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Diffstat (limited to 'src/backends/test/NormTestImpl.hpp')
| -rw-r--r-- | src/backends/test/NormTestImpl.hpp | 104 |
1 files changed, 104 insertions, 0 deletions
diff --git a/src/backends/test/NormTestImpl.hpp b/src/backends/test/NormTestImpl.hpp index dfa2f7072e..300eeceae0 100644 --- a/src/backends/test/NormTestImpl.hpp +++ b/src/backends/test/NormTestImpl.hpp @@ -5,6 +5,7 @@ #include <armnn/Exceptions.hpp> #include <armnn/LayerSupport.hpp> +#include "armnn/Types.hpp" #include <backends/CpuTensorHandle.hpp> #include <backends/WorkloadFactory.hpp> @@ -58,6 +59,7 @@ LayerTestResult<float,4> SimpleNormalizationTestImpl(armnn::IWorkloadFactory& wo 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; @@ -150,6 +152,108 @@ LayerTestResult<float,4> SimpleNormalizationTestImpl(armnn::IWorkloadFactory& wo return ret; } +// This is test implementation for CL and NEON, +// as currently, only Across Normalization is supported on CL and NEON for NHWC. +LayerTestResult<float,4> SimpleNormalizationNhwcClNeonTestImpl(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, 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]); + + workloadFactory.Finalize(); + workload->Execute(); + + 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, armnn::IWorkloadFactory& refWorkloadFactory, armnn::NormalizationAlgorithmChannel normChannel, |