| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "L2NormalizationTestImpl.hpp" |
| |
| #include <QuantizeHelper.hpp> |
| #include <ResolveType.hpp> |
| |
| #include <armnnUtils/TensorUtils.hpp> |
| #include <armnnUtils/Permute.hpp> |
| |
| #include <backendsCommon/test/TensorCopyUtils.hpp> |
| #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| |
| namespace |
| { |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> L2NormalizationTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::TensorShape& inputOutputTensorShape, |
| float scale, |
| int32_t offset, |
| const std::vector<float>& inputValues, |
| float outScale, |
| int32_t outOffset, |
| const std::vector<float>& expectedOutputValues, |
| const armnn::DataLayout layout, |
| float epsilon = 1e-12f) |
| { |
| boost::ignore_unused(memoryManager); |
| const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, ArmnnType, scale, offset); |
| const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, ArmnnType, outScale, outOffset); |
| |
| // at this point if we require it permute the input data |
| const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
| std::vector<float> inputData = inputValues; |
| if (layout == armnn::DataLayout::NHWC) |
| { |
| std::vector<float> tmp(inputData.size()); |
| armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); |
| inputData = tmp; |
| } |
| |
| auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, |
| armnnUtils::QuantizedVector<T>(inputData, |
| inputTensorInfo.GetQuantizationScale(), |
| inputTensorInfo.GetQuantizationOffset())); |
| |
| std::vector<float> expectedOutputData = expectedOutputValues; |
| if (layout == armnn::DataLayout::NHWC) |
| { |
| std::vector<float> tmp(expectedOutputData.size()); |
| armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, expectedOutputData.data(), tmp.data(), |
| sizeof(float)); |
| expectedOutputData = tmp; |
| } |
| |
| LayerTestResult<T, 4> result(outputTensorInfo); |
| result.outputExpected = |
| MakeTensor<T, 4>(outputTensorInfo, |
| armnnUtils::QuantizedVector<T>(expectedOutputData, |
| outputTensorInfo.GetQuantizationScale(), |
| outputTensorInfo.GetQuantizationOffset())); |
| |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| |
| armnn::L2NormalizationQueueDescriptor descriptor; |
| descriptor.m_Parameters.m_Eps = epsilon; |
| descriptor.m_Parameters.m_DataLayout = layout; |
| armnn::WorkloadInfo info; |
| |
| AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info); |
| |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]); |
| |
| workload->PostAllocationConfigure(); |
| ExecuteWorkload(*workload, memoryManager); |
| |
| CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| |
| return result; |
| } |
| |
| float CalcInvL2Norm(std::initializer_list<float> 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); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> L2NormalizationEpsilonTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float scale, |
| int32_t offset, |
| float outScale, |
| int32_t outOffset, |
| const armnn::DataLayout layout, |
| float epsilon) |
| { |
| // Width: 1 |
| // Height: 1 |
| // Channels: 3 |
| // BatchSize: 1 |
| unsigned int numberOfBatches = 1; |
| unsigned int numberOfChannels = 3; |
| unsigned int height = 1; |
| unsigned int width = 1; |
| |
| const armnn::TensorShape inputOutputShape = armnnUtils::GetTensorShape( |
| numberOfBatches, numberOfChannels, height, width, layout); |
| |
| // 0.0000001^2 + 0.00000002^2 + 0.00000003^2 < 1e-12 |
| std::vector<float> inputValues |
| { |
| // Batch 0, Channel 0, Height (1) x Width (1) |
| 0.00000001f, |
| |
| // Batch 0, Channel 1, Height (1) x Width (1) |
| 0.00000002f, |
| |
| // Batch 0, Channel 2, Height (1) x Width (1) |
| 0.00000003f, |
| }; |
| |
