From 82b15eda6f87a20bc31256f5e85eb4082d3d0591 Mon Sep 17 00:00:00 2001 From: Matthew Jackson Date: Thu, 25 Jul 2019 16:14:30 +0100 Subject: IVGCVSW-3537 Add support for L2 Normalization with < 4 dimensional tensors * Fix reference L2 Normalization workload to support < 4 dimensional tensors * Add unit test for L2 Normalization with 2d tensor to Reference, Neon and CL test suites * Fix typo in StackLayer Signed-off-by: Matthew Jackson Change-Id: I48a6a1289bcb02955b24f261bc70b467bd1abc23 --- src/armnn/layers/StackLayer.cpp | 2 +- src/backends/backendsCommon/WorkloadData.cpp | 6 +- src/backends/backendsCommon/test/LayerTests.cpp | 65 ++++++++++++++++++++++ src/backends/backendsCommon/test/LayerTests.hpp | 4 ++ src/backends/cl/test/ClLayerTests.cpp | 2 + src/backends/neon/test/NeonLayerTests.cpp | 2 + src/backends/reference/test/RefLayerTests.cpp | 2 + .../workloads/RefL2NormalizationWorkload.cpp | 37 +++++++++--- 8 files changed, 110 insertions(+), 10 deletions(-) diff --git a/src/armnn/layers/StackLayer.cpp b/src/armnn/layers/StackLayer.cpp index 59bc8d5a13..7f1dbec461 100644 --- a/src/armnn/layers/StackLayer.cpp +++ b/src/armnn/layers/StackLayer.cpp @@ -73,7 +73,7 @@ void StackLayer::ValidateTensorShapesFromInputs() TensorShape inputShape = GetInputSlot(i).GetConnection()->GetTensorInfo().GetShape(); if (inputShape != m_Param.m_InputShape) { - throw LayerValidationException("ConcatLayer: TensorShape set on InputSlot[" + + throw LayerValidationException("StackLayer: TensorShape set on InputSlot[" + std::to_string(i) + "] does not match defined input shape"); } diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp index 3d4e27cd9e..2000ce4a57 100644 --- a/src/backends/backendsCommon/WorkloadData.cpp +++ b/src/backends/backendsCommon/WorkloadData.cpp @@ -1120,8 +1120,10 @@ void L2NormalizationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const TensorInfo& inputTensorInfo = workloadInfo.m_InputTensorInfos[0]; const TensorInfo& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0]; - ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, "input"); - ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, "output"); + if (inputTensorInfo.GetNumDimensions() > 4) + { + throw InvalidArgumentException(descriptorName + ": Input tensors with rank greater than 4 are not supported."); + } ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output"); diff --git a/src/backends/backendsCommon/test/LayerTests.cpp b/src/backends/backendsCommon/test/LayerTests.cpp index f996edad65..46063803f0 100644 --- a/src/backends/backendsCommon/test/LayerTests.cpp +++ b/src/backends/backendsCommon/test/LayerTests.cpp @@ -7179,6 +7179,71 @@ LayerTestResult L2Normalization2dUint8Test( 1.f/128, 128, layout); } +LayerTestResult 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 inputData + { + 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f + }; + std::vector 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(inputTensorInfo, QuantizedVector( + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset(), + inputData)); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector( + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset(), + expectedOutputData)); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr 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 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; +} + template> LayerTestResult L2Normalization3dTestCommon( armnn::IWorkloadFactory& workloadFactory, diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp index 913c3a630f..fb7ce92702 100644 --- a/src/backends/backendsCommon/test/LayerTests.hpp +++ b/src/backends/backendsCommon/test/LayerTests.hpp @@ -1059,6 +1059,10 @@ LayerTestResult L2Normalization2dUint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout layout); +LayerTestResult L2Normalization2dShapeTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); + LayerTestResult L2Normalization3dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, diff --git a/src/backends/cl/test/ClLayerTests.cpp b/src/backends/cl/test/ClLayerTests.cpp index 160b3a9ae3..37af471658 100644 --- a/src/backends/cl/test/ClLayerTests.cpp +++ b/src/backends/cl/test/ClLayerTests.cpp @@ -302,6 +302,8 @@ ARMNN_AUTO_TEST_CASE(L2Normalization2dNhwc, L2Normalization2dTest, armnn::DataLa