Parameters.m_StrideY = strideY; - data.m_Parameters.m_PadLeft = padX; - data.m_Parameters.m_PadRight = padX; - data.m_Parameters.m_PadTop = padY; - data.m_Parameters.m_PadBottom = padY; - data.m_Parameters.m_BiasEnabled = true; - - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - armnn::Convolution2dQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr workload = workloadFactory.CreateConvolution2d(data, info); - std::unique_ptr workloadRef = refWorkloadFactory.CreateConvolution2d(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} - -template -LayerTestResult CompareDepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - unsigned int inputHeight = 8; - unsigned int inputWidth = 16; - unsigned int inputChannels = 3; - unsigned int inputNum = 5; - - unsigned int kernelHeight = 3; - unsigned int kernelWidth = 3; - unsigned int channelMultiplier = 1; - - unsigned int strideX = 2; - unsigned int strideY = 3; - unsigned int padX = 1; - unsigned int padY = 1; - - unsigned int outputNum = inputNum; - unsigned int outputChannels = inputChannels * channelMultiplier; - unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY; - unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo kernelDesc; - armnn::TensorInfo biasDesc; - - unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; - unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; - unsigned int kernelShape[] = { channelMultiplier, inputChannels, kernelHeight, kernelWidth }; - unsigned int biasShape[] = { outputChannels }; - - float inputsQScale = armnn::IsQuantizedType() ? 1.0f : 0; - float outputQScale = armnn::IsQuantizedType() ? 2.0f : 0; - int32_t qOffset = 0; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType(), inputsQScale, qOffset); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType(), outputQScale, qOffset); - kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType(), inputsQScale, qOffset); - biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetBiasDataType(armnn::GetDataType()), inputsQScale, qOffset); - - LayerTestResult ret(outputTensorInfo); - - auto input = MakeRandomTensor(inputTensorInfo, 124908, 0.0f, 255.0f); - auto kernel = MakeRandomTensor(kernelDesc, 891234, 0.0f, 255.0f); - auto bias = MakeRandomTensor::Type, 1>(biasDesc, 1028, 0.0f, 255.0f); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::DepthwiseConvolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); - armnn::ScopedCpuTensorHandle biasTensor(biasDesc); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; - data.m_Parameters.m_StrideX = strideX; - data.m_Parameters.m_StrideY = strideY; - data.m_Parameters.m_PadLeft = padX; - data.m_Parameters.m_PadRight = padX; - data.m_Parameters.m_PadTop = padY; - data.m_Parameters.m_PadBottom = padY; - data.m_Parameters.m_BiasEnabled = true; - - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - armnn::DepthwiseConvolution2dQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); - std::unique_ptr workloadRef = refWorkloadFactory.CreateDepthwiseConvolution2d(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} diff --git a/src/armnn/backends/test/ConvertFp16ToFp32TestImpl.hpp b/src/armnn/backends/test/ConvertFp16ToFp32TestImpl.hpp deleted file mode 100644 index b75879dea6..0000000000 --- a/src/armnn/backends/test/ConvertFp16ToFp32TestImpl.hpp +++ /dev/null @@ -1,55 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include -#include -#include - -#include -#include - -#include - -#include - -LayerTestResult SimpleConvertFp16ToFp32Test(armnn::IWorkloadFactory& workloadFactory) -{ - using namespace half_float::literal; - - const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16); - const armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, - { -37.5_h, -15.2_h, -8.76_h, -2.0_h, -1.5_h, -1.3_h, -0.5_h, -0.4_h, 0.0_h, - 1.0_h, 0.4_h, 0.5_h, 1.3_h, 1.5_h, 2.0_h, 8.76_h, 15.2_h, 37.5_h }); - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, - { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, - 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f }); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ConvertFp16ToFp32QueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateConvertFp16ToFp32(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} diff --git a/src/armnn/backends/test/ConvertFp32ToFp16TestImpl.hpp b/src/armnn/backends/test/ConvertFp32ToFp16TestImpl.hpp deleted file mode 100644 index 1325b4b054..0000000000 --- a/src/armnn/backends/test/ConvertFp32ToFp16TestImpl.hpp +++ /dev/null @@ -1,55 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include -#include -#include - -#include -#include - -#include - -#include - -LayerTestResult SimpleConvertFp32ToFp16Test(armnn::IWorkloadFactory& workloadFactory) -{ - using namespace half_float::literal; - - const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16); - - auto input = MakeTensor(inputTensorInfo, - { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, - 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f }); - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, - { -37.5_h, -15.2_h, -8.76_h, -2.0_h, -1.5_h, -1.3_h, -0.5_h, -0.4_h, 0.0_h, - 1.0_h, 0.4_h, 0.5_h, 1.3_h, 1.5_h, 2.0_h, 8.76_h, 15.2_h, 37.5_h }); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ConvertFp32ToFp16QueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateConvertFp32ToFp16(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} \ No newline at end of file diff --git a/src/armnn/backends/test/CreateWorkloadCl.cpp b/src/armnn/backends/test/CreateWorkloadCl.cpp deleted file mode 100644 index af3192cae2..0000000000 --- a/src/armnn/backends/test/CreateWorkloadCl.cpp +++ /dev/null @@ -1,564 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include "backends/ClWorkloadFactory.hpp" -#include "backends/RefWorkloadFactory.hpp" -#include "backends/MemCopyWorkload.hpp" -#include "backends/ClWorkloads/ClWorkloadUtils.hpp" -#include "backends/ClWorkloads.hpp" -#include "backends/ClTensorHandle.hpp" -#include "ClContextControlFixture.hpp" - -#include "test/CreateWorkloadClNeon.hpp" - -boost::test_tools::predicate_result CompareIClTensorHandleShape(IClTensorHandle* tensorHandle, - std::initializer_list expectedDimensions) -{ - return CompareTensorHandleShape(tensorHandle, expectedDimensions); -} - -BOOST_FIXTURE_TEST_SUITE(CreateWorkloadCl, ClContextControlFixture) - -template -static void ClCreateActivationWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - - auto workload = CreateActivationWorkloadTest(factory, graph); - - // Checks that inputs/outputs are as we expect them (see definition of CreateActivationWorkloadTest). - ActivationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {1})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {1})); -} - -BOOST_AUTO_TEST_CASE(CreateActivationFloatWorkload) -{ - ClCreateActivationWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateActivationFloat16Workload) -{ - ClCreateActivationWorkloadTest(); -} - -template -static void ClCreateArithmethicWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - auto workload = CreateArithmeticWorkloadTest(factory, graph); - - // Checks that inputs/outputs are as we expect them (see definition of CreateArithmeticWorkloadTest). - DescriptorType queueDescriptor = workload->GetData(); - auto inputHandle1 = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto inputHandle2 = boost::polymorphic_downcast(queueDescriptor.m_Inputs[1]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(CompareIClTensorHandleShape(inputHandle1, {2, 3})); - BOOST_TEST(CompareIClTensorHandleShape(inputHandle2, {2, 3})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {2, 3})); -} - -BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload) -{ - ClCreateArithmethicWorkloadTest, - AdditionQueueDescriptor, - AdditionLayer, - armnn::DataType::Float32>(); -} - -BOOST_AUTO_TEST_CASE(CreateAdditionFloat16Workload) -{ - ClCreateArithmethicWorkloadTest, - AdditionQueueDescriptor, - AdditionLayer, - armnn::DataType::Float16>(); -} - -BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) -{ - ClCreateArithmethicWorkloadTest, - SubtractionQueueDescriptor, - SubtractionLayer, - armnn::DataType::Float32>(); -} - -BOOST_AUTO_TEST_CASE(CreateSubtractionFloat16Workload) -{ - ClCreateArithmethicWorkloadTest, - SubtractionQueueDescriptor, - SubtractionLayer, - armnn::DataType::Float16>(); -} - -BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkloadTest) -{ - ClCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateMultiplicationFloat16WorkloadTest) -{ - ClCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateDivisionFloatWorkloadTest) -{ - ClCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateDivisionFloat16WorkloadTest) -{ - ClCreateArithmethicWorkloadTest(); -} - -template -static void ClCreateBatchNormalizationWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - - auto workload = CreateBatchNormalizationWorkloadTest - (factory, graph); - - // Checks that inputs/outputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). - BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {2, 3, 1, 1})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {2, 3, 1, 1})); -} - -BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatWorkload) -{ - ClCreateBatchNormalizationWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16Workload) -{ - ClCreateBatchNormalizationWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateConvertFp16ToFp32Workload) -{ - Graph graph; - ClWorkloadFactory factory; - auto workload = CreateConvertFp16ToFp32WorkloadTest(factory, graph); - - ConvertFp16ToFp32QueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {3, 2, 3})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {3, 2, 3})); - BOOST_TEST((inputHandle->GetTensor().info()->data_type() == arm_compute::DataType::F16)); - BOOST_TEST((outputHandle->GetTensor().info()->data_type() == arm_compute::DataType::F32)); -} - -BOOST_AUTO_TEST_CASE(CreateConvertFp32ToFp16Workload) -{ - Graph graph; - ClWorkloadFactory factory; - auto workload = CreateConvertFp32ToFp16WorkloadTest(factory, graph); - - ConvertFp32ToFp16QueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {3, 2, 3})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {3, 2, 3})); - BOOST_TEST((inputHandle->GetTensor().info()->data_type() == arm_compute::DataType::F32)); - BOOST_TEST((outputHandle->GetTensor().info()->data_type() == arm_compute::DataType::F16)); -} - -template -static void ClConvolution2dWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - auto workload = CreateConvolution2dWorkloadTest - (factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). - Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {2, 3, 8, 16})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {2, 2, 2, 10})); -} - -BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatWorkload) -{ - ClConvolution2dWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16Workload) -{ - ClConvolution2dWorkloadTest(); -} - - -template -static void ClDirectConvolution2dWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - auto workload = CreateDirectConvolution2dWorkloadTest( - factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateDirectConvolution2dWorkloadTest). - Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {2, 3, 6, 6})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {2, 2, 6, 6})); -} - -BOOST_AUTO_TEST_CASE(CreateDirectConvolution2dFloatWorkload) -{ - ClDirectConvolution2dWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateDirectConvolution2dFloat16Workload) -{ - ClDirectConvolution2dWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateDirectConvolution2dUint8Workload) -{ - ClDirectConvolution2dWorkloadTest(); -} - -template -static void ClCreateFullyConnectedWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - auto workload = - CreateFullyConnectedWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateFullyConnectedWorkloadTest). - FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {3, 1, 4, 5})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {3, 7})); -} - - -BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloatWorkloadTest) -{ - ClCreateFullyConnectedWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloat16WorkloadTest) -{ - ClCreateFullyConnectedWorkloadTest(); -} - -template -static void ClNormalizationWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - - auto workload = CreateNormalizationWorkloadTest - (factory, graph); - - // Checks that inputs/outputs are as we expect them (see definition of CreateNormalizationWorkloadTest). - NormalizationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {3, 5, 5, 1})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {3, 5, 5, 1})); -} - -BOOST_AUTO_TEST_CASE(CreateNormalizationFloatWorkload) -{ - ClNormalizationWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateNormalizationFloat16Workload) -{ - ClNormalizationWorkloadTest(); -} - -template -static void ClPooling2dWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - - auto workload = CreatePooling2dWorkloadTest(factory, graph); - - // Check that inputs/outputs are as we expect them (see definition of CreatePooling2dWorkloadTest). - Pooling2dQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {3, 2, 5, 5})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {3, 2, 2, 4})); -} - -BOOST_AUTO_TEST_CASE(CreatePooling2dFloatWorkload) -{ - ClPooling2dWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16Workload) -{ - ClPooling2dWorkloadTest(); -} - -template -static void ClCreateReshapeWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - - auto workload = CreateReshapeWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest). - ReshapeQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {4, 1})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {4})); // Leading size 1 dimensions are collapsed by ACL. -} - -BOOST_AUTO_TEST_CASE(CreateReshapeFloatWorkload) -{ - ClCreateReshapeWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateReshapeFloat16Workload) -{ - ClCreateReshapeWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) -{ - ClCreateReshapeWorkloadTest(); -} - -template -static void ClSoftmaxWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - - auto workload = CreateSoftmaxWorkloadTest(factory, graph); - - // Checks that inputs/outputs are as we expect them (see definition of ClSoftmaxFloatWorkload). - SoftmaxQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {4, 1})); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {4, 1})); -} - - -BOOST_AUTO_TEST_CASE(CreateSoftmaxFloatWorkloadTest) -{ - ClSoftmaxWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSoftmaxFloat16WorkloadTest) -{ - ClSoftmaxWorkloadTest(); -} - -template -static void ClSplitterWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - - auto workload = CreateSplitterWorkloadTest(factory, graph); - - // Checks that outputs are as we expect them (see definition of CreateSplitterWorkloadTest). - SplitterQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {5, 7, 7})); - - auto outputHandle1 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[1]); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle1, {2, 7, 7})); - - auto outputHandle2 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[2]); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle2, {2, 7, 7})); - - auto outputHandle0 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - // NOTE: At the moment the CL collapses the tensor to a 2 dim when dimension zero = 1 - // we are raising this difference between the NEON and CL libs as an issue with the compute library team. - BOOST_TEST(CompareIClTensorHandleShape(outputHandle0, {7, 7})); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterFloatWorkload) -{ - ClSplitterWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterFloat16Workload) -{ - ClSplitterWorkloadTest(); -} - -template -static void ClSplitterMergerTest() -{ - // Tests that it is possible to decide which output of the splitter layer - // should be lined to which input of the merger layer. - // We test that is is possible to specify 0th output - // of the splitter to be the 1st input to the merger and the 1st output of the splitter to be 0th input - // of the merger. - - Graph graph; - ClWorkloadFactory factory; - - auto workloads = - CreateSplitterMergerWorkloadTest - (factory, graph); - - auto wlSplitter = std::move(workloads.first); - auto wlMerger = std::move(workloads.second); - - //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. - armnn::ClSubTensorHandle* sOut0 = dynamic_cast(wlSplitter->GetData().m_Outputs[0]); - armnn::ClSubTensorHandle* sOut1 = dynamic_cast(wlSplitter->GetData().m_Outputs[1]); - armnn::ClSubTensorHandle* mIn0 = dynamic_cast(wlMerger->GetData().m_Inputs[0]); - armnn::ClSubTensorHandle* mIn1 = dynamic_cast(wlMerger->GetData().m_Inputs[1]); - - BOOST_TEST(sOut0); - BOOST_TEST(sOut1); - BOOST_TEST(mIn0); - BOOST_TEST(mIn1); - - //Fliped order of inputs/outputs. - bool validDataPointers = (sOut0 == mIn1) && (sOut1 == mIn0); - BOOST_TEST(validDataPointers); - - - //Also make sure that the inputs are subtensors of one tensor and outputs are sub tensors of another tensor. - bool validSubTensorParents = (mIn0->GetTensor().parent() == mIn1->GetTensor().parent()) - && (sOut0->GetTensor().parent() == sOut1->GetTensor().parent()); - - BOOST_TEST(validSubTensorParents); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterMergerFloatWorkload) -{ - ClSplitterMergerTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterMergerFloat16Workload) -{ - ClSplitterMergerTest(); -} - - -BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputs) -{ - // Test that it is possible to assign multiple (two) different layers to each of the outputs of a splitter layer. - // We create a splitter with two outputs. That each of those outputs is used by two different activation layers. - - Graph graph; - ClWorkloadFactory factory; - std::unique_ptr wlSplitter; - std::unique_ptr wlActiv0_0; - std::unique_ptr wlActiv0_1; - std::unique_ptr wlActiv1_0; - std::unique_ptr wlActiv1_1; - - CreateSplitterMultipleInputsOneOutputWorkloadTest(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, - wlActiv1_0, wlActiv1_1); - - //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. - armnn::ClSubTensorHandle* sOut0 = dynamic_cast(wlSplitter->GetData().m_Outputs[0]); - armnn::ClSubTensorHandle* sOut1 = dynamic_cast(wlSplitter->GetData().m_Outputs[1]); - armnn::ClSubTensorHandle* activ0_0Im = dynamic_cast(wlActiv0_0->GetData().m_Inputs[0]); - armnn::ClSubTensorHandle* activ0_1Im = dynamic_cast(wlActiv0_1->GetData().m_Inputs[0]); - armnn::ClSubTensorHandle* activ1_0Im = dynamic_cast(wlActiv1_0->GetData().m_Inputs[0]); - armnn::ClSubTensorHandle* activ1_1Im = dynamic_cast(wlActiv1_1->GetData().m_Inputs[0]); - - - BOOST_TEST(sOut0); - BOOST_TEST(sOut1); - BOOST_TEST(activ0_0Im); - BOOST_TEST(activ0_1Im); - BOOST_TEST(activ1_0Im); - BOOST_TEST(activ1_1Im); - - bool validDataPointers = (sOut0 == activ0_0Im) && (sOut0 == activ0_1Im) && - (sOut1 == activ1_0Im) && (sOut1 == activ1_1Im); - - BOOST_TEST(validDataPointers); -} - -BOOST_AUTO_TEST_CASE(CreateMemCopyWorkloadsCl) -{ - ClWorkloadFactory factory; - CreateMemCopyWorkloads(factory); -} - -BOOST_AUTO_TEST_CASE(CreateL2NormalizationWorkload) -{ - Graph graph; - ClWorkloadFactory factory; - - auto workload = CreateL2NormalizationWorkloadTest - (factory, graph); - - // Checks that inputs/outputs are as we expect them (see definition of CreateNormalizationWorkloadTest). - L2NormalizationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 5, 20, 50, 67 })); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 5, 20, 50, 67 })); -} - -template -static void ClCreateLstmWorkloadTest() -{ - Graph graph; - ClWorkloadFactory factory; - auto workload = CreateLstmWorkloadTest(factory, graph); - - LstmQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[1]); - BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 2, 2 })); - BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 2, 4 })); -} - -BOOST_AUTO_TEST_CASE(CreateLSTMWorkloadFloatWorkload) -{ - ClCreateLstmWorkloadTest(); -} - - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/CreateWorkloadNeon.cpp b/src/armnn/backends/test/CreateWorkloadNeon.cpp deleted file mode 100644 index fbe064e1c4..0000000000 --- a/src/armnn/backends/test/CreateWorkloadNeon.cpp +++ /dev/null @@ -1,455 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include "backends/NeonWorkloadFactory.hpp" -#include "backends/NeonWorkloadUtils.hpp" -#include "backends/NeonWorkloads.hpp" -#include "backends/MemCopyWorkload.hpp" -#include "backends/NeonTensorHandle.hpp" - -#include "test/CreateWorkloadClNeon.hpp" - -BOOST_AUTO_TEST_SUITE(CreateWorkloadNeon) - -namespace -{ - -bool TestNeonTensorHandleInfo(armnn::INeonTensorHandle* handle, const armnn::TensorInfo& expectedInfo) -{ - using namespace armnn::armcomputetensorutils; - - const arm_compute::ITensorInfo* handleInfo = handle->GetTensor().info(); - const arm_compute::TensorInfo expectedAclInfo = BuildArmComputeTensorInfo(expectedInfo); - - if (handleInfo->data_type() != expectedAclInfo.data_type()) - { - return false; - } - - if (handleInfo->num_dimensions() != expectedAclInfo.num_dimensions()) - { - return false; - } - - if (handleInfo->quantization_info() != expectedAclInfo.quantization_info()) - { - return false; - } - - for (std::size_t d = 0; d < expectedAclInfo.num_dimensions(); ++d) - { - if (handleInfo->dimension(d) != expectedAclInfo.dimension(d)) - { - return false; - } - } - - return true; -} - -} // namespace - -template -static void NeonCreateActivationWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateActivationWorkloadTest - (factory, graph); - - // Checks that inputs/outputs are as we expect them (see definition of CreateActivationWorkloadTest). - ActivationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({1, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 1}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateActivationFloat16Workload) -{ - NeonCreateActivationWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateActivationFloatWorkload) -{ - NeonCreateActivationWorkloadTest(); -} - -template -static void NeonCreateArithmethicWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateArithmeticWorkloadTest(factory, graph); - - DescriptorType queueDescriptor = workload->GetData(); - auto inputHandle1 = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto inputHandle2 = boost::polymorphic_downcast(queueDescriptor.m_Inputs[1]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle1, TensorInfo({2, 3}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle2, TensorInfo({2, 3}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateAdditionFloat16Workload) -{ - NeonCreateArithmethicWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload) -{ - NeonCreateArithmethicWorkloadTest(); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateSubtractionFloat16Workload) -{ - NeonCreateArithmethicWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) -{ - NeonCreateArithmethicWorkloadTest(); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateMultiplicationFloat16Workload) -{ - NeonCreateArithmethicWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkload) -{ - NeonCreateArithmethicWorkloadTest(); -} - -template -static void NeonCreateBatchNormalizationWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateBatchNormalizationWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). - BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({2, 3, 1, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3, 1, 1}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16Workload) -{ - NeonCreateBatchNormalizationWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatWorkload) -{ - NeonCreateBatchNormalizationWorkloadTest(); -} - -template -static void NeonCreateConvolution2dWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateConvolution2dWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). - Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({2, 3, 8, 16}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 2, 2, 10}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16Workload) -{ - NeonCreateConvolution2dWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatWorkload) -{ - NeonCreateConvolution2dWorkloadTest(); -} - -template -static void NeonCreateFullyConnectedWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateFullyConnectedWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateFullyConnectedWorkloadTest). - FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 1, 4, 5}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 7}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloat16Workload) -{ - NeonCreateFullyConnectedWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloatWorkload) -{ - NeonCreateFullyConnectedWorkloadTest(); -} - -template -static void NeonCreateNormalizationWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateNormalizationWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateNormalizationWorkloadTest). - NormalizationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 5, 5, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 5, 5, 1}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateNormalizationFloat16Workload) -{ - NeonCreateNormalizationWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateNormalizationFloatWorkload) -{ - NeonCreateNormalizationWorkloadTest(); -} - -template -static void NeonCreatePooling2dWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreatePooling2dWorkloadTest - (factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreatePooling2dWorkloadTest). - Pooling2dQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 2, 5, 5}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 2, 2, 4}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16Workload) -{ - NeonCreatePooling2dWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreatePooling2dFloatWorkload) -{ - NeonCreatePooling2dWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreatePooling2dUint8Workload) -{ - NeonCreatePooling2dWorkloadTest(); -} - -template -static void NeonCreateReshapeWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateReshapeWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest). - ReshapeQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 4}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateReshapeFloat16Workload) -{ - NeonCreateReshapeWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateReshapeFloatWorkload) -{ - NeonCreateReshapeWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) -{ - NeonCreateReshapeWorkloadTest(); -} - -template -static void NeonCreateSoftmaxWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateSoftmaxWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateSoftmaxWorkloadTest). - SoftmaxQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({4, 1}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateSoftmaxFloat16Workload) -{ - NeonCreateSoftmaxWorkloadTest(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateSoftmaxFloatWorkload) -{ - NeonCreateSoftmaxWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterWorkload) -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateSplitterWorkloadTest(factory, graph); - - // Checks that outputs are as we expect them (see definition of CreateSplitterWorkloadTest). - SplitterQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({5, 7, 7}, DataType::Float32))); - - auto outputHandle0 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle0, TensorInfo({1, 7, 7}, DataType::Float32))); - - auto outputHandle1 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[1]); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle1, TensorInfo({2, 7, 7}, DataType::Float32))); - - auto outputHandle2 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[2]); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle2, TensorInfo({2, 7, 7}, DataType::Float32))); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterMerger) -{ - // Tests that it is possible to decide which output of the splitter layer - // should be lined to which input of the merger layer. - // We tested that is is possible to specify 0th output - // of the splitter to be the 1st input to the merger, and the 1st output of the splitter to be 0th input - // of the merger. - - Graph graph; - NeonWorkloadFactory factory; - - auto workloads = - CreateSplitterMergerWorkloadTest(factory, graph); - - auto wlSplitter = std::move(workloads.first); - auto wlMerger = std::move(workloads.second); - - //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. - armnn::INeonTensorHandle* sOut0 = dynamic_cast(wlSplitter->GetData().m_Outputs[0]); - armnn::INeonTensorHandle* sOut1 = dynamic_cast(wlSplitter->GetData().m_Outputs[1]); - armnn::INeonTensorHandle* mIn0 = dynamic_cast(wlMerger->GetData().m_Inputs[0]); - armnn::INeonTensorHandle* mIn1 = dynamic_cast(wlMerger->GetData().m_Inputs[1]); - - BOOST_TEST(sOut0); - BOOST_TEST(sOut1); - BOOST_TEST(mIn0); - BOOST_TEST(mIn1); - - bool validDataPointers = (sOut0 == mIn1) && (sOut1 == mIn0); - - BOOST_TEST(validDataPointers); -} - -BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputs) -{ - // Tests that it is possible to assign multiple (two) different layers to each of the outputs of a splitter layer. - // We created a splitter with two outputs. That each of those outputs is used by two different activation layers - - Graph graph; - NeonWorkloadFactory factory; - std::unique_ptr wlSplitter; - std::unique_ptr wlActiv0_0; - std::unique_ptr wlActiv0_1; - std::unique_ptr wlActiv1_0; - std::unique_ptr wlActiv1_1; - - CreateSplitterMultipleInputsOneOutputWorkloadTest(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, - wlActiv1_0, wlActiv1_1); - - armnn::INeonTensorHandle* sOut0 = dynamic_cast(wlSplitter->GetData().m_Outputs[0]); - armnn::INeonTensorHandle* sOut1 = dynamic_cast(wlSplitter->GetData().m_Outputs[1]); - armnn::INeonTensorHandle* activ0_0Im = dynamic_cast(wlActiv0_0->GetData().m_Inputs[0]); - armnn::INeonTensorHandle* activ0_1Im = dynamic_cast(wlActiv0_1->GetData().m_Inputs[0]); - armnn::INeonTensorHandle* activ1_0Im = dynamic_cast(wlActiv1_0->GetData().m_Inputs[0]); - armnn::INeonTensorHandle* activ1_1Im = dynamic_cast(wlActiv1_1->GetData().m_Inputs[0]); - - - BOOST_TEST(sOut0); - BOOST_TEST(sOut1); - BOOST_TEST(activ0_0Im); - BOOST_TEST(activ0_1Im); - BOOST_TEST(activ1_0Im); - BOOST_TEST(activ1_1Im); - - bool validDataPointers = (sOut0 == activ0_0Im) && (sOut0 == activ0_1Im) && - (sOut1 == activ1_0Im) && (sOut1 == activ1_1Im); - - BOOST_TEST(validDataPointers); -} - -BOOST_AUTO_TEST_CASE(CreateMemCopyWorkloadsNeon) -{ - NeonWorkloadFactory factory; - CreateMemCopyWorkloads(factory); -} - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/CreateWorkloadRef.cpp b/src/armnn/backends/test/CreateWorkloadRef.cpp deleted file mode 100644 index 41419dafd0..0000000000 --- a/src/armnn/backends/test/CreateWorkloadRef.cpp +++ /dev/null @@ -1,478 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include "backends/RefWorkloadFactory.hpp" -#include "backends/RefWorkloads.hpp" -#include "backends/CpuTensorHandle.hpp" - -#include "test/CreateWorkload.hpp" - -namespace -{ - -template -void CheckInputOutput(std::unique_ptr workload, const TensorInfo& inputInfo, const TensorInfo& outputInfo) -{ - auto queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST((inputHandle->GetTensorInfo() == inputInfo)); - BOOST_TEST((outputHandle->GetTensorInfo() == outputInfo)); -} - -template -void CheckInputsOutput(std::unique_ptr workload, - const TensorInfo& inputInfo0, - const TensorInfo& inputInfo1, - const TensorInfo& outputInfo) -{ - auto queueDescriptor = workload->GetData(); - auto inputHandle0 = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - auto inputHandle1 = boost::polymorphic_downcast(queueDescriptor.m_Inputs[1]); - auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST((inputHandle0->GetTensorInfo() == inputInfo0)); - BOOST_TEST((inputHandle1->GetTensorInfo() == inputInfo1)); - BOOST_TEST((outputHandle->GetTensorInfo() == outputInfo)); -} -} - -BOOST_AUTO_TEST_SUITE(CreateWorkloadRef) - -template -static void RefCreateActivationWorkloadTest() -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateActivationWorkloadTest(factory, graph); - - // Checks that outputs are as we expect them (see definition of CreateActivationWorkloadTest). - CheckInputOutput(std::move(workload), - TensorInfo({ 1, 1 }, DataType), - TensorInfo({ 1, 1 }, DataType)); -} - -BOOST_AUTO_TEST_CASE(CreateActivationFloat32Workload) -{ - RefCreateActivationWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateActivationUint8Workload) -{ - RefCreateActivationWorkloadTest(); -} - -template -static void RefCreateArithmethicWorkloadTest() -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateArithmeticWorkloadTest(factory, graph); - - CheckInputsOutput(std::move(workload), - TensorInfo({ 2, 3 }, DataType), - TensorInfo({ 2, 3 }, DataType), - TensorInfo({ 2, 3 }, DataType)); -} - -BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload) -{ - RefCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateAdditionUint8Workload) -{ - RefCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) -{ - RefCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSubtractionUint8Workload) -{ - RefCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkload) -{ - RefCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateMultiplicationUint8Workload) -{ - RefCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateDivisionFloatWorkload) -{ - RefCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateDivisionUint8Workload) -{ - RefCreateArithmethicWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateBatchNormalizationWorkload) -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateBatchNormalizationWorkloadTest - (factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). - CheckInputOutput( - std::move(workload), TensorInfo({2, 3, 1, 1}, DataType::Float32), TensorInfo({2, 3, 1, 1}, DataType::Float32)); -} - -BOOST_AUTO_TEST_CASE(CreateConvertFp16ToFp32Float32Workload) -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateConvertFp16ToFp32WorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them - CheckInputOutput( - std::move(workload), TensorInfo({1, 3, 2, 3}, DataType::Float16), TensorInfo({1, 3, 2, 3}, DataType::Float32)); -} - -BOOST_AUTO_TEST_CASE(CreateConvertFp32ToFp16Float16Workload) -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateConvertFp32ToFp16WorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them - CheckInputOutput( - std::move(workload), TensorInfo({1, 3, 2, 3}, DataType::Float32), TensorInfo({1, 3, 2, 3}, DataType::Float16)); -} - -BOOST_AUTO_TEST_CASE(CreateConvolution2dWorkload) -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateConvolution2dWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). - CheckInputOutput(std::move(workload), - TensorInfo({2, 3, 8, 16}, DataType::Float32), - TensorInfo({2, 2, 2, 10}, DataType::Float32)); -} - -BOOST_AUTO_TEST_CASE(CreateDepthwiseConvolution2dWorkload) -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = - CreateDepthwiseConvolution2dWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). - CheckInputOutput(std::move(workload), - TensorInfo({2, 3, 8, 16}, DataType::Float32), - TensorInfo({2, 9, 2, 10}, DataType::Float32)); -} - -template -static void RefCreateFullyConnectedWorkloadTest() -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateFullyConnectedWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateFullyConnectedWorkloadTest). - float inputsQScale = DataType == armnn::DataType::QuantisedAsymm8 ? 1.0f : 0.0; - float outputQScale = DataType == armnn::DataType::QuantisedAsymm8 ? 