| const float approxInvL2Norm = 1.f / sqrtf(epsilon); |
| std::vector<float> expectedOutputValues |
| { |
| // Batch 0, Channel 0, Height (1) x Width (1) |
| 0.00000001f * approxInvL2Norm, |
| 0.00000002f * approxInvL2Norm, |
| 0.00000003f * approxInvL2Norm, |
| }; |
| |
| return L2NormalizationTestImpl<ArmnnType>( |
| workloadFactory, |
| memoryManager, |
| inputOutputShape, |
| scale, |
| offset, |
| inputValues, |
| outScale, |
| outOffset, |
| expectedOutputValues, |
| layout, |
| epsilon); |
| } |
| |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> L2Normalization1dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float scale, |
| int32_t offset, |
| float outScale, |
| int32_t outOffset, |
| const armnn::DataLayout layout) |
| { |
| // Width: 1 |
| // Height: 1 |
| // Channels: 10 |
| // BatchSize: 1 |
| unsigned int numberOfBatches = 1; |
| unsigned int numberOfChannels = 10; |
| unsigned int height = 1; |
| unsigned int width = 1; |
| |
| |
| const armnn::TensorShape inputOutputShape = armnnUtils::GetTensorShape( |
| numberOfBatches, numberOfChannels, height, width, layout); |
| std::vector<float> inputValues |
| { |
| // Batch 0, Channel 0, Height (1) x Width (1) |
| 1.0f, |
| |
| // Batch 0, Channel 1, Height (1) x Width (1) |
| 2.0f, |
| |
| // Batch 0, Channel 2, Height (1) x Width (1) |
| 3.0f, |
| |
| // Batch 0, Channel 3, Height (1) x Width (1) |
| 4.0f, |
| |
| // Batch 0, Channel 4, Height (1) x Width (1) |
| 5.0f, |
| |
| // Batch 0, Channel 5, Height (1) x Width (1) |
| 6.0f, |
| |
| // Batch 0, Channel 6, Height (1) x Width (1) |
| 7.0f, |
| |
| // Batch 0, Channel 7, Height (1) x Width (1) |
| 8.0f, |
| |
| // Batch 0, Channel 8, Height (1) x Width (1) |
| 9.0f, |
| |
| // Batch 0, Channel 9, Height (1) x Width (1) |
| 10.0f |
| }; |
| const float approxInvL2Norm = 0.050964719f; |
| std::vector<float> expectedOutputValues |
| { |
| // Batch 0, Channel 0, Height (1) x Width (1) |
| 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 |
| }; |
| |
| |
| return L2NormalizationTestImpl<ArmnnType>( |
| workloadFactory, |
| memoryManager, |
| inputOutputShape, |
| scale, |
| offset, |
| inputValues, |
| outScale, |
| outOffset, |
| expectedOutputValues, |
| layout); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> L2Normalization2dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float scale, |
| int32_t offset, |
| float outScale, |
| int32_t outOffset, |
| const armnn::DataLayout layout) |
| { |
| // Width: 5 |
| // Height: 1 |
| // Channels: 2 |
| // BatchSize: 1 |
| unsigned int numberOfBatches = 1; |
| unsigned int numberOfChannels = 2; |
| unsigned int height = 1; |
| unsigned int width = 5; |
| |
| const armnn::TensorShape inputOutputShape = armnnUtils::GetTensorShape( |
| numberOfBatches, numberOfChannels, height, width, layout); |
| std::vector<float> inputValues |
| { |
| // Batch 0, Channel 0, Height (1) x Width (5) |
| 1.0f, 3.0f, 5.0f, 7.0f, 9.0f, |
| |
| // Batch 0, Channel 1, Height (1) x Width (5) |
| 2.0f, 4.0f, 6.0f, 8.0f, 10.0f |
| }; |
| std::vector<float> expectedOutputValues |
| { |
| // Batch 0, Channel 0, Height (1) x Width (5) |
| 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 }), |
| |
| // Batch 0, Channel 1, Height (1) x Width (5) |
| 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 }) |
| }; |
| |
| return L2NormalizationTestImpl<ArmnnType>( |
| workloadFactory, |
| memoryManager, |
| inputOutputShape, |
| scale, |
| offset, |
| inputValues, |
| outScale, |
| outOffset, |
| expectedOutputValues, |
| layout); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> L2Normalization3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float scale, |
| int32_t offset, |
| float outScale, |
| int32_t outOffset, |
| const armnn::DataLayout layout) |
| { |
| // Width: 3 |