ARMNN_AUTO_TEST_CASE(L2Normalization3dNhwc, L2Normalization3dTest, armnn::DataLayout::NHWC) ARMNN_AUTO_TEST_CASE(L2Normalization4dNhwc, L2Normalization4dTest, armnn::DataLayout::NHWC) +ARMNN_AUTO_TEST_CASE(L2Normalization2dShape, L2Normalization2dShapeTest); + ARMNN_AUTO_TEST_CASE(L2NormalizationDefaultEpsilon, L2NormalizationDefaultEpsilonTest, armnn::DataLayout::NCHW) ARMNN_AUTO_TEST_CASE(L2NormalizationNonDefaultEpsilon, L2NormalizationNonDefaultEpsilonTest, armnn::DataLayout::NCHW) diff --git a/src/backends/neon/test/NeonLayerTests.cpp b/src/backends/neon/test/NeonLayerTests.cpp index d551431c73..9f7413c775 100644 --- a/src/backends/neon/test/NeonLayerTests.cpp +++ b/src/backends/neon/test/NeonLayerTests.cpp @@ -473,6 +473,8 @@ ARMNN_AUTO_TEST_CASE(L2Normalization2dNhwc, L2Normalization2dTest, armnn::DataLa ARMNN_AUTO_TEST_CASE(L2Normalization3dNhwc, L2Normalization3dTest, armnn::DataLayout::NHWC) ARMNN_AUTO_TEST_CASE(L2Normalization4dNhwc, L2Normalization4dTest, armnn::DataLayout::NHWC) +ARMNN_AUTO_TEST_CASE(L2Normalization2dShape, L2Normalization2dShapeTest); + ARMNN_AUTO_TEST_CASE(L2NormalizationDefaultEpsilon, L2NormalizationDefaultEpsilonTest, armnn::DataLayout::NCHW) ARMNN_AUTO_TEST_CASE(L2NormalizationNonDefaultEpsilon, L2NormalizationNonDefaultEpsilonTest, armnn::DataLayout::NCHW) diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp index 5cb804225b..f7fb78acbd 100644 --- a/src/backends/reference/test/RefLayerTests.cpp +++ b/src/backends/reference/test/RefLayerTests.cpp @@ -795,6 +795,8 @@ ARMNN_AUTO_TEST_CASE(L2Normalization2dUint8Nhwc, L2Normalization2dUint8Test, arm ARMNN_AUTO_TEST_CASE(L2Normalization3dUint8Nhwc, L2Normalization3dUint8Test, armnn::DataLayout::NHWC) ARMNN_AUTO_TEST_CASE(L2Normalization4dUint8Nhwc, L2Normalization4dUint8Test, armnn::DataLayout::NHWC) +ARMNN_AUTO_TEST_CASE(L2Normalization2dShape, L2Normalization2dShapeTest); + ARMNN_AUTO_TEST_CASE(L2NormalizationDefaultEpsilon, L2NormalizationDefaultEpsilonTest, armnn::DataLayout::NCHW) ARMNN_AUTO_TEST_CASE(L2NormalizationNonDefaultEpsilon, L2NormalizationNonDefaultEpsilonTest, armnn::DataLayout::NCHW) diff --git a/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp b/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp index 3b2ab50c8b..3764b9a49a 100644 --- a/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp +++ b/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp @@ -10,9 +10,10 @@ #include "Encoders.hpp" #include "DataLayoutIndexed.hpp" - #include "Profiling.hpp" +#include + #include using namespace armnnUtils; @@ -36,10 +37,32 @@ RefL2NormalizationWorkload::RefL2NormalizationWorkload( DataLayoutIndexed dataLayout(m_Data.m_Parameters.m_DataLayout); - const unsigned int batches = inputInfo.GetShape()[0]; - const unsigned int channels = inputInfo.GetShape()[dataLayout.GetChannelsIndex()]; - const unsigned int height = inputInfo.GetShape()[dataLayout.GetHeightIndex()]; - const unsigned int width = inputInfo.GetShape()[dataLayout.GetWidthIndex()]; + const TensorShape& shape = inputInfo.GetShape(); + unsigned int paddedShapeArray[4]; + const int idxShift = 4 - boost::numeric_cast(shape.GetNumDimensions()); + + const unsigned int batches = (idxShift == 0) ? shape[0] : 1; + paddedShapeArray[0] = batches; + + const int channelsIdx = boost::numeric_cast(dataLayout.GetChannelsIndex()); + const unsigned int channels = (channelsIdx - idxShift >= 0) + ? shape[boost::numeric_cast(channelsIdx - idxShift)] + : 1; + paddedShapeArray[channelsIdx] = channels; + + const int heightIdx = boost::numeric_cast(dataLayout.GetHeightIndex()); + const unsigned int height = (heightIdx - idxShift >= 0) + ? shape[boost::numeric_cast(heightIdx - idxShift)] + : 1; + paddedShapeArray[heightIdx] = height; + + const int widthIdx = boost::numeric_cast(dataLayout.GetWidthIndex()); + const unsigned int width = (widthIdx - idxShift >= 0) + ? shape[boost::numeric_cast(widthIdx - idxShift)] + : 1; + paddedShapeArray[widthIdx] = width; + + const TensorShape& paddedShape = TensorShape(4, paddedShapeArray); for (unsigned int n = 0; n < batches; ++n) { @@ -52,14 +75,14 @@ RefL2NormalizationWorkload::RefL2NormalizationWorkload( float reduction = 0.0; for (unsigned int d = 0; d < channels; ++d) { - unsigned int inputIndex = dataLayout.GetIndex(inputInfo.GetShape(), n, d, h, w); + unsigned int inputIndex = dataLayout.GetIndex(paddedShape, n, d, h, w); (*inputDecoder)[inputIndex]; const float value = inputDecoder->Get(); reduction += value * value; } - unsigned int index = dataLayout.GetIndex(inputInfo.GetShape(), n, c, h, w); + unsigned int index = dataLayout.GetIndex(paddedShape, n, c, h, w); float maximum = reduction < m_Data.m_Parameters.m_Eps ? m_Data.m_Parameters.m_Eps : reduction; -- cgit v1.2.1