2.0f : 0.0; - CheckInputOutput(std::move(workload), - TensorInfo({ 3, 1, 4, 5 }, DataType, inputsQScale), - TensorInfo({ 3, 7 }, DataType, outputQScale)); -} - -BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloat32Workload) -{ - RefCreateFullyConnectedWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateFullyConnectedUint8Workload) -{ - RefCreateFullyConnectedWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateNormalizationWorkload) -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateNormalizationWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateNormalizationWorkloadTest). - CheckInputOutput(std::move(workload), - TensorInfo({3, 5, 5, 1}, DataType::Float32), - TensorInfo({3, 5, 5, 1}, DataType::Float32)); -} - -template -static void RefCreatePooling2dWorkloadTest() -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreatePooling2dWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreatePooling2dWorkloadTest). - CheckInputOutput( - std::move(workload), - TensorInfo({3, 2, 5, 5}, DataType), - TensorInfo({3, 2, 2, 4}, DataType)); -} - -BOOST_AUTO_TEST_CASE(CreatePooling2dFloat32Workload) -{ - RefCreatePooling2dWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreatePooling2dUint8Workload) -{ - RefCreatePooling2dWorkloadTest(); -} - -template -static void RefCreateSoftmaxWorkloadTest() -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateSoftmaxWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateSoftmaxWorkloadTest). - CheckInputOutput( - std::move(workload), - TensorInfo({4, 1}, DataType), - TensorInfo({4, 1}, DataType)); -} - -BOOST_AUTO_TEST_CASE(CreateSoftmaxFloat32Workload) -{ - RefCreateSoftmaxWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSoftmaxUint8Workload) -{ - RefCreateSoftmaxWorkloadTest(); -} - -template -static void RefCreateSplitterWorkloadTest() -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateSplitterWorkloadTest(factory, graph); - - // Checks that outputs are as we expect them (see definition of CreateSplitterWorkloadTest). - SplitterQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); - BOOST_TEST((inputHandle->GetTensorInfo() == TensorInfo({ 5, 7, 7 }, DataType))); - - auto outputHandle0 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); - BOOST_TEST((outputHandle0->GetTensorInfo() == TensorInfo({ 1, 7, 7 }, DataType))); - - auto outputHandle1 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[1]); - BOOST_TEST((outputHandle1->GetTensorInfo() == TensorInfo({ 2, 7, 7 }, DataType))); - - auto outputHandle2 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[2]); - BOOST_TEST((outputHandle2->GetTensorInfo() == TensorInfo({ 2, 7, 7 }, DataType))); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterFloat32Workload) -{ - RefCreateSplitterWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterUint8Workload) -{ - RefCreateSplitterWorkloadTest(); -} - -template -static void RefCreateSplitterMergerWorkloadTest() -{ - // Tests that it is possible to decide which output of the splitter layer - // should be lined to which input of the merger layer. - // We tested that is is possible to specify 0th output - // of the splitter to be the 1st input to the merger and the 1st output of the splitter to be 0th input - // of the merger. - - Graph graph; - RefWorkloadFactory factory; - auto workloads = CreateSplitterMergerWorkloadTest - (factory, graph); - - auto wlSplitter = std::move(workloads.first); - auto wlMerger = std::move(workloads.second); - - //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. - armnn::CpuTensorHandle* sOut0 = dynamic_cast(wlSplitter->GetData().m_Outputs[0]); - armnn::CpuTensorHandle* sOut1 = dynamic_cast(wlSplitter->GetData().m_Outputs[1]); - armnn::CpuTensorHandle* mIn0 = dynamic_cast(wlMerger->GetData().m_Inputs[0]); - armnn::CpuTensorHandle* mIn1 = dynamic_cast(wlMerger->GetData().m_Inputs[1]); - - BOOST_TEST(sOut0); - BOOST_TEST(sOut1); - BOOST_TEST(mIn0); - BOOST_TEST(mIn1); - - bool validDataPointers = (sOut0 == mIn1) && (sOut1 == mIn0); - - BOOST_TEST(validDataPointers); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterMergerFloat32) -{ - RefCreateSplitterMergerWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterMergerUint8) -{ - RefCreateSplitterMergerWorkloadTest(); -} - -template -static void RefCreateSingleOutputMultipleInputsTest() -{ - // Tests that it is possible to assign multiple (two) different layers to each of the outputs of a splitter layer. - // We created a splitter with two outputs. That each of those outputs is used by two different activation layers. - - Graph graph; - RefWorkloadFactory factory; - std::unique_ptr wlSplitter; - std::unique_ptr wlActiv0_0; - std::unique_ptr wlActiv0_1; - std::unique_ptr wlActiv1_0; - std::unique_ptr wlActiv1_1; - - CreateSplitterMultipleInputsOneOutputWorkloadTest(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, wlActiv1_0, wlActiv1_1); - - armnn::CpuTensorHandle* sOut0 = dynamic_cast(wlSplitter->GetData().m_Outputs[0]); - armnn::CpuTensorHandle* sOut1 = dynamic_cast(wlSplitter->GetData().m_Outputs[1]); - armnn::CpuTensorHandle* activ0_0Im = dynamic_cast(wlActiv0_0->GetData().m_Inputs[0]); - armnn::CpuTensorHandle* activ0_1Im = dynamic_cast(wlActiv0_1->GetData().m_Inputs[0]); - armnn::CpuTensorHandle* activ1_0Im = dynamic_cast(wlActiv1_0->GetData().m_Inputs[0]); - armnn::CpuTensorHandle* activ1_1Im = dynamic_cast(wlActiv1_1->GetData().m_Inputs[0]); - - - BOOST_TEST(sOut0); - BOOST_TEST(sOut1); - BOOST_TEST(activ0_0Im); - BOOST_TEST(activ0_1Im); - BOOST_TEST(activ1_0Im); - BOOST_TEST(activ1_1Im); - - bool validDataPointers = (sOut0 == activ0_0Im) && (sOut0 == activ0_1Im) && - (sOut1 == activ1_0Im) && (sOut1 == activ1_1Im); - - BOOST_TEST(validDataPointers); -} - -BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputsFloat32) -{ - RefCreateSingleOutputMultipleInputsTest(); -} - -BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputsUint8) -{ - RefCreateSingleOutputMultipleInputsTest(); -} - -template -static void RefCreateResizeBilinearTest() -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateResizeBilinearWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateResizeBilinearWorkloadTest). - CheckInputOutput( - std::move(workload), - TensorInfo({ 2, 3, 4, 4 }, DataType), - TensorInfo({ 2, 3, 2, 2 }, DataType)); -} - -BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat32) -{ - RefCreateResizeBilinearTest(); -} - -BOOST_AUTO_TEST_CASE(CreateResizeBilinearUint8) -{ - RefCreateResizeBilinearTest(); -} - -BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat32) -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateL2NormalizationWorkloadTest - (factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateL2NormalizationWorkloadTest). - CheckInputOutput( - std::move(workload), - TensorInfo({ 5, 20, 50, 67 }, armnn::DataType::Float32), - TensorInfo({ 5, 20, 50, 67 }, armnn::DataType::Float32)); -} - -template -static void RefCreateReshapeWorkloadTest() -{ - Graph graph; - RefWorkloadFactory factory; - auto workload = CreateReshapeWorkloadTest(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest). - CheckInputOutput( - std::move(workload), - TensorInfo({ 4, 1 }, DataType), - TensorInfo({ 1, 4 }, DataType)); -} - -BOOST_AUTO_TEST_CASE(CreateReshapeFloat32Workload) -{ - RefCreateReshapeWorkloadTest(); -} - -BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) -{ - RefCreateReshapeWorkloadTest(); -} - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/FullyConnectedTestImpl.hpp b/src/armnn/backends/test/FullyConnectedTestImpl.hpp deleted file mode 100644 index 125b7e62b1..0000000000 --- a/src/armnn/backends/test/FullyConnectedTestImpl.hpp +++ /dev/null @@ -1,287 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -template -LayerTestResult SimpleFullyConnectedTestImpl( - armnn::IWorkloadFactory& workloadFactory, - armnn::TensorInfo inputTensorInfo, - armnn::TensorInfo outputTensorInfo, - armnn::TensorInfo weightsDesc, - armnn::TensorInfo biasesDesc, - boost::multi_array& weights, - boost::multi_array& bias, - boost::multi_array& input, - bool biasEnabled, - bool transposeWeights) -{ - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::FullyConnectedQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(weightsDesc); - armnn::ScopedCpuTensorHandle biasTensor(biasesDesc); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &weights[0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; - data.m_Parameters.m_BiasEnabled = biasEnabled; - data.m_Parameters.m_TransposeWeightMatrix = transposeWeights; - - std::unique_ptr workload = workloadFactory.CreateFullyConnected(data, info); - LayerTestResult result(outputTensorInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get()); - - return result; -} - -LayerTestResult FullyConnectedFloat32Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled, - bool transposeWeights) -{ - unsigned int inputWidth = 1; - unsigned int inputHeight = 1; - unsigned int inputChannels = 5; - unsigned int inputNum = 2; - - unsigned int outputChannels = 3; - unsigned int outputNum = 2; - - // Define the tensor descriptors. - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo weightsDesc; - armnn::TensorInfo biasesDesc; - - unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; - unsigned int outputShape[] = { outputNum, outputChannels }; - unsigned int weightsShape[] = { inputChannels, outputChannels }; - if (transposeWeights) - { - std::swap(weightsShape[0], weightsShape[1]); - } - unsigned int biasShape[] = { outputChannels }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32); - weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32); - biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32); - - LayerTestResult result(outputTensorInfo); - - boost::multi_array input = MakeTensor(inputTensorInfo, std::vector( - { - 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, - - 5.0f, 4.0f, 3.0f, 2.0f, 1.0f - }) - ); - - boost::multi_array weights = MakeTensor(weightsDesc, std::vector( - { - .5f, 2.f, .5f, - .5f, 2.f, 1.f, - .5f, 2.f, 2.f, - .5f, 2.f, 3.f, - .5f, 2.f, 4.f - })); - - if (transposeWeights) - { - weights = MakeTensor(weightsDesc, std::vector( - { - .5f, .5f, .5f, .5f, .5f, - 2.f, 2.f, 2.f, 2.f, 2.f, - .5f, 1.f, 2.f, 3.f, 4.f - })); - } - - - std::vector biasValues({0.f, 0.f, 0.f}); - if (biasEnabled) - { - biasValues = std::vector({10.f, 20.f, 30.f}); - } - boost::multi_array bias = MakeTensor(biasesDesc, biasValues); - - result = SimpleFullyConnectedTestImpl( - workloadFactory, - inputTensorInfo, outputTensorInfo, - weightsDesc, biasesDesc, - weights, bias, input, - biasEnabled, transposeWeights - ); - - result.outputExpected = MakeTensor(outputTensorInfo, std::vector( - { - 0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0], - 2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1], - 0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2], - - 2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0], - 10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1], - 2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2] - }) - ); - - return result; -} - -LayerTestResult FullyConnectedUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) -{ - constexpr static unsigned int inputWidth = 3u; - constexpr static unsigned int inputHeight = 2u; - constexpr static unsigned int inputChannels = 1u; - - constexpr static unsigned int inputSize = inputWidth * inputHeight * inputChannels; - - constexpr static unsigned int outputChannels = 2u; - - armnn::TensorInfo inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(0.1f); - inputTensorInfo.SetQuantizationOffset(63); - - armnn::TensorInfo outputTensorInfo({ 1, outputChannels }, armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(5.f); - outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10); - - armnn::TensorInfo weightsDesc({ outputChannels, inputSize }, armnn::DataType::QuantisedAsymm8); - weightsDesc.SetQuantizationScale(0.2f); - weightsDesc.SetQuantizationOffset(93); - - armnn::TensorInfo biasesDesc({ outputChannels }, armnn::DataType::Signed32); - biasesDesc.SetQuantizationScale(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale()); - biasesDesc.SetQuantizationOffset(0); - - LayerTestResult result(outputTensorInfo); - - auto input = MakeTensor(inputTensorInfo, std::vector{51, 124, 28, - 251, 8, 92}); - - auto weights = MakeTensor(weightsDesc, std::vector{51, 193, 42, 53, 175, 34, - 210, 145, 23, 74, 34, 150}); - - // scale = 0.02 - // offset = 0 - auto bias = MakeTensor(biasesDesc, std::vector{9250, 67500}); - - result = SimpleFullyConnectedTestImpl( - workloadFactory, - inputTensorInfo, outputTensorInfo, - weightsDesc, biasesDesc, - weights, bias, input, - biasEnabled, true - ); - - // Manually calculated. - // Note one of these values has been clamped to 0. - if (biasEnabled) - { - result.outputExpected = MakeTensor(outputTensorInfo, std::vector{0, 242}); - } - else - { - result.outputExpected = MakeTensor(outputTensorInfo, std::vector{0, 32}); - } - - return result; -} - - - -// -// ArmNN variant of the AndroidNN fully_connected_float_large test. -// -// Tests the fully connected layer with large values, optionally transposing weights. -// Note this is templated for consistency, but the nature of this tests makes it unlikely to be useful in Uint8 mode. -// -template -LayerTestResult FullyConnectedLargeTestCommon(armnn::IWorkloadFactory& workloadFactory, - bool transposeWeights, - float qScale = 0.0f, - int32_t qOffset = 0) -{ - unsigned int inputWidth = 1; - unsigned int inputHeight = 1; - unsigned int inputChannels = 5; - unsigned int inputNum = 1; - - unsigned int outputChannels = 1; - unsigned int outputNum = 1; - - // Define the tensor descriptors. - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo weightsDesc; - armnn::TensorInfo biasesDesc; - - unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; - unsigned int outputShape[] = { outputNum, outputChannels }; - unsigned int weightsShape[] = { inputChannels, outputChannels }; - if (transposeWeights) - { - std::swap(weightsShape[0], weightsShape[1]); - } - - unsigned int biasShape[] = { outputChannels }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType()); - outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::GetDataType()); - weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::GetDataType()); - biasesDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - LayerTestResult result(outputTensorInfo); - - boost::multi_array input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f, - }) - ); - - boost::multi_array weights = MakeTensor(weightsDesc, - QuantizedVector(qScale, qOffset, { - 2.0f, 3.0f, 4.0f, 5.0f, 6.0f - }) - ); - - std::vector biasValues({900000.f}); - boost::multi_array bias = MakeTensor(biasesDesc, biasValues); - - result = SimpleFullyConnectedTestImpl( - workloadFactory, - inputTensorInfo, outputTensorInfo, - weightsDesc, biasesDesc, - weights, bias, input, - true, transposeWeights - ); - - result.outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 965432.0f, - }) - ); - - return result; -} diff --git a/src/armnn/backends/test/IsLayerSupportedTest.cpp b/src/armnn/backends/test/IsLayerSupportedTest.cpp deleted file mode 100644 index 97d3de5e38..0000000000 --- a/src/armnn/backends/test/IsLayerSupportedTest.cpp +++ /dev/null @@ -1,239 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include - -#include "test/TensorHelpers.hpp" -#include "LayerTests.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/RefWorkloadFactory.hpp" - -#include -#include -#include -#include - -#include "IsLayerSupportedTestImpl.hpp" -#include "ClContextControlFixture.hpp" - -#include "layers/ConvertFp16ToFp32Layer.hpp" -#include "layers/ConvertFp32ToFp16Layer.hpp" - -BOOST_AUTO_TEST_SUITE(IsLayerSupported) - -BOOST_AUTO_TEST_CASE(IsLayerSupportedLayerTypeMatches) -{ - LayerTypeMatchesTest(); -} - -BOOST_AUTO_TEST_CASE(IsLayerSupportedFloat16Reference) -{ - armnn::RefWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_AUTO_TEST_CASE(IsLayerSupportedFloat32Reference) -{ - armnn::RefWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_AUTO_TEST_CASE(IsLayerSupportedUint8Reference) -{ - armnn::RefWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_AUTO_TEST_CASE(IsConvertFp16ToFp32SupportedReference) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(result); -} - -BOOST_AUTO_TEST_CASE(IsConvertFp16ToFp32SupportedFp32InputReference) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(!result); - BOOST_CHECK_EQUAL(reasonIfUnsupported, "Layer is not supported with float32 data type input"); -} - -BOOST_AUTO_TEST_CASE(IsConvertFp16ToFp32SupportedFp16OutputReference) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(!result); - BOOST_CHECK_EQUAL(reasonIfUnsupported, "Layer is not supported with float16 data type output"); -} - -BOOST_AUTO_TEST_CASE(IsConvertFp32ToFp16SupportedReference) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(result); -} - -BOOST_AUTO_TEST_CASE(IsConvertFp32ToFp16SupportedFp16InputReference) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(!result); - BOOST_CHECK_EQUAL(reasonIfUnsupported, "Layer is not supported with float16 data type input"); -} - -BOOST_AUTO_TEST_CASE(IsConvertFp32ToFp16SupportedFp32OutputReference) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(!result); - BOOST_CHECK_EQUAL(reasonIfUnsupported, "Layer is not supported with float32 data type output"); -} - -#ifdef ARMCOMPUTENEON_ENABLED -BOOST_AUTO_TEST_CASE(IsLayerSupportedFloat16Neon) -{ - armnn::NeonWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_AUTO_TEST_CASE(IsLayerSupportedFloat32Neon) -{ - armnn::NeonWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_AUTO_TEST_CASE(IsLayerSupportedUint8Neon) -{ - armnn::NeonWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_AUTO_TEST_CASE(IsConvertFp16ToFp32SupportedNeon) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(result); -} - -BOOST_AUTO_TEST_CASE(IsConvertFp32ToFp16SupportedNeon) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(result); -} -#endif //#ifdef ARMCOMPUTENEON_ENABLED. - - -#ifdef ARMCOMPUTECL_ENABLED - -BOOST_FIXTURE_TEST_CASE(IsLayerSupportedFloat16Cl, ClContextControlFixture) -{ - armnn::ClWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_FIXTURE_TEST_CASE(IsLayerSupportedFloat32Cl, ClContextControlFixture) -{ - armnn::ClWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_FIXTURE_TEST_CASE(IsLayerSupportedUint8Cl, ClContextControlFixture) -{ - armnn::ClWorkloadFactory factory; - IsLayerSupportedTests(&factory); -} - -BOOST_FIXTURE_TEST_CASE(IsConvertFp16ToFp32SupportedCl, ClContextControlFixture) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(result); -} - -BOOST_FIXTURE_TEST_CASE(IsConvertFp16ToFp32SupportedFp32InputCl, ClContextControlFixture) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(!result); - BOOST_CHECK_EQUAL(reasonIfUnsupported, "Input should be Float16"); -} - -BOOST_FIXTURE_TEST_CASE(IsConvertFp16ToFp32SupportedFp16OutputCl, ClContextControlFixture) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(!result); - BOOST_CHECK_EQUAL(reasonIfUnsupported, "Output should be Float32"); -} - -BOOST_FIXTURE_TEST_CASE(IsConvertFp32ToFp16SupportedCl, ClContextControlFixture) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(result); -} - -BOOST_FIXTURE_TEST_CASE(IsConvertFp32ToFp16SupportedFp16InputCl, ClContextControlFixture) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(!result); - BOOST_CHECK_EQUAL(reasonIfUnsupported, "Input should be Float32"); -} - -BOOST_FIXTURE_TEST_CASE(IsConvertFp32ToFp16SupportedFp32OutputCl, ClContextControlFixture) -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests(reasonIfUnsupported); - - BOOST_CHECK(!result); - BOOST_CHECK_EQUAL(reasonIfUnsupported, "Output should be Float16"); -} -#endif //#ifdef ARMCOMPUTECL_ENABLED. - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/IsLayerSupportedTestImpl.hpp b/src/armnn/backends/test/IsLayerSupportedTestImpl.hpp deleted file mode 100644 index c5389df06e..0000000000 --- a/src/armnn/backends/test/IsLayerSupportedTestImpl.hpp +++ /dev/null @@ -1,565 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include "Graph.hpp" - -#include - -namespace -{ -armnn::Graph dummyGraph; - -// Make a dummy TensorInfo object. -template -armnn::TensorInfo MakeDummyTensorInfo() -{ - return armnn::TensorInfo({2,2,2,2}, DataType); -} - - -// Make a dummy WorkloadInfo using a dummy TensorInfo. -template -armnn::WorkloadInfo MakeDummyWorkloadInfo(unsigned int numInputs, unsigned int numOutputs) -{ - armnn::WorkloadInfo info; - for (unsigned int i=0; i < numInputs; i++) - { - info.m_InputTensorInfos.push_back(MakeDummyTensorInfo()); - } - for (unsigned int o=0; o < numOutputs; o++) - { - info.m_OutputTensorInfos.push_back(MakeDummyTensorInfo()); - } - return info; -} - -// Template class to create a dummy layer (2 parameters). -template -struct DummyLayer -{ - DummyLayer() - { - m_Layer = dummyGraph.AddLayer(DescType(), ""); - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - LayerType* m_Layer; -}; - -// Template class to create a dummy layer (1 parameter). -template -struct DummyLayer -{ - DummyLayer() - { - m_Layer = dummyGraph.AddLayer(""); - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - LayerType* m_Layer; -}; - -template<> -struct DummyLayer -{ - DummyLayer() - { - m_Layer = dummyGraph.AddLayer(armnn::BatchNormalizationDescriptor(), ""); - m_Layer->m_Mean = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_Variance = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_Beta = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_Gamma = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - armnn::BatchNormalizationLayer* m_Layer; - -}; - -template<> -struct DummyLayer -{ - DummyLayer() - { - m_Layer = dummyGraph.AddLayer(""); - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - armnn::ConstantLayer* m_Layer; -}; - -template<> -struct DummyLayer -{ - DummyLayer() - { - m_Layer = dummyGraph.AddLayer(armnn::LayerBindingId(), ""); - - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - armnn::InputLayer* m_Layer; -}; - -template<> -struct DummyLayer -{ - DummyLayer() - { - armnn::OriginsDescriptor desc(2); - m_Layer = dummyGraph.AddLayer(desc, ""); - - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - armnn::MergerLayer* m_Layer; -}; - -template<> -struct DummyLayer -{ - DummyLayer() - { - m_Layer = dummyGraph.AddLayer(armnn::LayerBindingId(), ""); - - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - armnn::OutputLayer* m_Layer; -}; - -template<> -struct DummyLayer -{ - DummyLayer() - { - armnn::ViewsDescriptor desc(1); - m_Layer = dummyGraph.AddLayer(desc, ""); - - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - armnn::SplitterLayer* m_Layer; -}; - -template -struct DummyConvolutionLayer -{ - DummyConvolutionLayer() - { - typename ConvolutionLayerType::DescriptorType desc; - m_Layer = dummyGraph.AddLayer(desc, ""); - m_Layer->m_Weight = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_Bias = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - } - ~DummyConvolutionLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - ConvolutionLayerType* m_Layer; -}; - -template<> -struct DummyLayer - : public DummyConvolutionLayer -{ -}; - -template<> -struct DummyLayer - : public DummyConvolutionLayer -{ -}; - -template -struct DummyLstmLayer -{ - DummyLstmLayer() - { - typename LstmLayerType::DescriptorType desc; - desc.m_CifgEnabled = false; - - m_Layer = dummyGraph.AddLayer(armnn::LstmDescriptor(), ""); - m_Layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_BasicParameters.m_InputToCellWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_BasicParameters.m_ForgetGateBias = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_BasicParameters.m_CellBias = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_BasicParameters.m_OutputGateBias = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - - m_Layer->m_CifgParameters.m_InputToInputWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_CifgParameters.m_CellToInputWeights = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - m_Layer->m_CifgParameters.m_InputGateBias = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - } - ~DummyLstmLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - armnn::LstmLayer* m_Layer; -}; - -template<> -struct DummyLayer - : public DummyLstmLayer -{ -}; - -template<> -struct DummyLayer -{ - DummyLayer() - { - armnn::FullyConnectedLayer::DescriptorType desc; - m_Layer = dummyGraph.AddLayer(desc, ""); - m_Layer->m_Weight = std::make_unique( - armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); - } - ~DummyLayer() - { - dummyGraph.EraseLayer(m_Layer); - } - armnn::FullyConnectedLayer* m_Layer; -}; - -// Tag for giving LayerType entries a unique strong type each. -template -struct Tag{}; - -#define DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, descType) \ -template \ -struct LayerTypePolicy \ -{ \ - using Type = armnn::name##Layer; \ - using Desc = descType; \ - using QueueDesc = armnn::name##QueueDescriptor; \ - constexpr static const char* NameStr = #name; \ - \ - static std::unique_ptr MakeDummyWorkload(armnn::IWorkloadFactory *factory, \ - unsigned int nIn, unsigned int nOut) \ - { \ - QueueDesc desc; \ - armnn::WorkloadInfo info = MakeDummyWorkloadInfo(nIn, nOut); \ - return factory->Create##name(desc, info); \ - } \ -}; - -// Define a layer policy specialization for use with the IsLayerSupported tests. -// Use this version for layers whose constructor takes 1 parameter(name). -#define DECLARE_LAYER_POLICY_1_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, void) - -// Define a layer policy specialization for use with the IsLayerSupported tests. -// Use this version for layers whose constructor takes 2 parameters(descriptor and name). -#define DECLARE_LAYER_POLICY_2_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, armnn::name##Descriptor) - -// Layer policy template. -template -struct LayerTypePolicy; - -// Every entry in the armnn::LayerType enum must be accounted for below. -DECLARE_LAYER_POLICY_2_PARAM(Activation) - -DECLARE_LAYER_POLICY_1_PARAM(Addition) - -DECLARE_LAYER_POLICY_2_PARAM(BatchNormalization) - -DECLARE_LAYER_POLICY_1_PARAM(Constant) - -DECLARE_LAYER_POLICY_1_PARAM(ConvertFp16ToFp32) - -DECLARE_LAYER_POLICY_1_PARAM(ConvertFp32ToFp16) - -DECLARE_LAYER_POLICY_2_PARAM(Convolution2d) - -DECLARE_LAYER_POLICY_1_PARAM(MemCopy) - -DECLARE_LAYER_POLICY_2_PARAM(DepthwiseConvolution2d) - -DECLARE_LAYER_POLICY_2_PARAM(FakeQuantization) - -DECLARE_LAYER_POLICY_1_PARAM(Floor) - -DECLARE_LAYER_POLICY_2_PARAM(FullyConnected) - -DECLARE_LAYER_POLICY_CUSTOM_PARAM(Input, armnn::LayerBindingId) - -DECLARE_LAYER_POLICY_1_PARAM(L2Normalization) - -DECLARE_LAYER_POLICY_2_PARAM(Lstm) - -DECLARE_LAYER_POLICY_2_PARAM(Mean) - -DECLARE_LAYER_POLICY_2_PARAM(Merger) - -DECLARE_LAYER_POLICY_1_PARAM(Multiplication) - -DECLARE_LAYER_POLICY_2_PARAM(Normalization) - -DECLARE_LAYER_POLICY_CUSTOM_PARAM(Output, armnn::LayerBindingId) - -DECLARE_LAYER_POLICY_2_PARAM(Permute) - -DECLARE_LAYER_POLICY_2_PARAM(Pooling2d) - -DECLARE_LAYER_POLICY_1_PARAM(Division) - -DECLARE_LAYER_POLICY_2_PARAM(ResizeBilinear) - -DECLARE_LAYER_POLICY_2_PARAM(Reshape) - -DECLARE_LAYER_POLICY_2_PARAM(Softmax) - -DECLARE_LAYER_POLICY_2_PARAM(Splitter) - -DECLARE_LAYER_POLICY_1_PARAM(Subtraction) - - -// Generic implementation to get the number of input slots for a given layer type; -template -unsigned int GetNumInputs(const armnn::Layer& layer) -{ - return layer.GetNumInputSlots(); -} - -// Generic implementation to get the number of output slots for a given layer type; -template -unsigned int GetNumOutputs(const armnn::Layer& layer) -{ - return layer.GetNumOutputSlots(); -} - -template<> -unsigned int GetNumInputs(const armnn::Layer& layer) -{ - boost::ignore_unused(layer); - return 2; -} - -// Tests that the IsLayerSupported() function returns the correct value. -// We determined the correct value by *trying* to create the relevant workload and seeing if it matches what we expect. -// Returns true if expectations are met, otherwise returns false. -template -bool IsLayerSupportedTest(FactoryType *factory, Tag) -{ - using LayerPolicy = LayerTypePolicy; - using LayerType = typename LayerPolicy::Type; - using LayerDesc = typename LayerPolicy::Desc; - DummyLayer layer; - - unsigned int numIn = GetNumInputs(*layer.m_Layer); - unsigned int numOut = GetNumOutputs(*layer.m_Layer); - - // Make another dummy layer just to make IsLayerSupported have valid inputs. - DummyLayer previousLayer; - // Set output of the previous layer to a dummy tensor. - armnn::TensorInfo output = MakeDummyTensorInfo(); - previousLayer.m_Layer->GetOutputSlot(0).SetTensorInfo(output); - // Connect all outputs of the previous layer to inputs of tested layer. - for (unsigned int i = 0; i < numIn; i++) - { - armnn::IOutputSlot& previousLayerOutputSlot = previousLayer.m_Layer->GetOutputSlot(0); - armnn::IInputSlot& layerInputSlot = layer.m_Layer->GetInputSlot(i); - previousLayerOutputSlot.Connect(layerInputSlot); - } - // Set outputs of tested layer to a dummy tensor. - for (unsigned int i = 0; i < numOut; i++) - { - layer.m_Layer->GetOutputSlot(0).SetTensorInfo(output); - } - - std::string layerName = LayerPolicy::NameStr; - std::string reasonIfUnsupported; - if (FactoryType::IsLayerSupported(*layer.m_Layer, DataType, reasonIfUnsupported)) - { - std::string errorMsg = " layer expected support but found none."; - try - { - bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() != nullptr; - // hacky way (it has to be replaced): for Lstm, we only support F32 right now -// BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg); - return retVal; - } - catch(const armnn::InvalidArgumentException& e) - { - boost::ignore_unused(e); - // This is ok since we throw InvalidArgumentException when creating the dummy workload. - return true; - } - catch(const std::exception& e) - { - errorMsg = e.what(); - BOOST_TEST_ERROR(layerName << ": " << errorMsg); - return false; - } - catch(...) - { - errorMsg = "Unexpected error while testing support for "; - BOOST_TEST_ERROR(errorMsg << layerName); - return false; - } - } - else - { - std::string errorMsg = "layer expected no support (giving reason: " + reasonIfUnsupported + ") but found some."; - try - { - bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() == nullptr; - BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg); - return retVal; - } - // These two exceptions are ok: For workloads that are partially supported, attempting to instantiate them - // using parameters that make IsLayerSupported() return false should throw an - // InvalidArgumentException or UnimplementedException. - catch(const armnn::InvalidArgumentException& e) - { - boost::ignore_unused(e); - return true; - } - catch(const armnn::UnimplementedException& e) - { - boost::ignore_unused(e); - return true; - } - catch(const std::exception& e) - { - errorMsg = e.what(); - BOOST_TEST_ERROR(layerName << ": " << errorMsg); - return false; - } - catch(...) - { - errorMsg = "Unexpected error while testing support for "; - BOOST_TEST_ERROR(errorMsg << layerName); - return false; - } - } -} - -// Helper function to compute the next type in the LayerType enum. -constexpr armnn::LayerType NextType(armnn::LayerType type) -{ - return static_cast(static_cast(type)+1); -} - -// Termination function for determining the end of the LayerType enumeration. -template -bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag) -{ - return IsLayerSupportedTest(factory, Tag()); -}; - -// Recursive function to test and enter in the LayerType enum and then iterate on the next entry. -template -bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag) -{ - bool v = IsLayerSupportedTest(factory, Tag()); - - return v && - IsLayerSupportedTestsImpl - (factory, Tag()); -}; - -// Helper function to pass through to the test framework. -template -bool IsLayerSupportedTests(FactoryType *factory) -{ - return IsLayerSupportedTestsImpl(factory, Tag()); -}; - -template -bool TestLayerTypeMatches() -{ - using LayerPolicy = LayerTypePolicy; - using LayerType = typename LayerPolicy::Type; - using LayerDesc = typename LayerPolicy::Desc; - DummyLayer layer; - - std::stringstream ss; - ss << LayerPolicy::NameStr << " layer type mismatches expected layer type value."; - bool v = Type == layer.m_Layer->GetType(); - BOOST_CHECK_MESSAGE(v, ss.str()); - return v; -}; - -template -bool LayerTypeMatchesTestImpl(Tag) -{ - return TestLayerTypeMatches(); -}; - -template -bool LayerTypeMatchesTestImpl(Tag) -{ - return TestLayerTypeMatches() && - LayerTypeMatchesTestImpl(Tag()); -}; - -bool LayerTypeMatchesTest() -{ - return LayerTypeMatchesTestImpl(Tag()); -}; - -template -bool IsConvertLayerSupportedTests(std::string& reasonIfUnsupported) -{ - armnn::Graph graph; - LayerType* const layer = graph.AddLayer("LayerName"); - - armnn::Layer* const input = graph.AddLayer(0, "input"); - armnn::Layer* const output = graph.AddLayer(0, "output"); - - armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, InputDataType); - armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, OutputDataType); - - input->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); - input->GetOutputHandler(0).SetTensorInfo(inputTensorInfo); - layer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - layer->GetOutputHandler(0).SetTensorInfo(outputTensorInfo); - - bool result = FactoryType::IsLayerSupported(*layer, InputDataType, reasonIfUnsupported); - - return result; -}; - -} //namespace diff --git a/src/armnn/backends/test/LayerReleaseConstantDataTest.cpp b/src/armnn/backends/test/LayerReleaseConstantDataTest.cpp deleted file mode 100644 index 7566c72352..0000000000 --- a/src/armnn/backends/test/LayerReleaseConstantDataTest.cpp +++ /dev/null @@ -1,212 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include -#include - -#include "backends/WorkloadData.hpp" -#include "Graph.hpp" - -#include - -#include "backends/CpuTensorHandle.hpp" -#include "backends/ClWorkloadFactory.hpp" - -using namespace armnn; -using namespace std; - -// connects two layers -void Connect(Layer* from, Layer* to, const TensorInfo& tensorInfo, unsigned int fromIndex = 0, unsigned int toIndex = 0) -{ - from->GetOutputSlot(fromIndex).Connect(to->GetInputSlot(toIndex)); - from->GetOutputHandler(fromIndex).SetTensorInfo(tensorInfo); -} - -///////////////////////////////////////////////////////////////////////////////////////////// -// The following test are created specifically to test ReleaseConstantData() method in the Layer -// They build very simple graphs including the layer will be checked. -// Checks weights and biases before the method called and after. -///////////////////////////////////////////////////////////////////////////////////////////// - -BOOST_AUTO_TEST_SUITE(LayerReleaseConstantDataTest) - -BOOST_AUTO_TEST_CASE(ReleaseBatchNormalizationLayerConstantDataTest) -{ - Graph graph; - ClWorkloadFactory factory; - - // create the layer we're testing - BatchNormalizationDescriptor layerDesc; - layerDesc.m_Eps = 0.05f; - BatchNormalizationLayer* const layer = graph.AddLayer(layerDesc, "layer"); - - armnn::TensorInfo weightInfo({3}, armnn::DataType::Float32); - layer->m_Mean = std::make_unique(weightInfo); - layer->m_Variance = std::make_unique(weightInfo); - layer->m_Beta = std::make_unique(weightInfo); - layer->m_Gamma = std::make_unique(weightInfo); - layer->m_Mean->Allocate(); - layer->m_Variance->Allocate(); - layer->m_Beta->Allocate(); - layer->m_Gamma->Allocate(); - - // create extra layers - Layer* const input = graph.AddLayer(0, "input"); - Layer* const output = graph.AddLayer(0, "output"); - - // connect up - armnn::TensorInfo tensorInfo({2, 3, 1, 1}, armnn::DataType::Float32); - Connect(input, layer, tensorInfo); - Connect(layer, output, tensorInfo); - - // check the constants that they are not NULL - BOOST_CHECK(layer->m_Mean != nullptr); - BOOST_CHECK(layer->m_Variance != nullptr); - BOOST_CHECK(layer->m_Beta != nullptr); - BOOST_CHECK(layer->m_Gamma != nullptr); - - // free up the constants.. - layer->ReleaseConstantData(); - - // check the constants that they are NULL now - BOOST_CHECK(layer->m_Mean == nullptr); - BOOST_CHECK(layer->m_Variance == nullptr); - BOOST_CHECK(layer->m_Beta == nullptr); - BOOST_CHECK(layer->m_Gamma == nullptr); - - } - - - BOOST_AUTO_TEST_CASE(ReleaseConvolution2dLayerConstantDataTest) - { - Graph graph; - ClWorkloadFactory factory; - - // create the layer we're testing - Convolution2dDescriptor layerDesc; - layerDesc.m_PadLeft = 3; - layerDesc.m_PadRight = 3; - layerDesc.m_PadTop = 1; - layerDesc.m_PadBottom = 1; - layerDesc.m_StrideX = 2; - layerDesc.m_StrideY = 4; - layerDesc.m_BiasEnabled = true; - - Convolution2dLayer* const layer = graph.AddLayer(layerDesc, "layer"); - - layer->m_Weight = std::make_unique(TensorInfo({2, 3, 5, 3}, - armnn::DataType::Float32)); - layer->m_Bias = std::make_unique - (TensorInfo({2}, GetBiasDataType(armnn::DataType::Float32))); - - layer->m_Weight->Allocate(); - layer->m_Bias->Allocate(); - - // create extra layers - Layer* const input = graph.AddLayer(0, "input"); - Layer* const output = graph.AddLayer(0, "output"); - - // connect up - Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32)); - Connect(layer, output, TensorInfo({2, 2, 2, 10}, armnn::DataType::Float32)); - - // check the constants that they are not NULL - BOOST_CHECK(layer->m_Weight != nullptr); - BOOST_CHECK(layer->m_Bias != nullptr); - - // free up the constants.. - layer->ReleaseConstantData(); - - // check the constants that they are NULL now - BOOST_CHECK(layer->m_Weight == nullptr); - BOOST_CHECK(layer->m_Bias == nullptr); -} - -BOOST_AUTO_TEST_CASE(ReleaseDepthwiseConvolution2dLayerConstantDataTest) -{ - Graph graph; - ClWorkloadFactory factory; - - // create the layer we're testing - DepthwiseConvolution2dDescriptor layerDesc; - layerDesc.m_PadLeft = 3; - layerDesc.m_PadRight = 3; - layerDesc.m_PadTop = 1; - layerDesc.m_PadBottom = 1; - layerDesc.m_StrideX = 2; - layerDesc.m_StrideY = 4; - layerDesc.m_BiasEnabled = true; - - DepthwiseConvolution2dLayer* const layer = graph.AddLayer(layerDesc, "layer"); - - layer->m_Weight = std::make_unique(TensorInfo({3, 3, 5, 3}, DataType::Float32)); - layer->m_Bias = std::make_unique(TensorInfo({9}, DataType::Float32)); - layer->m_Weight->Allocate(); - layer->m_Bias->Allocate(); - - // create extra layers - Layer* const input = graph.AddLayer(0, "input"); - Layer* const output = graph.AddLayer(0, "output"); - - // connect up - Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32)); - Connect(layer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32)); - - // check the constants that they are not NULL - BOOST_CHECK(layer->m_Weight != nullptr); - BOOST_CHECK(layer->m_Bias != nullptr); - - // free up the constants.. - layer->ReleaseConstantData(); - - // check the constants that they are NULL now - BOOST_CHECK(layer->m_Weight == nullptr); - BOOST_CHECK(layer->m_Bias == nullptr); -} - -BOOST_AUTO_TEST_CASE(ReleaseFullyConnectedLayerConstantDataTest) -{ - Graph graph; - ClWorkloadFactory factory; - - // create the layer we're testing - FullyConnectedDescriptor layerDesc; - layerDesc.m_BiasEnabled = true; - layerDesc.m_TransposeWeightMatrix = true; - - FullyConnectedLayer* const layer = graph.AddLayer(layerDesc, "layer"); - - float inputsQScale = 1.0f; - float outputQScale = 2.0f; - - layer->m_Weight = std::make_unique(TensorInfo({7, 20}, - DataType::QuantisedAsymm8, inputsQScale, 0)); - layer->m_Bias = std::make_unique(TensorInfo({7}, - GetBiasDataType(DataType::QuantisedAsymm8), inputsQScale)); - layer->m_Weight->Allocate(); - layer->m_Bias->Allocate(); - - // create extra layers - Layer* const input = graph.AddLayer(0, "input"); - Layer* const output = graph.AddLayer(0, "output"); - - // connect up - Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType::QuantisedAsymm8, inputsQScale)); - Connect(layer, output, TensorInfo({3, 7}, DataType::QuantisedAsymm8, outputQScale)); - - // check the constants that they are not NULL - BOOST_CHECK(layer->m_Weight != nullptr); - BOOST_CHECK(layer->m_Bias != nullptr); - - // free up the constants.. - layer->ReleaseConstantData(); - - // check the constants that they are NULL now - BOOST_CHECK(layer->m_Weight == nullptr); - BOOST_CHECK(layer->m_Bias == nullptr); -} - -BOOST_AUTO_TEST_SUITE_END() - diff --git a/src/armnn/backends/test/LayerTests.cpp b/src/armnn/backends/test/LayerTests.cpp deleted file mode 100644 index 4dcc36fdb2..0000000000 --- a/src/armnn/backends/test/LayerTests.cpp +++ /dev/null @@ -1,4750 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include "LayerTests.hpp" - -#include "test/TensorHelpers.hpp" -#include "TensorCopyUtils.hpp" -#include "Permute.hpp" - -#include -#include - -#include "armnn/LayerSupport.