| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NormalizationTestImpl.hpp" |
| |
| #include <armnn/Exceptions.hpp> |
| #include <armnn/LayerSupport.hpp> |
| |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| |
| #include <backendsCommon/test/TensorCopyUtils.hpp> |
| #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| |
| namespace |
| { |
| |
| LayerTestResult<float,4> SimpleNormalizationTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::NormalizationAlgorithmChannel normChannel, |
| armnn::NormalizationAlgorithmMethod normMethod) |
| { |
| boost::ignore_unused(memoryManager); |
| 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<float,4> ret(outputTensorInfo); |
| |
| auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
| // 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<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> 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; |
| data.m_Parameters.m_DataLayout = armnn::DataLayout::NCHW; |
| |
| 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<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| |
| ExecuteWorkload(*workload, memoryManager); |
| |
| 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<int>(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<float, 4>(outputTensorInfo, |
| std::vector<float>({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<float> outputVector; |
| for (int n = 0; n < boost::numeric_cast<int>(inputNum); ++n) |
| { |
| for (int h = 0; h < boost::numeric_cast<int>(inputHeight); ++h) |
| { |
| for (int w = 0; w < boost::numeric_cast<int>(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<float, 4>(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<float,4> SimpleNormalizationNhwcTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 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, inputHeight, inputWidth, inputChannels }; |
| unsigned int outputShape[] = { outputNum, outputHeight, outputWidth, outputChannels }; |
| |
| auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| |
| LayerTestResult<float,4> ret(outputTensorInfo); |
| |
| auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
| // 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<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> 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; |
| data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| 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<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| |
| ExecuteWorkload(*workload, memoryManager); |
| |
| CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| |
| switch (normMethod) |
| { |
| case armnn::NormalizationAlgorithmMethod::LocalBrightness: |
| { |
| switch (normChannel) |
| { |
| case armnn::NormalizationAlgorithmChannel::Across: |
| { |
| std::vector<float> expectedOutput{ 0.5f, 0.400000006f, 0.300000012f, 0.235294119f, |
| 0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f }; |
| ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, expectedOutput); |
| break; |
| } |
| default: |
| { |
| throw armnn::UnimplementedException("Unsupported normalisation channel type, " |
| "Only Cross-map is supported for NHWC layout"); |
| } |
| } |
| break; |
| } |
| case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. |
| default: |
| { |
| throw armnn::UnimplementedException("Unsupported normalisation method type, " |
| "only LocalBrightness is supported"); |
| } |
| } |
| |
| return ret; |
| } |
| |
| LayerTestResult<float,4> CompareNormalizationTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 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<float,4> ret(outputTensorInfo); |
| |
| auto input = MakeRandomTensor<float, 4>(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<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> 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<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> 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::BackendId backend = workloadFactory.GetBackendId(); |
| const size_t reasonIfUnsupportedMaxLen = 255; |
| char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
| ret.supported = armnn::IsNormalizationSupported(backend, inputTensorInfo, outputTensorInfo, data.m_Parameters, |
| reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| if (!ret.supported) |
| { |
| return ret; |
| } |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| std::unique_ptr<armnn::IWorkload> 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]); |
| |
| ExecuteWorkload(*workload, memoryManager); |
| |
| workloadRef->Execute(); |
| |
| CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); |
| |
| return ret; |
| } |
| |
| } // anonymous namespace |
| |
| LayerTestResult<float,4> SimpleNormalizationAcrossTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
| return SimpleNormalizationTestImpl(workloadFactory, memoryManager, normChannel, normMethod); |
| } |
| |
| LayerTestResult<float,4> SimpleNormalizationWithinTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| auto normChannel = armnn::NormalizationAlgorithmChannel::Within; |
| return SimpleNormalizationTestImpl(workloadFactory, memoryManager, normChannel, normMethod); |
| } |
| |
| LayerTestResult<float,4> SimpleNormalizationAcrossNhwcTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
| return SimpleNormalizationNhwcTestImpl(workloadFactory, memoryManager, normChannel, normMethod); |
| } |
| |
| LayerTestResult<float,4> CompareNormalizationTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::IWorkloadFactory& refWorkloadFactory, |
| armnn::NormalizationAlgorithmChannel normChannel, |
| armnn::NormalizationAlgorithmMethod normMethod) |
| { |
| return CompareNormalizationTestImpl(workloadFactory, memoryManager, refWorkloadFactory, normChannel, normMethod); |
| } |