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 <matthew.jackson@arm.com>
Change-Id: I48a6a1289bcb02955b24f261bc70b467bd1abc23
diff --git a/src/armnn/layers/StackLayer.cpp b/src/armnn/layers/StackLayer.cpp
index 59bc8d5..7f1dbec 100644
--- a/src/armnn/layers/StackLayer.cpp
+++ b/src/armnn/layers/StackLayer.cpp
@@ -73,7 +73,7 @@
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 3d4e27c..2000ce4 100644
--- a/src/backends/backendsCommon/WorkloadData.cpp
+++ b/src/backends/backendsCommon/WorkloadData.cpp
@@ -1120,8 +1120,10 @@
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 f996eda..4606380 100644
--- a/src/backends/backendsCommon/test/LayerTests.cpp
+++ b/src/backends/backendsCommon/test/LayerTests.cpp
@@ -7179,6 +7179,71 @@
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, QuantizedVector<float>(
+ inputTensorInfo.GetQuantizationScale(),
+ inputTensorInfo.GetQuantizationOffset(),
+ inputData));
+
+ LayerTestResult<float, 2> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, QuantizedVector<float>(
+ outputTensorInfo.GetQuantizationScale(),
+ outputTensorInfo.GetQuantizationOffset(),
+ 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;
+}
+
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> L2Normalization3dTestCommon(
armnn::IWorkloadFactory& workloadFactory,
diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp
index 913c3a6..fb7ce92 100644
--- a/src/backends/backendsCommon/test/LayerTests.hpp
+++ b/src/backends/backendsCommon/test/LayerTests.hpp
@@ -1059,6 +1059,10 @@
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::DataLayout layout);
+LayerTestResult<float, 2> L2Normalization2dShapeTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager);
+
LayerTestResult<float, 4> 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 160b3a9..37af471 100644
--- a/src/backends/cl/test/ClLayerTests.cpp
+++ b/src/backends/cl/test/ClLayerTests.cpp
@@ -302,6 +302,8 @@
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 d551431..9f7413c 100644
--- a/src/backends/neon/test/NeonLayerTests.cpp
+++ b/src/backends/neon/test/NeonLayerTests.cpp
@@ -473,6 +473,8 @@
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 5cb8042..f7fb78a 100644
--- a/src/backends/reference/test/RefLayerTests.cpp
+++ b/src/backends/reference/test/RefLayerTests.cpp
@@ -795,6 +795,8 @@
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 3b2ab50..3764b9a 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 <boost/numeric/conversion/cast.hpp>
+
#include <cmath>
using namespace armnnUtils;
@@ -36,10 +37,32 @@
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<int>(shape.GetNumDimensions());
+
+ const unsigned int batches = (idxShift == 0) ? shape[0] : 1;
+ paddedShapeArray[0] = batches;
+
+ const int channelsIdx = boost::numeric_cast<int>(dataLayout.GetChannelsIndex());
+ const unsigned int channels = (channelsIdx - idxShift >= 0)
+ ? shape[boost::numeric_cast<unsigned int>(channelsIdx - idxShift)]
+ : 1;
+ paddedShapeArray[channelsIdx] = channels;
+
+ const int heightIdx = boost::numeric_cast<int>(dataLayout.GetHeightIndex());
+ const unsigned int height = (heightIdx - idxShift >= 0)
+ ? shape[boost::numeric_cast<unsigned int>(heightIdx - idxShift)]
+ : 1;
+ paddedShapeArray[heightIdx] = height;
+
+ const int widthIdx = boost::numeric_cast<int>(dataLayout.GetWidthIndex());
+ const unsigned int width = (widthIdx - idxShift >= 0)
+ ? shape[boost::numeric_cast<unsigned int>(widthIdx - idxShift)]
+ : 1;
+ paddedShapeArray[widthIdx] = width;
+
+ const TensorShape& paddedShape = TensorShape(4, paddedShapeArray);
for (unsigned int n = 0; n < batches; ++n)
{
@@ -52,14 +75,14 @@
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;