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -#ifdef ARMCOMPUTECL_ENABLED -#include "backends/ClTensorHandle.hpp" -#include "backends/ArmComputeTensorUtils.hpp" -#endif - -#include -#include - -#include "WorkloadTestUtils.hpp" -#include "Conv2dTestImpl.hpp" -#include "BatchNormTestImpl.hpp" -#include "ActivationTestImpl.hpp" -#include "Pooling2dTestImpl.hpp" -#include "ReshapeTestImpl.hpp" -#include "FullyConnectedTestImpl.hpp" -#include "SplitterTestImpl.hpp" -#include "SoftmaxTestImpl.hpp" -#include "NormTestImpl.hpp" -#include "PermuteTestImpl.hpp" -#include "LstmTestImpl.hpp" -#include "ConvertFp16ToFp32TestImpl.hpp" -#include "ConvertFp32ToFp16TestImpl.hpp" - -// 3-channel 16x8 image used as common input data for a number of Conv2d tests. -static std::vector ConvInput3x8x16({ - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 -}); - -// 2-channel bias used by a number of Conv2d tests. -static std::vector Bias2({0, 2}); - -// Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled. -template -boost::multi_array GetBias2(bool biasEnabled, float qScale, int32_t qOffset) -{ - if(biasEnabled) - { - armnn::TensorInfo biasDesc({static_cast(Bias2.size())}, armnn::GetDataType()); - boost::multi_array bias = MakeTensor(biasDesc, QuantizedVector(qScale, qOffset, Bias2)); - return bias; - } - else - { - return boost::multi_array(); - } -} - -template -LayerTestResult SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - // Use common single-batch 3-channel 16x8 image. - armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType()); - boost::multi_array input = MakeTensor(inputDesc, QuantizedVector(qScale, qOffset, ConvInput3x8x16)); - - // Use a 2-element batch with 3-channel 3x5 kernels. - armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType()); - boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( - QuantizedVector(qScale, qOffset, { - 1, 1, 1, - 1, -1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0 - }))); - - // Expected output is 2 batch elements of a 1-channel 14x4 image. - armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType()); - boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( - QuantizedVector(qScale, qOffset, { - -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, - -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, - -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, - -23.5f, -23.5f, -23.5f, - -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, - -23.5f, -23.5f, -23.5f, - - 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 - }))); - - return SimpleConvolution2dTestImpl(workloadFactory, - input, - kernel, - GetBias2::Type>(biasEnabled, qScale, qOffset), - expectedOutput, - qScale, - qOffset); -} - -template -LayerTestResult SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - // Use a 3x3 kernel, which exercises ArmCompute's direct convolution path. - - // Use common single-batch 3-channel 16x8 image. - armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType()); - boost::multi_array input = MakeTensor(inputDesc, QuantizedVector(qScale, qOffset, ConvInput3x8x16)); - - // Use a 2-element batch of 3-channel 3x3 kernels. - armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType()); - boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( - QuantizedVector(qScale, qOffset, { - 1, 1, 1, - 1, -1, 1, - 1, 1, 1, - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0 - }))); - - // Expected output is 1 batch of a 2-channel 14x6 image. - armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType()); - boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( - QuantizedVector(qScale, qOffset, { - -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, - -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, - -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, - -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, - -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, - -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, - - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 - }))); - - return SimpleConvolution2dTestImpl(workloadFactory, - input, - kernel, - GetBias2::Type>(biasEnabled, qScale, qOffset), - expectedOutput, - qScale, - qOffset); -} - -LayerTestResult SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return SimpleConvolution2d3x5TestCommon(workloadFactory, 0.f, 0, biasEnabled); -} - -LayerTestResult SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return SimpleConvolution2d3x5TestCommon(workloadFactory, 0.5f, 50, biasEnabled); -} - -LayerTestResult SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return SimpleConvolution2d3x3TestCommon(workloadFactory, 0.f, 0, biasEnabled); -} - -LayerTestResult SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return SimpleConvolution2d3x3TestCommon(workloadFactory, 0.5f, 50, biasEnabled); -} - -template -LayerTestResult Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon( - armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - // Use a single-batch 1-channel 3x3 image as input. - armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType()); - boost::multi_array input = MakeTensor(inputDesc, std::vector( - QuantizedVector(qScale, qOffset, { - 11,21,31, - 12,22,32, - 13,23,33 - }))); - - // Use 1 batch of a 1-channel 2x2 kernel. - armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType()); - boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( - QuantizedVector(qScale, qOffset, { - -11,-21, - -12,-22, - }))); - -// Expected output is 1 batch of a 1-channel 6x8 image. -// Manually calculated like this: -//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..] -//[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..] -//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..] -//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..] -//[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..] -//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..] -//[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..] - armnn::TensorInfo outputDesc({1, 1, 8, 6}, armnn::GetDataType()); - boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( - QuantizedVector(qScale, qOffset, { - 0, 0, 0, 0, 0, 0, - -242, -594, -934, -372, 0, 0, - -495, -1190, -1850, -725, 0, 0, - -538, -1256, -1916, -748, 0, 0, - -273, -626, -946, -363, 0, 0, - 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0 - }))); - - return SimpleConvolution2dTestImpl(workloadFactory, - input, - kernel, - GetBias2::Type>(false, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - 1, // Padding left. - 2, // Padding top. - 3, // Padding right. - 4); // Padding bottom. -} - -template -LayerTestResult SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - // Use a single-batch 1-channel 5x5 image as input. - armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType()); - boost::multi_array input = MakeTensor(inputDesc, std::vector( - QuantizedVector(qScale, qOffset, { - 11,21,31,41,51, - 12,22,32,42,52, - 13,23,33,43,53, - 14,24,34,44,54, - 15,25,35,45,55, - }))); - - // Use 1 batch of a 1-channel 4x4 kernel. - armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType()); - boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( - QuantizedVector(qScale, qOffset, { - -11,-21,-31,-41, - -12,-22,-32,-42, - -13,-23,-33,-43, - -14,-24,-34,-44, - }))); - - // Expected output is 1 batch of a 1-channel 5x5 image. - armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType()); - std::vector myVec(outputDesc.GetNumElements(), 0); - boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( - QuantizedVector(qScale, qOffset, { - -7140, -10580, -13940, -9300, -5230, - -9590, -14120, -18520, -12290, -6860, - -9980, -14560, -18960, -12560, -7000, - -7518, -10904, -14144, -9318, -5152, - -5032, -7256, -9376, -6142, -3368, - }))); - - return SimpleConvolution2dTestImpl(workloadFactory, - input, - kernel, - GetBias2::Type>(false, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - 1, // Padding left. - 1, // Padding top. - 2, // Padding right. - 2); // Padding bottom. -} - -template -LayerTestResult DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - // Use a single-batch 2-channel 5x5 image as input. - armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType()); - auto input = MakeTensor(inputTensorInfo, std::vector( - QuantizedVector(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 0, 1, 2, 3, 4, - 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, - 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, - - 25, 26, 27, 28, 29, - 30, 31, 32, 33, 34, - 35, 36, 37, 38, 39, - 40, 41, 42, 43, 44, - 45, 46, 47, 48, 49 - }))); - - // Use a depth multiplier of 1 on a 2-channel 4x4 kernel. - armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType()); - auto kernel = MakeTensor(kernelTensorInfo, std::vector( - QuantizedVector(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { - 32, 31, 30, 29, - 28, 27, 26, 25, - 24, 23, 22, 21, - 20, 19, 18, 17, - - 16, 15, 14, 13, - 12, 11, 10, 9, - 8, 7, 6, 5, - 4, 3, 2, 1 - }))); - - // Expected output is 1 batch of a 2-channel 5x5 image. - // Calculated using the python tensorflow library with strideX=1, strideY=1. - armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType()); - boost::multi_array expectedOutput = MakeTensor(outputTensorInfo, std::vector( - QuantizedVector(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { - 1062, 1580, 1850, 1530, 1117, - 2140, 3108, 3500, 2842, 2042, - 3580, 5068, 5460, 4342, 3062, - 3618, 5072, 5390, 4248, 2971, - 3074, 4282, 4510, 3533, 2457, - 1550, 2284, 2362, 1955, 1428, - 2910, 4206, 4342, 3528, 2536, - 3390, 4886, 5022, 4068, 2916, - 3566, 5056, 5182, 4133, 2922, - 3100, 4352, 4452, 3517, 2465 - }))); - - return DepthwiseConvolution2dAsymmetricTestImpl(workloadFactory, - input, - kernel, - GetBias2::Type>(biasEnabled, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - 1, // Padding left. - 1, // Padding top. - 2, // Padding right. - 2, // Padding bottom. - 1, // strideX - 1); // strideY -} - -LayerTestResult -Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(workloadFactory, 0.0f, 0); -} - -LayerTestResult Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleConvolution2dAsymmetricPaddingTestCommon(workloadFactory, 0.0f, 0); -} - -LayerTestResult DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dTestImpl(workloadFactory, 0.0f, 0, biasEnabled); -} - -LayerTestResult DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dDepthMul1TestImpl(workloadFactory, 0.0f, 0, biasEnabled); -} - -LayerTestResult DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dAsymmetricTestCommon(workloadFactory, 0.0f, 0, biasEnabled); -} - -LayerTestResult DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dTestImpl(workloadFactory, 0.5f, 50, biasEnabled); -} - -LayerTestResult DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dDepthMul1TestImpl(workloadFactory, 0.5f, 50, biasEnabled); -} - -LayerTestResult Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) -{ - return Convolution1dTestImpl(workloadFactory, 0.0f, 0, biasEnabled); -} - -LayerTestResult Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) -{ - return Convolution1dTestImpl(workloadFactory, 0.1f, 128, biasEnabled); -} - -LayerTestResult CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - return CompareConvolution2dTestImpl(workloadFactory, refWorkloadFactory); -} - -template -LayerTestResult CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - return CompareDepthwiseConvolution2dTestImpl(workloadFactory, refWorkloadFactory); -} - -template LayerTestResult CompareDepthwiseConvolution2dTest( - armnn::IWorkloadFactory&, armnn::IWorkloadFactory&); -template LayerTestResult CompareDepthwiseConvolution2dTest( - armnn::IWorkloadFactory&, armnn::IWorkloadFactory&); - -LayerTestResult SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory) -{ - auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; - auto normChannel = armnn::NormalizationAlgorithmChannel::Across; - return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); -} - -LayerTestResult SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory) -{ - auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; - auto normChannel = armnn::NormalizationAlgorithmChannel::Within; - return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); -} - -LayerTestResult SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta) -{ - return SimpleSoftmaxTestImpl(workloadFactory, beta); -} - -LayerTestResult SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta) -{ - return SimpleSoftmaxTestImpl(workloadFactory, beta); -} - -LayerTestResult CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::NormalizationAlgorithmChannel normChannel, - armnn::NormalizationAlgorithmMethod normMethod) -{ - return CompareNormalizationTestImpl(workloadFactory, refWorkloadFactory, normChannel, normMethod); -} - -LayerTestResult CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - float beta) -{ - return CompareSoftmaxTestImpl(workloadFactory, refWorkloadFactory, beta); -} - -LayerTestResult CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - float beta) -{ - return CompareSoftmaxTestImpl(workloadFactory, refWorkloadFactory, beta); -} - -std::vector> SplitterTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SplitterTestCommon(workloadFactory); -} - -std::vector> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SplitterTestCommon(workloadFactory, 1.0f, 0); -} - -LayerTestResult CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory) -{ - return CopyViaSplitterTestImpl(workloadFactory, 0.0f, 0); -} - -LayerTestResult CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return CopyViaSplitterTestImpl(workloadFactory, 1.0f, 0); -} - -LayerTestResult LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest( - armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType()); - boost::multi_array input = MakeTensor(inputDesc, std::vector( - { 2., 3., 3., 4. })); - - armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType()); - boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( - {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, - -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f})); - return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); -} - -LayerTestResult LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest( - armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType()); - boost::multi_array input = MakeTensor(inputDesc, std::vector( - {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, - 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f})); - - armnn::TensorInfo outputDesc({ 2, 16 }, armnn::GetDataType()); - boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( - {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, - -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, - -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, - 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, - -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, - 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, - 0.02168f})); - return LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(workloadFactory, input, expectedOutput); -} - -LayerTestResult LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType()); - boost::multi_array input = MakeTensor(inputDesc, std::vector( - {2., 3., 3., 4.})); - - - armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType()); - boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( - {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, - -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}})); - - return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); -} - -LayerTestResult MergerTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int outputWidth = 3; - unsigned int outputHeight = 6; - unsigned int outputChannels = 3; - - unsigned int inputWidth1 = 3; - unsigned int inputHeight1 = 6; - unsigned int inputChannels1 = 2; - - unsigned int inputWidth2 = 3; - unsigned int inputHeight2 = 6; - unsigned int inputChannels2 = 1; - - // Define the tensor descriptors. - armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32); - armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32); - armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32); - - LayerTestResult ret(outputTensorInfo); - - ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( - { - 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, - }) - ); - - auto input1 = MakeTensor(inputTensorInfo1, std::vector( - { - 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, - }) - ); - - auto input2 = MakeTensor(inputTensorInfo2, std::vector( - { - 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, - }) - ); - - std::vector wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. - armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); - - std::vector wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. - armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); - - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - - std::unique_ptr inputHandle1 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : - workloadFactory.CreateTensorHandle(inputTensorInfo1); - - std::unique_ptr inputHandle2 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : - workloadFactory.CreateTensorHandle(inputTensorInfo2); - - armnn::MergerQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_ViewOrigins.push_back(window1); - data.m_ViewOrigins.push_back(window2); - - std::unique_ptr workload = workloadFactory.CreateMerger(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult AdditionTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int batchSize = 2; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; - - armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape[] = {batchSize, channels, height, width}; - - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - - - auto input1 = MakeTensor(inputTensorInfo1, std::vector( - { - 0.0f, 2.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - - 1.0f, 2.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - - 0.0f, 2.0f, 1.0f, - 4.2f, 1.0f, 2.0f, - - 0.0f, 0.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - })); - - auto input2 = MakeTensor(inputTensorInfo2, std::vector( - { - 1.0f, 2.0f, 1.0f, - 0.0f, 1.0f, 2.0f, - - 1.0f, 2.0f, -2.0f, - 0.2f, 1.0f, 2.0f, - - 0.0f, 2.0f, 1.0f, - 4.2f, 0.0f, -3.0f, - - 0.0f, 0.0f, 1.0f, - 0.7f, 1.0f, 5.0f, - })); - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( - { - 1.0f, 4.0f, 2.0f, - 0.2f, 2.0f, 4.0f, - - 2.0f, 4.0f, -1.0f, - 0.4f, 2.0f, 4.0f, - - 0.0f, 4.0f, 2.0f, - 8.4f, 1.0f, -1.0f, - - 0.0f, 0.0f, 2.0f, - 0.9f, 2.0f, 7.0f, - })); - - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateAddition(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template -LayerTestResult AdditionBroadcastTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, armnn::GetDataType()); - armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); - - if (armnn::IsQuantizedType()) - { - inputTensorInfo1.SetQuantizationScale(qScale); - inputTensorInfo1.SetQuantizationOffset(qOffset); - inputTensorInfo2.SetQuantizationScale(qScale); - inputTensorInfo2.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input1 = MakeTensor(inputTensorInfo1, QuantizedVector(qScale, qOffset, - { - 0.0f, - 1.0f, - - 2.0f, - 3.0f, - - 4.0f, - 5.0f, - })); - - auto input2 = MakeTensor(inputTensorInfo2, QuantizedVector(qScale, qOffset, - { - 0.5f, 1.5f, 2.5f, - 3.5f, 4.5f, 5.5f, - })); - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, - { - 0.5f, 1.5f, 2.5f, - 4.5f, 5.5f, 6.5f, - - 2.5f, 3.5f, 4.5f, - 6.5f, 7.5f, 8.5f, - - 4.5f, 5.5f, 6.5f, - 8.5f, 9.5f, 10.5f, - })); - - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateAddition(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template -LayerTestResult AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); - armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); - - if (armnn::IsQuantizedType()) - { - inputTensorInfo1.SetQuantizationScale(qScale); - inputTensorInfo1.SetQuantizationOffset(qOffset); - inputTensorInfo2.SetQuantizationScale(qScale); - inputTensorInfo2.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input1 = MakeTensor(inputTensorInfo1, QuantizedVector(qScale, qOffset, - { - 0.0f, 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, - })); - - auto input2 = MakeTensor(inputTensorInfo2, QuantizedVector(qScale, qOffset, - { - 0.5f, - })); - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, - { - 0.5f, 1.5f, 2.5f, - 3.5f, 4.5f, 5.5f, - 6.5f, 7.5f, 8.5f, - 9.5f, 10.5f, 11.5f, - 12.5f, 13.5f, 14.5f, - 15.5f, 16.5f, 17.5f, - })); - - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateAddition(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) -{ - return AdditionBroadcastTestImpl(workloadFactory, 0.0f, 0); -} - -LayerTestResult AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return AdditionBroadcastTestImpl(workloadFactory, 2.f, 0); -} - -LayerTestResult AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) -{ - return AdditionBroadcast1ElementTestImpl(workloadFactory, 0.0f, 0); -} - -LayerTestResult AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return AdditionBroadcast1ElementTestImpl(workloadFactory, 0.1333333f, 128); -} - -LayerTestResult CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - unsigned int batchSize = 4; - unsigned int channels = 1; - unsigned int height = 2; - unsigned int width = 3; - - armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape[] = {batchSize, channels, height, width}; - - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - - auto input1 = MakeRandomTensor(inputTensorInfo1, 1232); - auto input2 = MakeRandomTensor(inputTensorInfo2, 456); - - LayerTestResult ret(outputTensorInfo); - - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::unique_ptr inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - armnn::AdditionQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get()); - SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr workload = workloadFactory.CreateAddition(data, info); - std::unique_ptr workloadRef = refWorkloadFactory.CreateAddition(refData, refInfo); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - inputHandle1Ref->Allocate(); - inputHandle2Ref->Allocate(); - outputHandleRef->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2Ref.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} - -namespace { -template -LayerTestResult DivisionTestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int shape0[4], - const std::vector& values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector & outValues, - float outScale, - int32_t outOffset) -{ - auto dataType = (std::is_same::value ? - armnn::DataType::QuantisedAsymm8 : - armnn::DataType::Float32); - - armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); - armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); - armnn::TensorInfo outputTensorInfo(4, outShape, dataType); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor(inputTensorInfo0, values0); - auto input1 = MakeTensor(inputTensorInfo1, values1); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, outValues); - - std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::DivisionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateDivision(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector input0({ - 1.f, 1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f, - -1.f, -1.f, -1.f, -1.f, 5.f, 5.f, 5.f, 5.f }); - - std::vector input1({ - 0.f, 0.f, -0.f, -0.f, 0.f, 0.f, -0.f, -0.f, - 0.f, 0.f, -0.f, -0.f, 5.f, 5.f, 5.f, 5.f }); - - std::vector output({ - INFINITY, INFINITY, -INFINITY, -INFINITY, NAN, NAN, -NAN, -NAN, - -INFINITY, -INFINITY, INFINITY, INFINITY, 1, 1, 1, 1 }); - - return DivisionTestHelper(workloadFactory, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 1.0f, 0); -} - -LayerTestResult DivisionTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector input0({ - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector input1({ - 1, 1, 1, 1, 2, 2, 2, 2, - 4, 4, 4, 4, 4, 4, 4, 4 }); - - std::vector output({ - 2, 2, 2, 2, 1.5, 1.5, 1.5, 1.5, - 1, 1, 1, 1, 1.25, 1.25, 1.25, 1.25 }); - - - return DivisionTestHelper(workloadFactory, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 1.0f, 0); -} - -LayerTestResult DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector input0({ 2, 4, 6, 8, 10, 12, 14, 16}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector input1({ 2 }); - - std::vector output({ 1, 2, 3, 4, 5, 6, 7, 8}); - - - return DivisionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector input0({ - 1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector input1({ 1, 2 }); - - std::vector output({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - return DivisionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - - -LayerTestResult DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector input0({2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector input1({1, 1, 1, 1, 2, 2, 2, 2, - 4, 4, 4, 4, 4, 4, 4, 4 }); - - std::vector output({8, 8, 8, 8, 6, 6, 6, 6, - 4, 4, 4, 4, 5, 5, 5, 5}); - - - return DivisionTestHelper(workloadFactory, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 0.25f, 0); -} - -LayerTestResult DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector input0({ 2, 4, 6, 8, 10, 12, 14, 16}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector input1({ 2 }); - - std::vector output({ 1, 2, 3, 4, 5, 6, 7, 8}); - - return DivisionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector input0({1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector input1({ 1, 2 }); - - std::vector output({1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - return DivisionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -namespace { -LayerTestResult MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int shape0[4], - const std::vector & values0, - const unsigned int shape1[4], - const std::vector & values1, - const unsigned int outShape[4], - const std::vector & outValues) -{ - const size_t dimensionCount = 4; - armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32}; - armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32}; - armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32}; - - auto input0 = MakeTensor(inputTensorInfo0, values0); - auto input1 = MakeTensor(inputTensorInfo1, values1); - - LayerTestResult ret(outputTensorInfo); - - std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateMultiplication(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - ret.outputExpected = MakeTensor(outputTensorInfo, outValues); - return ret; -} -} // anonymous namespace - - -LayerTestResult MultiplicationTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector input0({ - 1, 1, 1, 1, 2, 2, 2, 2, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector input1({ - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector output({ - 2, 2, 2, 2, 6, 6, 6, 6, - 12, 12, 12, 12, 20, 20, 20, 20 }); - - return MultiplicationTestHelper(workloadFactory, - shape, - input0, - shape, - input1, - shape, - output); -} - -LayerTestResult MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector input0({ 1, 2, 3, 4, 5, 6, 7, 8}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector input1({ 2 }); - - std::vector output({ 2, 4, 6, 8, 10, 12, 14, 16}); - - return MultiplicationTestHelper(workloadFactory, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector input1({ 1, 2 }); - - std::vector output({ - 1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - return MultiplicationTestHelper(workloadFactory, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - const unsigned int width = 16; - const unsigned int height = 32; - const unsigned int channelCount = 2; - const unsigned int batchSize = 5; - - armnn::TensorInfo inputTensorInfo0; - armnn::TensorInfo inputTensorInfo1; - armnn::TensorInfo outputTensorInfo; - - constexpr unsigned int shape[] = { batchSize, channelCount, height, width }; - - inputTensorInfo0 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - - LayerTestResult comparisonResult(outputTensorInfo); - - auto input0 = MakeRandomTensor(inputTensorInfo0, 803506992); - auto input1 = MakeRandomTensor(inputTensorInfo1, 54902257); - - std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::unique_ptr inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - armnn::MultiplicationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo0, inputHandle0Ref.get()); - SetWorkloadInput(refData, refInfo, 1, inputTensorInfo1, inputHandle1Ref.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr workload = workloadFactory.CreateMultiplication(data, info); - std::unique_ptr workloadRef = refWorkloadFactory.CreateMultiplication(refData, refInfo); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - inputHandle0Ref->Allocate(); - inputHandle1Ref->Allocate(); - outputHandleRef->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle0Ref.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); - - return comparisonResult; -} - -LayerTestResult CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 3; - const unsigned int channels = 5; - const unsigned int batchSize = 3; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo tensorInfo; - - constexpr unsigned int shape[] = {batchSize, channels, height, width}; - constexpr unsigned int tensorShape[] = {channels}; - - inputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - tensorInfo = armnn::TensorInfo(1, tensorShape, armnn::DataType::Float32); - - auto input = MakeRandomTensor(inputTensorInfo, 21312); - - auto mean = MakeRandomTensor(tensorInfo, 123); - auto variance = MakeRandomTensor(tensorInfo, 234, 0.0f); - auto beta = MakeRandomTensor(tensorInfo, 123); - auto gamma = MakeRandomTensor(tensorInfo, 345); - - LayerTestResult ret(outputTensorInfo); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::BatchNormalizationQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle meanTensor(tensorInfo); - armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo); - armnn::ScopedCpuTensorHandle betaTensor(tensorInfo); - armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo); - - AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]); - AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]); - AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]); - AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Mean = &meanTensor; - data.m_Variance = &varianceTensor; - data.m_Beta = &betaTensor; - data.m_Gamma = &gammaTensor; - data.m_Parameters.m_Eps = 0.01f; - - armnn::BatchNormalizationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr workload = workloadFactory.CreateBatchNormalization(data, info); - std::unique_ptr workloadRef = refWorkloadFactory.CreateBatchNormalization(refData, refInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - inputHandleRef->Allocate(); - outputHandleRef->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} - -template -void PermuteTensorData( - armnn::IWorkloadFactory& workloadFactory, - const armnn::PermutationVector& mappings, - armnn::TensorInfo & inputTensorInfo, - const T * inputData, - std::vector& outputData) -{ - BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null"); - if (inputData == nullptr) - { - // Nullptr is an error in the test. By returning without doing the concatenation - // I expect the caller to fail the test. It still makes sense to report this as - // an assert for Debug builds. - return; - } - - armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::PermuteQueueDescriptor queueDescriptor; - queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings}; - armnn::WorkloadInfo workloadInfo; - AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), inputData); - - workload->Execute(); - - outputData.resize(outputTensorInfo.GetNumElements()); - CopyDataFromITensorHandle(&outputData[0], outputHandle.get()); - inputTensorInfo = outputTensorInfo; -} - -armnn::OriginsDescriptor CreateMergerDescriptorForConcatenation( - const std::vector & inputTensorInfos, - unsigned int concatDim) -{ - std::vector shapes; - shapes.reserve(inputTensorInfos.size()); - for (const armnn::TensorInfo& it: inputTensorInfos) - { - shapes.push_back(it.GetShape()); - } - - return armnn::CreateMergerDescriptorForConcatenation(shapes.begin(), - shapes.end(), - concatDim); -} - -// -// Concatenation is only supported for N and C dimensions for NCHW. In case of -// <4 dimensions we need to make sure that the concat dimensions are at least -// the 3rd slowest iterating one. -// - -bool NeedPermuteForConcat( - const std::vector & inputTensorInfos, - unsigned int concatDim) -{ - // See note above. Additionally we expect the input shapes to have the - // same number of dimensions. - unsigned int nDimensions = 0; - - // Determine the number of dimensions as well as sanity check them - // agains test implementation issues. - for (auto && tensorInfo : inputTensorInfos) - { - if (!nDimensions) - { - nDimensions = tensorInfo.GetShape().GetNumDimensions(); - } - else - { - BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(), - "Input shapes must have the same number of dimensions"); - } - } - - return (nDimensions-concatDim) < 3; -} - -armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape) -{ - unsigned int numDims = inputShape.GetNumDimensions(); - if (numDims >= 3) - { - // Nothing to do if the inputShape has at least 3 dimensions. - return inputShape; - } - - std::vector newDims(size_t(3), 1u); - unsigned int expandedBy = 3 - numDims; - for (unsigned int i=0; i & permutations) -{ - BOOST_ASSERT_MSG(numDimensions <= 3, - "Only dimensions 1,2 and 3 are supported by this helper"); - - unsigned int expandedBy = 3 - numDimensions; - unsigned int expandedConcatAxis = concatDim + expandedBy; - - if (expandedConcatAxis == 2) - { - concatDim = 0; - armnn::PermutationVector forwardPermutation({1, 2, 0}); - armnn::PermutationVector reversePermutation({2, 0, 1}); - permutations = std::make_pair(forwardPermutation, reversePermutation); - } - else if (expandedConcatAxis == 1) - { - concatDim = 0; - armnn::PermutationVector forwardPermutation({2, 0, 1}); - armnn::PermutationVector reversePermutation({1, 2, 0}); - permutations = std::make_pair(forwardPermutation, reversePermutation); - } - else - { - BOOST_ASSERT(expandedConcatAxis == 0); - concatDim = 0; - } -} - -// -// Permute the input tensors so we can do a supported concatenation. -// Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions -// at the front. Finally this function tells what the output shape -// of the permuted concatenated tensor is going to be. -// -template -void PermuteInputsForConcat( - armnn::IWorkloadFactory& workloadFactory, - std::vector & inputTensorInfos, - std::vector & inputData, - std::vector> & inputDataStorage, - armnn::PermutationVector & permuteVector, - unsigned int & concatDim, - armnn::TensorInfo & outputTensorInfo) -{ - BOOST_ASSERT_MSG(inputTensorInfos.size() > 1, - "Expecting more than one tensor to be concatenated here"); - - unsigned int numDims = 0; - unsigned int nthInput = 0; - const armnn::PermutationVector identity({0, 1, 2}); - - std::pair permutations = - std::make_pair(identity, identity); - - inputDataStorage.resize(inputData.size()); - - for (auto && tensorInfo : inputTensorInfos) - { - if (numDims == 0) - { - numDims = tensorInfo.GetShape().GetNumDimensions(); - Generate3dPermuteVectorForConcat(numDims, concatDim, permutations); - // Store the reverese permutation. - permuteVector = permutations.second; - BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity), - "Test logic error, we don't need permutation, so we shouldn't arrive here"); - } - else - { - BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(), - "All inputs must have the same number of dimensions"); - } - - armnn::TensorInfo newTensorInfo = tensorInfo; - newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape())); - - PermuteTensorData(workloadFactory, - permutations.first, - newTensorInfo, - inputData[nthInput], - inputDataStorage[nthInput]); - - inputData[nthInput] = inputDataStorage[nthInput].data(); - inputTensorInfos[nthInput] = newTensorInfo; - - ++nthInput; - } - - outputTensorInfo.SetShape( - armnnUtils::Permuted( - ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()), - permutations.first)); -} - - -// -// This is the pair of PermuteInputsForConcat(...) which permutes back -// the output of the concatenation so we can check it against an expected -// output. -// -template -void PermuteOutputForConcat( - armnn::IWorkloadFactory& workloadFactory, - const armnn::TensorInfo & tensorInfo, - const armnn::PermutationVector & permuteVector, - std::unique_ptr && inputDataHandle, - T * data) -{ - BOOST_ASSERT_MSG(data != nullptr, "data must not be null"); - if (data == nullptr) - { - // Nullptr is an error in the test. By returning without doing the permutation - // I expect the caller to fail the test. It still makes sense to report this as - // an assert for Debug builds. - return; - } - - armnn::TensorInfo resultTensorInfo = tensorInfo; - std::vector inputData(tensorInfo.GetNumElements()); - std::vector outputData; - - CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get()); - - PermuteTensorData(workloadFactory, - permuteVector, - resultTensorInfo, - &inputData[0], - outputData); - - ::memcpy(data, &outputData[0], sizeof(T)*outputData.size()); -} - -template -void Concatenate(armnn::IWorkloadFactory& workloadFactory, - std::initializer_list inputTensorInfosOrig, - std::initializer_list inputsOrig, - const armnn::TensorInfo& outputTensorInfoOrig, - T * output, - unsigned int concatDim) -{ - BOOST_ASSERT_MSG(output != nullptr, "output must not be null"); - if (output == nullptr) - { - // Nullptr is an error in the test. By returning without doing the permutation - // I expect the caller to fail the test. It still makes sense to report this as - // an assert for Debug builds. - return; - } - - armnn::MergerQueueDescriptor queueDescriptor; - - // Saves a copy of the parameters which we might need to change. - std::vector inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); - std::vector inputs = inputsOrig; - armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig; - - armnn::PermutationVector permuteVector{0, 1, 2}; - - // Holds and automatically releases memory for the reshaped input data. - std::vector> tmpInputDataStorage; - - const size_t inputCount = inputTensorInfos.size(); - - bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim); - - if (needPermuteForConcat) - { - // - // We need to permute the inputs, because concatenation along - // the requested axis is not supported. - // - PermuteInputsForConcat(workloadFactory, - inputTensorInfos, - inputs, - tmpInputDataStorage, - permuteVector, - concatDim, - outputTensorInfo); - } - - armnn::OriginsDescriptor viewsDescriptor = CreateMergerDescriptorForConcatenation(inputTensorInfos, concatDim); - - queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews()); - for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i) - { - queueDescriptor.m_ViewOrigins.emplace_back(std::vector(viewsDescriptor.GetViewOrigin(i), - viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions())); - } - - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::vector> inputHandles; - inputHandles.reserve(inputCount); - - const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - for (unsigned int i = 0; i < inputCount; ++i) - { - const armnn::TensorInfo& inputTensorInfo = inputTensorInfos[i]; - - std::unique_ptr inputHandle = subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo.GetShape(), - queueDescriptor.m_ViewOrigins[i].m_Origin.data()) - : workloadFactory.CreateTensorHandle(inputTensorInfo); - - inputHandles.emplace_back(std::move(inputHandle)); - } - - armnn::WorkloadInfo workloadInfo; - - for (unsigned int i = 0; i < inputCount; ++i) - { - AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get()); - } - - AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateMerger(queueDescriptor, workloadInfo); - - for (auto& inputHandle : inputHandles) - { - inputHandle->Allocate(); - } - - outputHandle->Allocate(); - - unsigned int nextInputId = 0; - for (auto& inputHandle : inputHandles) - { - CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]); - ++nextInputId; - } - - workloadFactory.Finalize(); - workload->Execute(); - - if (needPermuteForConcat) - { - PermuteOutputForConcat(workloadFactory, - outputTensorInfo, - permuteVector, - std::move(outputHandle), - output); - } - else - { - CopyDataFromITensorHandle(output, outputHandle.get()); - } -} - -template -LayerTestResult Concatenation1dTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) -{ - armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType()); - - auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 1.0f, 2.0f, 3.0f })); - auto input1 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 4.0f, 5.0f, 6.0f })); - auto input2 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 7.0f, 8.0f, 9.0f })); - - armnn::TensorInfo outputTensorInfo({ 9 }, armnn::GetDataType()); - - LayerTestResult result(outputTensorInfo); - - std::vector output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate(workloadFactory, - { inputTensorInfo, inputTensorInfo, inputTensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 0); - - result.output = MakeTensor(outputTensorInfo, output); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f - })); - - return result; -} - -LayerTestResult Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation1dTestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - const armnn::TensorInfo& outputTensorInfo, - unsigned int dimension, - const float qScale, - const int32_t qOffset) -{ - armnn::TensorInfo inputTensorInfo({ 2, 3 }, armnn::GetDataType()); - - auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); - - auto input1 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, - - // Batch 1 - 13.0f, 14.0f, 15.0f, - })); - - auto input2 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 7.0f, 8.0f, 9.0f, - - // Batch 1 - 16.0f, 17.0f, 18.0f, - })); - - LayerTestResult result(outputTensorInfo); - - std::vector output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate(workloadFactory, - { inputTensorInfo, inputTensorInfo, inputTensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - dimension); - - result.output = MakeTensor(outputTensorInfo, output); - return result; -} - -template -LayerTestResult Concatenation2dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType()); - - LayerTestResult result = Concatenation2dTestImpl(workloadFactory, outputTensorInfo, 0, qScale, qOffset); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - - // Batch 2 - 4.0f, 5.0f, 6.0f, - - // Batch 3 - 13.0f, 14.0f, 15.0f, - - // Batch 4 - 7.0f, 8.0f, 9.0f, - - // Batch 5 - 16.0f, 17.0f, 18.0f, - })); - - return result; -} - -LayerTestResult Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim0TestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation2dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType()); - - LayerTestResult result = Concatenation2dTestImpl(workloadFactory, outputTensorInfo, 1, qScale, qOffset); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f - })); - - return result; -} - -LayerTestResult Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim1TestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation2dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType()); - auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); - - armnn::TensorInfo input1TensorInfo({ 3, 3 }, armnn::GetDataType()); - auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, - - // Batch 1 - 13.0f, 14.0f, 15.0f, - - // Batch 0 - 7.0f, 8.0f, 9.0f, - })); - - armnn::TensorInfo input2TensorInfo({ 1, 3 }, armnn::GetDataType()); - auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 1 - 16.0f, 17.0f, 18.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType()); - LayerTestResult result(outputTensorInfo); - - std::vector output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 0); - - result.output = MakeTensor(outputTensorInfo, output); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - - // Batch 2 - 4.0f, 5.0f, 6.0f, - - // Batch 3 - 13.0f, 14.0f, 15.0f, - - // Batch 4 - 7.0f, 8.0f, 9.0f, - - // Batch 5 - 16.0f, 17.0f, 18.0f, - })); - - return result; -} - -LayerTestResult Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim0DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation2dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType()); - auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); - - armnn::TensorInfo input1TensorInfo({ 2, 5 }, armnn::GetDataType()); - auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, - - // Batch 1 - 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, - })); - - armnn::TensorInfo input2TensorInfo({ 2, 1 }, armnn::GetDataType()); - auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 9.0f, - - // Batch 1 - 18.0f - })); - - armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType()); - LayerTestResult result(outputTensorInfo); - - std::vector output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 1); - - result.output = MakeTensor(outputTensorInfo, output); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, - })); - - return result; -} - -LayerTestResult Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim1DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation3dTestImpl(armnn::IWorkloadFactory& workloadFactory, - const armnn::TensorInfo& outputTensorInfo, - unsigned int dimension, - float qScale, - int32_t qOffset) -{ - armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType()); - - auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f - })); - - auto input1 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, 8.0f, - - // Batch 0, Channel 1 - 9.0f, 10.0f, - - // Batch 0, Channel 2 - 11.0f, 12.0f, - - // Batch 1, Channel 0 - 25.0f, 26.0f, - - // Batch 1, Channel 1 - 27.0f, 28.0f, - - // Batch 1, Channel 2 - 29.0f, 30.0f - })); - - auto input2 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 13.0f, 14.0f, - - // Batch 0, Channel 1 - 15.0f, 16.0f, - - // Batch 0, Channel 2 - 17.0f, 18.0f, - - // Batch 1, Channel 0 - 31.0f, 32.0f, - - // Batch 1, Channel 1 - 33.0f, 34.0f, - - // Batch 1, Channel 2 - 35.0f, 36.0f - })); - - LayerTestResult result(outputTensorInfo); - - std::vector output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate(workloadFactory, - { inputTensorInfo, inputTensorInfo, inputTensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - dimension); - - result.output = MakeTensor(outputTensorInfo, output); - return result; -} - -template -LayerTestResult Concatenation3dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType()); - - LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 0, - qScale, qOffset); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, - - // Batch 2, Channel 0 - 7.0f, 8.0f, - - // Batch 2, Channel 1 - 9.0f, 10.0f, - - // Batch 2, Channel 2 - 11.0f, 12.0f, - - // Batch 3, Channel 0 - 25.0f, 26.0f, - - // Batch 3, Channel 1 - 27.0f, 28.0f, - - // Batch 3, Channel 2 - 29.0f, 30.0f, - - // Batch 4, Channel 0 - 13.0f, 14.0f, - - // Batch 4, Channel 1 - 15.0f, 16.0f, - - // Batch 4, Channel 2 - 17.0f, 18.0f, - - // Batch 5, Channel 0 - 31.0f, 32.0f, - - // Batch 5, Channel 1 - 33.0f, 34.0f, - - // Batch 5, Channel 2 - 35.0f, 36.0f - })); - return result; -} - -LayerTestResult Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim0TestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation3dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType()); - - LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 1, qScale, qOffset); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 0, Channel 3 - 7.0f, 8.0f, - - // Batch 0, Channel 4 - 9.0f, 10.0f, - - // Batch 0, Channel 5 - 11.0f, 12.0f, - - // Batch 0, Channel 6 - 13.0f, 14.0f, - - // Batch 0, Channel 7 - 15.0f, 16.0f, - - // Batch 0, Channel 8 - 17.0f, 18.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, - - // Batch 1, Channel 3 - 25.0f, 26.0f, - - // Batch 1, Channel 4 - 27.0f, 28.0f, - - // Batch 1, Channel 5 - 29.0f, 30.0f, - - // Batch 1, Channel 6 - 31.0f, 32.0f, - - // Batch 1, Channel 7 - 33.0f, 34.0f, - - // Batch 1, Channel 8 - 35.0f, 36.0f - })); - - return result; -} - -LayerTestResult Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim1TestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation3dDim2TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType()); - - LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 2, qScale, qOffset); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f, - })); - - return result; -} - -LayerTestResult Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim2TestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation3dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); - auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f - })); - - armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, armnn::GetDataType()); - auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, 8.0f, - - // Batch 0, Channel 1 - 9.0f, 10.0f, - - // Batch 0, Channel 2 - 11.0f, 12.0f, - })); - - armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, armnn::GetDataType()); - auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 25.0f, 26.0f, - - // Batch 0, Channel 1 - 27.0f, 28.0f, - - // Batch 0, Channel 2 - 29.0f, 30.0f, - - // Batch 1, Channel 0 - 13.0f, 14.0f, - - // Batch 1, Channel 1 - 15.0f, 16.0f, - - // Batch 1, Channel 2 - 17.0f, 18.0f, - - // Batch 2, Channel 0 - 31.0f, 32.0f, - - // Batch 2, Channel 1 - 33.0f, 34.0f, - - // Batch 2, Channel 2 - 35.0f, 36.0f - })); - - armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType()); - LayerTestResult result(outputTensorInfo); - - std::vector output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 0); - - result.output = MakeTensor(outputTensorInfo, output); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, - - // Batch 2, Channel 0 - 7.0f, 8.0f, - - // Batch 2, Channel 1 - 9.0f, 10.0f, - - // Batch 2, Channel 2 - 11.0f, 12.0f, - - // Batch 3, Channel 0 - 25.0f, 26.0f, - - // Batch 3, Channel 1 - 27.0f, 28.0f, - - // Batch 3, Channel 2 - 29.0f, 30.0f, - - // Batch 4, Channel 0 - 13.0f, 14.0f, - - // Batch 4, Channel 1 - 15.0f, 16.0f, - - // Batch 4, Channel 2 - 17.0f, 18.0f, - - // Batch 5, Channel 0 - 31.0f, 32.0f, - - // Batch 5, Channel 1 - 33.0f, 34.0f, - - // Batch 5, Channel 2 - 35.0f, 36.0f - })); - - return result; -} - -LayerTestResult Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim0DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation3dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); - auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f - })); - - armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, armnn::GetDataType()); - auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, 8.0f, - - // Batch 0, Channel 1 - 9.0f, 10.0f, - - // Batch 0, Channel 2 - 11.0f, 12.0f, - - // Batch 0, Channel 3 - 25.0f, 26.0f, - - // Batch 1, Channel 0 - 27.0f, 28.0f, - - // Batch 1, Channel 1 - 29.0f, 30.0f, - - // Batch 1, Channel 2 - 13.0f, 14.0f, - - // Batch 1, Channel 3 - 15.0f, 16.0f, - })); - - armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, armnn::GetDataType()); - auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 17.0f, 18.0f, - - // Batch 1, Channel 0 - 31.0f, 32.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, armnn::GetDataType()); - LayerTestResult result(outputTensorInfo); - - std::vector output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 1); - - result.output = MakeTensor(outputTensorInfo, output); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 0, Channel 3 - 7.0f, 8.0f, - - // Batch 0, Channel 4 - 9.0f, 10.0f, - - // Batch 0, Channel 5 - 11.0f, 12.0f, - - // Batch 0, Channel 6 - 25.0f, 26.0f, - - // Batch 0, Channel 7 - 17.0f, 18.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, - - // Batch 1, Channel 3 - 27.0f, 28.0f, - - // Batch 1, Channel 4 - 29.0f, 30.0f, - - // Batch 1, Channel 5 - 13.0f, 14.0f, - - // Batch 1, Channel 6 - 15.0f, 16.0f, - - // Batch 1, Channel 7 - 31.0f, 32.0f, - })); - - return result; -} - -LayerTestResult Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim1DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); -} - -template -LayerTestResult Concatenation3dDim2DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); - auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f - })); - - armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, armnn::GetDataType()); - auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, - - // Batch 0, Channel 1 - 9.0f, - - // Batch 0, Channel 2 - 11.0f, - - // Batch 1, Channel 0 - 25.0f, - - // Batch 1, Channel 1 - 27.0f, - - // Batch 1, Channel 2 - 29.0f - })); - - armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, armnn::GetDataType()); - auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 13.0f, 14.0f, 50.0f, - - // Batch 0, Channel 1 - 15.0f, 16.0f, 51.0f, - - // Batch 0, Channel 2 - 17.0f, 18.0f, 52.0f, - - // Batch 1, Channel 0 - 31.0f, 32.0f, 53.0f, - - // Batch 1, Channel 1 - 33.0f, 34.0f, 54.0f, - - // Batch 1, Channel 2 - 35.0f, 36.0f, 55.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType()); - LayerTestResult result(outputTensorInfo); - - std::vector output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 2); - - result.output = MakeTensor(outputTensorInfo, output); - result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f, - })); - - return result; -} - -LayerTestResult Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim2DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); -} - -LayerTestResult ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 4; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = input; - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 2; - constexpr unsigned int inputHeight = 2; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth / 2; - constexpr unsigned int outputHeight = inputHeight / 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1.0f, 255.0f, - 200.0f, 250.f, - })); - - // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, - // then figures out the interpolants and weights. Note this is different to projecting the centre of the - // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value - // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting - // the centre). - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ - 1.0f - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 4; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth / 2; - constexpr unsigned int outputHeight = inputHeight / 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ - 1.f, 3.f, - 3.f, 5.f - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 5; - constexpr unsigned int inputHeight = 3; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = 3; - constexpr unsigned int outputHeight = 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1.0f, 2.0f, 3.0f, 5.0f, 8.0f, - 13.0f, 21.0f, 34.0f, 55.0f, 89.0f, - 144.0f, 233.0f, 377.0f, 610.0f, 987.0f - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ - 1.0f, 2.6666f, 6.0f, - 78.5f, 179.3333f, 401.f - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 2; - constexpr unsigned int inputHeight = 3; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = 5; - constexpr unsigned int outputHeight = 3; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1.0f, 2.0f, - 13.0f, 21.0f, - 144.0f, 233.0f - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ - 1.0f, 1.4f, 1.8f, 2.f, 2.f, - 13.f, 16.2f, 19.4f, 21.f, 21.f, - 144.f, 179.6f, 215.2f, 233.f, 233.f - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int width = 2; - constexpr unsigned int height = 3; - - const armnn::TensorInfo tensorInfo({height, width }, - armnn::DataType::Float32); - auto input = MakeTensor(tensorInfo, std::vector({ - -10.0f, -5.0f, - 0.0f, 5.0f, - 10.0f, 10.0f - })); - - LayerTestResult ret(tensorInfo); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(tensorInfo); - - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(tensorInfo); - - armnn::FakeQuantizationQueueDescriptor data; - armnn::WorkloadInfo info; - - AddInputToWorkload(data, info, tensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, tensorInfo, outputHandle.get()); - float min = -10.f; - float max = 10.f; - - data.m_Parameters.m_Min = min; - data.m_Parameters.m_Max = max; - - armnn::PassthroughCpuTensorHandle refHandle(tensorInfo, &ret.outputExpected[0][0]); - armnn::FakeQuantizationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadOutput(refData, refInfo, 0, tensorInfo, &refHandle); - - std::unique_ptr workload = workloadFactory.CreateFakeQuantization(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); - - ret.outputExpected = MakeTensor(tensorInfo, std::vector({ - 0.0f, 63.0f, - 128.0f, 191.0f, - 255.0f, 255.0f - })); - return ret; -} - -LayerTestResult L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 1; - constexpr unsigned int inputHeight = 1; - constexpr unsigned int inputChannels = 10; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f - })); - - const float approxInvL2Norm = 0.050964719f; - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(inputTensorInfo, std::vector({ - 1.0f * approxInvL2Norm, - 2.0f * approxInvL2Norm, - 3.0f * approxInvL2Norm, - 4.0f * approxInvL2Norm, - 5.0f * approxInvL2Norm, - 6.0f * approxInvL2Norm, - 7.0f * approxInvL2Norm, - 8.0f * approxInvL2Norm, - 9.0f * approxInvL2Norm, - 10.0f * approxInvL2Norm - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::L2NormalizationQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateL2Normalization(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -namespace -{ - -float CalcInvL2Norm(std::initializer_list elements) -{ - const float reduction = std::accumulate(elements.begin(), elements.end(), 0.0f, - [](float acc, float element) { return acc + element * element; }); - return 1.0f / sqrtf(reduction); -} - -} - -LayerTestResult L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 5; - constexpr unsigned int inputHeight = 1; - constexpr unsigned int inputChannels = 2; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1.0f, 3.0f, 5.0f, 7.0f, 9.0f, - 2.0f, 4.0f, 6.0f, 8.0f, 10.0f - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(inputTensorInfo, std::vector({ - 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }), - 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }), - 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }), - 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }), - 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }), - - 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }), - 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }), - 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }), - 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }), - 10.0f * CalcInvL2Norm({ 9.0f, 10.0f }) - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::L2NormalizationQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateL2Normalization(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 3; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 2; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - // Channel 0 - 119.0f, 21.0f, 150.0f, - 149.0f, 32.0f, 179.0f, - 15.0f, 227.0f, 141.0f, - 147.0f, 199.0f, 220.0f, - - // Channel 1 - 110.0f, 140.0f, 73.0f, - 211.0f, 212.0f, 89.0f, - 24.0f, 138.0f, 188.0f, - 162.0f, 12.0f, 161.0f, - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(inputTensorInfo, std::vector({ - 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }), - 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }), - 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }), - 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }), - 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }), - 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }), - 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }), - 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }), - 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }), - 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }), - 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }), - 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }), - - 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }), - 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }), - 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }), - 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }), - 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }), - 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }), - 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }), - 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }), - 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }), - 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }), - 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }), - 161.0f * CalcInvL2Norm({ 220.0f, 161.0f }), - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::L2NormalizationQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateL2Normalization(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 3; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 3; - constexpr unsigned int inputBatchSize = 2; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - // Batch 0, Channel 0 - 235.0f, 46.0f, 178.0f, - 100.0f, 123.0f, 19.0f, - 172.0f, 74.0f, 250.0f, - 6.0f, 195.0f, 80.0f, - - // Batch 0, Channel 1 - 113.0f, 95.0f, 202.0f, - 77.0f, 114.0f, 71.0f, - 122.0f, 246.0f, 166.0f, - 82.0f, 28.0f, 37.0f, - - // Batch 0, Channel 2 - 56.0f, 170.0f, 162.0f, - 194.0f, 89.0f, 254.0f, - 12.0f, 209.0f, 200.0f, - 1.0f, 64.0f, 54.0f, - - // Batch 1, Channel 0 - 67.0f, 90.0f, 49.0f, - 7.0f, 163.0f, 18.0f, - 25.0f, 117.0f, 103.0f, - 247.0f, 59.0f, 189.0f, - - // Batch 1, Channel 1 - 239.0f, 104.0f, 199.0f, - 17.0f, 124.0f, 153.0f, - 222.0f, 217.0f, 75.0f, - 32.0f, 126.0f, 21.0f, - - // Batch 1, Channel 2 - 97.0f, 145.0f, 215.0f, - 115.0f, 116.0f, 238.0f, - 226.0f, 16.0f, 132.0f, - 92.0f, 125.0f, 88.0f, - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(inputTensorInfo, std::vector({ - - // Batch 0, Channel 0 - 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), - 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), - 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), - 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), - 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), - 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), - 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), - 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), - 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), - 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), - 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), - 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), - - // Batch 0, Channel 1 - 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), - 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), - 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), - 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), - 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), - 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), - 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), - 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), - 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), - 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), - 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), - 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), - - // Batch 0, Channel 2 - 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), - 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), - 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), - 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), - 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), - 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), - 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), - 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), - 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), - 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), - 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), - 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), - - // Batch 1, Channel 0 - 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), - 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), - 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), - 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), - 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), - 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), - 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), - 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), - 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), - 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), - 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), - 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), - - // Batch 1, Channel 1 - 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), - 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), - 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), - 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), - 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), - 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), - 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), - 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), - 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), - 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), - 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), - 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), - - // Batch 1, Channel 2 - 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), - 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), - 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), - 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), - 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), - 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), - 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), - 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), - 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), - 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), - 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), - 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::L2NormalizationQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateL2Normalization(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -template -LayerTestResult ConstantTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - constexpr unsigned int inputWidth = 3; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 3; - constexpr unsigned int inputBatchSize = 2; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::GetDataType()); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, std::vector( - QuantizedVector(qScale, qOffset, { - // Batch 0, Channel 0 - 235.0f, 46.0f, 178.0f, - 100.0f, 123.0f, 19.0f, - 172.0f, 74.0f, 250.0f, - 6.0f, 195.0f, 80.0f, - - // Batch 0, Channel 1 - 113.0f, 95.0f, 202.0f, - 77.0f, 114.0f, 71.0f, - 122.0f, 246.0f, 166.0f, - 82.0f, 28.0f, 37.0f, - - // Batch 0, Channel 2 - 56.0f, 170.0f, 162.0f, - 194.0f, 89.0f, 254.0f, - 12.0f, 209.0f, 200.0f, - 1.0f, 64.0f, 54.0f, - - // Batch 1, Channel 0 - 67.0f, 90.0f, 49.0f, - 7.0f, 163.0f, 18.0f, - 25.0f, 117.0f, 103.0f, - 247.0f, 59.0f, 189.0f, - - // Batch 1, Channel 1 - 239.0f, 104.0f, 199.0f, - 17.0f, 124.0f, 153.0f, - 222.0f, 217.0f, 75.0f, - 32.0f, 126.0f, 21.0f, - - // Batch 1, Channel 2 - 97.0f, 145.0f, 215.0f, - 115.0f, 116.0f, 238.0f, - 226.0f, 16.0f, 132.0f, - 92.0f, 125.0f, 88.0f, - }))); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = input; - - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ScopedCpuTensorHandle constantTensor(inputTensorInfo); - AllocateAndCopyDataToITensorHandle(&constantTensor, &input[0][0][0][0]); - - armnn::ConstantQueueDescriptor descriptor; - descriptor.m_LayerOutput = &constantTensor; - - armnn::WorkloadInfo info; - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateConstant(descriptor, info); - - outputHandle->Allocate(); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult ConstantTest(armnn::IWorkloadFactory& workloadFactory) -{ - return ConstantTestImpl(workloadFactory, 0.0f, 0); -} - -LayerTestResult ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory) -{ - return ConstantTestImpl(workloadFactory, 1.0f, 0); -} - -LayerTestResult MergerUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int outputWidth = 3; - unsigned int outputHeight = 6; - unsigned int outputChannels = 3; - - unsigned int inputWidth1 = 3; - unsigned int inputHeight1 = 6; - unsigned int inputChannels1 = 2; - - unsigned int inputWidth2 = 3; - unsigned int inputHeight2 = 6; - unsigned int inputChannels2 = 1; - - // Defines the tensor descriptors. - armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8); - - // Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize them. - const float scale = 0.13497836f; - const int32_t offset = -7; - - outputTensorInfo.SetQuantizationScale(scale); - outputTensorInfo.SetQuantizationOffset(offset); - inputTensorInfo1.SetQuantizationScale(scale); - inputTensorInfo1.SetQuantizationOffset(offset); - inputTensorInfo2.SetQuantizationScale(scale); - inputTensorInfo2.SetQuantizationOffset(offset); - - LayerTestResult ret(outputTensorInfo); - - ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( - { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9, - 10, 11, 12, - 13, 14, 15, - 16, 17, 18, - - 19, 20, 21, - 22, 23, 24, - 25, 26, 27, - 28, 29, 30, - 31, 32, 33, - 34, 35, 36, - - 37, 38, 39, - 40, 41, 42, - 43, 44, 45, - 46, 47, 48, - 49, 50, 51, - 52, 53, 54, - }) - ); - - auto input1 = MakeTensor(inputTensorInfo1, std::vector( - { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9, - 10, 11, 12, - 13, 14, 15, - 16, 17, 18, - - 19, 20, 21, - 22, 23, 24, - 25, 26, 27, - 28, 29, 30, - 31, 32, 33, - 34, 35, 36, - }) - ); - - auto input2 = MakeTensor(inputTensorInfo2, std::vector( - { - 37, 38, 39, - 40, 41, 42, - 43, 44, 45, - 46, 47, 48, - 49, 50, 51, - 52, 53, 54, - }) - ); - - std::vector wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. - armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); - - std::vector wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. - armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); - - - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - - std::unique_ptr inputHandle1 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : - workloadFactory.CreateTensorHandle(inputTensorInfo1); - - std::unique_ptr inputHandle2 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : - workloadFactory.CreateTensorHandle(inputTensorInfo2); - - - armnn::MergerQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_ViewOrigins.push_back(window1); - data.m_ViewOrigins.push_back(window2); - - std::unique_ptr workload = workloadFactory.CreateMerger(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int batchSize = 1; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; - - const float scale = 7.0f; - const int32_t offset = 3; - - armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; - armnn::TensorInfo outputTensorInfo; - - const unsigned int shape[] = { batchSize, channels, height, width }; - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - inputTensorInfo1.SetQuantizationScale(scale); - inputTensorInfo1.SetQuantizationOffset(offset); - - inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - inputTensorInfo2.SetQuantizationScale(scale); - inputTensorInfo2.SetQuantizationOffset(offset); - - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(scale); - outputTensorInfo.SetQuantizationOffset(offset); - - // See dequantized values to the right. - auto input1 = MakeTensor(inputTensorInfo1, std::vector( - { - 63, 35, 77, 70, 56, 112, // 420, 224, 518, 469, 371, 763 - 203, 28, 252, 168, 245, 91 // 1400, 175, 1743, 1155, 1694, 616 - })); - - // See dequantized values to the right. - auto input2 = MakeTensor(inputTensorInfo1, std::vector( - { - 21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449 - 126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861 - })); - - // See dequantized values to the right. - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector( - { - 81, 39, 249, 255, 228, 255, // 546, 252, 1722, 2065(clamped), 1575, 2212(clamped) - 255, 186, 255, 186, 255, 214, // 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477 - })); - - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateAddition(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} - -namespace -{ -LayerTestResult MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int shape0[4], - const std::vector & values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector & outValues, - float outScale, - int32_t outOffset) -{ - armnn::TensorInfo inputTensorInfo0(4, shape0, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo inputTensorInfo1(4, shape1, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo outputTensorInfo(4, outShape, armnn::DataType::QuantisedAsymm8); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor(inputTensorInfo0, values0); - auto input1 = MakeTensor(inputTensorInfo1, values1); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, outValues); - - std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateMultiplication(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int batchSize = 1; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; - const unsigned int shape[] = { batchSize, channels, height, width }; - - // See dequantized values to the right. - std::vector input0({ - 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440, - 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120 - }); - - // See dequantized values to the right. - std::vector input1({ - 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747, - 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297 - }); - - // See dequantized values to the right. - std::vector output( - { - 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680, - 77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640 - }); - - return MultiplicationUint8TestHelper(workloadFactory, - shape, - input0, - 4.0f, - 1, - shape, - input1, - 3.0f, - -2, - shape, - output, - 1366.255f, // Scale/offset chosen to have output values out of range. - -5); -} - -LayerTestResult MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector input1({2}); - - std::vector output({ - 2, 4, 6, 8, 10, 12, - 14, 16, 18, 20, 22, 24 - }); - - return MultiplicationUint8TestHelper(workloadFactory, - shape0, - input0, - 1.0f, - 0, - shape1, - input1, - 1.0f, - 0, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector input1({1, 2, 3}); - - std::vector output({ - 1, 4, 9, 4, 10, 18, - 7, 16, 27, 10, 22, 36 - }); - - return MultiplicationUint8TestHelper(workloadFactory, - shape0, - input0, - 1.0f, - 0, - shape1, - input1, - 1.0f, - 0, - shape0, - output, - 1.0f, - 0); -} - -namespace -{ -template -LayerTestResult SubtractionTestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int shape0[4], - const std::vector& values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector & outValues, - float outScale, - int32_t