IVGCVSW-1812 Adding Ref implementation and tests of MeanWorkloads
Change-Id: I6fb15c407024e3b91d5abf4513f8090be5821760
diff --git a/src/armnn/layers/MeanLayer.cpp b/src/armnn/layers/MeanLayer.cpp
index 6bbb094..01f1133 100644
--- a/src/armnn/layers/MeanLayer.cpp
+++ b/src/armnn/layers/MeanLayer.cpp
@@ -60,7 +60,7 @@
{
outputRank = 1;
}
- else if (m_Param.m_Axis.size() <= input.GetNumDimensions())
+ else if (m_Param.m_Axis.size() >= input.GetNumDimensions())
{
throw LayerValidationException("MeanLayer: Dimensions to reduce can not be bigger than input dimensions");
}
diff --git a/src/armnn/test/NetworkTests.cpp b/src/armnn/test/NetworkTests.cpp
index 11c26da..2f36f4d 100644
--- a/src/armnn/test/NetworkTests.cpp
+++ b/src/armnn/test/NetworkTests.cpp
@@ -845,6 +845,39 @@
}
}
+BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefMeanLayer)
+{
+ // Create runtime in which test will run
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::Compute> backends = {armnn::Compute::CpuRef};
+
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+
+ armnn::MeanDescriptor descriptor({ 0, 1 }, false);
+ armnn::IConnectableLayer* meanLayer = net->AddMeanLayer(descriptor);
+
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(meanLayer->GetInputSlot(0));
+ meanLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 4, 3, 2 }, armnn::DataType::Float32));
+ meanLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 2 }, armnn::DataType::Float32));
+
+ // optimize the network
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ BOOST_CHECK_EQUAL(armnn::Compute::CpuRef, layer->GetComputeDevice());
+ }
+}
+
BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnCpuRef)
{
// Test to check when FP16 Turbo mode set
diff --git a/src/backends/reference/RefLayerSupport.cpp b/src/backends/reference/RefLayerSupport.cpp
index d56cdeb..12a2817 100644
--- a/src/backends/reference/RefLayerSupport.cpp
+++ b/src/backends/reference/RefLayerSupport.cpp
@@ -392,7 +392,12 @@
const MeanDescriptor& descriptor,
std::string* reasonIfUnsupported)
{
- return false;
+ ignore_unused(output);
+ ignore_unused(descriptor);
+ return IsSupportedForDataTypeRef(reasonIfUnsupported,
+ input.GetDataType(),
+ &TrueFunc<>,
+ &TrueFunc<>);
}
}
diff --git a/src/backends/reference/RefWorkloadFactory.cpp b/src/backends/reference/RefWorkloadFactory.cpp
index 5cefd1b..582c691 100644
--- a/src/backends/reference/RefWorkloadFactory.cpp
+++ b/src/backends/reference/RefWorkloadFactory.cpp
@@ -242,7 +242,7 @@
std::unique_ptr<armnn::IWorkload> RefWorkloadFactory::CreateMean(
const MeanQueueDescriptor& descriptor, const WorkloadInfo& info) const
{
- return MakeWorkload<NullWorkload, NullWorkload>(descriptor, info);
+ return MakeWorkload<RefMeanFloat32Workload, RefMeanUint8Workload>(descriptor, info);
}
std::unique_ptr<IWorkload> RefWorkloadFactory::CreatePad(const PadQueueDescriptor& descriptor,
diff --git a/src/backends/reference/backend.mk b/src/backends/reference/backend.mk
index 23dab11..e5345c0 100644
--- a/src/backends/reference/backend.mk
+++ b/src/backends/reference/backend.mk
@@ -15,6 +15,7 @@
workloads/Broadcast.cpp \
workloads/ConvImpl.cpp \
workloads/FullyConnected.cpp \
+ workloads/Mean.cpp \
workloads/Pooling2d.cpp \
workloads/RefActivationFloat32Workload.cpp \
