IVGCVSW-2023 CL and Neon implementation of BatchNorm with NHWC
Change-Id: I962e986607e5d045cd97b9eaeaea2f5ae624db35
diff --git a/include/armnn/Descriptors.hpp b/include/armnn/Descriptors.hpp
index a5b1d64..a7eca43 100644
--- a/include/armnn/Descriptors.hpp
+++ b/include/armnn/Descriptors.hpp
@@ -293,7 +293,7 @@
{}
float m_Eps;
- DataLayout m_DataLayout;
+ DataLayoutIndexed m_DataLayout;
};
struct FakeQuantizationDescriptor
diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp
index db1773a..01c5e9f 100644
--- a/src/armnn/test/CreateWorkload.hpp
+++ b/src/armnn/test/CreateWorkload.hpp
@@ -133,8 +133,20 @@
template <typename BatchNormalizationFloat32Workload, armnn::DataType DataType>
std::unique_ptr<BatchNormalizationFloat32Workload> CreateBatchNormalizationWorkloadTest(
- armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW)
+ armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayoutIndexed dataLayout = DataLayout::NCHW)
{
+
+ TensorShape tensorShape;
+ switch (dataLayout.GetDataLayout())
+ {
+ case DataLayout::NHWC:
+ tensorShape = { 2, 4, 4, 3 };
+ break;
+ case DataLayout::NCHW:
+ default:
+ tensorShape = { 2, 3, 4, 4 };
+ }
+
// Creates the layer we're testing.
BatchNormalizationDescriptor layerDesc;
layerDesc.m_Eps = 0.05f;
@@ -156,25 +168,23 @@
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
- TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{ 2, 3, 1, 1 } : TensorShape{ 2, 1, 1, 3 };
- TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{ 2, 3, 1, 1 } : TensorShape{ 2, 1, 1, 3 };
-
- // Connects up.
- Connect(input, layer, TensorInfo(inputShape, DataType));
- Connect(layer, output, TensorInfo(outputShape, DataType));
+ //Connects up.
+ armnn::TensorInfo tensorInfo(tensorShape, DataType);
+ Connect(input, layer, tensorInfo);
+ Connect(layer, output, tensorInfo);
CreateTensorHandles(graph, factory);
// Makes the workload and checks it.
auto workload = MakeAndCheckWorkload<BatchNormalizationFloat32Workload>(*layer, graph, factory);
BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData();
BOOST_TEST(queueDescriptor.m_Parameters.m_Eps == 0.05f);
- BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);
BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);
BOOST_TEST((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType)));
BOOST_TEST((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType)));
BOOST_TEST((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType)));
BOOST_TEST((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType)));
+ BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout.GetDataLayout() == dataLayout));
// Returns so we can do extra, backend-specific tests.
return workload;
diff --git a/src/backends/cl/test/ClCreateWorkloadTests.cpp b/src/backends/cl/test/ClCreateWorkloadTests.cpp
index 756b4a6..b5fc031 100644
--- a/src/backends/cl/test/ClCreateWorkloadTests.cpp
+++ b/src/backends/cl/test/ClCreateWorkloadTests.cpp
@@ -144,31 +144,53 @@
}
template <typename BatchNormalizationWorkloadType, armnn::DataType DataType>
-static void ClCreateBatchNormalizationWorkloadTest()
+static void ClCreateBatchNormalizationWorkloadTest(DataLayout dataLayout)
{
Graph graph;
ClWorkloadFactory factory;
auto workload = CreateBatchNormalizationWorkloadTest<BatchNormalizationWorkloadType, DataType>
- (factory, graph);
+ (factory, graph, dataLayout);
// Checks that inputs/outputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest).
BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData();
auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]);
auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]);
- BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {2, 3, 1, 1}));
- BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {2, 3, 1, 1}));
+ switch (dataLayout)
+ {
+ case DataLayout::NHWC:
+ BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 2, 4, 4, 3 }));
+ BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 2, 4, 4, 3 }));
+ break;
+ default: // NCHW
+ BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 2, 3, 4, 4 }));
+ BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 2, 3, 4, 4 }));
+ }
}
-BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatWorkload)
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatNchwWorkload)
{
- ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload, armnn::DataType::Float32>();
+ ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload,
+ armnn::DataType::Float32>(DataLayout::NCHW);
}
-BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16Workload)
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16NchwWorkload)
{
- ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload, armnn::DataType::Float16>();
+ ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload,
+ armnn::DataType::Float16>(DataLayout::NCHW);
+}
+
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatNhwcWorkload)
+{
+ ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload,
+ armnn::DataType::Float32>(DataLayout::NHWC);
+}
+
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationNhwcFloat16NhwcWorkload)
+{
+ ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload,
+ armnn::DataType::Float16>(DataLayout::NHWC);
}
BOOST_AUTO_TEST_CASE(CreateConvertFp16ToFp32Workload)
diff --git a/src/backends/cl/test/ClLayerTests.cpp b/src/backends/cl/test/ClLayerTests.cpp
index 3b1603c..a4f824a 100755
--- a/src/backends/cl/test/ClLayerTests.cpp
+++ b/src/backends/cl/test/ClLayerTests.cpp
@@ -181,6 +181,7 @@
// Batch Norm
ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest)
+ARMNN_AUTO_TEST_CASE(BatchNormNhwc, BatchNormNhwcTest)
// L2 Normalization
ARMNN_AUTO_TEST_CASE(L2Normalization1d, L2Normalization1dTest)
diff --git a/src/backends/cl/workloads/ClBatchNormalizationFloatWorkload.cpp b/src/backends/cl/workloads/ClBatchNormalizationFloatWorkload.cpp
index 5bff7a6..24be7cd 100644
--- a/src/backends/cl/workloads/ClBatchNormalizationFloatWorkload.cpp
+++ b/src/backends/cl/workloads/ClBatchNormalizationFloatWorkload.cpp
@@ -23,12 +23,20 @@
const TensorInfo& gamma,
const BatchNormalizationDescriptor &desc)
{
- const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input);
- const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output);
- const arm_compute::TensorInfo aclMeanInfo = BuildArmComputeTensorInfo(mean);
- const arm_compute::TensorInfo aclVarInfo = BuildArmComputeTensorInfo(var);
- const arm_compute::TensorInfo aclBetaInfo = BuildArmComputeTensorInfo(beta);
- const arm_compute::TensorInfo aclGammaInfo = BuildArmComputeTensorInfo(gamma);
+ const DataLayout dataLayout = desc.m_DataLayout.GetDataLayout();
+
+ const arm_compute::TensorInfo aclInputInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(input, dataLayout);
+ const arm_compute::TensorInfo aclOutputInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(output, dataLayout);
+ const arm_compute::TensorInfo aclMeanInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(mean, dataLayout);
+ const arm_compute::TensorInfo aclVarInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(var, dataLayout);
+ const arm_compute::TensorInfo aclBetaInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(beta, dataLayout);
+ const arm_compute::TensorInfo aclGammaInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(gamma, dataLayout);
return arm_compute::CLBatchNormalizationLayer::validate(&aclInputInfo,
&aclOutputInfo,
@@ -60,6 +68,10 @@
arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+ arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout.GetDataLayout());
+ input.info()->set_data_layout(aclDataLayout);
+ output.info()->set_data_layout(aclDataLayout);
+
m_Layer.configure(&input,
&output,
m_Mean.get(),
diff --git a/src/backends/neon/test/NeonCreateWorkloadTests.cpp b/src/backends/neon/test/NeonCreateWorkloadTests.cpp
index a588a3e..8d5574c 100644
--- a/src/backends/neon/test/NeonCreateWorkloadTests.cpp
+++ b/src/backends/neon/test/NeonCreateWorkloadTests.cpp
@@ -153,30 +153,45 @@
}
template <typename BatchNormalizationWorkloadType, typename armnn::DataType DataType>
-static void NeonCreateBatchNormalizationWorkloadTest()
+static void NeonCreateBatchNormalizationWorkloadTest(DataLayout dataLayout)
{
Graph graph;
NeonWorkloadFactory factory;
- auto workload = CreateBatchNormalizationWorkloadTest<BatchNormalizationWorkloadType, DataType>(factory, graph);
+ auto workload = CreateBatchNormalizationWorkloadTest<BatchNormalizationWorkloadType, DataType>
+ (factory, graph, dataLayout);
// Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest).
BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData();
auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]);
auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]);
- BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({2, 3, 1, 1}, DataType)));
- BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3, 1, 1}, DataType)));
+
+ TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 4, 4} : TensorShape{2, 4, 4, 3};
+ TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 4, 4} : TensorShape{2, 4, 4, 3};
+
+ BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType)));
+ BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType)));
}
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16Workload)
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16NchwWorkload)
{
- NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float16>();
+ NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float16>(DataLayout::NCHW);
+}
+
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16NhwcWorkload)
+{
+ NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float16>(DataLayout::NHWC);
}
#endif
-BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatWorkload)
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatNchwWorkload)
{
- NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float32>();
+ NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float32>(DataLayout::NCHW);
+}
+
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatNhwcWorkload)
+{
+ NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float32>(DataLayout::NHWC);
}
template <typename armnn::DataType DataType>
diff --git a/src/backends/neon/test/NeonLayerTests.cpp b/src/backends/neon/test/NeonLayerTests.cpp
index 31ee7d8..568a236 100644
--- a/src/backends/neon/test/NeonLayerTests.cpp
+++ b/src/backends/neon/test/NeonLayerTests.cpp
@@ -338,6 +338,7 @@
// Batch Norm
ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest)
+ARMNN_AUTO_TEST_CASE(BatchNormNhwc, BatchNormNhwcTest)
// Constant
ARMNN_AUTO_TEST_CASE(Constant, ConstantTest)
diff --git a/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp b/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp
index f7056a5..95cfdce 100644
--- a/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp
+++ b/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp
@@ -21,12 +21,20 @@
const TensorInfo& gamma,
const BatchNormalizationDescriptor& descriptor)
{
- const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input);
- const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output);
- const arm_compute::TensorInfo aclMeanInfo = BuildArmComputeTensorInfo(mean);
- const arm_compute::TensorInfo aclVarInfo = BuildArmComputeTensorInfo(var);
- const arm_compute::TensorInfo aclBetaInfo = BuildArmComputeTensorInfo(beta);
- const arm_compute::TensorInfo aclGammaInfo = BuildArmComputeTensorInfo(gamma);
+ const DataLayout dataLayout = descriptor.m_DataLayout.GetDataLayout();
+
+ const arm_compute::TensorInfo aclInputInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(input, dataLayout);
+ const arm_compute::TensorInfo aclOutputInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(output, dataLayout);
+ const arm_compute::TensorInfo aclMeanInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(mean, dataLayout);
+ const arm_compute::TensorInfo aclVarInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(var, dataLayout);
+ const arm_compute::TensorInfo aclBetaInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(beta, dataLayout);
+ const arm_compute::TensorInfo aclGammaInfo =
+ armcomputetensorutils::BuildArmComputeTensorInfo(gamma, dataLayout);
return arm_compute::NEBatchNormalizationLayer::validate(&aclInputInfo,
&aclOutputInfo,
@@ -46,6 +54,10 @@
arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+ arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout.GetDataLayout());
+ input.info()->set_data_layout(aclDataLayout);
+ output.info()->set_data_layout(aclDataLayout);
+
m_Mean = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_Mean, m_Data.m_Mean->GetTensorInfo());