outOffset) -{ - auto dataType = (std::is_same::value ? - armnn::DataType::QuantisedAsymm8 : - armnn::DataType::Float32); - - armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); - armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); - armnn::TensorInfo outputTensorInfo(4, outShape, dataType); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor(inputTensorInfo0, values0); - auto input1 = MakeTensor(inputTensorInfo1, values1); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, outValues); - - std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::SubtractionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateSubtraction(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 2 }; - - std::vector input0({ 10, 12, 14, 16 }); - std::vector input1({ 1, 2, 1, 2 }); - std::vector output({ 3, 3, 5, 5 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 0.5f, 2, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector input0({ 10, 12, 14, 16 }); - std::vector input1({ 2 }); - std::vector output({ 5, 6, 7, 8 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 0.5f, 2, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 3); -} - -LayerTestResult SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 1 }; - - std::vector input0({ 10, 12, 14, 16 }); - std::vector input1({ 2, 1 }); - std::vector output({ 8, 11, 12, 15 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult SubtractionTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 2 }; - - std::vector input0({ 1, 2, 3, 4 }); - std::vector input1({ 1, -1, 0, 2 }); - std::vector output({ 0, 3, 3, 2 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector input0({ 1, 2, 3, 4 }); - std::vector input1({ 10 }); - std::vector output({ -9, -8, -7, -6 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 2 }; - - std::vector input0({ 1, 2, 3, 4 }); - std::vector input1({ 10, -5 }); - std::vector output({ -9, 7, -7, 9 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 4; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(1.5f); - inputTensorInfo.SetQuantizationOffset(-3); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(1.5f); - outputTensorInfo.SetQuantizationOffset(-3); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1, 2, 3, 4, - 2, 3, 4, 5, - 3, 4, 5, 6, - 4, 5, 6, 7 - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = input; - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 2; - constexpr unsigned int inputHeight = 2; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth / 2; - constexpr unsigned int outputHeight = inputHeight / 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(0.1567f); - inputTensorInfo.SetQuantizationOffset(1); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(0.1567f); - outputTensorInfo.SetQuantizationOffset(1); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1, 255, - 200, 250 - })); - - // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, - // then figures out the interpolants and weights. Note this is different to projecting the centre of the - // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value - // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting - // the centre). - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ - 1 - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 4; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth / 2; - constexpr unsigned int outputHeight = inputHeight / 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(3.141592f); - inputTensorInfo.SetQuantizationOffset(3); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(3.141592f); - outputTensorInfo.SetQuantizationOffset(3); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1, 2, 3, 4, - 2, 3, 4, 5, - 3, 4, 5, 6, - 4, 5, 6, 7 - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ - 1, 3, - 3, 5 - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 3; - constexpr unsigned int inputHeight = 2; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = 2; - constexpr unsigned int outputHeight = 1; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(1.5f); - inputTensorInfo.SetQuantizationOffset(-1); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(1.5f); - outputTensorInfo.SetQuantizationOffset(-1); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 1, 2, 3, // 3.0, 4.5, 6.0 - 5, 8, 13 // 9.0, 13.5, 21.0 - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ - 1, 3 // 3.0, 5.25 - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 2; - constexpr unsigned int inputHeight = 3; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = 5; - constexpr unsigned int outputHeight = 3; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(0.010765f); - inputTensorInfo.SetQuantizationOffset(7); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(0.010132f); - outputTensorInfo.SetQuantizationOffset(-18); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - 24, 228, // 0.183005, 2.379065, - 105, 128, // 1.05497, 1.302565 - 230, 71 // 2.400595, 0.68896 - })); - - LayerTestResult result(outputTensorInfo); - result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ - 0, 87, 173, 217, 217, // 0.18300501, 1.06142902, 1.93985295, 2.37906504, 2.37906504 - 86, 96, 106, 111, 111, // 1.05497003, 1.15400803, 1.25304604, 1.30256498, 1.30256498 - 219, 151, 84, 50, 50 // 2.40059495, 1.71594095, 1.03128707, 0.68896002, 0.68896002 - })); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult BatchNormTest(armnn::IWorkloadFactory& workloadFactory) -{ - auto ret = BatchNormTestImpl(workloadFactory, 0.f, 0); - return ret; -} - -LayerTestResult BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - auto ret = BatchNormTestImpl(workloadFactory, 1.f/20.f, 50); - return ret; -} - -LayerTestResult ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return ConstantTestImpl(workloadFactory, 2e-6f, 1); -} - -LayerTestResult Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation1dTestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim0TestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim1TestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim0DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim1DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim0TestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim1TestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim2TestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim0TestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim1DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim2DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); -} - -LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return SimpleMaxPooling2dSize2x2Stride2x2TestCommon(workloadFactory, forceNoPadding); -} - -LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return SimpleMaxPooling2dSize2x2Stride2x2TestCommon(workloadFactory, forceNoPadding, 3.0f, -5); -} - -LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return SimpleMaxPooling2dSize3x3Stride2x4TestCommon(workloadFactory, forceNoPadding); -} - -LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return SimpleMaxPooling2dSize3x3Stride2x4TestCommon(workloadFactory, forceNoPadding, 0.1f, 128); -} - -LayerTestResult SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleAveragePooling2dTestCommon(workloadFactory); -} - -LayerTestResult SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleAveragePooling2dTestCommon(workloadFactory, 0.5, -1); -} - -LayerTestResult IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon(workloadFactory, forceNoPadding); -} - -LayerTestResult LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return LargeTensorsAveragePooling2dTestCommon(workloadFactory); -} - -LayerTestResult LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return LargeTensorsAveragePooling2dTestCommon(workloadFactory, 0.5, -1); -} - -LayerTestResult SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleL2Pooling2dTestCommon(workloadFactory); -} - -LayerTestResult SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleL2Pooling2dTestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride1TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride1TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride3TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride3TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride4TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride4TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize7TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize7TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize9TestCommon(workloadFactory); -} - -LayerTestResult L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize9TestCommon(workloadFactory); -} - -LayerTestResult AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return AsymmetricNonSquarePooling2dTestCommon(workloadFactory); -} - -LayerTestResult AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return AsymmetricNonSquarePooling2dTestCommon(workloadFactory); -} - -LayerTestResult ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::PoolingAlgorithm poolingType) -{ - return ComparePooling2dTestCommon(workloadFactory, refWorkloadFactory, poolingType); -} - -LayerTestResult ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::PoolingAlgorithm poolingType) -{ - return ComparePooling2dTestCommon(workloadFactory, refWorkloadFactory, poolingType, 0.1f, 128); -} - -LayerTestResult FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory, - bool transposeWeights) -{ - return FullyConnectedLargeTestCommon(workloadFactory, transposeWeights); -} - -LayerTestResult IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleMaxPooling2dTestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleMaxPooling2dTestCommon(workloadFactory, 1.0f, -5); -} - -LayerTestResult IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingMaxPooling2dSize3TestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingMaxPooling2dSize3TestCommon(workloadFactory, 1.0f, -5); -} - -LayerTestResult IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleAveragePooling2dTestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleAveragePooling2dTestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test( - armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingAveragePooling2dSize3TestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingAveragePooling2dSize3TestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleL2Pooling2dTestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleL2Pooling2dTestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingL2Pooling2dSize3TestCommon(workloadFactory); -} - -LayerTestResult IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingL2Pooling2dSize3TestCommon(workloadFactory); -} - -LayerTestResult SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimplePermuteFloat32TestCommon(workloadFactory); -}; - -LayerTestResult SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimplePermuteUint8TestCommon(workloadFactory); -}; - -LayerTestResult PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory) -{ - return PermuteFloat32ValueSet1TestCommon(workloadFactory); -}; - -LayerTestResult PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory) -{ - return PermuteFloat32ValueSet2TestCommon(workloadFactory); -}; - -LayerTestResult PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return PermuteFloat32ValueSet3TestCommon(workloadFactory); -}; \ No newline at end of file diff --git a/src/armnn/backends/test/LayerTests.hpp b/src/armnn/backends/test/LayerTests.hpp deleted file mode 100644 index 365a1f53d4..0000000000 --- a/src/armnn/backends/test/LayerTests.hpp +++ /dev/null @@ -1,345 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include "armnn/ArmNN.hpp" -#include "armnn/Tensor.hpp" -#include "Half.hpp" - -#include -#include -#include - -// Layer callables. - -namespace armnn -{ -class IWorkloadFactory; -} - -template -boost::array GetTensorShapeAsArray(const armnn::TensorInfo& tensorInfo) -{ - BOOST_ASSERT_MSG(n == tensorInfo.GetNumDimensions(), - "Attempting to construct a shape array of mismatching size"); - - boost::array shape; - for (unsigned int i = 0; i < n; i++) - { - shape[i] = tensorInfo.GetShape()[i]; - } - return shape; -} - -template -struct LayerTestResult -{ - LayerTestResult(const armnn::TensorInfo& outputInfo) - { - auto shape( GetTensorShapeAsArray(outputInfo) ); - output.resize(shape); - outputExpected.resize(shape); - supported = true; - } - - boost::multi_array output; - boost::multi_array outputExpected; - bool supported; -}; - -LayerTestResult SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled); - -LayerTestResult SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled); - -LayerTestResult -Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory); - - -LayerTestResult Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled); -LayerTestResult Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled); - -LayerTestResult DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled); - -LayerTestResult DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled); - -LayerTestResult DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled); - -LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding); -LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding); -LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding); -LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding ); -LayerTestResult IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding); -LayerTestResult IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test( - armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::PoolingAlgorithm poolingType); -LayerTestResult ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::PoolingAlgorithm poolingType); - -LayerTestResult ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta); -LayerTestResult SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta); - -LayerTestResult SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleReshapeFloat32Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SimpleReshapeUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleFloorTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory); - -template -LayerTestResult CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory); - -LayerTestResult CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::NormalizationAlgorithmChannel normChannel, - armnn::NormalizationAlgorithmMethod normMethod); - -LayerTestResult CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, float beta); - -LayerTestResult FullyConnectedFloat32Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled, - bool transposeWeights); - -std::vector> SplitterTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult MergerTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult AdditionTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory); - -LayerTestResult SubtractionTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult CompareActivationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::ActivationFunction f, - unsigned int batchSize); - -LayerTestResult DivisionTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult MultiplicationTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory); - -LayerTestResult BatchNormTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory); - -LayerTestResult BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - float upperBound, - float lowerBound); - -// Tests that the output should be identical to the input when the output dimensions match the input ones. -LayerTestResult ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory); - -// Tests the behaviour of the resize bilinear operation when rescaling a 2x2 image into a 1x1 image. -LayerTestResult SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory); - -// Tests the resize bilinear for minification of a square input matrix (also: input dimensions are a -// multiple of output dimensions). -LayerTestResult ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory); - -// Tests the resize bilinear for minification (output dimensions smaller than input dimensions). -LayerTestResult ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory); - -// Tests the resize bilinear for magnification (output dimensions bigger than input dimensions). -LayerTestResult ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult BatchNormTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult ConstantTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult BoundedReLuUint8Test(armnn::IWorkloadFactory& workloadFactory, float upperBound); -LayerTestResult BoundedReLuUint8Test(armnn::IWorkloadFactory& workloadFactory, - float upperBound, - float lowerBound); - -LayerTestResult FullyConnectedUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled); - -std::vector> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult MergerUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::ActivationFunction f); - -LayerTestResult CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - float beta); - -LayerTestResult MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled); - -LayerTestResult SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled); - -LayerTestResult DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled); - -LayerTestResult DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled); - -LayerTestResult ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); - - -LayerTestResult FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory, - bool transposeWeights); -LayerTestResult SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest - (armnn::IWorkloadFactory& workloadFactory); -LayerTestResult - LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult -LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory& workloadFactory); - -LayerTestResult SimpleConvertFp16ToFp32Test(armnn::IWorkloadFactory& workloadFactory); -LayerTestResult SimpleConvertFp32ToFp16Test(armnn::IWorkloadFactory& workloadFactory); diff --git a/src/armnn/backends/test/LstmTestImpl.hpp b/src/armnn/backends/test/LstmTestImpl.hpp deleted file mode 100644 index 2c4e166084..0000000000 --- a/src/armnn/backends/test/LstmTestImpl.hpp +++ /dev/null @@ -1,1150 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include - -#include "test/TensorHelpers.hpp" -#include "QuantizeHelper.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include -#include "backends/WorkloadFactory.hpp" - -LayerTestResult LstmNoCifgNoPeepholeNoProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory, - const boost::multi_array& input, - const boost::multi_array& outputExpected) -{ - unsigned int batchSize = boost::numeric_cast(input.shape()[0]); - unsigned int inputSize = boost::numeric_cast(input.shape()[1]); - unsigned int outputSize = boost::numeric_cast(outputExpected.shape()[1]); - // cellSize and outputSize have the same size when there is no projection. - unsigned numUnits = outputSize; - - - armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType()); - armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::GetDataType()); - armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::GetDataType()); - - - armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, armnn::GetDataType()); - armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, armnn::GetDataType()); - armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType()); - - - LayerTestResult ret(outputTensorInfo); - - std::vector inputVector; - inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); - auto inputTensor = MakeTensor(inputTensorInfo, inputVector); - - std::vector cellStateInVector(batchSize * numUnits, 0.f); - auto cellStateInTensor = MakeTensor(cellStateInTensorInfo, cellStateInVector); - - std::vector outputStateInVector(batchSize * outputSize, 0.f); - auto outputStateInTensor = MakeTensor(outputStateInTensorInfo, outputStateInVector); - - std::vector scratchBufferVector(batchSize * numUnits * 3, 0.f); - auto scratchBufferTensor = MakeTensor(scratchBufferTensorInfo, scratchBufferVector); - - std::vector outputStateOutVector(batchSize * outputSize, 0.f); - auto outputStateOutTensor = MakeTensor(outputStateOutTensorInfo, outputStateOutVector); - - std::vector cellStateOutVector(batchSize * numUnits, 0.f); - auto cellStateOutTensor = MakeTensor(cellStateOutTensorInfo, cellStateOutVector); - - std::vector outputVector; - outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); - ret.outputExpected = MakeTensor(outputTensorInfo, outputVector); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr cellStateInHandle = - workloadFactory.CreateTensorHandle(cellStateInTensorInfo); - std::unique_ptr outputStateInHandle = - workloadFactory.CreateTensorHandle(outputStateInTensorInfo); - - std::unique_ptr scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo); - std::unique_ptr outputStateOutHandle = - workloadFactory.CreateTensorHandle(outputStateOutTensorInfo); - std::unique_ptr cellStateOutHandle = - workloadFactory.CreateTensorHandle(cellStateOutTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - - armnn::LstmQueueDescriptor data; - armnn::WorkloadInfo info; - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); - AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); - - AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get()); - AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); - AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - armnn::TensorInfo tensorInfo4({numUnits}, armnn::GetDataType()); - armnn::TensorInfo tensorInfo8({numUnits, 2}, armnn::GetDataType()); - armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::GetDataType()); - - auto inputToInputWeights = MakeTensor(tensorInfo8, {-0.45018822f, -0.02338299f, -0.0870589f, - -0.34550029f, 0.04266912f, -0.15680569f, - -0.34856534f, 0.43890524f}); - - auto inputToForgetWeights = MakeTensor(tensorInfo8, {0.09701663f, 0.20334584f, -0.50592935f, - -0.31343272f, -0.40032279f, 0.44781327f, - 0.01387155f, -0.35593212f}); - - auto inputToCellWeights = MakeTensor(tensorInfo8, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, - -0.20583314f, 0.44344562f, 0.22077113f, - -0.29909778f}); - - auto inputToOutputWeights = MakeTensor(tensorInfo8, {-0.25065863f, -0.28290087f, 0.04613829f, - 0.40525138f, 0.44272184f, 0.03897077f, - -0.1556896f, 0.19487578f}); - - auto recurrentToInputWeights = MakeTensor(tensorInfo16, {-0.0063535f, -0.2042388f, 0.31454784f, - -0.35746509f, 0.28902304f, 0.08183324f, - -0.16555229f, 0.02286911f, -0.13566875f, - 0.03034258f, 0.48091322f, -0.12528998f, - 0.24077177f, -0.51332325f, -0.33502164f, - 0.10629296f}); - - auto recurrentToForgetWeights = MakeTensor(tensorInfo16, {-0.48684245f, -0.06655136f, 0.42224967f, - 0.2112639f, 0.27654213f, 0.20864892f, - -0.07646349f, 0.45877004f, 0.00141793f, - -0.14609534f, 0.36447752f, 0.09196436f, - 0.28053468f, 0.01560611f, -0.20127171f, - -0.01140004f}); - - auto recurrentToCellWeights = MakeTensor(tensorInfo16, {-0.3407414f, 0.24443203f, -0.2078532f, - 0.26320225f, 0.05695659f, -0.00123841f, - -0.4744786f, -0.35869038f, -0.06418842f, - -0.13502428f, -0.501764f, 0.22830659f, - -0.46367589f, 0.26016325f, -0.03894562f, - -0.16368064f}); - - auto recurrentToOutputWeights = MakeTensor(tensorInfo16, {0.43385774f, -0.17194885f, 0.2718237f, - 0.09215671f, 0.24107647f, -0.39835793f, - 0.18212086f, 0.01301402f, 0.48572797f, - -0.50656658f, 0.20047462f, -0.20607421f, - -0.51818722f, -0.15390486f, 0.0468148f, - 0.39922136f}); - - auto cellToInputWeights = MakeTensor(tensorInfo4, {0., 0., 0., 0.}); - - auto inputGateBias = MakeTensor(tensorInfo4, {0., 0., 0., 0.}); - - auto forgetGateBias = MakeTensor(tensorInfo4, {1., 1., 1., 1.}); - - auto cellBias = MakeTensor(tensorInfo4, {0., 0., 0., 0.}); - - auto outputGateBias = MakeTensor(tensorInfo4, {0., 0., 0., 0.}); - - armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo8); - armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo8); - armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo8); - armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo8); - armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); - armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo16); - armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo16); - armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); - armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo4); - armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo4); - armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo4); - armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo4); - armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo4); - - AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]); - AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]); - AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]); - AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]); - AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]); - - data.m_InputToInputWeights = &inputToInputWeightsTensor; - data.m_InputToForgetWeights = &inputToForgetWeightsTensor; - data.m_InputToCellWeights = &inputToCellWeightsTensor; - data.m_InputToOutputWeights = &inputToOutputWeightsTensor; - data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; - data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; - data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; - data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; - data.m_CellToInputWeights = &cellToInputWeightsTensor; - data.m_InputGateBias = &inputGateBiasTensor; - data.m_ForgetGateBias = &forgetGateBiasTensor; - data.m_CellBias = &cellBiasTensor; - data.m_OutputGateBias = &outputGateBiasTensor; - - - // Flags to set test configuration - data.m_Parameters.m_ActivationFunc = 4; - data.m_Parameters.m_CifgEnabled = false; - data.m_Parameters.m_PeepholeEnabled = false; - data.m_Parameters.m_ProjectionEnabled = false; - - - std::unique_ptr workload = workloadFactory.CreateLstm(data, info); - inputHandle->Allocate(); - outputStateInHandle->Allocate(); - cellStateInHandle->Allocate(); - - scratchHandle->Allocate(); - outputStateOutHandle->Allocate(); - cellStateOutHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); - CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]); - CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); - - return ret; -} - - -LayerTestResult -LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory, - const boost::multi_array& input, - const boost::multi_array& outputExpected) { - - unsigned int batchSize = 2; - unsigned int outputSize = 16; - unsigned int inputSize = 5; - unsigned numUnits = 20; - - armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType()); - armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::GetDataType()); - armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::GetDataType()); - - // Scratch buffer size without CIFG [batchSize, numUnits * 3] - armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, armnn::GetDataType()); - armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, armnn::GetDataType()); - armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType()); - - LayerTestResult ret(outputTensorInfo); - - std::vector inputVector; - inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); - auto inputTensor = MakeTensor(inputTensorInfo, inputVector); - - std::vector cellStateInVector(batchSize * numUnits, 0.f); - auto cellStateInTensor = MakeTensor(cellStateInTensorInfo, cellStateInVector); - - std::vector outputStateInVector(batchSize * outputSize, 0.f); - auto outputStateInTensor = MakeTensor(outputStateInTensorInfo, outputStateInVector); - - std::vector scratchBufferVector(batchSize * numUnits * 3, 0.f); - auto scratchBufferTensor = MakeTensor(scratchBufferTensorInfo, scratchBufferVector); - - std::vector outputStateOutVector(batchSize * outputSize, 0.f); - auto outputStateOutTensor = MakeTensor(outputStateOutTensorInfo, outputStateOutVector); - - std::vector cellStateOutVector(batchSize * numUnits, 0.f); - auto cellStateOutTensor = MakeTensor(cellStateOutTensorInfo, cellStateOutVector); - - std::vector outputVector; - outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); - ret.outputExpected = MakeTensor(outputTensorInfo, outputVector); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr cellStateInHandle = - workloadFactory.CreateTensorHandle(cellStateInTensorInfo); - std::unique_ptr outputStateInHandle = - workloadFactory.CreateTensorHandle(outputStateInTensorInfo); - - std::unique_ptr scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo); - std::unique_ptr outputStateOutHandle = - workloadFactory.CreateTensorHandle(outputStateOutTensorInfo); - std::unique_ptr cellStateOutHandle = - workloadFactory.CreateTensorHandle(cellStateOutTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::LstmQueueDescriptor data; - armnn::WorkloadInfo info; - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); - AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); - - AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get()); - AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); - AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - armnn::TensorInfo tensorInfo16({outputSize}, armnn::GetDataType()); - armnn::TensorInfo tensorInfo20({numUnits}, armnn::GetDataType()); - armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::GetDataType()); - armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::GetDataType()); - armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::GetDataType()); - - auto inputToInputWeights = - MakeTensor(tensorInfo20x5, {0.021393683f,0.06124551f, 0.046905167f,-0.014657677f,-0.03149463f, - 0.09171803f, 0.14647801f,0.10797193f, -0.0057968358f,0.0019193048f, - -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, - -0.022599787f,-0.09121155f, -0.008675967f, -0.045206103f,-0.0821282f, - -0.008045952f,0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, - -0.06453243f,0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, - -0.1671009f, -0.15519552f, -0.16819797f,-0.13971269f,-0.11953059f, - 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, - 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, - 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, - -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, - 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, - -0.028545704f,0.024939531f,0.050929718f,0.0076203286f,-0.0029723682f, - -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f,-0.16327988f, - -0.2273378f, -0.0993647f, -0.017155107f,0.0023917493f,0.049272764f, - 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, - -0.04544234f, -0.0497073f,-0.07135631f, -0.048929106f,-0.004042012f, - -0.009284026f, 0.018042054f, 0.0036860977f,-0.07427302f, -0.11434604f, - -0.018995456f, 0.031487543f, 0.012834908f,0.019977754f,0.044256654f, - -0.39292613f, -0.18519334f, -0.11651281f,-0.06809892f, 0.011373677f - }); - - auto inputToForgetWeights = - MakeTensor(tensorInfo20x5, {-0.0018401089f, -0.004852237f,0.03698424f, 0.014181704f,0.028273236f, - -0.016726194f, -0.05249759f,-0.10204261f, 0.00861066f,-0.040979505f, - -0.009899187f,0.01923892f,-0.028177269f, -0.08535103f,-0.14585495f, - 0.10662567f,-0.01909731f,-0.017883534f,-0.0047269356f,-0.045103323f, - 0.0030784295f,0.076784775f,0.07463696f, 0.094531395f,0.0814421f, - -0.12257899f, -0.033945758f,-0.031303465f, 0.045630626f,0.06843887f, - -0.13492945f, -0.012480007f,-0.0811829f, -0.07224499f,-0.09628791f, - 0.045100946f,0.0012300825f, 0.013964662f, 0.099372394f,0.02543059f, - 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f,0.052625068f, - 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f,0.07241905f, - 0.018668644f, -0.037377294f,-0.06277783f,-0.08833636f,-0.040120605f, - -0.011405586f,-0.007808335f,-0.010301386f,-0.005102167f,0.027717464f, - 0.05483423f, 0.11449111f, 0.11289652f,0.10939839f, 0.13396506f, - -0.08402166f,-0.01901462f, -0.044678304f,-0.07720565f,0.014350063f, - -0.11757958f, -0.0652038f, -0.08185733f,-0.076754324f,-0.092614375f, - 0.10405491f, 0.052960336f, 0.035755895f,0.035839386f,-0.012540553f, - 0.036881298f, 0.02913376f, 0.03420159f,0.05448447f,-0.054523353f, - 0.02582715f, 0.02327355f, -0.011857179f,-0.0011980024f,-0.034641717f, - -0.026125094f,-0.17582615f,-0.15923657f,-0.27486774f,-0.0006143371f, - 0.0001771948f, -8.470171e-05f, 0.02651807f,0.045790765f,0.06956496f - }); - - auto inputToCellWeights = - MakeTensor(tensorInfo20x5, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, - -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, - -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, - -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, - -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, - 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f, - -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f, - 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f, - -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f, - -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f, - -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f, - 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f, - 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f, - 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f, - -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f, - -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f, - -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f, - -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f, - -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f, - -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f, - 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f, - 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f, - 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f, - 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f, - 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f - }); - - auto inputToOutputWeights = - MakeTensor(tensorInfo20x5, {-0.0998932f, -0.07201956f, -0.052803773f,-0.15629593f,-0.15001918f, - -0.07650751f,0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f, - 0.029517552f, -0.04751393f, 0.010350531f,-0.02664851f, -0.016839722f, - -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f,-0.0129761f, - -0.021737747f,-0.038305793f,-0.06870586f, -0.01481247f,-0.001285394f, - 0.10124236f, 0.083122835f, 0.053313006f,-0.062235646f,-0.075637154f, - -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f, - -0.086665764f,-0.037162706f,-0.038880914f,-0.035832845f,-0.014481564f, - -0.09825003f,-0.12048569f,-0.097665586f,-0.05287633f, -0.0964047f, - -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f,0.050649304f, - 0.05798951f, -0.021852335f,-0.099848844f,0.014740475f,-0.078897946f, - 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f, - 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f,0.06945813f, - -0.078907564f,-0.06707616f, -0.11844508f, -0.09986688f,-0.07509403f, - 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f,0.14639415f, - 0.0015017595f, -0.014267382f, -0.03417257f,0.012711468f,0.0028300495f, - -0.024758482f, -0.05098548f,-0.0821182f, 0.014225672f, 0.021544158f, - 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f,0.06476295f, - -0.022907063f,0.027562456f,0.040185735f, 0.019567577f,-0.015598739f, - -0.049097303f, -0.017121866f, -0.083368234f,-0.02332002f,-0.0840956f - }); - - auto inputGateBias = - MakeTensor(tensorInfo20, {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, - -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, - -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, - 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f - }); - - auto forgetGateBias = - MakeTensor(tensorInfo20, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, - 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f, - 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f, - -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f, - 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f - }); - - auto cellBias = - MakeTensor(tensorInfo20, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, - -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f, - -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f, - -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f, - 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f - }); - - auto outputGateBias = - MakeTensor(tensorInfo20, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, - 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, - 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, - -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f - }); - - auto recurrentToInputWeights = - MakeTensor(tensorInfo20x16, {-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, - -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, - -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, - -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, - 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, - 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, - -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, - 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f, - -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f, - 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f, - -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f, - 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f, - -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f, - 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f, - -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f, - -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f, - -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f, - -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f, - -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f, - 0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f, - 0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f, - 0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f, - 0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f, - 