workloads/RefActivationUint8Workload.cpp \
@@ -36,6 +37,8 @@
workloads/RefFullyConnectedUint8Workload.cpp \
workloads/RefL2NormalizationFloat32Workload.cpp \
workloads/RefLstmFloat32Workload.cpp \
+ workloads/RefMeanFloat32Workload.cpp \
+ workloads/RefMeanUint8Workload.cpp \
workloads/RefMergerFloat32Workload.cpp \
workloads/RefMergerUint8Workload.cpp \
workloads/RefNormalizationFloat32Workload.cpp \
diff --git a/src/backends/reference/workloads/CMakeLists.txt b/src/backends/reference/workloads/CMakeLists.txt
index 7343b70..5a756e4 100644
--- a/src/backends/reference/workloads/CMakeLists.txt
+++ b/src/backends/reference/workloads/CMakeLists.txt
@@ -94,6 +94,12 @@
Softmax.hpp
Splitter.hpp
TensorBufferArrayView.hpp
+ Mean.cpp
+ Mean.hpp
+ RefMeanFloat32Workload.cpp
+ RefMeanFloat32Workload.hpp
+ RefMeanUint8Workload.cpp
+ RefMeanUint8Workload.hpp
)
add_library(armnnRefBackendWorkloads STATIC ${armnnRefBackendWorkloads_sources})
diff --git a/src/backends/reference/workloads/Mean.cpp b/src/backends/reference/workloads/Mean.cpp
new file mode 100644
index 0000000..0db67a0
--- /dev/null
+++ b/src/backends/reference/workloads/Mean.cpp
@@ -0,0 +1,136 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "Mean.hpp"
+#include "backends/WorkloadData.hpp"
+
+#include <boost/numeric/conversion/cast.hpp>
+
+#include <cmath>
+#include <cstddef>
+#include <functional>
+#include <limits>
+
+namespace armnn
+{
+bool NextIndex(const unsigned int numDims, const armnn::TensorShape& dims, std::vector<unsigned int>& current)
+{
+ unsigned int carry = 1;
+
+ for (unsigned int idx = numDims; idx-- > 0; )
+ {
+ unsigned int current_val = current[idx] + carry;
+ if (dims[idx] == current_val)
+ {
+ current[idx] = 0;
+ }
+ else
+ {
+ current[idx] = current_val;
+ carry = 0;
+ break;
+ }
+ }
+ return (carry == 0);
+}
+
+std::size_t ReducedOutputOffset(const unsigned int numDims, const armnn::TensorShape& dims,
+ std::vector<unsigned int>& index, const unsigned int numAxis,
+ const std::vector<unsigned int>& axis) {
+ std::size_t offset = 0;
+ for (unsigned int idx = 0; idx < numDims; ++idx)
+ {
+ bool isAxis = false;
+ if (!axis.empty())
+ {
+ for (unsigned int axisIdx = 0; axisIdx < numAxis; ++axisIdx)
+ {
+ if (idx == axis[axisIdx])
+ {
+ isAxis = true;
+ break;
+ }
+ }
+ }
+ if (!isAxis)
+ {
+ offset = offset * boost::numeric_cast<size_t>(dims[idx]) + boost::numeric_cast<size_t>(index[idx]);
+ }
+ }
+ return offset;
+}
+} // namespace
+
+namespace armnn
+{
+void Mean(const armnn::TensorInfo& inputInfo,
+ const armnn::TensorInfo& outputInfo,
+ const std::vector<unsigned int>& axis,
+ const float* inputData,
+ float* outputData) {
+
+ unsigned int inputNumDims = inputInfo.GetNumDimensions();
+ unsigned int outputNumDims = outputInfo.GetNumDimensions();
+
+ armnn::TensorShape outputDims = outputInfo.GetShape();
+ armnn::TensorShape inputDims = inputInfo.GetShape();
+
+ // Initialise output data.
+ size_t numOutputs = 1;
+ for (unsigned int idx = 0; idx < outputNumDims; ++idx)
+ {
+ numOutputs *= boost::numeric_cast<size_t>(outputDims[idx]);
+ }
+
+ std::vector<float> tempSum(numOutputs);
+ for (size_t idx = 0; idx < numOutputs; ++idx)
+ {
+ outputData[idx] = 0.0f;
+ tempSum[idx] = 0.0f;
+ }
+
+ // Initialise temp index.