| // Height: 4 |
| // Channels: 2 |
| // BatchSize: 1 |
| unsigned int numberOfBatches = 1; |
| unsigned int numberOfChannels = 2; |
| unsigned int height = 4; |
| unsigned int width = 3; |
| |
| const armnn::TensorShape inputOutputShape = armnnUtils::GetTensorShape( |
| numberOfBatches, numberOfChannels, height, width, layout); |
| std::vector<float> inputValues |
| { |
| // Batch 0, Channel 0, Height (4) x Width (3) |
| 119.0f, 21.0f, 150.0f, |
| 149.0f, 32.0f, 179.0f, |
| 15.0f, 227.0f, 141.0f, |
| 147.0f, 199.0f, 220.0f, |
| |
| // Batch 0, Channel 1, Height (4) x Width (3) |
| 110.0f, 140.0f, 73.0f, |
| 211.0f, 212.0f, 89.0f, |
| 24.0f, 138.0f, 188.0f, |
| 162.0f, 12.0f, 161.0f |
| }; |
| std::vector<float> expectedOutputValues |
| { |
| // Batch 0, Channel 0, Height (4) x Width (3) |
| 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 }), |
| |
| // Batch 0, Channel 1, Height (4) x Width (3) |
| 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 }) |
| }; |
| |
| return L2NormalizationTestImpl<ArmnnType>( |
| workloadFactory, |
| memoryManager, |
| inputOutputShape, |
| scale, |
| offset, |
| inputValues, |
| outScale, |
| outOffset, |
| expectedOutputValues, |
| layout); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> L2Normalization4dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float scale, |
| int32_t offset, |
| float outScale, |
| int32_t outOffset, |
| const armnn::DataLayout layout) |
| { |
| // Width: 3 |
| // Height: 4 |
| // Channels: 3 |
| // BatchSize: 2 |
| unsigned int numberOfBatches = 2; |
| unsigned int numberOfChannels = 3; |
| unsigned int height = 4; |
| unsigned int width = 3; |
| |
| const armnn::TensorShape inputOutputShape = armnnUtils::GetTensorShape( |
| numberOfBatches, numberOfChannels, height, width, layout); |
| std::vector<float> inputValues |
| { |
| // Batch 0, Channel 0, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 97.0f, 145.0f, 215.0f, |
| 115.0f, 116.0f, 238.0f, |
| 226.0f, 16.0f, 132.0f, |
| 92.0f, 125.0f, 88.0f |
| }; |
| std::vector<float> expectedOutputValues |
| { |
| // Batch 0, Channel 0, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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, Height (4) x Width (3) |
| 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 }) |
| }; |
| |
| return L2NormalizationTestImpl<ArmnnType>( |
| workloadFactory, |
| memoryManager, |
| inputOutputShape, |
| scale, |
| offset, |
| inputValues, |
| outScale, |
| outOffset, |
| expectedOutputValues, |
| layout); |
| } |
| |
| } // anonymous namespace |
| |
| LayerTestResult<float, 4> L2NormalizationDefaultEpsilonTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| // Dummy descriptor to get the default value of epsilon. |
| armnn::L2NormalizationDescriptor descriptor; |
| |
| return L2NormalizationEpsilonTestCommon<armnn::DataType::Float32>( |
| workloadFactory, |
| memoryManager, |
| 0.f, |
| 0, |
| 0.f, |
| 0, |
| layout, |
| descriptor.m_Eps); |
| } |
| |
| LayerTestResult<float, 4> L2NormalizationNonDefaultEpsilonTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2NormalizationEpsilonTestCommon<armnn::DataType::Float32>( |
| workloadFactory, |
| memoryManager, |
| 0.f, |
| 0, |
| 0.f, |
| 0, |
| layout, |
| 1e-9f); |
| } |
| |
| LayerTestResult<float, 4> L2Normalization1dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, |
| memoryManager, |
| 0.f, |
| 0, |
| 0.f, |
| 0, |
| layout); |
| } |
| |
| LayerTestResult<int16_t, 4> L2Normalization1dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, |
| memoryManager, |
| 1.f, |
| 0, |
| 1.f, |
| 0, |
| layout); |
| } |
| |
| LayerTestResult<uint8_t, 4> L2Normalization1dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, |
| memoryManager, |
| 1.f, |
| 0, |
| 1.f / 128, |
| 128, |
| layout); |
| } |
| |