0.012092817f, -0.04989123f, -0.086576f, -0.003399834f, - -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f, - -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f, - 0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f, - -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f, - -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f, - -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f, - -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f, - -0.011337001f, 0.035530265f, -0.010912711f, 0.0706555f, - -0.005894094f, 0.051841937f, -0.1401738f, -0.02351249f, - 0.0365468f, 0.07590991f, 0.08838724f, 0.021681072f, - -0.10086113f, 0.019608743f, -0.06195883f, 0.077335775f, - 0.023646897f, -0.095322326f, 0.02233014f, 0.09756986f, - -0.048691444f, -0.009579111f, 0.07595467f, 0.11480546f, - -0.09801813f, 0.019894179f, 0.08502348f, 0.004032281f, - 0.037211012f, 0.068537936f, -0.048005626f, -0.091520436f, - -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f, - -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f, - 0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f, - -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f, - 0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f, - 0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f, - 0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f, - 0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f, - 0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f, - 0.05052217f, -0.058925f, -0.011402121f, -0.024987547f, - -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f, - -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f, - -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f, - -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f, - -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f, - 0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f, - 0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f, - -0.017081132f, 0.019358726f, 0.0027079724f, 0.004635139f, - 0.062634714f, -0.02338735f, -0.039547626f, -0.02050681f, - 0.03385117f, -0.083611414f, 0.002862572f, -0.09421313f, - 0.058618143f, -0.08598433f, 0.00972939f, 0.023867095f, - -0.053934585f, -0.023203006f, 0.07452513f, -0.048767887f, - -0.07314807f, -0.056307215f, -0.10433547f, -0.06440842f, - 0.04328182f, 0.04389765f, -0.020006588f, -0.09076438f, - -0.11652589f, -0.021705797f, 0.03345259f, -0.010329105f, - -0.025767034f, 0.013057034f, -0.07316461f, -0.10145612f, - 0.06358255f, 0.18531723f, 0.07759293f, 0.12006465f, - 0.1305557f, 0.058638252f, -0.03393652f, 0.09622831f, - -0.16253184f, -2.4580743e-06f, 0.079869635f, -0.070196845f, - -0.005644518f, 0.06857898f, -0.12598175f, -0.035084512f, - 0.03156317f, -0.12794146f, -0.031963028f, 0.04692781f, - 0.030070418f, 0.0071660685f, -0.095516115f, -0.004643372f, - 0.040170413f, -0.062104587f, -0.0037324072f, 0.0554317f, - 0.08184801f, -0.019164372f, 0.06791302f, 0.034257166f, - -0.10307039f, 0.021943003f, 0.046745934f, 0.0790918f, - -0.0265588f, -0.007824208f, 0.042546265f, -0.00977924f, - -0.0002440307f, -0.017384544f, -0.017990116f, 0.12252321f, - -0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f, - 0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f, - -0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f, - -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f - }); - - auto recurrentToForgetWeights = - MakeTensor(tensorInfo20x16, {-0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, - 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, - -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, - 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, - 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, - -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, - -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, - 0.061878487f, -0.04729229f, 0.034919553f, -0.07585433f, - -0.04421272f, -0.044019096f, 0.085488975f, 0.04058006f, - -0.06890133f, -0.030951202f, -0.024628663f, -0.07672815f, - 0.034293607f, 0.08556707f, -0.05293577f, -0.033561368f, - -0.04899627f, 0.0241671f, 0.015736353f, -0.095442444f, - -0.029564252f, 0.016493602f, -0.035026584f, 0.022337519f, - -0.026871363f, 0.004780428f, 0.0077918363f, -0.03601621f, - 0.016435321f, -0.03263031f, -0.09543275f, -0.047392778f, - 0.013454138f, 0.028934088f, 0.01685226f, -0.086110644f, - -0.046250615f, -0.01847454f, 0.047608484f, 0.07339695f, - 0.034546845f, -0.04881143f, 0.009128804f, -0.08802852f, - 0.03761666f, 0.008096139f, -0.014454086f, 0.014361001f, - -0.023502491f, -0.0011840804f, -0.07607001f, 0.001856849f, - -0.06509276f, -0.006021153f, -0.08570962f, -0.1451793f, - 0.060212336f, 0.055259194f, 0.06974018f, 0.049454916f, - -0.027794661f, -0.08077226f, -0.016179763f, 0.1169753f, - 0.17213494f, -0.0056326236f, -0.053934924f, -0.0124349f, - -0.11520337f, 0.05409887f, 0.088759385f, 0.0019655675f, - 0.0042065294f, 0.03881498f, 0.019844765f, 0.041858196f, - -0.05695512f, 0.047233116f, 0.038937137f, -0.06542224f, - 0.014429736f, -0.09719407f, 0.13908425f, -0.05379757f, - 0.012321099f, 0.082840554f, -0.029899208f, 0.044217527f, - 0.059855383f, 0.07711018f, -0.045319796f, 0.0948846f, - -0.011724666f, -0.0033288454f, -0.033542685f, -0.04764985f, - -0.13873616f, 0.040668588f, 0.034832682f, -0.015319203f, - -0.018715994f, 0.046002675f, 0.0599172f, -0.043107376f, - 0.0294216f, -0.002314414f, -0.022424703f, 0.0030315618f, - 0.0014641669f, 0.0029166266f, -0.11878115f, 0.013738511f, - 0.12375372f, -0.0006038222f, 0.029104086f, 0.087442465f, - 0.052958444f, 0.07558703f, 0.04817258f, 0.044462286f, - -0.015213451f, -0.08783778f, -0.0561384f, -0.003008196f, - 0.047060397f, -0.002058388f, 0.03429439f, -0.018839769f, - 0.024734668f, 0.024614193f, -0.042046934f, 0.09597743f, - -0.0043254104f, 0.04320769f, 0.0064070094f, -0.0019131786f, - -0.02558259f, -0.022822596f, -0.023273505f, -0.02464396f, - -0.10991725f, -0.006240552f, 0.0074488563f, 0.024044557f, - 0.04383914f, -0.046476185f, 0.028658995f, 0.060410924f, - 0.050786525f, 0.009452605f, -0.0073054377f, -0.024810238f, - 0.0052906186f, 0.0066939713f, -0.0020913032f, 0.014515517f, - 0.015898481f, 0.021362653f, -0.030262267f, 0.016587038f, - -0.011442813f, 0.041154444f, -0.007631438f, -0.03423484f, - -0.010977775f, 0.036152758f, 0.0066366293f, 0.11915515f, - 0.02318443f, -0.041350313f, 0.021485701f, -0.10906167f, - -0.028218046f, -0.00954771f, 0.020531068f, -0.11995105f, - -0.03672871f, 0.024019798f, 0.014255957f, -0.05221243f, - -0.00661567f, -0.04630967f, 0.033188973f, 0.10107534f, - -0.014027541f, 0.030796422f, -0.10270911f, -0.035999842f, - 0.15443139f, 0.07684145f, 0.036571592f, -0.035900835f, - -0.0034699554f, 0.06209149f, 0.015920248f, -0.031122351f, - -0.03858649f, 0.01849943f, 0.13872518f, 0.01503974f, - 0.069941424f, -0.06948533f, -0.0088794185f, 0.061282158f, - -0.047401894f, 0.03100163f, -0.041533746f, -0.10430945f, - 0.044574402f, -0.01425562f, -0.024290353f, 0.034563623f, - 0.05866852f, 0.023947537f, -0.09445152f, 0.035450947f, - 0.02247216f, -0.0042998926f, 0.061146557f, -0.10250651f, - 0.020881841f, -0.06747029f, 0.10062043f, -0.0023941975f, - 0.03532124f, -0.016341697f, 0.09685456f, -0.016764693f, - 0.051808182f, 0.05875331f, -0.04536488f, 0.001626336f, - -0.028892258f, -0.01048663f, -0.009793449f, -0.017093895f, - 0.010987891f, 0.02357273f, -0.00010856845f, 0.0099760275f, - -0.001845119f, -0.03551521f, 0.0018358806f, 0.05763657f, - -0.01769146f, 0.040995963f, 0.02235177f, -0.060430344f, - 0.11475477f, -0.023854522f, 0.10071741f, 0.0686208f, - -0.014250481f, 0.034261297f, 0.047418304f, 0.08562733f, - -0.030519066f, 0.0060542435f, 0.014653856f, -0.038836084f, - 0.04096551f, 0.032249358f, -0.08355519f, -0.026823482f, - 0.056386515f, -0.010401743f, -0.028396193f, 0.08507674f, - 0.014410365f, 0.020995233f, 0.17040324f, 0.11511526f, - 0.02459721f, 0.0066619175f, 0.025853224f, -0.023133837f, - -0.081302024f, 0.017264642f, -0.009585969f, 0.09491168f, - -0.051313367f, 0.054532815f, -0.014298593f, 0.10657464f, - 0.007076659f, 0.10964551f, 0.0409152f, 0.008275321f, - -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f - }); - - auto recurrentToCellWeights = - MakeTensor(tensorInfo20x16, {-0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, - 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, - 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, - -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, - 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, - 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, - -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, - -0.019443132f, -0.030755889f, -0.0040000007f, 0.04465846f, - -0.021585021f, 0.0031670958f, 0.0053199246f, -0.056117613f, - -0.10893326f, 0.076739706f, -0.08509834f, -0.027997585f, - 0.037871376f, 0.01449768f, -0.09002357f, -0.06111149f, - -0.046195522f, 0.0422062f, -0.005683705f, -0.1253618f, - -0.012925729f, -0.04890792f, 0.06985068f, 0.037654128f, - 0.03398274f, -0.004781977f, 0.007032333f, -0.031787455f, - 0.010868644f, -0.031489216f, 0.09525667f, 0.013939797f, - 0.0058680447f, 0.0167067f, 0.02668468f, -0.04797466f, - -0.048885044f, -0.12722108f, 0.035304096f, 0.06554885f, - 0.00972396f, -0.039238118f, -0.05159735f, -0.11329045f, - 0.1613692f, -0.03750952f, 0.06529313f, -0.071974665f, - -0.11769596f, 0.015524369f, -0.0013754242f, -0.12446318f, - 0.02786344f, -0.014179351f, 0.005264273f, 0.14376344f, - 0.015983658f, 0.03406988f, -0.06939408f, 0.040699873f, - 0.02111075f, 0.09669095f, 0.041345075f, -0.08316494f, - -0.07684199f, -0.045768797f, 0.032298047f, -0.041805092f, - 0.0119405f, 0.0061010392f, 0.12652606f, 0.0064572375f, - -0.024950314f, 0.11574242f, 0.04508852f, -0.04335324f, - 0.06760663f, -0.027437469f, 0.07216407f, 0.06977076f, - -0.05438599f, 0.034033038f, -0.028602652f, 0.05346137f, - 0.043184172f, -0.037189785f, 0.10420091f, 0.00882477f, - -0.054019816f, -0.074273005f, -0.030617684f, -0.0028467078f, - 0.024302477f, -0.0038869337f, 0.005332455f, 0.0013399826f, - 0.04361412f, -0.007001822f, 0.09631092f, -0.06702025f, - -0.042049985f, -0.035070654f, -0.04103342f, -0.10273396f, - 0.0544271f, 0.037184782f, -0.13150354f, -0.0058036847f, - -0.008264958f, 0.042035464f, 0.05891794f, 0.029673764f, - 0.0063542654f, 0.044788733f, 0.054816857f, 0.062257513f, - -0.00093483756f, 0.048938446f, -0.004952862f, -0.007730018f, - -0.04043371f, -0.017094059f, 0.07229206f, -0.023670016f, - -0.052195564f, -0.025616996f, -0.01520939f, 0.045104615f, - -0.007376126f, 0.003533447f, 0.006570588f, 0.056037236f, - 0.12436656f, 0.051817212f, 0.028532185f, -0.08686856f, - 0.11868599f, 0.07663395f, -0.07323171f, 0.03463402f, - -0.050708205f, -0.04458982f, -0.11590894f, 0.021273347f, - 0.1251325f, -0.15313013f, -0.12224372f, 0.17228661f, - 0.023029093f, 0.086124025f, 0.006445803f, -0.03496501f, - 0.028332196f, 0.04449512f, -0.042436164f, -0.026587414f, - -0.006041347f, -0.09292539f, -0.05678812f, 0.03897832f, - 0.09465633f, 0.008115513f, -0.02171956f, 0.08304309f, - 0.071401566f, 0.019622514f, 0.032163795f, -0.004167056f, - 0.02295182f, 0.030739572f, 0.056506045f, 0.004612461f, - 0.06524936f, 0.059999723f, 0.046395954f, -0.0045512207f, - -0.1335546f, -0.030136576f, 0.11584653f, -0.014678886f, - 0.0020118146f, -0.09688814f, -0.0790206f, 0.039770417f, - -0.0329582f, 0.07922767f, 0.029322514f, 0.026405897f, - 0.04207835f, -0.07073373f, 0.063781224f, 0.0859677f, - -0.10925287f, -0.07011058f, 0.048005477f, 0.03438226f, - -0.09606514f, -0.006669445f, -0.043381985f, 0.04240257f, - -0.06955775f, -0.06769346f, 0.043903265f, -0.026784198f, - -0.017840602f, 0.024307009f, -0.040079936f, -0.019946516f, - 0.045318738f, -0.12233574f, 0.026170589f, 0.0074471775f, - 0.15978073f, 0.10185836f, 0.10298046f, -0.015476589f, - -0.039390966f, -0.072174534f, 0.0739445f, -0.1211869f, - -0.0347889f, -0.07943156f, 0.014809798f, -0.12412325f, - -0.0030663363f, 0.039695457f, 0.0647603f, -0.08291318f, - -0.018529687f, -0.004423833f, 0.0037507233f, 0.084633216f, - -0.01514876f, -0.056505352f, -0.012800942f, -0.06994386f, - 0.012962922f, -0.031234352f, 0.07029052f, 0.016418684f, - 0.03618972f, 0.055686004f, -0.08663945f, -0.017404709f, - -0.054761406f, 0.029065743f, 0.052404847f, 0.020238016f, - 0.0048197987f, -0.0214882f, 0.07078733f, 0.013016777f, - 0.06262858f, 0.009184685f, 0.020785125f, -0.043904778f, - -0.0270329f, -0.03299152f, -0.060088247f, -0.015162964f, - -0.001828936f, 0.12642565f, -0.056757294f, 0.013586685f, - 0.09232601f, -0.035886683f, 0.06000002f, 0.05229691f, - -0.052580316f, -0.082029596f, -0.010794592f, 0.012947712f, - -0.036429964f, -0.085508935f, -0.13127148f, -0.017744139f, - 0.031502828f, 0.036232427f, -0.031581745f, 0.023051167f, - -0.05325106f, -0.03421577f, 0.028793324f, -0.034633752f, - -0.009881397f, -0.043551125f, -0.018609839f, 0.0019097115f, - -0.008799762f, 0.056595087f, 0.0022273948f, 0.055752404f - }); - - auto recurrentToOutputWeights = - MakeTensor(tensorInfo20x16, {0.025825322f, -0.05813119f, 0.09495884f,-0.045984812f, -0.01255415f, - -0.0026479573f,-0.08196161f,-0.054914974f,-0.0046604523f, - -0.029587349f, -0.044576716f, -0.07480124f, -0.082868785f, - 0.023254942f, 0.027502948f, -0.0039728214f, -0.08683098f, - -0.08116779f, -0.014675607f, -0.037924774f, -0.023314456f, - -0.007401714f, -0.09255757f, 0.029460307f, -0.08829125f, - -0.005139627f, -0.08989442f, -0.0555066f, 0.13596267f, - -0.025062224f, -0.048351806f, -0.03850004f, 0.07266485f, - -0.022414139f, 0.05940088f, 0.075114764f, 0.09597592f, - -0.010211725f, -0.0049794707f, -0.011523867f, -0.025980417f, - 0.072999895f, 0.11091378f, -0.081685916f, 0.014416728f, - 0.043229222f, 0.034178585f, -0.07530371f, 0.035837382f, - -0.085607f, -0.007721233f, -0.03287832f, -0.043848954f, - -0.06404588f, -0.06632928f, -0.073643476f, 0.008214239f, - -0.045984086f, 0.039764922f, 0.03474462f, 0.060612556f, - -0.080590084f, 0.049127717f, 0.04151091f, -0.030063879f, - 0.008801774f, -0.023021035f, -0.019558564f, 0.05158114f, - -0.010947698f, -0.011825728f, 0.0075720972f, 0.0699727f, - -0.0039981045f, 0.069350146f, 0.08799282f, 0.016156472f, - 0.035502106f, 0.11695009f, 0.006217345f, 0.13392477f, - -0.037875112f, 0.025745004f, 0.08940699f, -0.00924166f, - 0.0046702605f, -0.036598757f, -0.08811812f, 0.10522024f, - -0.032441203f, 0.008176899f, -0.04454919f, 0.07058152f, - 0.0067963637f, 0.039206743f, 0.03259838f, 0.03725492f, - -0.09515802f, 0.013326398f, -0.052055415f, -0.025676316f, - 0.03198509f, -0.015951829f, -0.058556724f, 0.036879618f, - 0.043357447f, 0.028362012f, -0.05908629f, 0.0059240665f, - -0.04995891f, -0.019187413f,0.0276265f, -0.01628143f, 0.0025863599f, - 0.08800015f, 0.035250366f, -0.022165963f, -0.07328642f, - -0.009415526f, -0.07455109f, 0.11690406f, 0.0363299f, - 0.07411125f, 0.042103454f, -0.009660886f, 0.019076364f, - 0.018299393f, -0.046004917f, 0.08891175f,0.0431396f, -0.026327137f, - -0.051502608f, 0.08979574f, -0.051670972f, 0.04940282f, - -0.07491107f, -0.021240504f, 0.022596184f, -0.034280192f, - 0.060163025f, -0.058211457f, -0.051837247f, -0.01349775f, - -0.04639988f, -0.035936575f, -0.011681591f, 0.064818054f, - 0.0073146066f, -0.021745546f, -0.043124277f, -0.06471268f, - -0.07053354f, -0.029321948f, -0.05330136f, 0.016933719f, - -0.053782392f, 0.13747959f, -0.1361751f, -0.11569455f, - 0.0033329215f, 0.05693899f, -0.053219706f, 0.063698f, - 0.07977434f, -0.07924483f, 0.06936997f, 0.0034815092f, - -0.007305279f, -0.037325785f, -0.07251102f, -0.033633437f, - -0.08677009f, 0.091591336f, -0.14165086f, 0.021752775f, - 0.019683983f, 0.0011612234f, -0.058154266f, 0.049996935f, - 0.0288841f, -0.0024567875f, -0.14345716f, 0.010955264f,-0.10234828f, - 0.1183656f, -0.0010731248f, -0.023590032f,-0.072285876f,-0.0724771f, - -0.026382286f, -0.0014920527f, 0.042667855f, 0.0018776858f, - 0.02986552f, 0.009814309f, 0.0733756f, 0.12289186f, - 0.018043943f, -0.0458958f, 0.049412545f, 0.033632483f, - 0.05495232f, 0.036686596f, -0.013781798f, -0.010036754f, - 0.02576849f, -0.08307328f, 0.010112348f, 0.042521734f, - -0.05869831f, -0.071689695f, 0.03876447f, -0.13275425f, -0.0352966f, - -0.023077697f, 0.10285965f, 0.084736146f, 0.15568255f, - -0.00040734606f, 0.027835453f, -0.10292561f, -0.032401145f, - 0.10053256f, -0.026142767f, -0.08271222f, -0.0030240538f, - -0.016368777f, 0.1070414f, 0.042672627f, 0.013456989f, - -0.0437609f, -0.022309763f, 0.11576483f, 0.04108048f, - 0.061026827f, -0.0190714f, -0.0869359f, 0.037901703f, 0.0610107f, - 0.07202949f, 0.01675338f, 0.086139716f, -0.08795751f, - -0.014898893f, -0.023771819f, -0.01965048f, 0.007955471f, - -0.043740474f, 0.03346837f, -0.10549954f, 0.090567775f, - 0.042013682f, -0.03176985f, 0.12569028f, -0.02421228f, - -0.029526481f, 0.023851605f, 0.031539805f, 0.05292009f, - -0.02344001f, -0.07811758f, -0.08834428f, 0.10094801f, - 0.16594367f, -0.06861939f, -0.021256343f, -0.041093912f, - -0.06669611f, 0.035498552f, 0.021757556f, -0.09302526f, - -0.015403468f, -0.06614931f, -0.051798206f, -0.013874718f, - 0.03630673f, 0.010412845f, -0.08077351f, 0.046185967f, - 0.0035662893f, 0.03541868f, -0.094149634f, -0.034814864f, - 0.003128424f, -0.020674974f, -0.03944324f, -0.008110165f, - -0.11113267f, 0.08484226f, 0.043586485f, 0.040582247f, - 0.0968012f, -0.065249965f, -0.028036479f, 0.0050708856f, - 0.0017462453f, 0.0326779f, 0.041296225f, 0.09164146f, - -0.047743853f, -0.015952192f, -0.034451712f, 0.084197424f, - -0.05347844f, -0.11768019f, 0.085926116f, -0.08251791f, - -0.045081906f, 0.0948852f, 0.068401024f, 0.024856757f, - 0.06978981f, -0.057309967f, -0.012775832f, -0.0032452994f, - 0.01977615f, -0.041040014f, -0.024264973f,0.063464895f, 0.05431621f - }); - - auto cellToInputWeights = - MakeTensor(tensorInfo20, {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, - -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, - -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f,-0.052169047f, - 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f - }); - - - auto cellToForgetWeights = - MakeTensor(tensorInfo20, {-0.01998659f,-0.15568835f,-0.24248174f, -0.012770197f, 0.041331276f, - -0.072311886f, -0.052123554f,-0.0066330447f,-0.043891653f,0.036225766f, - -0.047248036f, 0.021479502f,0.033189066f, 0.11952997f, -0.020432774f, - 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f - }); - - auto cellToOutputWeights = - MakeTensor(tensorInfo20, {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, - -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, - -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, - 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f - }); - - auto projectionWeights = - MakeTensor(tensorInfo16x20, - {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, - 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, - -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, - -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, - 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, - 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, - 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, - 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, - -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, - -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, - -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, - 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, - 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, - 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, - 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, - 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, - -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, - 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, - -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, - 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, - -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, - -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, - 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, - -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, - 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, - -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, - -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, - 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, - -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, - -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, - -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, - 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, - 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, - -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, - 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, - 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, - 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, - 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, - 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, - -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, - -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, - 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, - -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, - -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, - 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, - 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, - 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, - -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, - -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, - -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, - 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, - -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, - 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, - 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, - -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, - -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, - -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, - 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, - -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, - -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, - -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, - 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, - 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, - 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f - }); - - std::vector projectionBiasVector(outputSize, 0.f); - auto projectionBias = MakeTensor(tensorInfo16, projectionBiasVector); - - armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo20x5); - armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo20x5); - armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo20x5); - armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo20x5); - armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo20x16); - armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo20x16); - armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo20x16); - armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo20x16); - armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo20); - armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo20); - armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo20); - armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo20); - armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo20); - armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfo20); - armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfo20); - armnn::ScopedCpuTensorHandle projectionWeightsTensor(tensorInfo16x20); - armnn::ScopedCpuTensorHandle projectionBiasTensor(tensorInfo16); - - AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]); - AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]); - AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]); - AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]); - AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]); - AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]); - AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]); - AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, &projectionBias[0]); - - data.m_InputToInputWeights = &inputToInputWeightsTensor; - data.m_InputToForgetWeights = &inputToForgetWeightsTensor; - data.m_InputToCellWeights = &inputToCellWeightsTensor; - data.m_InputToOutputWeights = &inputToOutputWeightsTensor; - data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; - data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; - data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; - data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; - data.m_CellToInputWeights = &cellToInputWeightsTensor; - data.m_InputGateBias = &inputGateBiasTensor; - data.m_ForgetGateBias = &forgetGateBiasTensor; - data.m_CellBias = &cellBiasTensor; - data.m_OutputGateBias = &outputGateBiasTensor; - data.m_CellToForgetWeights = &cellToForgetWeightsTensor; - data.m_CellToOutputWeights = &cellToOutputWeightsTensor; - data.m_ProjectionWeights = &projectionWeightsTensor; - data.m_ProjectionBias = &projectionBiasTensor; - - // Flags to set test configuration - data.m_Parameters.m_ActivationFunc = 4; - data.m_Parameters.m_CifgEnabled = false; - data.m_Parameters.m_PeepholeEnabled = true; - data.m_Parameters.m_ProjectionEnabled = true; - - - std::unique_ptr workload = workloadFactory.CreateLstm(data, info); - inputHandle->Allocate(); - outputStateInHandle->Allocate(); - cellStateInHandle->Allocate(); - - scratchHandle->Allocate(); - outputStateOutHandle->Allocate(); - cellStateOutHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); - CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]); - CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); - - return ret; - -} - - -LayerTestResult LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory, - const boost::multi_array& input, - const boost::multi_array& outputExpected) -{ - bool cifgEnabled = true; - bool peepholeEnabled = true; - bool projectionEnabled = false; - // These are not the input and the output of Lstm yet - unsigned int batchSize = boost::numeric_cast(input.shape()[0]); - unsigned int inputSize = boost::numeric_cast(input.shape()[1]); - - unsigned int outputSize = boost::numeric_cast(outputExpected.shape()[1]); - - const unsigned int cellSize = outputSize; - - // Decide the shape of all input tensors - armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType()); - armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::GetDataType()); - armnn::TensorInfo cellStateInTensorInfo({batchSize, cellSize}, armnn::GetDataType()); - - unsigned int scratchBufferSize = cifgEnabled ? cellSize * 4 : cellSize * 3; - armnn::TensorInfo scratchBufferTensorInfo({batchSize, scratchBufferSize}, armnn::GetDataType()); - armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType()); - armnn::TensorInfo cellStateOutTensorInfo({batchSize, cellSize}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType()); - - // List of inputs - std::vector inputData; - inputData.assign(input.data(), input.data() + batchSize*inputSize); - auto inputTensor = MakeTensor(inputTensorInfo, inputData); - - std::vector outputStateInVector(batchSize * outputSize, 0.f); - auto outputStateInTensor = MakeTensor(outputStateInTensorInfo, outputStateInVector); - - std::vector cellStateInVector(batchSize * cellSize, 0.f); - auto cellStateInTensor = MakeTensor(cellStateInTensorInfo, cellStateInVector); - - - // Prepare all the weights in the descriptor for LSTM - armnn::LstmQueueDescriptor data; - armnn::TensorInfo tensorInfoInput({cellSize, inputSize}, armnn::GetDataType()); - armnn::TensorInfo tensorInfoOutput({cellSize, outputSize}, armnn::GetDataType()); - armnn::TensorInfo tensorInfoNumUnits({cellSize}, armnn::GetDataType()); - - auto inputToCellWeights = MakeTensor(tensorInfoInput, - {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, - 0.04717243f, 0.48944736f, -0.38535351f, - -0.17212132f}); - auto inputToForgetWeights = MakeTensor(tensorInfoInput, - {-0.55291498f, -0.42866567f, 0.13056988f, - -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, - 0.33826375f}); - auto inputToOutputWeights = MakeTensor(tensorInfoInput, - {0.10725588f, -0.02335852f, -0.55932593f, - -0.09426838f, -0.44257352f, 0.54939759f, - 0.01533556f, 0.42751634f}); - auto cellBias = MakeTensor(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f}); - auto forgetGateBias = MakeTensor(tensorInfoNumUnits, {1.f, 1.f, 1.f, 1.f}); - auto outputGateBias = MakeTensor(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f}); - - auto recurrentToCellWeights = MakeTensor(tensorInfoOutput, - {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, - 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, - 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, - 0.21193194f}); - auto recurrentToForgetWeights = MakeTensor(tensorInfoOutput, - {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, - 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, - -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}); - - auto recurrentToOutputWeights = MakeTensor(tensorInfoOutput, - {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, - -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, - 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}); - - auto cellToForgetWeights = MakeTensor(tensorInfoNumUnits, - {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}); - auto cellToOutputWeights = MakeTensor(tensorInfoNumUnits, - {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}); - - armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfoInput); - armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfoInput); - armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfoInput); - - armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfoNumUnits); - armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfoNumUnits); - armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfoNumUnits); - - armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfoOutput); - armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfoOutput); - armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfoOutput); - - - armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfoNumUnits); - armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfoNumUnits); - - AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]); - - AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]); - AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]); - AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]); - - AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]); - AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]); - - AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]); - AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]); - - - data.m_InputToCellWeights = &inputToCellWeightsTensor; - data.m_InputToForgetWeights = &inputToForgetWeightsTensor; - data.m_InputToOutputWeights = &inputToOutputWeightsTensor; - - data.m_CellBias = &cellBiasTensor; - data.m_ForgetGateBias = &forgetGateBiasTensor; - data.m_OutputGateBias = &outputGateBiasTensor; - - data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; - data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; - data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; - - data.m_CellToForgetWeights = &cellToForgetWeightsTensor; - data.m_CellToOutputWeights = &cellToOutputWeightsTensor; - - // other parameters for the descriptor - data.m_Parameters.m_CifgEnabled = cifgEnabled; - data.m_Parameters.m_ProjectionEnabled = projectionEnabled; - data.m_Parameters.m_PeepholeEnabled = peepholeEnabled; - - data.m_Parameters.m_ActivationFunc = 4; - data.m_Parameters.m_ClippingThresProj = 0.0; - data.m_Parameters.m_ClippingThresCell = 0.0; - - - // List of outputs - std::vector scratchBufferVector(batchSize * scratchBufferSize, 0.f); - auto scratchBufferTensor = MakeTensor(scratchBufferTensorInfo, scratchBufferVector); - LayerTestResult ret0(scratchBufferTensorInfo); - - // Output state for a certain time step - std::vector outputStateOutVector(batchSize * outputSize, 0.f); - auto outputStateOutTensor = MakeTensor(outputStateOutTensorInfo, outputStateOutVector); - LayerTestResult ret1(outputStateOutTensorInfo); - - // Cell state for a certain time step - std::vector cellStateOutVector(batchSize * cellSize, 0.f); - auto cellStateOutTensor = MakeTensor(cellStateOutTensorInfo, cellStateOutVector); - LayerTestResult ret2(cellStateOutTensorInfo); - - // Output for a certain time step - std::vector outputVector(batchSize * outputSize, 0.f); - auto outputTensor = MakeTensor(outputTensorInfo, outputVector); - std::vector outputData; - outputData.assign(outputExpected.data(), outputExpected.data() + batchSize*outputSize); - LayerTestResult ret3(outputTensorInfo); - ret3.outputExpected = MakeTensor(outputTensorInfo, outputData); - - // Prepare the inputs and outputs for the workload - std::unique_ptr inputHandle = - workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputStateInHandle = - workloadFactory.CreateTensorHandle(outputStateInTensorInfo); - std::unique_ptr cellStateInHandle = - workloadFactory.CreateTensorHandle(cellStateInTensorInfo); - - std::unique_ptr scratchBufferHandle = - workloadFactory.CreateTensorHandle(scratchBufferTensorInfo); - std::unique_ptr outputStateOutHandle = - workloadFactory.CreateTensorHandle(outputStateOutTensorInfo); - std::unique_ptr cellStateOutHandle = - workloadFactory.CreateTensorHandle(cellStateOutTensorInfo); - std::unique_ptr outputHandle = - workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); - AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); - - AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchBufferHandle.get()); - AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); - AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateLstm(data, info); - - - inputHandle->Allocate(); - outputStateInHandle->Allocate(); - cellStateInHandle->Allocate(); - - scratchBufferHandle->Allocate(); - outputStateOutHandle->Allocate(); - cellStateOutHandle->Allocate(); - outputHandle->Allocate(); - - - CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); - CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]); - CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]); - - CopyDataToITensorHandle(scratchBufferHandle.get(), &scratchBufferTensor[0][0]); - CopyDataToITensorHandle(outputStateOutHandle.get(), &outputStateOutTensor[0][0]); - CopyDataToITensorHandle(cellStateOutHandle.get(), &cellStateOutTensor[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret0.output[0][0], scratchBufferHandle.get()); - CopyDataFromITensorHandle(&ret1.output[0][0], outputStateOutHandle.get()); - CopyDataFromITensorHandle(&ret2.output[0][0], cellStateOutHandle.get()); - CopyDataFromITensorHandle(&ret3.output[0][0], outputHandle.get()); - - return ret3; -} diff --git a/src/armnn/backends/test/MemCopyTests.cpp b/src/armnn/backends/test/MemCopyTests.cpp deleted file mode 100644 index 44089c9d65..0000000000 --- a/src/armnn/backends/test/MemCopyTests.cpp +++ /dev/null @@ -1,180 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include -#include - -#include "armnn/ArmNN.hpp" -#include "backends/RefWorkloadFactory.hpp" -#if ARMCOMPUTECL_ENABLED -#include "backends/ClWorkloadFactory.hpp" -#endif -#if ARMCOMPUTENEON_ENABLED -#include "backends/NeonWorkloadFactory.hpp" -#endif -#include "backends/CpuTensorHandle.hpp" -#include "test/TensorHelpers.hpp" - -#include "TensorCopyUtils.hpp" -#include "WorkloadTestUtils.hpp" - -#if ARMCOMPUTECL_ENABLED || ARMCOMPUTENEON_ENABLED -#include "../ArmComputeTensorUtils.hpp" -#endif - -BOOST_AUTO_TEST_SUITE(MemCopyTestSuite) - -void MemCopyTest(armnn::IWorkloadFactory& srcWorkloadFactory, armnn::IWorkloadFactory& dstWorkloadFactory, - bool withSubtensors) -{ - const std::array shapeData = { { 1u, 1u, 6u, 5u } }; - const armnn::TensorShape tensorShape(4, shapeData.data()); - const armnn::TensorInfo tensorInfo(tensorShape, armnn::DataType::Float32); - boost::multi_array inputData = MakeTensor(tensorInfo, std::vector( - { - 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, - }) - ); - - boost::multi_array outputData(shapeData); - - auto inputTensorHandle = srcWorkloadFactory.CreateTensorHandle(tensorInfo); - auto outputTensorHandle = dstWorkloadFactory.CreateTensorHandle(tensorInfo); - - AllocateAndCopyDataToITensorHandle(inputTensorHandle.get(), inputData.data()); - outputTensorHandle->Allocate(); - - armnn::MemCopyQueueDescriptor memCopyQueueDesc; - armnn::WorkloadInfo workloadInfo; - - const unsigned int origin[4] = {}; - - auto workloadInput = (withSubtensors && srcWorkloadFactory.SupportsSubTensors()) - ? srcWorkloadFactory.CreateSubTensorHandle(*inputTensorHandle, tensorShape, origin) - : std::move(inputTensorHandle); - auto workloadOutput = (withSubtensors && dstWorkloadFactory.SupportsSubTensors()) - ? dstWorkloadFactory.CreateSubTensorHandle(*outputTensorHandle, tensorShape, origin) - : std::move(outputTensorHandle); - - AddInputToWorkload(memCopyQueueDesc, workloadInfo, tensorInfo, workloadInput.get()); - AddOutputToWorkload(memCopyQueueDesc, workloadInfo, tensorInfo, workloadOutput.get()); - - dstWorkloadFactory.CreateMemCopy(memCopyQueueDesc, workloadInfo)->Execute(); - - CopyDataFromITensorHandle(outputData.data(), workloadOutput.get()); - - BOOST_TEST(CompareTensors(inputData, outputData)); -} - -template -void MemCopyTest(bool withSubtensors) -{ - SrcWorkloadFactory srcWorkloadFactory; - DstWorkloadFactory dstWorkloadFactory; - MemCopyTest(srcWorkloadFactory, dstWorkloadFactory, withSubtensors); -} - -#if ARMCOMPUTECL_ENABLED || ARMCOMPUTENEON_ENABLED - -BOOST_AUTO_TEST_CASE(AclTypeConversions) -{ - arm_compute::Strides strides(1,2,3,4); - armnn::TensorShape convertedStrides = armnn::armcomputetensorutils::GetStrides(strides); - BOOST_TEST(convertedStrides[0] == 4); - BOOST_TEST(convertedStrides[1] == 3); - BOOST_TEST(convertedStrides[2] == 2); - BOOST_TEST(convertedStrides[3] == 1); - - arm_compute::TensorShape shape(5,6,7,8); - armnn::TensorShape convertedshape = armnn::armcomputetensorutils::GetShape(shape); - BOOST_TEST(convertedshape[0] == 8); - BOOST_TEST(convertedshape[1] == 7); - BOOST_TEST(convertedshape[2] == 6); - BOOST_TEST(convertedshape[3] == 5); -} -#endif - -#if ARMCOMPUTECL_ENABLED - -BOOST_AUTO_TEST_CASE(CopyBetweenCpuAndGpu) -{ - MemCopyTest(false); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenGpuAndCpu) -{ - MemCopyTest(false); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenCpuAndGpuWithSubtensors) -{ - MemCopyTest(true); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenGpuAndCpuWithSubtensors) -{ - MemCopyTest(true); -} - -#endif // ARMCOMPUTECL_ENABLED - -#if ARMCOMPUTENEON_ENABLED - -BOOST_AUTO_TEST_CASE(CopyBetweenCpuAndNeon) -{ - MemCopyTest(false); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenNeonAndCpu) -{ - MemCopyTest(false); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenCpuAndNeonWithSubtensors) -{ - MemCopyTest(true); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenNeonAndCpuWithSubtensors) -{ - MemCopyTest(true); -} - -#endif // ARMCOMPUTENEON_ENABLED - -#if ARMCOMPUTECL_ENABLED && ARMCOMPUTENEON_ENABLED - -BOOST_AUTO_TEST_CASE(CopyBetweenNeonAndGpu) -{ - MemCopyTest(false); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenGpuAndNeon) -{ - MemCopyTest(false); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenNeonAndGpuWithSubtensors) -{ - MemCopyTest(true); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenGpuAndNeonWithSubtensors) -{ - MemCopyTest(true); -} - -#endif - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/NormTestImpl.hpp b/src/armnn/backends/test/NormTestImpl.hpp deleted file mode 100644 index 2690313655..0000000000 --- a/src/armnn/backends/test/NormTestImpl.hpp +++ /dev/null @@ -1,241 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "armnn/Exceptions.hpp" -#include "armnn/LayerSupport.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -LayerTestResult SimpleNormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::NormalizationAlgorithmChannel normChannel, - armnn::NormalizationAlgorithmMethod normMethod) -{ - const unsigned int inputHeight = 2; - const unsigned int inputWidth = 2; - const unsigned int inputChannels = 1; - const unsigned int inputNum = 2; - - unsigned int outputHeight = inputHeight; - unsigned int outputWidth = inputWidth; - unsigned int outputChannels = inputChannels; - unsigned int outputNum = inputNum; - - unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; - unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; - - auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - LayerTestResult ret(outputTensorInfo); - - auto input = MakeTensor(inputTensorInfo, std::vector({ - // Batch #0 - 1.0f, 2.0f, - 3.0f, 4.0f, - // Batch #1 - 5.0f, 6.0f, - 7.0f, 8.0f - })); - - float alpha = 1.f; - float beta = 1.f; - float kappa = 1.f; - uint32_t normSize = 3; - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::NormalizationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Parameters.m_NormChannelType = normChannel; - data.m_Parameters.m_NormMethodType = normMethod; - data.m_Parameters.m_NormSize = normSize; - data.m_Parameters.m_Alpha = alpha; - data.m_Parameters.m_Beta = beta; - data.m_Parameters.m_K = kappa; - - armnn::PassthroughCpuTensorHandle refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]); - armnn::NormalizationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); - - std::unique_ptr workload = workloadFactory.CreateNormalization(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - switch (normMethod) - { - case armnn::NormalizationAlgorithmMethod::LocalBrightness: - { - switch (normChannel) - { - case armnn::NormalizationAlgorithmChannel::Within: - { - // When normalising within channels, the 3x3 kernel covers the entire 2x2 input at every index. - // Therefore, all output values should equal the inputs, but divided by: - // pow((kappa + (accumulatedScale * alpha)), beta) - // ...where accumulatedScale is the sum of every element squared. - float divisor[inputNum]; - for(int i = 0; i < boost::numeric_cast(inputNum); i++) - { - float accumulatedScale = input[i][0][0][0]*input[i][0][0][0] + - input[i][0][0][1]*input[i][0][0][1] + - input[i][0][1][0]*input[i][0][1][0] + - input[i][0][1][1]*input[i][0][1][1]; - divisor[i] = powf((kappa + accumulatedScale * alpha), beta); - } - ret.outputExpected = MakeTensor(outputTensorInfo, - std::vector({input[0][0][0][0]/divisor[0], - input[0][0][0][1]/divisor[0], - input[0][0][1][0]/divisor[0], - input[0][0][1][1]/divisor[0], - input[1][0][0][0]/divisor[1], - input[1][0][0][1]/divisor[1], - input[1][0][1][0]/divisor[1], - input[1][0][1][1]/divisor[1]})); - break; - } - case armnn::NormalizationAlgorithmChannel::Across: - { - // When normalising across channels, all output values should equal the inputs, but multiplied by: - // pow((kappa + (accumulatedScale * alpha)), -beta) - // ...where accumulatedScale is the sum of the inputs for adjacent channels for this element squared - // ...where adjacent channels means within half the normSize for the channel - // The test data has only one channel, so this is simplified below. - std::vector outputVector; - for (int n = 0; n < boost::numeric_cast(inputNum); ++n) - { - for (int h = 0; h < boost::numeric_cast(inputHeight); ++h) - { - for (int w = 0; w < boost::numeric_cast(inputWidth); ++w) - { - float accumulatedScale = input[n][0][h][w]*input[n][0][h][w]; - float scale = powf((kappa + accumulatedScale * alpha), -beta); - outputVector.push_back(input[n][0][h][w] * scale); - } - } - } - ret.outputExpected = MakeTensor(outputTensorInfo, outputVector); - break; - } - default: - { - throw armnn::UnimplementedException("Unsupported normalisation channel type, " - "only Across and Within are supported"); - } - } - break; - } - case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. - default: - { - throw armnn::UnimplementedException("Unsupported normalisation method type, " - "only LocalBrightness is supported"); - } - } - - return ret; -} - -LayerTestResult CompareNormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::NormalizationAlgorithmChannel normChannel, - armnn::NormalizationAlgorithmMethod normMethod) -{ - constexpr unsigned int inputNum = 5; - constexpr unsigned int inputChannels = 3; - constexpr unsigned int inputHeight = 32; - constexpr unsigned int inputWidth = 24; - - constexpr unsigned int outputNum = inputNum; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputWidth = inputWidth; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; - unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - LayerTestResult ret(outputTensorInfo); - - auto input = MakeRandomTensor(inputTensorInfo, 111234); - - constexpr float alpha = 1.f; - constexpr float beta = 1.f; - constexpr float kappa = 1.f; - constexpr uint32_t normSize = 5; - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::NormalizationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Parameters.m_NormChannelType = normChannel; - data.m_Parameters.m_NormMethodType = normMethod; - data.m_Parameters.m_NormSize = normSize; - data.m_Parameters.m_Alpha = alpha; - data.m_Parameters.m_Beta = beta; - data.m_Parameters.m_K = kappa; - - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - armnn::NormalizationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - // Don't execute if Normalization is not supported for the method and channel types, as an exception will be raised. - armnn::Compute compute = workloadFactory.GetCompute(); - const size_t reasonIfUnsupportedMaxLen = 255; - char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; - ret.supported = armnn::IsNormalizationSupported(compute, inputTensorInfo, outputTensorInfo, data.m_Parameters, - reasonIfUnsupported, reasonIfUnsupportedMaxLen); - if (!ret.supported) - { - return ret; - } - - std::unique_ptr workload = workloadFactory.CreateNormalization(data, info); - std::unique_ptr workloadRef = refWorkloadFactory.CreateNormalization(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} - diff --git a/src/armnn/backends/test/PermuteTestImpl.hpp b/src/armnn/backends/test/PermuteTestImpl.hpp deleted file mode 100644 index b49c539b2e..0000000000 --- a/src/armnn/backends/test/PermuteTestImpl.hpp +++ /dev/null @@ -1,225 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include -#include - -#include "test/TensorHelpers.hpp" -#include "QuantizeHelper.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -template -LayerTestResult SimplePermuteTestImpl( - armnn::IWorkloadFactory& workloadFactory, - armnn::PermuteDescriptor descriptor, - armnn::TensorInfo inputTensorInfo, - armnn::TensorInfo outputTensorInfo, - const std::vector& inputData, - const std::vector& outputExpectedData) -{ - auto input = MakeTensor(inputTensorInfo, inputData); - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, outputExpectedData); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::PermuteQueueDescriptor data; - data.m_Parameters = descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreatePermute(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult SimplePermuteFloat32TestCommon(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 1, 2, 2, 2 }; - unsigned int outputShape[] = { 1, 2, 2, 2 }; - - armnn::PermuteDescriptor descriptor; - descriptor.m_DimMappings = {0U, 3U, 1U, 2U}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - std::vector input = std::vector( - { - 1.0f, 2.0f, - 3.0f, 4.0f, - - 5.0f, 6.0f, - 7.0f, 8.0f - }); - - std::vector outputExpected = std::vector( - { - 1.0f, 5.0f, 2.0f, 6.0f, - 3.0f, 7.0f, 4.0f, 8.0f - }); - - return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, - outputTensorInfo, input, outputExpected); -} - -LayerTestResult SimplePermuteUint8TestCommon(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 1, 2, 2, 2 }; - unsigned int outputShape[] = { 1, 2, 2, 2 }; - - armnn::PermuteDescriptor descriptor; - descriptor.m_DimMappings = {0U, 3U, 1U, 2U}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(1.0f); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(1.0f); - - std::vector input = std::vector( - { - 1, 2, - 3, 4, - - 5, 6, - 7, 8 - }); - - std::vector outputExpected = std::vector( - { - 1, 5, 2, 6, - 3, 7, 4, 8 - }); - - return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, - outputTensorInfo, input, outputExpected); -} - -LayerTestResult -PermuteFloat32ValueSet1TestCommon(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 1, 2, 2, 3 }; - unsigned int outputShape[] = { 1, 3, 2, 2 }; - - armnn::PermuteDescriptor descriptor; - descriptor.m_DimMappings = {0U, 2U, 3U, 1U}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - std::vector input = std::vector( - { - 1.0f, 2.0f, 3.0f, - 11.0f, 12.0f, 13.0f, - 21.0f, 22.0f, 23.0f, - 31.0f, 32.0f, 33.0f, - }); - - std::vector outputExpected = std::vector( - { - 1.0f, 11.0f, 21.0f, 31.0f, - 2.0f, 12.0f, 22.0f, 32.0f, - 3.0f, 13.0f, 23.0f, 33.0f, - }); - - return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, - outputTensorInfo, input, outputExpected); -} - -LayerTestResult -PermuteFloat32ValueSet2TestCommon(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 1, 3, 2, 2 }; - unsigned int outputShape[] = { 1, 2, 2, 3 }; - - armnn::PermuteDescriptor descriptor; - descriptor.m_DimMappings = {0U, 3U, 1U, 2U}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - std::vector input = std::vector( - { - 1.0f, 11.0f, 21.0f, 31.0f, - 2.0f, 12.0f, 22.0f, 32.0f, - 3.0f, 13.0f, 23.0f, 33.0f, - }); - - std::vector outputExpected = std::vector( - { - 1.0f, 2.0f, 3.0f, - 11.0f, 12.0f, 13.0f, - 21.0f, 22.0f, 23.0f, - 31.0f, 32.0f, 33.0f, - }); - - return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, - outputTensorInfo, input, outputExpected); -} - -LayerTestResult -PermuteFloat32ValueSet3TestCommon(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 1, 2, 3, 3 }; - unsigned int outputShape[] = { 1, 3, 2, 3 }; - - armnn::PermuteDescriptor descriptor; - descriptor.m_DimMappings = {0U, 2U, 3U, 1U}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - std::vector input = std::vector( - { - 1.0f, 2.0f, 3.0f, - 11.0f, 12.0f, 13.0f, - 21.0f, 22.0f, 23.0f, - 31.0f, 32.0f, 33.0f, - 41.0f, 42.0f, 43.0f, - 51.0f, 52.0f, 53.0f, - }); - - std::vector outputExpected = std::vector( - { - 1.0f, 11.0f, 21.0f, 31.0f, 41.0f, 51.0f, - 2.0f, 12.0f, 22.0f, 32.0f, 42.0f, 52.0f, - 3.0f, 13.0f, 23.0f, 33.0f, 43.0f, 53.0f, - }); - - return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, - outputTensorInfo, input, outputExpected); -} diff --git a/src/armnn/backends/test/Pooling2dTestImpl.hpp b/src/armnn/backends/test/Pooling2dTestImpl.hpp deleted file mode 100644 index e8c7e86e9d..0000000000 --- a/src/armnn/backends/test/Pooling2dTestImpl.hpp +++ /dev/null @@ -1,1116 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include -#include - -#include "test/TensorHelpers.hpp" -#include "QuantizeHelper.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -#include - -template -LayerTestResult SimplePooling2dTestImpl( - armnn::IWorkloadFactory& workloadFactory, - armnn::Pooling2dDescriptor descriptor, - float qScale, - int32_t qOffset, - const boost::multi_array& input, - const boost::multi_array& outputExpected) -{ - unsigned int inputHeight = boost::numeric_cast(input.shape()[2]); - unsigned int inputWidth = boost::numeric_cast(input.shape()[3]); - unsigned int inputChannels = boost::numeric_cast(input.shape()[1]); - unsigned int inputBatchSize = boost::numeric_cast(input.shape()[0]); - - unsigned int outputHeight = boost::numeric_cast(outputExpected.shape()[2]); - unsigned int outputWidth = boost::numeric_cast(outputExpected.shape()[3]); - unsigned int outputChannels = boost::numeric_cast(outputExpected.shape()[1]); - unsigned int outputBatchSize = boost::numeric_cast(outputExpected.shape()[0]); - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - LayerTestResult result(outputTensorInfo); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::Pooling2dQueueDescriptor queueDescriptor; - queueDescriptor.m_Parameters = descriptor; - armnn::WorkloadInfo workloadInfo; - AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - - // Don't execute if Pooling is not supported, as an exception will be raised. - armnn::Compute compute = workloadFactory.GetCompute(); - const size_t reasonIfUnsupportedMaxLen = 255; - char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; - result.supported = armnn::IsPooling2dSupported(compute, inputTensorInfo, outputTensorInfo, - queueDescriptor.m_Parameters, - reasonIfUnsupported, reasonIfUnsupportedMaxLen); - if (!result.supported) - { - return result; - } - - std::unique_ptr workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - result.outputExpected = outputExpected; - - return result; -} - -// -// Tests max pooling with the following parameters: -// -// Pooling size: 3x3 -// Stride: (2,4) -// input size: 8x13 -// channels: 2 -// batch size: 2 -// -template -LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4TestCommon(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 4; - // forceNoPadding is mainly used for compatibility with ARM Compute. - // As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size. - descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; - descriptor.m_PadTop = descriptor.m_PadBottom = 0; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - unsigned int inputWidth = 8; - unsigned int inputHeight = 13; - unsigned int outputWidth = - (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / - descriptor.m_StrideX; - unsigned int outputHeight = - (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / - descriptor.m_StrideY; - unsigned int channels = 2; - unsigned int batchSize = 2; - - armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - std::vector singleChannelData({ - 0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f, - 1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f, - 8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f, - 8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f, - 5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f, - 1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f, - 9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f, - 1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f, - 6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f, - 8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f, - 7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f, - 4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f, - 3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f, - }); - - // Constructs input data. - std::vector inputData; - auto negator = [](float f) { return -f; }; - - // First image (two channels where the second channel is the negative of the first one). - inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); - std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); - - // Second image (same as first image). - inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); - std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); - - auto input = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, inputData)); - - // These were calculated manually. - auto shape(GetTensorShapeAsArray<4>(outputTensorInfo)); - boost::multi_array outputExpected(shape); - if (forceNoPadding) - { - outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 8.0f, 8.0f, 8.0f, - 9.0f, 7.0f, 9.0f, - 9.0f, 9.0f, 9.0f, - - 0.0f, 0.0f, -3.0f, - -1.0f, 0.0f, 0.0f, - -1.0f, -1.0f, -1.0f, - - 8.0f, 8.0f, 8.0f, - 9.0f, 7.0f, 9.0f, - 9.0f, 9.0f, 9.0f, - - 0.0f, 0.0f, -3.0f, - -1.0f, 0.0f, 0.0f, - -1.0f, -1.0f, -1.0f - })); - } - else - { - outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, - 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, - 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, - - 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, - 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f, - - 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, - 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, - 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, - - 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, - 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f - })); - } - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = descriptor.m_StrideY = 2; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - })); - - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 2.5f, 4.0f, - 1.0f, 2.5f, 4.0f, - 1.0f, 2.5f, 4.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 100; - descriptor.m_StrideX = descriptor.m_StrideY = 5; - descriptor.m_PadLeft = 50; - descriptor.m_PadRight = 50; - descriptor.m_PadTop = 50; - descriptor.m_PadBottom = 50; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - std::vector inputVec; - - for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i) - { - inputVec.push_back(1); - } - - auto input = MakeTensor(inputTensorInfo, inputVec); - - std::vector outputVec; - - for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i) - { - outputVec.push_back(1); - } - - auto outputExpected = MakeTensor(outputTensorInfo, outputVec); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult SimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = descriptor.m_StrideY = 2; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 7.0f, 1.0f, 7.0f, - 1.0f, 7.0f, 1.0f, 7.0f, - 1.0f, 7.0f, 1.0f, 7.0f, - 1.0f, 7.0f, 1.0f, 7.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 5.0f, 5.0f, - 5.0f, 5.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult L2Pooling2dSize3Stride1TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 2.0f, 1.0f, 5.0f, 2.0f, - 1.0f, 2.0f, 2.0f, 1.0f, - 5.0f, 4.0f, 1.0f, 5.0f, - 2.0f, 1.0f, 5.0f, 2.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 3.0f, 3.0f, - 3.0f, 3.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult L2Pooling2dSize3Stride3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 3; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType()); - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 3.0f, 3.0f, 3.0f, - 3.0f, 3.0f, 3.0f, - 3.0f, 3.0f, 3.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult L2Pooling2dSize3Stride4TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 4; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType()); - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 3.0f, 3.0f, - 3.0f, 3.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult L2Pooling2dSize7TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 7; - descriptor.m_StrideX = descriptor.m_StrideY = 7; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType()); - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f, - 8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f, - 0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType()); - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 3.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult L2Pooling2dSize9TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 9; - descriptor.m_StrideX = descriptor.m_StrideY = 9; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType()); - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType()); - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 3.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult AsymmetricNonSquarePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); - - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = 2; - descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 1; - descriptor.m_PadLeft = 2; - descriptor.m_PadRight = 0; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 2; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - // Construct input data. - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 3.0f, 4.0f, - })); - - // These were calculated manually. - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 0.0f, 3.0f, 0.0f, 3.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult ComparePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::PoolingAlgorithm poolingType, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - const unsigned int inputWidth = 16; - const unsigned int inputHeight = 32; - const unsigned int channelCount = 2; - const unsigned int batchSize = 5; - - const unsigned int poolSize = 3; - const unsigned int strideX = 2; - const unsigned int strideY = 4; - const unsigned int padX = 0; - const unsigned int padY = 0; - - const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX; - const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth }; - unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType()); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - boost::multi_array input = MakeRandomTensor(inputTensorInfo, 81715); - - LayerTestResult comparisonResult(outputTensorInfo); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::Pooling2dQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Parameters.m_PoolType = poolingType; - data.m_Parameters.m_PoolWidth = poolSize; - data.m_Parameters.m_PoolHeight = poolSize; - data.m_Parameters.m_StrideX = strideX; - data.m_Parameters.m_StrideY = strideY; - data.m_Parameters.m_PadLeft = padX; - data.m_Parameters.m_PadRight = padX; - data.m_Parameters.m_PadTop = padY; - data.m_Parameters.m_PadBottom = padY; - data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - // Don't execute if Pooling is not supported, as an exception will be raised. - armnn::Compute compute = workloadFactory.GetCompute(); - const size_t reasonIfUnsupportedMaxLen = 255; - char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; - comparisonResult.supported = armnn::IsPooling2dSupported(compute, inputTensorInfo, outputTensorInfo, - data.m_Parameters, - reasonIfUnsupported, reasonIfUnsupportedMaxLen); - if (!comparisonResult.supported) - { - return comparisonResult; - } - - armnn::Pooling2dQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr workload = workloadFactory.CreatePooling2d(data, info); - std::unique_ptr workloadRef = refWorkloadFactory.CreatePooling2d(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); - - workload->Execute(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); - - return comparisonResult; -} - -// -// Tests max pooling with the following parameters: -// -// Pooling size: 2x2 -// Stride: (2,2) -// input size: 4x4 -// channels: 1 -// batch size: 1 -// -template -LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2TestCommon(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 2; - descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; - descriptor.m_PadTop = descriptor.m_PadBottom = 0; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - unsigned int inputWidth = 4; - unsigned int inputHeight = 4; - unsigned int outputWidth = - (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / - descriptor.m_StrideX; - unsigned int outputHeight = - (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / - descriptor.m_StrideY; - unsigned int channels = 1; - unsigned int batchSize = 1; - - std::vector inputData = { - 510.0f, 222.0f, 780.0f, 654.0f, - 141.0f, 276.0f, 15.0f, 546.0f, - 303.0f, 618.0f, 582.0f, 339.0f, - 438.0f, 564.0f, 573.0f, 402.0f - }; - - // Note that left and right edges will be 0.f, due to the 2x2 max pooling only accessing zeros here. - std::vector expectedOutputDataWithPadding = { - 0.0f, 510.0f, 780.0f, 654.0f, 0.0f, - 0.0f, 438.0f, 618.0f, 402.0f, 0.0f - }; - - std::vector expectedOutputDataNoPadding = { - 510.0f, 780.0f, - 618.0f, 582.0f - }; - - armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType()); - - // Scale and offset should match input - we're just calculating maximum values. - armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, inputData)); - - auto outputExpected = MakeTensor(outputTensorInfo, - forceNoPadding ? QuantizedVector(qScale, qOffset, expectedOutputDataNoPadding) : - QuantizedVector(qScale, qOffset, expectedOutputDataWithPadding)); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -// -// Tests max pooling with the following parameters: -// -// Pooling size: 3x2 -// Stride: (2,2) -// input size: 3x2 -// channels: 1 -// batch size: 1 -// -template -LayerTestResult IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( - armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = 3; - descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 2; - descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1; - descriptor.m_PadRight = descriptor.m_PadLeft; - descriptor.m_PadTop = 0; - descriptor.m_PadBottom = 0; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - unsigned int inputWidth = 3; - unsigned int inputHeight = 2; - unsigned int outputWidth = - (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / - descriptor.m_StrideX; - unsigned int outputHeight = - (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / - descriptor.m_StrideY; - unsigned int channels = 1; - unsigned int batchSize = 1; - - std::vector inputData = { - 3.0f, 6.0f, 9.0f, - 12.0f, 15.0f, 18.0f, - }; - - std::vector expectedOutputDataWithPadding = { - 6.0f, 8.0f, - }; - - std::vector expectedOutputDataNoPadding = { - 10.5f, - }; - - armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType()); - - // Scale and offset should match input - we're just calculating average values. - armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, inputData)); - - auto outputExpected = MakeTensor(outputTensorInfo, - forceNoPadding ? QuantizedVector(qScale, qOffset, expectedOutputDataNoPadding) : - QuantizedVector(qScale, qOffset, expectedOutputDataWithPadding)); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - - -template -LayerTestResult IgnorePaddingSimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = descriptor.m_StrideY = 2; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - -1.0f, -2.0f, 3.0f, 4.0f, - -1.0f, -2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, -3.0f, -4.0f, - })); - - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - -1.0f, 3.0f, 4.0f, - 1.0f, 3.0f, 4.0f, - 1.0f, 2.0f, -4.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult IgnorePaddingMaxPooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 1; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - -1.0f, -2.0f, 3.0f, 4.0f, - -1.0f, -2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, -3.0f, -4.0f, - })); - - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - -1.0f, 3.0f, 4.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 4.0f, - 2.0f, 2.0f, 2.0f, -3.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult IgnorePaddingSimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = descriptor.m_StrideY = 2; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 12.0f, 20.0f, 32.0f, 40.0f, - 12.0f, 20.0f, 32.0f, 40.0f, - 12.0f, 20.0f, 32.0f, 40.0f, - 12.0f, 20.0f, 32.0f, 40.0f, - })); - - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 3.0f, 13.0f, 10.0f, - 6.0f, 26.0f, 20.0f, - 3.0f, 13.0f, 10.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 2; - descriptor.m_PadLeft = 0; - descriptor.m_PadRight = 0; - descriptor.m_PadTop = 0; - descriptor.m_PadBottom = 0; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4}, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - })); - - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 2.0f, 3.5f, - 2.0f, 3.5f - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult IgnorePaddingAveragePooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 1; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 9.0f, 27.0f, 18.0f, 36.0f, - 18.0f, 9.0f, 18.0f, 9.0f, - 27.0f, 18.0f, 9.0f, 27.0f, - 9.0f, 27.0f, 9.0f, 18.0f, - })); - - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 7.0f, 11.0f, 13.0f, 9.0f, - 12.0f, 17.0f, 19.0f, 13.0f, - 12.0f, 16.0f, 16.0f, 10.0f, - 9.0f, 11.0f, 12.0f, 7.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult IgnorePaddingSimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = descriptor.m_StrideY = 2; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 2.0f, 4.0f, 8.0f, 16.0f, - 4.0f, 2.0f, 2.0f, 4.0f, - 8.0f, 2.0f, 4.0f, 2.0f, - 16.0f, 2.0f, 2.0f, 8.0f, - })); - - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 4.4721f, 8.0f, - 4.4721f, 2.6457f, 2.236f, - 8.0f, 1.4142f, 4.0f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template -LayerTestResult IgnorePaddingL2Pooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 1; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor(inputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - })); - - auto outputExpected = MakeTensor(outputTensorInfo, - QuantizedVector(qScale, qOffset, { - 1.0540f, 1.7638f, 2.5385f, 2.3570f, - 1.2909f, 2.1602f, 3.1091f, 2.8867f, - 1.2909f, 2.1602f, 3.1091f, 2.8867f, - 1.0540f, 1.7638f, 2.5385f, 2.3570f, - })); - - return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} diff --git a/src/armnn/backends/test/QuantizeHelper.hpp b/src/armnn/backends/test/QuantizeHelper.hpp deleted file mode 100644 index bb4e561d59..0000000000 --- a/src/armnn/backends/test/QuantizeHelper.hpp +++ /dev/null @@ -1,91 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include - -#include -#include -#include -#include - -template -struct SelectiveQuantizer -{ - static T Quantize(float value, float scale, int32_t offset) - { - return armnn::Quantize(value, scale, offset); - } - - static float Dequantize(T value, float scale, int32_t offset) - { - return armnn::Dequantize(value, scale, offset); - } -}; - -template -struct SelectiveQuantizer -{ - static T Quantize(float value, float scale, int32_t offset) - { - boost::ignore_unused(scale, offset); - return value; - } - - static float Dequantize(T value, float scale, int32_t offset) - { - boost::ignore_unused(scale, offset); - return value; - } -}; - -template -T SelectiveQuantize(float value, float scale, int32_t offset) -{ - return SelectiveQuantizer()>::Quantize(value, scale, offset); -}; - -template -float SelectiveDequantize(T value, float scale, int32_t offset) -{ - return SelectiveQuantizer()>::Dequantize(value, scale, offset); -}; - -template -struct IsFloatingPointIterator -{ - static constexpr bool value=std::is_floating_point::value_type>::value; -}; - -template ::value, int>::type=0 // Makes sure fp iterator is valid. -> -std::vector QuantizedVector(float qScale, int32_t qOffset, FloatIt first, FloatIt last) -{ - std::vector quantized; - quantized.reserve(boost::numeric_cast(std::distance(first, last))); - - for (auto it = first; it != last; ++it) - { - auto f = *it; - T q =SelectiveQuantize(f, qScale, qOffset); - quantized.push_back(q); - } - - return quantized; -} - -template -std::vector QuantizedVector(float qScale, int32_t qOffset, const std::vector& array) -{ - return QuantizedVector(qScale, qOffset, array.begin(), array.end()); -} - -template -std::vector QuantizedVector(float qScale, int32_t qOffset, std::initializer_list array) -{ - return QuantizedVector(qScale, qOffset, array.begin(), array.end()); -} diff --git a/src/armnn/backends/test/Reference.cpp b/src/armnn/backends/test/Reference.cpp deleted file mode 100644 index 62786a9ec4..0000000000 --- a/src/armnn/backends/test/Reference.cpp +++ /dev/null @@ -1,253 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include - -#include "LayerTests.hpp" -#include "test/TensorHelpers.hpp" - -#include "backends/RefWorkloadFactory.hpp" - -#include "test/UnitTests.hpp" - -BOOST_AUTO_TEST_SUITE(Compute_Reference) -using FactoryType = armnn::RefWorkloadFactory; - -// ============================================================================ -// UNIT tests - -// Convolution -ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x5, SimpleConvolution2d3x5Test, true) -ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x5Uint8, SimpleConvolution2d3x5Uint8Test, true) - -ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false) -ARMNN_AUTO_TEST_CASE(UnbiasedConvolutionUint8, SimpleConvolution2d3x5Uint8Test, false) - -ARMNN_AUTO_TEST_CASE(SimpleConvolution1d, Convolution1dTest, true) -ARMNN_AUTO_TEST_CASE(SimpleConvolution1dUint8, Convolution1dUint8Test, true) - -ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x3, SimpleConvolution2d3x3Test, true) -ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x3Uint8, SimpleConvolution2d3x3Uint8Test, true) - -ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2dSquare, SimpleConvolution2d3x3Test, false) - -ARMNN_AUTO_TEST_CASE(SimpleConvolution2dAsymmetricPaddingLargerThanHalfKernelSize, - Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest) -ARMNN_AUTO_TEST_CASE(SimpleConvolution2dAsymmetricPadding, Convolution2dAsymmetricPaddingTest) - -// Depthwise Convolution -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2d, DepthwiseConvolution2dTest, true) -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dUint8, DepthwiseConvolution2dUint8Test, true) - -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2d, DepthwiseConvolution2dTest, false) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dUint8, DepthwiseConvolution2dUint8Test, false) - -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, true) -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, true) - -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, false) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, false) - -ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dAsymmetric, DepthwiseConvolution2dAsymmetricTest, true) -ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dAsymmetric, DepthwiseConvolution2dAsymmetricTest, false) - -// Pooling -ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize2x2Stride2x2, SimpleMaxPooling2dSize2x2Stride2x2Test, false) -ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize2x2Stride2x2Uint8, SimpleMaxPooling2dSize2x2Stride2x2Uint8Test, false) - -ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4, SimpleMaxPooling2dSize3x3Stride2x4Test, false) -ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4Uint8, SimpleMaxPooling2dSize3x3Stride2x4Uint8Test, false) - -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleMaxPooling2d, IgnorePaddingSimpleMaxPooling2dTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleMaxPooling2dUint8, IgnorePaddingSimpleMaxPooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingMaxPooling2dSize3, IgnorePaddingMaxPooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingMaxPooling2dSize3Uint8, IgnorePaddingMaxPooling2dSize3Uint8Test) - -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2d, IgnorePaddingSimpleAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dUint8, IgnorePaddingSimpleAveragePooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPadding, IgnorePaddingSimpleAveragePooling2dNoPaddingTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPaddingUint8, - IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3, IgnorePaddingAveragePooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3Uint8, IgnorePaddingAveragePooling2dSize3Uint8Test) - -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleL2Pooling2d, IgnorePaddingSimpleL2Pooling2dTest) -ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleL2Pooling2dUint8, IgnorePaddingSimpleL2Pooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingL2Pooling2dSize3, IgnorePaddingL2Pooling2dSize3Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingL2Pooling2dSize3Uint8, IgnorePaddingL2Pooling2dSize3Uint8Test) - -ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2d, SimpleAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2dUint8, SimpleAveragePooling2dUint8Test) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2, - IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, false) -ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2NoPadding, - IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, true) - -ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2d, LargeTensorsAveragePooling2dTest) -ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2dUint8, LargeTensorsAveragePooling2dUint8Test) - -ARMNN_AUTO_TEST_CASE(SimpleL2Pooling2d, SimpleL2Pooling2dTest) -ARMNN_AUTO_TEST_CASE(SimpleL2Pooling2dUint8, SimpleL2Pooling2dUint8Test) - -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize7, L2Pooling2dSize7Test) -ARMNN_AUTO_TEST_CASE(L2Pooling2dSize7Uint8, L2Pooling2dSize7Uint8Test) - -ARMNN_AUTO_TEST_CASE(AsymmNonSquarePooling2d, AsymmetricNonSquarePooling2dTest) -ARMNN_AUTO_TEST_CASE(AsymmNonSquarePooling2dUint8, AsymmetricNonSquarePooling2dUint8Test) - -// Activation -ARMNN_AUTO_TEST_CASE(ConstantLinearActivation, ConstantLinearActivationTest) -ARMNN_AUTO_TEST_CASE(ConstantLinearActivationUint8, ConstantLinearActivationUint8Test) - -ARMNN_AUTO_TEST_CASE(SimpleNormalizationAcross, SimpleNormalizationAcrossTest) -ARMNN_AUTO_TEST_CASE(SimpleNormalizationWithin, SimpleNormalizationWithinTest) - -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1, SimpleSoftmaxTest, 1.0f) -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2, SimpleSoftmaxTest, 2.0f) -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1Uint8, SimpleSoftmaxUint8Test, 1.0f) -ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2Uint8, SimpleSoftmaxUint8Test, 2.0f) - -ARMNN_AUTO_TEST_CASE(SimpleSigmoid, SimpleSigmoidTest) -ARMNN_AUTO_TEST_CASE(SimpleSigmoidUint8, SimpleSigmoidUint8Test) - -ARMNN_AUTO_TEST_CASE(ReLu1, BoundedReLuUpperAndLowerBoundTest) -ARMNN_AUTO_TEST_CASE(ReLu6, BoundedReLuUpperBoundOnlyTest) -ARMNN_AUTO_TEST_CASE(ReLu1Uint8, BoundedReLuUint8UpperAndLowerBoundTest) -ARMNN_AUTO_TEST_CASE(ReLu6Uint8, BoundedReLuUint8UpperBoundOnlyTest) - -// Fully Conected -ARMNN_AUTO_TEST_CASE(SimpleFullyConnected, FullyConnectedFloat32Test, false, false) -ARMNN_AUTO_TEST_CASE(FullyConnectedUint8, FullyConnectedUint8Test, false) -ARMNN_AUTO_TEST_CASE(SimpleFullyConnectedWithBias, FullyConnectedFloat32Test, true, false) -ARMNN_AUTO_TEST_CASE(FullyConnectedBiasedUint8, FullyConnectedUint8Test, true) -ARMNN_AUTO_TEST_CASE(SimpleFullyConnectedWithTranspose, FullyConnectedFloat32Test, false, true) - -ARMNN_AUTO_TEST_CASE(FullyConnectedLarge, FullyConnectedLargeTest, false) -ARMNN_AUTO_TEST_CASE(FullyConnectedLargeTransposed, FullyConnectedLargeTest, true) - -// Splitter -ARMNN_AUTO_TEST_CASE(SimpleSplitter, SplitterTest) -ARMNN_AUTO_TEST_CASE(SimpleSplitterUint8, SplitterUint8Test) - -ARMNN_AUTO_TEST_CASE(CopyViaSplitter, CopyViaSplitterTest) -ARMNN_AUTO_TEST_CASE(CopyViaSplitterUint8, CopyViaSplitterUint8Test) - -// Merger -ARMNN_AUTO_TEST_CASE(SimpleMerger, MergerTest) -ARMNN_AUTO_TEST_CASE(MergerUint8, MergerUint8Test) - -// Add -ARMNN_AUTO_TEST_CASE(SimpleAdd, AdditionTest) -ARMNN_AUTO_TEST_CASE(AddBroadcast1Element, AdditionBroadcast1ElementTest) -ARMNN_AUTO_TEST_CASE(AddBroadcast, AdditionBroadcastTest) - -ARMNN_AUTO_TEST_CASE(AdditionUint8, AdditionUint8Test) -ARMNN_AUTO_TEST_CASE(AddBroadcastUint8, AdditionBroadcastUint8Test) -ARMNN_AUTO_TEST_CASE(AddBroadcast1ElementUint8, AdditionBroadcast1ElementUint8Test) - -// Sub -ARMNN_AUTO_TEST_CASE(SimpleSub, SubtractionTest) -ARMNN_AUTO_TEST_CASE(SubBroadcast1Element, SubtractionBroadcast1ElementTest) -ARMNN_AUTO_TEST_CASE(SubBroadcast, SubtractionBroadcastTest) - -ARMNN_AUTO_TEST_CASE(SubtractionUint8, SubtractionUint8Test) -ARMNN_AUTO_TEST_CASE(SubBroadcastUint8, SubtractionBroadcastUint8Test) -ARMNN_AUTO_TEST_CASE(SubBroadcast1ElementUint8, SubtractionBroadcast1ElementUint8Test) - -// Div -ARMNN_AUTO_TEST_CASE(SimpleDivision, DivisionTest) -ARMNN_AUTO_TEST_CASE(DivisionByZero, DivisionByZeroTest) -ARMNN_AUTO_TEST_CASE(DivisionBroadcast1Element, DivisionBroadcast1ElementTest) -ARMNN_AUTO_TEST_CASE(DivisionBroadcast1DVector, DivisionBroadcast1DVectorTest) -// NOTE: division by zero for quantized div needs more attention -// see IVGCVSW-1849 -ARMNN_AUTO_TEST_CASE(DivisionUint8, DivisionUint8Test) -ARMNN_AUTO_TEST_CASE(DivisionUint8Broadcast1Element, DivisionBroadcast1ElementUint8Test) -ARMNN_AUTO_TEST_CASE(DivisionUint8Broadcast1DVector, DivisionBroadcast1DVectorUint8Test) - -// Mul -ARMNN_AUTO_TEST_CASE(SimpleMultiplication, MultiplicationTest) -ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1Element, MultiplicationBroadcast1ElementTest) -ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVector, MultiplicationBroadcast1DVectorTest) -ARMNN_AUTO_TEST_CASE(MultiplicationUint8, MultiplicationUint8Test) -ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1ElementUint8, MultiplicationBroadcast1ElementUint8Test) -ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVectorUint8, MultiplicationBroadcast1DVectorUint8Test) - -// Batch Norm -ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest) -ARMNN_AUTO_TEST_CASE(BatchNormUint8, BatchNormUint8Test) - -// Resize Bilinear -ARMNN_AUTO_TEST_CASE(SimpleResizeBilinear, SimpleResizeBilinearTest) -ARMNN_AUTO_TEST_CASE(SimpleResizeBilinearUint8, SimpleResizeBilinearUint8Test) -ARMNN_AUTO_TEST_CASE(ResizeBilinearNop, ResizeBilinearNopTest) -ARMNN_AUTO_TEST_CASE(ResizeBilinearNopUint8, ResizeBilinearNopUint8Test) -ARMNN_AUTO_TEST_CASE(ResizeBilinearSqMin, ResizeBilinearSqMinTest) -ARMNN_AUTO_TEST_CASE(ResizeBilinearSqMinUint8, ResizeBilinearSqMinUint8Test) -ARMNN_AUTO_TEST_CASE(ResizeBilinearMin, ResizeBilinearMinTest) -ARMNN_AUTO_TEST_CASE(ResizeBilinearMinUint8, ResizeBilinearMinUint8Test) -ARMNN_AUTO_TEST_CASE(ResizeBilinearMag, ResizeBilinearMagTest) -ARMNN_AUTO_TEST_CASE(ResizeBilinearMagUint8, ResizeBilinearMagUint8Test) - -// Fake Quantization -ARMNN_AUTO_TEST_CASE(FakeQuantization, FakeQuantizationTest) - -// L2 Noramlization -ARMNN_AUTO_TEST_CASE(L2Normalization1d, L2Normalization1dTest) -ARMNN_AUTO_TEST_CASE(L2Normalization2d, L2Normalization2dTest) -ARMNN_AUTO_TEST_CASE(L2Normalization3d, L2Normalization3dTest) -ARMNN_AUTO_TEST_CASE(L2Normalization4d, L2Normalization4dTest) - -// Constant -ARMNN_AUTO_TEST_CASE(Constant, ConstantTest) -ARMNN_AUTO_TEST_CASE(ConstantUint8, ConstantUint8Test) - -// Concat -ARMNN_AUTO_TEST_CASE(Concatenation1d, Concatenation1dTest) -ARMNN_AUTO_TEST_CASE(Concatenation1dUint8, Concatenation1dUint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0, Concatenation2dDim0Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0Uint8, Concatenation2dDim0Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1, Concatenation2dDim1Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1Uint8, Concatenation2dDim1Uint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0DiffInputDims, Concatenation2dDim0DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim0DiffInputDimsUint8, Concatenation2dDim0DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1DiffInputDims, Concatenation2dDim1DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation2dDim1DiffInputDimsUint8, Concatenation2dDim1DiffInputDimsUint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0, Concatenation3dDim0Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0Uint8, Concatenation3dDim0Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1, Concatenation3dDim1Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1Uint8, Concatenation3dDim1Uint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2, Concatenation3dDim2Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2Uint8, Concatenation3dDim2Uint8Test) - -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0DiffInputDims, Concatenation3dDim0DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim0DiffInputDimsUint8, Concatenation3dDim0DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1DiffInputDims, Concatenation3dDim1DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim1DiffInputDimsUint8, Concatenation3dDim1DiffInputDimsUint8Test) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2DiffInputDims, Concatenation3dDim2DiffInputDimsTest) -ARMNN_AUTO_TEST_CASE(Concatenation3dDim2DiffInputDimsUint8, Concatenation3dDim2DiffInputDimsUint8Test) - -// Floor -ARMNN_AUTO_TEST_CASE(SimpleFloor, SimpleFloorTest) - -// Reshape -ARMNN_AUTO_TEST_CASE(SimpleReshapeFloat32, SimpleReshapeFloat32Test) -ARMNN_AUTO_TEST_CASE(SimpleReshapeUint8, SimpleReshapeUint8Test) - -// Permute -ARMNN_AUTO_TEST_CASE(SimplePermuteFloat32, SimplePermuteFloat32Test) -ARMNN_AUTO_TEST_CASE(SimplePermuteUint8, SimplePermuteUint8Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet1, PermuteFloat32ValueSet1Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet2, PermuteFloat32ValueSet2Test) -ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test) - -// Convert from Float16 to Float32 -ARMNN_AUTO_TEST_CASE(SimpleConvertFp16ToFp32, SimpleConvertFp16ToFp32Test) -// Convert from Float32 to Float16 -ARMNN_AUTO_TEST_CASE(SimpleConvertFp32ToFp16, SimpleConvertFp32ToFp16Test) - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/ReshapeTestImpl.hpp b/src/armnn/backends/test/ReshapeTestImpl.hpp deleted file mode 100644 index 5d32d9d3a6..0000000000 --- a/src/armnn/backends/test/ReshapeTestImpl.hpp +++ /dev/null @@ -1,177 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include -#include - -#include "test/TensorHelpers.hpp" -#include "QuantizeHelper.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -template -LayerTestResult SimpleReshapeTestImpl( - armnn::IWorkloadFactory& workloadFactory, - armnn::TensorInfo inputTensorInfo, - armnn::TensorInfo outputTensorInfo, - const std::vector& inputData, - const std::vector& outputExpectedData) -{ - auto input = MakeTensor(inputTensorInfo, inputData); - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, outputExpectedData); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ReshapeQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateReshape(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult SimpleReshapeFloat32Test(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 2, 2, 3, 3 }; - unsigned int outputShape[] = { 2, 2, 9, 1 }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - std::vector input = std::vector( - { - 0.0f, 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, - }); - - std::vector outputExpected = std::vector( - { - 0.0f, 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, - }); - - return SimpleReshapeTestImpl(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected); -} - -LayerTestResult SimpleFloorTest(armnn::IWorkloadFactory& workloadFactory) -{ - const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo(inputTensorInfo); - - auto input = MakeTensor(inputTensorInfo, - { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, - 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f }); - - LayerTestResult ret(outputTensorInfo); - ret.outputExpected = MakeTensor(outputTensorInfo, - { -38.0f, -16.0f, -9.0f, -2.0f, -2.0f, -2.0f, -1.0f, -1.0f, 0.0f, - 1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 2.0f, 8.0f, 15.0f, 37.0f }); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::FloorQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateFloor(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult SimpleReshapeUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 2, 2, 3, 3 }; - unsigned int outputShape[] = { 2, 2, 9, 1 }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(1.0f); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(1.0f); - - std::vector input = std::vector( - { - 0, 1, 2, - 3, 4, 5, - 6, 7, 8, - - 9, 10, 11, - 12, 13, 14, - 15, 16, 17, - - 18, 19, 20, - 21, 22, 23, - 24, 25, 26, - - 27, 28, 29, - 30, 31, 32, - 33, 34, 35, - }); - - std::vector outputExpected = std::vector( - { - 0, 1, 2, 3, 4, 5, 6, 7, 8, - - 9, 10, 11, 12, 13, 14, 15, 16, 17, - - 18, 19, 20, 21, 22, 23, 24, 25, 26, - - 27, 28, 29, 30, 31, 32, 33, 34, 35, - }); - - return SimpleReshapeTestImpl(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected); -} diff --git a/src/armnn/backends/test/SoftmaxTestImpl.hpp b/src/armnn/backends/test/SoftmaxTestImpl.hpp deleted file mode 100644 index 5bc13fa21c..0000000000 --- a/src/armnn/backends/test/SoftmaxTestImpl.hpp +++ /dev/null @@ -1,153 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include -#include - -#include "test/TensorHelpers.hpp" -#include "QuantizeHelper.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -#include - -template -LayerTestResult SimpleSoftmaxTestImpl(armnn::IWorkloadFactory& workloadFactory, float beta) -{ - using std::exp; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 2, 4 }; - - inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType()); - float qScale = 1.f / 256.f; - int qOffset = 0; - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - - outputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType()); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - - LayerTestResult ret(outputTensorInfo); - - // Each row is independently softmax'd. - auto input = MakeTensor(inputTensorInfo, std::vector( - QuantizedVector(qScale, 0, { - 0.f, 1.f, 0.f, 0.f, - .5f, 0.f, 0.f, 0.f, - }))); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::SoftmaxQueueDescriptor data; - data.m_Parameters.m_Beta = beta; - - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr workload = workloadFactory.CreateSoftmax(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); - - float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta), - exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) }; - float sum0 = x0[0] + x0[1] + x0[2] + x0[3]; - float x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta), - exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) }; - float sum1 = x1[0] + x1[1] + x1[2] + x1[3]; - - ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( - QuantizedVector(qScale, qOffset, { - x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0, - x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 - }))); - - return ret; -} - -template -LayerTestResult CompareSoftmaxTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - float beta) -{ - - const int batchSize = 20; - const int channels = 30; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { batchSize, channels }; - - inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType()); - outputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType()); - float qScale = 1.f / 256.f; - int qOffset = 0; - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - - - LayerTestResult ret(outputTensorInfo); - auto input = MakeRandomTensor(inputTensorInfo, 0xF00D, 0.0f, 1.0f); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::SoftmaxQueueDescriptor data; - data.m_Parameters.m_Beta = beta; - - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - - armnn::SoftmaxQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr workload = workloadFactory.CreateSoftmax(data, info); - std::unique_ptr workloadRef = refWorkloadFactory.CreateSoftmax(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0], outputHandleRef.get()); - - return ret; -} diff --git a/src/armnn/backends/test/SplitterTestImpl.hpp b/src/armnn/backends/test/SplitterTestImpl.hpp deleted file mode 100644 index 5dcc412d0e..0000000000 --- a/src/armnn/backends/test/SplitterTestImpl.hpp +++ /dev/null @@ -1,307 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include -#include - -#include "test/TensorHelpers.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -#include "backends/test/QuantizeHelper.hpp" - - -template -std::vector> 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()); - - // Outputs of the original split. - armnn::TensorInfo outputTensorInfo1({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo2({ outputChannels2, outputHeight2, outputWidth2 }, armnn::GetDataType()); - - // Outputs of the subsequent subtensor split. - armnn::TensorInfo outputTensorInfo3({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType()); - armnn::TensorInfo outputTensorInfo4({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType()); - - // 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()) - { - 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 ret1(outputTensorInfo1); - LayerTestResult ret2(outputTensorInfo2); - LayerTestResult ret3(outputTensorInfo3); - LayerTestResult ret4(outputTensorInfo4); - - auto input = MakeTensor(inputTensorInfo, std::vector( - QuantizedVector(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(outputTensorInfo1, std::vector( - QuantizedVector(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(outputTensorInfo2, std::vector( - QuantizedVector(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(outputTensorInfo3, std::vector( - QuantizedVector(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(outputTensorInfo4, std::vector( - QuantizedVector(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 wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of output[0]. - armnn::SplitterQueueDescriptor::ViewOrigin window1(wOrigin1); - - std::vector wOrigin2 = {1, 0, 0}; //Extent of the window is defined by size of output[1]. - armnn::SplitterQueueDescriptor::ViewOrigin window2(wOrigin2); - - std::vector wOrigin3 = {0, 0, 0}; //Extent of the window is defined by size of output[2]. - armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3); - - std::vector 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 inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - - std::unique_ptr outputHandle1 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo1.GetShape(), wOrigin1.data()) : - workloadFactory.CreateTensorHandle(outputTensorInfo1); - - std::unique_ptr outputHandle2 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo2.GetShape(), wOrigin2.data()) : - workloadFactory.CreateTensorHandle(outputTensorInfo2); - - std::unique_ptr outputHandle3 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo3.GetShape(), wOrigin3.data()) : - workloadFactory.CreateTensorHandle(outputTensorInfo3); - - std::unique_ptr 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 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 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> ret = {ret1, ret2, ret3, ret4,}; - - return ret; -} - - -template -LayerTestResult CopyViaSplitterTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) -{ - const armnn::TensorInfo tensorInfo({ 3, 6, 5 }, armnn::GetDataType()); - auto input = MakeTensor(tensorInfo, QuantizedVector(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 origin = { 0, 0, 0 }; - armnn::SplitterQueueDescriptor::ViewOrigin window(origin); - - const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - - std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(tensorInfo); - - std::unique_ptr 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 workload = workloadFactory.CreateSplitter(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0]); - - workload->Execute(); - - LayerTestResult ret(tensorInfo); - CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); - ret.outputExpected = input; - - return ret; -} diff --git a/src/armnn/backends/test/TensorCopyUtils.cpp b/src/armnn/backends/test/TensorCopyUtils.cpp deleted file mode 100644 index dc5864b285..0000000000 --- a/src/armnn/backends/test/TensorCopyUtils.cpp +++ /dev/null @@ -1,159 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include -#include -#include -#include - -#include "TensorCopyUtils.hpp" - -#ifdef ARMCOMPUTECL_ENABLED -#include "backends/ClTensorHandle.hpp" -#endif - -#if ARMCOMPUTENEON_ENABLED -#include "backends/NeonTensorHandle.hpp" -#endif - -#if ARMCOMPUTECLENABLED || ARMCOMPUTENEON_ENABLED -#include "backends/ArmComputeTensorUtils.hpp" -#endif - -#include "backends/CpuTensorHandle.hpp" - -void CopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem) -{ - switch (tensorHandle->GetType()) - { - case armnn::ITensorHandle::Cpu: - { - auto handle = boost::polymorphic_downcast(tensorHandle); - memcpy(handle->GetTensor(), mem, handle->GetTensorInfo().GetNumBytes()); - break; - } -#ifdef ARMCOMPUTECL_ENABLED - case armnn::ITensorHandle::CL: - { - using armnn::armcomputetensorutils::CopyArmComputeITensorData; - auto handle = boost::polymorphic_downcast(tensorHandle); - handle->Map(true); - switch(handle->GetDataType()) - { - case arm_compute::DataType::F32: - CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); - break; - case arm_compute::DataType::QASYMM8: - CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); - break; - case arm_compute::DataType::F16: - CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); - break; - default: - { - throw armnn::UnimplementedException(); - } - } - handle->Unmap(); - break; - } -#endif -#if ARMCOMPUTENEON_ENABLED - case armnn::ITensorHandle::Neon: - { - using armnn::armcomputetensorutils::CopyArmComputeITensorData; - auto handle = boost::polymorphic_downcast(tensorHandle); - switch (handle->GetDataType()) - { - case arm_compute::DataType::F32: - CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); - break; - case arm_compute::DataType::QASYMM8: - CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); - break; - default: - { - throw armnn::UnimplementedException(); - } - } - break; - } -#endif - default: - { - throw armnn::UnimplementedException(); - } - } -} - -void CopyDataFromITensorHandle(void* mem, const armnn::ITensorHandle* tensorHandle) -{ - switch (tensorHandle->GetType()) - { - case armnn::ITensorHandle::Cpu: - { - auto handle = boost::polymorphic_downcast(tensorHandle); - memcpy(mem, handle->GetTensor(), handle->GetTensorInfo().GetNumBytes()); - break; - } -#ifdef ARMCOMPUTECL_ENABLED - case armnn::ITensorHandle::CL: - { - using armnn::armcomputetensorutils::CopyArmComputeITensorData; - auto handle = boost::polymorphic_downcast(tensorHandle); - const_cast(handle)->Map(true); - switch(handle->GetDataType()) - { - case arm_compute::DataType::F32: - CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); - break; - case arm_compute::DataType::QASYMM8: - CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); - break; - case arm_compute::DataType::F16: - CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); - break; - default: - { - throw armnn::UnimplementedException(); - } - } - const_cast(handle)->Unmap(); - break; - } -#endif -#if ARMCOMPUTENEON_ENABLED - case armnn::ITensorHandle::Neon: - { - using armnn::armcomputetensorutils::CopyArmComputeITensorData; - auto handle = boost::polymorphic_downcast(tensorHandle); - switch (handle->GetDataType()) - { - case arm_compute::DataType::F32: - CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); - break; - case arm_compute::DataType::QASYMM8: - CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); - break; - default: - { - throw armnn::UnimplementedException(); - } - } - break; - } -#endif - default: - { - throw armnn::UnimplementedException(); - } - } -} - -void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem) -{ - tensorHandle->Allocate(); - CopyDataToITensorHandle(tensorHandle, mem); -} diff --git a/src/armnn/backends/test/TensorCopyUtils.hpp b/src/armnn/backends/test/TensorCopyUtils.hpp deleted file mode 100644 index 0cec839903..0000000000 --- a/src/armnn/backends/test/TensorCopyUtils.hpp +++ /dev/null @@ -1,14 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include "armnn/Tensor.hpp" -#include "backends/ITensorHandle.hpp" - -void CopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem); - -void CopyDataFromITensorHandle(void* mem, const armnn::ITensorHandle* tensorHandle); - -void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem); \ No newline at end of file diff --git a/src/armnn/backends/test/WorkloadDataValidation.cpp b/src/armnn/backends/test/WorkloadDataValidation.cpp deleted file mode 100644 index a5cfbd1270..0000000000 --- a/src/armnn/backends/test/WorkloadDataValidation.cpp +++ /dev/null @@ -1,471 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include -#include -#include -#include -#include - -#include - -#include "WorkloadTestUtils.hpp" - -using namespace armnn; - -BOOST_AUTO_TEST_SUITE(WorkloadInfoValidation) - - - -BOOST_AUTO_TEST_CASE(QueueDescriptor_Validate_WrongNumOfInputsOutputs) -{ - InputQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - //Invalid argument exception is expected, because no inputs and no outputs were defined. - BOOST_CHECK_THROW(RefWorkloadFactory().CreateInput(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - -BOOST_AUTO_TEST_CASE(RefPooling2dFloat32Workload_Validate_WrongDimTensor) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = {2, 3, 4}; // <- Invalid - input tensor has to be 4D. - unsigned int outputShape[] = {2, 3, 4, 5}; - - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - inputTensorInfo = armnn::TensorInfo(3, inputShape, armnn::DataType::Float32); - - Pooling2dQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); - - // Invalid argument exception is expected, input tensor has to be 4D. - BOOST_CHECK_THROW(RefPooling2dFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - -BOOST_AUTO_TEST_CASE(SoftmaxQueueDescriptor_Validate_WrongInputHeight) -{ - unsigned int inputHeight = 1; - unsigned int inputWidth = 1; - unsigned int inputChannels = 4; - unsigned int inputNum = 2; - - unsigned int outputChannels = inputChannels; - unsigned int outputHeight = inputHeight + 1; //Makes data invalid - Softmax expects height and width to be 1. - unsigned int outputWidth = inputWidth; - unsigned int outputNum = inputNum; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; - unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - SoftmaxQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - - //Invalid argument exception is expected, because height != 1. - BOOST_CHECK_THROW(RefSoftmaxFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - -BOOST_AUTO_TEST_CASE(FullyConnectedQueueDescriptor_Validate_RequiredDataMissing) -{ - unsigned int inputWidth = 1; - unsigned int inputHeight = 1; - unsigned int inputChannels = 5; - unsigned int inputNum = 2; - - unsigned int outputWidth = 1; - unsigned int outputHeight = 1; - unsigned int outputChannels = 3; - unsigned int outputNum = 2; - - // Define the tensor descriptors. - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo weightsDesc; - armnn::TensorInfo biasesDesc; - - unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; - unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; - unsigned int weightsShape[] = { 1, 1, inputChannels, outputChannels }; - unsigned int biasShape[] = { 1, outputChannels, outputHeight, outputWidth }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - weightsDesc = armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32); - biasesDesc = armnn::TensorInfo(4, biasShape, armnn::DataType::Float32); - - FullyConnectedQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - ScopedCpuTensorHandle weightTensor(weightsDesc); - ScopedCpuTensorHandle biasTensor(biasesDesc); - - AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - invalidData.m_Weight = &weightTensor; - invalidData.m_Bias = &biasTensor; - invalidData.m_Parameters.m_BiasEnabled = true; - invalidData.m_Parameters.m_TransposeWeightMatrix = false; - - - //Invalid argument exception is expected, because not all required fields have been provided. - //In particular inputsData[0], outputsData[0] and weightsData can not be null. - BOOST_CHECK_THROW(RefFullyConnectedFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - - -BOOST_AUTO_TEST_CASE(NormalizationQueueDescriptor_Validate_WrongInputHeight) -{ - constexpr unsigned int inputNum = 5; - constexpr unsigned int inputHeight = 32; - constexpr unsigned int inputWidth = 24; - constexpr unsigned int inputChannels = 3; - - constexpr unsigned int outputNum = inputNum; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputHeight = inputHeight + 1; //Makes data invalid - normalization requires. - //Input and output to have the same dimensions. - constexpr unsigned int outputWidth = inputWidth; - - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; - unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - - armnn::NormalizationAlgorithmMethod normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; - armnn::NormalizationAlgorithmChannel normChannel = armnn::NormalizationAlgorithmChannel::Across; - float alpha = 1.f; - float beta = 1.f; - float kappa = 1.f; - uint32_t normSize = 5; - - NormalizationQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - invalidData.m_Parameters.m_NormChannelType = normChannel; - invalidData.m_Parameters.m_NormMethodType = normMethod; - invalidData.m_Parameters.m_NormSize = normSize; - invalidData.m_Parameters.m_Alpha = alpha; - invalidData.m_Parameters.m_Beta = beta; - invalidData.m_Parameters.m_K = kappa; - - //Invalid argument exception is expected, because input height != output height. - BOOST_CHECK_THROW(RefNormalizationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - -BOOST_AUTO_TEST_CASE(SplitterQueueDescriptor_Validate_WrongWindow) -{ - constexpr unsigned int inputNum = 1; - constexpr unsigned int inputHeight = 32; - constexpr unsigned int inputWidth = 24; - constexpr unsigned int inputChannels = 3; - - constexpr unsigned int outputNum = inputNum; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputHeight = 18; - constexpr unsigned int outputWidth = inputWidth; - - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; - unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - SplitterQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - - // Invalid, since it has only 3 dimensions while the input tensor is 4d. - std::vector wOrigin = {0, 0, 0}; - armnn::SplitterQueueDescriptor::ViewOrigin window(wOrigin); - invalidData.m_ViewOrigins.push_back(window); - - BOOST_TEST_INFO("Invalid argument exception is expected, because split window dimensionality does not " - "match input."); - BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - - // Invalid, since window extends past the boundary of input tensor. - std::vector wOrigin3 = {0, 0, 15, 0}; - armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3); - invalidData.m_ViewOrigins[0] = window3; - BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ outputHeight > inputHeight"); - BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - - - std::vector wOrigin4 = {0, 0, 0, 0}; - armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4); - invalidData.m_ViewOrigins[0] = window4; - - std::vector wOrigin5 = {1, 16, 20, 2}; - armnn::SplitterQueueDescriptor::ViewOrigin window5(wOrigin4); - invalidData.m_ViewOrigins.push_back(window5); - - BOOST_TEST_INFO("Invalid exception due to number of split windows not matching number of outputs."); - BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - - -BOOST_AUTO_TEST_CASE(MergerQueueDescriptor_Validate_WrongWindow) -{ - constexpr unsigned int inputNum = 1; - constexpr unsigned int inputChannels = 3; - constexpr unsigned int inputHeight = 32; - constexpr unsigned int inputWidth = 24; - - constexpr unsigned int outputNum = 1; - constexpr unsigned int outputChannels = 3; - constexpr unsigned int outputHeight = 32; - constexpr unsigned int outputWidth = 24; - - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; - unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - MergerQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - - // Invalid, since it has only 3 dimensions while the input tensor is 4d. - std::vector wOrigin = {0, 0, 0}; - armnn::MergerQueueDescriptor::ViewOrigin window(wOrigin); - invalidData.m_ViewOrigins.push_back(window); - - BOOST_TEST_INFO("Invalid argument exception is expected, because merge window dimensionality does not " - "match input."); - BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - - // Invalid, since window extends past the boundary of output tensor. - std::vector wOrigin3 = {0, 0, 15, 0}; - armnn::MergerQueueDescriptor::ViewOrigin window3(wOrigin3); - invalidData.m_ViewOrigins[0] = window3; - BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ inputHeight > outputHeight"); - BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - - - std::vector wOrigin4 = {0, 0, 0, 0}; - armnn::MergerQueueDescriptor::ViewOrigin window4(wOrigin4); - invalidData.m_ViewOrigins[0] = window4; - - std::vector wOrigin5 = {1, 16, 20, 2}; - armnn::MergerQueueDescriptor::ViewOrigin window5(wOrigin4); - invalidData.m_ViewOrigins.push_back(window5); - - BOOST_TEST_INFO("Invalid exception due to number of merge windows not matching number of inputs."); - BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - -BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputNumbers) -{ - armnn::TensorInfo input1TensorInfo; - armnn::TensorInfo input2TensorInfo; - armnn::TensorInfo input3TensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape[] = {1, 1, 1, 1}; - - input1TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - input2TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - input3TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - - AdditionQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - - // Too few inputs. - BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - - AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); - - // Correct. - BOOST_CHECK_NO_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo)); - - AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, nullptr); - - // Too many inputs. - BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - -BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputShapes) -{ - armnn::TensorInfo input1TensorInfo; - armnn::TensorInfo input2TensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape1[] = {1, 1, 2, 1}; - unsigned int shape2[] = {1, 1, 3, 2}; - - // Incompatible shapes even with broadcasting. - { - input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); - input2TensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); - - AdditionQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); - AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - - BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - } - - // Output size not compatible with input sizes. - { - input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); - input2TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32); - - AdditionQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); - AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - - // Output differs. - BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - } -} - -BOOST_AUTO_TEST_CASE(MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch) -{ - armnn::TensorInfo input0TensorInfo; - armnn::TensorInfo input1TensorInfo; - armnn::TensorInfo outputTensorInfo; - - constexpr unsigned int input0Shape[] = { 2, 2, 4, 4 }; - constexpr std::size_t dimensionCount = std::extent::value; - - // Checks dimension consistency for input tensors. - for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex) - { - unsigned int input1Shape[dimensionCount]; - for (unsigned int i = 0; i < dimensionCount; ++i) - { - input1Shape[i] = input0Shape[i]; - } - - ++input1Shape[dimIndex]; - - input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); - input1TensorInfo = armnn::TensorInfo(dimensionCount, input1Shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); - - MultiplicationQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr); - AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); - - BOOST_CHECK_THROW(RefMultiplicationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - } - - // Checks dimension consistency for input and output tensors. - for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex) - { - unsigned int outputShape[dimensionCount]; - for (unsigned int i = 0; i < dimensionCount; ++i) - { - outputShape[i] = input0Shape[i]; - } - - ++outputShape[dimIndex]; - - input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); - input1TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(dimensionCount, outputShape, armnn::DataType::Float32); - - MultiplicationQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr); - AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); - - BOOST_CHECK_THROW(RefMultiplicationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); - } -} - -BOOST_AUTO_TEST_CASE(ReshapeQueueDescriptor_Validate_MismatchingNumElements) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - // The input and output shapes should have the same number of elements, but these don't. - unsigned int inputShape[] = { 1, 1, 2, 3 }; - unsigned int outputShape[] = { 1, 1, 1, 2 }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); - - ReshapeQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - - // InvalidArgumentException is expected, because the number of elements don't match. - BOOST_CHECK_THROW(RefReshapeFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); -} - - -BOOST_AUTO_TEST_CASE(LstmQueueDescriptor_Validate) -{ - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 1, 2 }; - unsigned int outputShape[] = { 1 }; - - inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(1, outputShape, armnn::DataType::Float32); - - LstmQueueDescriptor invalidData; - WorkloadInfo invalidInfo; - - AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); - AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); - - BOOST_CHECK_THROW(invalidData.Validate(invalidInfo), armnn::InvalidArgumentException); -} - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/backends/test/WorkloadTestUtils.hpp b/src/armnn/backends/test/WorkloadTestUtils.hpp deleted file mode 100644 index a7b75309f7..0000000000 --- a/src/armnn/backends/test/WorkloadTestUtils.hpp +++ /dev/null @@ -1,55 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include -#include - -namespace armnn -{ -class ITensorHandle; -} - -template -void AddInputToWorkload(QueueDescriptor& descriptor, - armnn::WorkloadInfo& info, - const armnn::TensorInfo& tensorInfo, - armnn::ITensorHandle* tensorHandle) -{ - descriptor.m_Inputs.push_back(tensorHandle); - info.m_InputTensorInfos.push_back(tensorInfo); -} - -template -void AddOutputToWorkload(QueueDescriptor& descriptor, - armnn::WorkloadInfo& info, - const armnn::TensorInfo& tensorInfo, - armnn::ITensorHandle* tensorHandle) -{ - descriptor.m_Outputs.push_back(tensorHandle); - info.m_OutputTensorInfos.push_back(tensorInfo); -} - -template -void SetWorkloadInput(QueueDescriptor& descriptor, - armnn::WorkloadInfo& info, - unsigned int index, - const armnn::TensorInfo& tensorInfo, - armnn::ITensorHandle* tensorHandle) -{ - descriptor.m_Inputs[index] = tensorHandle; - info.m_InputTensorInfos[index] = tensorInfo; -} - -template -void SetWorkloadOutput(QueueDescriptor& descriptor, - armnn::WorkloadInfo& info, - unsigned int index, - const armnn::TensorInfo& tensorInfo, - armnn::ITensorHandle* tensorHandle) -{ - descriptor.m_Outputs[index] = tensorHandle; - info.m_OutputTensorInfos[index] = tensorInfo; -} \ No newline at end of file -- cgit v1.2.1