+ std::vector<unsigned int> tempIndex(inputNumDims);
+ for (unsigned int idx = 0; idx < inputNumDims; ++idx)
+ {
+ tempIndex[idx] = 0;
+ }
+
+ std::vector<unsigned int> resolvedAxis = axis;
+ if (resolvedAxis.empty())
+ {
+ for (unsigned int idx = 0; idx < inputNumDims; ++idx)
+ {
+ resolvedAxis.push_back(idx);
+ }
+ }
+ unsigned int numResolvedAxis = boost::numeric_cast<unsigned int>(resolvedAxis.size());
+
+ // Iterates through input_data and sum up the reduced axis.
+ for (bool hasNext = true; hasNext; hasNext = NextIndex(inputNumDims, inputDims, tempIndex))
+ {
+ size_t inputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex, 0, {});
+ size_t outputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex,
+ numResolvedAxis, resolvedAxis);
+ tempSum[outputOffset] += inputData[inputOffset];
+ }
+
+ // Takes average by num of elements added to get mean.
+ size_t numElementsInAxis = 1;
+ for (unsigned int idx = 0; idx < numResolvedAxis; ++idx)
+ {
+ size_t current = boost::numeric_cast<size_t>(inputDims[resolvedAxis[idx]]);
+ BOOST_ASSERT(boost::numeric_cast<float>(current) <
+ (std::numeric_limits<float>::max() / boost::numeric_cast<float>(numElementsInAxis)));
+ numElementsInAxis *= current;
+ }
+ if (numElementsInAxis > 0) {
+ for (size_t idx = 0; idx < numOutputs; ++idx)
+ {
+ outputData[idx] = tempSum[idx] / boost::numeric_cast<float>(numElementsInAxis);
+ }
+ }
+}
+} //namespace armnn
diff --git a/src/backends/reference/workloads/Mean.hpp b/src/backends/reference/workloads/Mean.hpp
new file mode 100644
index 0000000..38c2e39
--- /dev/null
+++ b/src/backends/reference/workloads/Mean.hpp
@@ -0,0 +1,21 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "armnn/DescriptorsFwd.hpp"
+#include "armnn/Tensor.hpp"
+
+#include <vector>
+
+namespace armnn
+{
+void Mean(const TensorInfo& inputInfo,
+ const TensorInfo& outputInfo,
+ const std::vector<unsigned int>& axis,
+ const float* inputData,
+ float* outputData);
+} //namespace armnn
+
diff --git a/src/backends/reference/workloads/RefMeanFloat32Workload.cpp b/src/backends/reference/workloads/RefMeanFloat32Workload.cpp
new file mode 100644
index 0000000..a23906b
--- /dev/null
+++ b/src/backends/reference/workloads/RefMeanFloat32Workload.cpp
@@ -0,0 +1,35 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "RefMeanFloat32Workload.hpp"
+
+#include "Mean.hpp"
+#include "RefWorkloadUtils.hpp"
+
+#include "Profiling.hpp"
+#include "vector"
+
+namespace armnn
+{
+
+RefMeanFloat32Workload::RefMeanFloat32Workload(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info)
+ :Float32Workload<MeanQueueDescriptor>(descriptor, info) {}
+
+
+void RefMeanFloat32Workload::Execute() const
+{
+ ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefMeanFloat32Workload_Execute");
+
+ const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]);
+ const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);
+ const float* inputData = GetInputTensorDataFloat(0, m_Data);
+ float* outputData = GetOutputTensorDataFloat(0, m_Data);
+
+ Mean(inputInfo, outputInfo, m_Data.m_Parameters.m_Axis, inputData, outputData);
+}
+
+} //namespace armnn
+
+
diff --git a/src/backends/reference/workloads/RefMeanFloat32Workload.hpp b/src/backends/reference/workloads/RefMeanFloat32Workload.hpp
new file mode 100644
index 0000000..a4c559f
--- /dev/null
+++ b/src/backends/reference/workloads/RefMeanFloat32Workload.hpp
@@ -0,0 +1,22 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "backends/Workload.hpp"
+#include "backends/WorkloadData.hpp"
+
+namespace armnn
+{
+
+
+class RefMeanFloat32Workload : public Float32Workload<MeanQueueDescriptor>
+{
+public:
+ explicit RefMeanFloat32Workload (const MeanQueueDescriptor& descriptor, const WorkloadInfo& info);
+ virtual void Execute() const override;
+};
+
+}//namespace armnn
diff --git a/src/backends/reference/workloads/RefMeanUint8Workload.cpp b/src/backends/reference/workloads/RefMeanUint8Workload.cpp
new file mode 100644
index 0000000..4ebffcf
--- /dev/null
+++ b/src/backends/reference/workloads/RefMeanUint8Workload.cpp
@@ -0,0 +1,39 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "RefMeanUint8Workload.hpp"
+
+#include "Mean.hpp"
+#include "RefWorkloadUtils.hpp"
+
+#include "Profiling.hpp"
+
+#include <vector>
+
+namespace armnn
+{
+
+RefMeanUint8Workload::RefMeanUint8Workload(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info)
+ :Uint8Workload<MeanQueueDescriptor>(descriptor, info) {}
+
+
+void RefMeanUint8Workload::Execute() const
+{
+ ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefMeanUint8Workload_Execute");
+
+ const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]);
+ const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);
+
+ auto dequant = Dequantize(GetInputTensorDataU8(0, m_Data), inputInfo);
+
+ std::vector<float> results(outputInfo.GetNumElements());
+
+ Mean(inputInfo, outputInfo, m_Data.m_Parameters.m_Axis, dequant.data(), results.data());
+
+ Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), outputInfo);
+}
+
+} //namespace armnn
+
diff --git a/src/backends/reference/workloads/RefMeanUint8Workload.hpp b/src/backends/reference/workloads/RefMeanUint8Workload.hpp
new file mode 100644
index 0000000..21cf72b
--- /dev/null
+++ b/src/backends/reference/workloads/RefMeanUint8Workload.hpp
@@ -0,0 +1,21 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "backends/Workload.hpp"
+#include "backends/WorkloadData.hpp"
+
+namespace armnn
+{
+
+class RefMeanUint8Workload : public Uint8Workload<MeanQueueDescriptor>
+{
+public:
+ explicit RefMeanUint8Workload (const MeanQueueDescriptor& descriptor, const WorkloadInfo& info);
+ virtual void Execute() const override;
+};
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/RefWorkloads.hpp b/src/backends/reference/workloads/RefWorkloads.hpp
index e5c6e1e..7e89cab 100644
--- a/src/backends/reference/workloads/RefWorkloads.hpp
+++ b/src/backends/reference/workloads/RefWorkloads.hpp
@@ -51,3 +51,5 @@
#include "RefLstmFloat32Workload.hpp"
#include "RefConvertFp16ToFp32Workload.hpp"
#include "RefConvertFp32ToFp16Workload.hpp"
+#include "RefMeanUint8Workload.hpp"
+#include "RefMeanFloat32Workload.hpp"
\ No newline at end of file
diff --git a/src/backends/test/LayerTests.cpp b/src/backends/test/LayerTests.cpp
index 8f06690..4f6cb93 100644
--- a/src/backends/test/LayerTests.cpp
+++ b/src/backends/test/LayerTests.cpp
@@ -4747,4 +4747,185 @@
LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory)
{
return PermuteFloat32ValueSet3TestCommon(workloadFactory);
-};
\ No newline at end of file
+};
+
+namespace
+{
+template <typename T, std::size_t InputDim, std::size_t OutputDim>
+LayerTestResult<T, OutputDim> MeanTestHelper(armnn::IWorkloadFactory& workloadFactory,
+ const unsigned int* inputShape,
+ const std::vector<T>& inputData,
+ const std::vector<unsigned int>& axis,
+ bool keepDims,
+ const unsigned int* outputShape,
+ const std::vector<T>& outputData,
+ float scale = 1.0f,
+ int32_t offset = 0)
+{
+ auto dataType = (std::is_same<T, uint8_t>::value ?
+ armnn::DataType::QuantisedAsymm8 :
+ armnn::DataType::Float32);
+
+ armnn::TensorInfo inputTensorInfo(InputDim, inputShape, dataType);
+ armnn::TensorInfo outputTensorInfo(OutputDim, outputShape, dataType);
+
+ inputTensorInfo.SetQuantizationScale(scale);
+ inputTensorInfo.SetQuantizationOffset(offset);
+
+ outputTensorInfo.SetQuantizationScale(scale);
+ outputTensorInfo.SetQuantizationOffset(offset);
+
+ auto input = MakeTensor<T, InputDim>(inputTensorInfo, inputData);
+
+ LayerTestResult<T, OutputDim> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<T, OutputDim>(outputTensorInfo, outputData);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::MeanQueueDescriptor data;
+ data.m_Parameters.m_Axis = axis;
+ data.m_Parameters.m_KeepDims = keepDims;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMean(data, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), input.origin());
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(result.output.origin(), outputHandle.get());
+
+ return result;
+}
+} // anonymous namespace
+
+LayerTestResult<uint8_t, 1> MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 3, 2 };
+ const unsigned int outputShape[] = { 1 };
+
+ std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
+ std::vector<uint8_t> output({ 2 });
+
+ return MeanTestHelper<uint8_t, 2, 1>(workloadFactory, inputShape, input, {}, false, outputShape, output);
+}
+
+LayerTestResult<uint8_t, 3> MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 1, 1, 3, 2 };
+ const unsigned int outputShape[] = { 1, 1, 2 };
+
+ std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
+ std::vector<uint8_t> output({ 2, 2 });
+
+ return MeanTestHelper<uint8_t, 4, 3>(workloadFactory, inputShape, input, {2}, false, outputShape, output);
+}
+
+LayerTestResult<uint8_t, 4> MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 1, 1, 3, 2 };
+ const unsigned int outputShape[] = { 1, 1, 1, 2 };
+
+ std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
+ std::vector<uint8_t> output({ 2, 2 });
+
+ return MeanTestHelper<uint8_t, 4, 4>(workloadFactory, inputShape, input, {2}, true, outputShape, output);
+}
+
+LayerTestResult<uint8_t, 4> MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 2, 3, 1, 2 };
+ const unsigned int outputShape[] = { 1, 3, 1, 1 };
+
+ std::vector<uint8_t> input({ 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6});
+ std::vector<uint8_t> output({ 1, 3, 5 });
+
+ return MeanTestHelper<uint8_t, 4, 4>(workloadFactory, inputShape, input, {0, 3}, true, outputShape, output);
+}
+
+LayerTestResult<uint8_t, 1> MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = {4, 3, 2};
+ const unsigned int outputShape[] = { 2 };
+
+ std::vector<uint8_t> input({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
+ std::vector<uint8_t> output({12, 13});
+
+ return MeanTestHelper<uint8_t, 3, 1>(workloadFactory, inputShape, input, {0, 1}, false, outputShape,
+ output, 0.8f, 5);
+}
+
+LayerTestResult<float, 1> MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 3, 2 };
+ const unsigned int outputShape[] = { 1 };
+
+ std::vector<float> input({ 1., 1., 2., 2., 3., 3. });
+ std::vector<float> output({ 2. });
+
+ return MeanTestHelper<float, 2, 1>(workloadFactory, inputShape, input, {}, false, outputShape, output);
+}
+
+LayerTestResult<float, 3> MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 2, 3, 1, 2 };
+ const unsigned int outputShape[] = { 3, 1, 2 };
+
+ std::vector<float> input({ 1., 2., 3., 4., 5., 6., 1., 2., 3., 4., 5., 6.});
+ std::vector<float> output({ 1., 2., 3., 4., 5., 6. });
+
+ return MeanTestHelper<float, 4, 3>(workloadFactory, inputShape, input, {0}, false, outputShape, output);
+}
+
+LayerTestResult<float, 4> MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 1, 1, 3, 2 };
+ const unsigned int outputShape[] = { 1, 1, 1, 2 };
+
+ std::vector<float> input({ 1., 1., 2., 2., 3., 3. });
+ std::vector<float> output({ 2., 2. });
+
+ return MeanTestHelper<float, 4, 4>(workloadFactory, inputShape, input, {2}, true, outputShape, output);
+}
+
+LayerTestResult<float, 4> MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 2, 3, 1, 2 };
+ const unsigned int outputShape[] = { 1, 3, 1, 1 };
+
+ std::vector<float> input({ 1., 2., 3., 4., 5., 6., 1., 2., 3., 4., 5., 6.});
+ std::vector<float> output({ 1.5, 3.5, 5.5 });
+
+ return MeanTestHelper<float, 4, 4>(workloadFactory, inputShape, input, {0, 3}, true, outputShape, output);
+}
+
+LayerTestResult<float, 1> MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = {4, 3, 2};
+ const unsigned int outputShape[] = { 2 };
+
+ std::vector<float> input({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
+ 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f});
+ std::vector<float> output({12.0f, 13.0f});
+
+ return MeanTestHelper<float, 3, 1>(workloadFactory, inputShape, input, {0, 1}, false, outputShape, output);
+}
+
+LayerTestResult<float, 3> MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = {4, 3, 2};
+ const unsigned int outputShape[] = {1, 3, 1 };
+
+ std::vector<float> input({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
+ 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f});
+ std::vector<float> output({10.5f, 12.5f, 14.5f});
+
+ return MeanTestHelper<float, 3, 3>(workloadFactory, inputShape, input, {0, 2}, true, outputShape, output);
+}
diff --git a/src/backends/test/LayerTests.hpp b/src/backends/test/LayerTests.hpp
index 365a1f5..9dc3afa 100644
--- a/src/backends/test/LayerTests.hpp
+++ b/src/backends/test/LayerTests.hpp
@@ -343,3 +343,16 @@
LayerTestResult<float, 4> SimpleConvertFp16ToFp32Test(armnn::IWorkloadFactory& workloadFactory);
LayerTestResult<armnn::Half, 4> SimpleConvertFp32ToFp16Test(armnn::IWorkloadFactory& workloadFactory);
+
+
+LayerTestResult<uint8_t, 1> MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<uint8_t, 3> MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<uint8_t, 4> MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<uint8_t, 4> MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<uint8_t, 1> MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<float, 1> MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<float, 3> MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<float, 4> MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<float, 4> MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<float, 1> MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory);
+LayerTestResult<float, 3> MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory);
diff --git a/src/backends/test/Reference.cpp b/src/backends/test/Reference.cpp
index 97a209d..30a8f8e 100644
--- a/src/backends/test/Reference.cpp
+++ b/src/backends/test/Reference.cpp
@@ -250,4 +250,18 @@
// Convert from Float32 to Float16
ARMNN_AUTO_TEST_CASE(SimpleConvertFp32ToFp16, SimpleConvertFp32ToFp16Test)
+// Mean
+ARMNN_AUTO_TEST_CASE(MeanUint8Simple, MeanUint8SimpleTest)
+ARMNN_AUTO_TEST_CASE(MeanUint8SimpleAxis, MeanUint8SimpleAxisTest)
+ARMNN_AUTO_TEST_CASE(MeanUint8KeepDims, MeanUint8KeepDimsTest)
+ARMNN_AUTO_TEST_CASE(MeanUint8MultipleDims, MeanUint8MultipleDimsTest)
+ARMNN_AUTO_TEST_CASE(MeanVtsUint8, MeanVtsUint8Test)
+
+ARMNN_AUTO_TEST_CASE(MeanFloatSimple, MeanFloatSimpleTest)
+ARMNN_AUTO_TEST_CASE(MeanFloatSimpleAxis, MeanFloatSimpleAxisTest)
+ARMNN_AUTO_TEST_CASE(MeanFloatKeepDims, MeanFloatKeepDimsTest)
+ARMNN_AUTO_TEST_CASE(MeanFloatMultipleDims, MeanFloatMultipleDimsTest)
+ARMNN_AUTO_TEST_CASE(MeanVtsFloat1, MeanVtsFloat1Test)
+ARMNN_AUTO_TEST_CASE(MeanVtsFloat2, MeanVtsFloat2Test)
+
BOOST_AUTO_TEST_SUITE_END()