| LayerTestResult<float, 4> L2Normalization2dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization2dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, |
| memoryManager, |
| 0.f, |
| 0, |
| 0.f, |
| 0, |
| layout); |
| } |
| |
| LayerTestResult<int16_t, 4> L2Normalization2dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, |
| memoryManager, |
| 1.f, |
| 0, |
| 1.f, |
| 0, |
| layout); |
| } |
| |
| LayerTestResult<uint8_t, 4> L2Normalization2dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, |
| memoryManager, |
| 1.f, |
| 0, |
| 1.f / 128, |
| 128, |
| layout); |
| } |
| |
| LayerTestResult<float, 2> L2Normalization2dShapeTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| { |
| const armnn::DataLayout layout = armnn::DataLayout::NHWC; |
| const armnn::TensorShape inputOutputTensorShape = armnn::TensorShape({ 5, 2 }); |
| |
| std::vector<float> inputData |
| { |
| 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f |
| }; |
| std::vector<float> expectedOutputData |
| { |
| 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }), |
| 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }), |
| 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }), |
| 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }), |
| 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }), |
| 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }), |
| 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }), |
| 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }), |
| 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }), |
| 10.0f * CalcInvL2Norm({ 9.0f, 10.0f }) |
| }; |
| |
| const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32, 0.f, 0); |
| const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32, 0.f, 0); |
| |
| auto inputTensor = MakeTensor<float, 2>(inputTensorInfo, inputData); |
| |
| LayerTestResult<float, 2> result(outputTensorInfo); |
| result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, expectedOutputData); |
| |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| |
| armnn::L2NormalizationQueueDescriptor descriptor; |
| descriptor.m_Parameters.m_Eps = 1e-12f; |
| descriptor.m_Parameters.m_DataLayout = layout; |
| armnn::WorkloadInfo info; |
| |
| AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info); |
| |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); |
| |
| workload->PostAllocationConfigure(); |
| ExecuteWorkload(*workload, memoryManager); |
| |
| CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get()); |
| |
| return result; |
| } |
| |
| LayerTestResult<float, 4> L2Normalization3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, |
| memoryManager, |
| 0.f, |
| 0, |
| 0.f, |
| 0, |
| layout); |
| } |
| |
| LayerTestResult<int16_t, 4> L2Normalization3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, |
| memoryManager, |
| 1.f, |
| 0, |
| 1.f, |
| 0, |
| layout); |
| } |
| |
| LayerTestResult<uint8_t, 4> L2Normalization3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, |
| memoryManager, |
| 1.f, |
| 0, |
| 1.f / 128, |
| 128, |
| layout); |
| } |
| |
| LayerTestResult<float, 4> L2Normalization4dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization4dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, |
| memoryManager, |
| 0.f, |
| 0, |
| 0.f, |
| 0, |
| layout); |
| } |
| |
| LayerTestResult<int16_t, 4> L2Normalization4dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, |
| memoryManager, |
| 1.f, |
| 0, |
| 1.f, |
| 0, |
| layout); |
| } |
| |
| LayerTestResult<uint8_t, 4> L2Normalization4dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::DataLayout layout) |
| { |
| return L2Normalization1dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, |
| memoryManager, |
| 1.f, |
| 0, |
| 1.f / 128, |
| 128, |
| layout); |
| } |