diff --git a/src/backends/reference/test/RefCreateWorkloadTests.cpp b/src/backends/reference/test/RefCreateWorkloadTests.cpp
index d258b81..8bad549 100644
--- a/src/backends/reference/test/RefCreateWorkloadTests.cpp
+++ b/src/backends/reference/test/RefCreateWorkloadTests.cpp
@@ -157,13 +157,13 @@
switch (dataLayout)
{
case DataLayout::NHWC:
- inputShape = { 2, 1, 1, 3 };
- outputShape = { 2, 1, 1, 3 };
+ inputShape = { 2, 4, 4, 3 };
+ outputShape = { 2, 4, 4, 3 };
break;
case DataLayout::NCHW:
default:
- inputShape = { 2, 3, 1, 1 };
- outputShape = { 2, 3, 1, 1 };
+ inputShape = { 2, 3, 4, 4 };
+ outputShape = { 2, 3, 4, 4 };
break;
}
diff --git a/src/backends/test/BatchNormTestImpl.hpp b/src/backends/test/BatchNormTestImpl.hpp
index 4941b00..166c444 100644
--- a/src/backends/test/BatchNormTestImpl.hpp
+++ b/src/backends/test/BatchNormTestImpl.hpp
@@ -95,3 +95,93 @@
return result;
}
+
+
+template<typename T>
+LayerTestResult<T,4> BatchNormTestNhwcImpl(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset)
+{
+ const unsigned int width = 2;
+ const unsigned int height = 3;
+ const unsigned int channels = 2;
+ const unsigned int num = 1;
+
+ armnn::TensorInfo inputTensorInfo({num, height, width, channels}, armnn::GetDataType<T>());
+ armnn::TensorInfo outputTensorInfo({num, height, width, channels}, armnn::GetDataType<T>());
+ armnn::TensorInfo tensorInfo({channels}, armnn::GetDataType<T>());
+
+ // Set quantization parameters if the requested type is a quantized type.
+ if(armnn::IsQuantizedType<T>())
+ {
+ inputTensorInfo.SetQuantizationScale(qScale);
+ inputTensorInfo.SetQuantizationOffset(qOffset);
+ outputTensorInfo.SetQuantizationScale(qScale);
+ outputTensorInfo.SetQuantizationOffset(qOffset);
+ tensorInfo.SetQuantizationScale(qScale);
+ tensorInfo.SetQuantizationOffset(qOffset);
+ }
+
+ auto input = MakeTensor<T, 4>(inputTensorInfo,
+ QuantizedVector<T>(qScale, qOffset,
+ {
+ 1.f, 1.f, 4.f, 1.f,
+ 4.f, 4.f, 2.f, 1.f,
+ 1.f, -2.f, 6.f, 4.f
+ }));
+ // These values are per-channel of the input.
+ auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
+ auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9}));
+ auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2}));
+ auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1}));
+ LayerTestResult<T,4> ret(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::BatchNormalizationQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
+ armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
+ armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
+ armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
+
+ AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
+ AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
+ AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
+ AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
+
+ AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+ data.m_Mean = &meanTensor;
+ data.m_Variance = &varianceTensor;
+ data.m_Beta = &betaTensor;
+ data.m_Gamma = &gammaTensor;
+ data.m_Parameters.m_Eps = 0.0f;
+ data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC;
+
+ // For each channel:
+ // substract mean, divide by standard deviation (with an epsilon to avoid div by 0),
+ // multiply by gamma and add beta
+ ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
+ QuantizedVector<T>(qScale, qOffset,
+ {
+ 1.f, 3.f, 4.f, 3.f,
+ 4.f, 4.f, 2.f, 3.f,
+ 1.f, 2.f, 6.f, 4.f
+ }));
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(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());
+
+ return ret;
+}
\ No newline at end of file
diff --git a/src/backends/test/LayerTests.hpp b/src/backends/test/LayerTests.hpp
index b6651ce..925e3e6 100644
--- a/src/backends/test/LayerTests.hpp
+++ b/src/backends/test/LayerTests.hpp
@@ -256,6 +256,7 @@
LayerTestResult<float, 4> ResizeBilinearMagNhwcTest(armnn::IWorkloadFactory& workloadFactory);
LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<float, 4> BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory);
LayerTestResult<float, 2> FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory);