IVGCVSW-1946: Remove armnn/src from the include paths
Change-Id: I663a0a0fccb43ee960ec070121a59df9db0bb04e
diff --git a/src/backends/backendsCommon/test/ActivationTestImpl.hpp b/src/backends/backendsCommon/test/ActivationTestImpl.hpp
new file mode 100644
index 0000000..3b3ee93
--- /dev/null
+++ b/src/backends/backendsCommon/test/ActivationTestImpl.hpp
@@ -0,0 +1,560 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "ActivationFixture.hpp"
+#include "QuantizeHelper.hpp"
+
+#include <armnn/ArmNN.hpp>
+#include <armnn/Tensor.hpp>
+#include <armnn/TypesUtils.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+#include <test/TensorHelpers.hpp>
+
+#include <algorithm>
+
+template<typename T>
+LayerTestResult<T, 4> BoundedReLuTestCommon(armnn::IWorkloadFactory& workloadFactory,
+ float upperBound, float lowerBound,
+ float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset,
+ const std::vector<T>& inputData, const std::vector<T>& outputExpectedData,
+ unsigned int inputWidth, unsigned int inputHeight,
+ unsigned int inputChannels, unsigned int inputBatchSize)
+{
+ unsigned int outputWidth = inputWidth;
+ unsigned int outputHeight = inputHeight;
+ unsigned int outputChannels = inputChannels;
+ unsigned int outputBatchSize = inputBatchSize;
+
+ armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::GetDataType<T>());
+
+ armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ armnn::GetDataType<T>());
+
+ if(armnn::IsQuantizedType<T>())
+ {
+ inputTensorInfo.SetQuantizationScale(inputScale);
+ inputTensorInfo.SetQuantizationOffset(inputOffset);
+
+ outputTensorInfo.SetQuantizationScale(outputScale);
+ outputTensorInfo.SetQuantizationOffset(outputOffset);
+ }
+
+ LayerTestResult<T, 4> result(inputTensorInfo);
+
+ auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ // Setup bounded ReLu.
+ armnn::ActivationQueueDescriptor descriptor;
+ armnn::WorkloadInfo workloadInfo;
+ AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
+
+ descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu;
+ descriptor.m_Parameters.m_A = upperBound;
+ descriptor.m_Parameters.m_B = lowerBound;
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+ result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData);
+
+ return result;
+}
+
+LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int inputWidth = 4u;
+ unsigned int inputHeight = 5u;
+ unsigned int inputChannels = 1u;
+ unsigned int inputBatchSize = 1;
+
+ std::vector<float> input = std::vector<float>{
+ -2.0f, 0.1f, 0.5f, 1.25f,
+ 0.786f, 0.9875f, -1.5f, 0.384f,
+ 1.0001f, 3.5f, 7.5f, 0.896f,
+ 2.126f, 2.0f, 0.3f, 0.15f,
+ 0.999f, 1.2f, 0.89f, 6.1f,
+ };
+
+ // Calculated manually.
+ std::vector<float> output = std::vector<float>{
+ -1.0f, 0.1f, 0.5f, 1.0f,
+ 0.786f, 0.9875f, -1.0f, 0.384f,
+ 1.0f, 1.0f, 1.0f, 0.896f,
+ 1.0f, 1.0f, 0.3f, 0.15f,
+ 0.999f, 1.0f, 0.89f, 1.0f,
+ };
+
+ return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output,
+ inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int inputWidth = 4u;
+ unsigned int inputHeight = 5u;
+ unsigned int inputChannels = 1u;
+ unsigned int inputBatchSize = 1;
+
+ std::vector<float> input = std::vector<float>{
+ -1.0f, 0.1f, 0.5f, 6.25f,
+ 0.786f, 5.9875f, -0.5f, 0.384f,
+ 6.0001f, 3.5f, 7.5f, 0.896f,
+ 2.126f, 12.0f, 0.3f, 0.15f,
+ 0.999f, 1.2f, 0.89f, 6.1f,
+ };
+
+ // Calculated manually.
+ std::vector<float> output = std::vector<float>{
+ 0.0f, 0.1f, 0.5f, 6.0f,
+ 0.786f, 5.9875f, 0.0f, 0.384f,
+ 6.0f, 3.5f, 6.0f, 0.896f,
+ 2.126f, 6.0f, 0.3f, 0.15f,
+ 0.999f, 1.2f, 0.89f, 6.0f,
+ };
+
+ return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output,
+ inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int inputWidth = 3u;
+ unsigned int inputHeight = 2u;
+ unsigned int inputChannels = 1u;
+ unsigned int inputBatchSize = 1;
+
+ std::vector<uint8_t> input = std::vector<uint8_t>{
+ 51, 124, 28,
+ 251, 8, 92
+ };
+
+ // Calculated manually.
+ std::vector<uint8_t> output = std::vector<uint8_t>{
+ 0, 122, 0,
+ 255, 0, 58
+ };
+
+ float inputScale = 12.0f / 255.0f;
+ int32_t inputOffset = 63;
+ float outputScale = 6.0f / 255.0f;
+ int32_t outputOffset = 0;
+
+ return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f,
+ inputScale, inputOffset, outputScale, outputOffset,
+ input, output,
+ inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int inputWidth = 3u;
+ unsigned int inputHeight = 2u;
+ unsigned int inputChannels = 1u;
+ unsigned int inputBatchSize = 1;
+
+ std::vector<uint8_t> input = std::vector<uint8_t>{
+ 51, 230, 28,
+ 251, 8, 92
+ };
+
+ // Calculated manually.
+ std::vector<uint8_t> output = std::vector<uint8_t>{
+ 51, 192, 32,
+ 192, 32, 92
+ };
+
+ int32_t inputOffset = 112;
+ float inputScale = 0.0125f;
+
+ return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f,
+ inputScale, inputOffset, inputScale, inputOffset, // Input/output scale & offset same.
+ input, output,
+ inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+namespace
+{
+
+struct BoundedReLuRandomInputTestTraits
+{
+ constexpr static unsigned int inputHeight = 31u;
+ constexpr static unsigned int inputWidth = 19u;
+ constexpr static unsigned int inputChannels = 4u;
+ constexpr static unsigned int inputBatchSize = 2;
+
+ constexpr static unsigned int outputHeight = inputHeight;
+ constexpr static unsigned int outputWidth = inputWidth;
+ constexpr static unsigned int outputChannels = inputChannels;
+ constexpr static unsigned int outputBatchSize = inputBatchSize;
+
+ static armnn::TensorInfo GetInputTensorInfo()
+ {
+ return armnn::TensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::DataType::Float32);
+ }
+
+ static armnn::TensorInfo GetOutputTensorInfo()
+ {
+ return armnn::TensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ armnn::DataType::Float32);
+ }
+};
+
+boost::multi_array<float, 4> BoundedReLuRandomInputTest(armnn::IWorkloadFactory& workloadFactory,
+ float lowerBound,
+ float upperBound,
+ const armnn::ActivationDescriptor& activationDescriptor)
+{
+ const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo();
+ const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo();
+
+ boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo));
+
+ // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu
+ // range [lowerBound, upperBound].
+ auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ // Set up bounded ReLu.
+ armnn::ActivationQueueDescriptor descriptor;
+ armnn::WorkloadInfo workloadInfo;
+ AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
+ descriptor.m_Parameters = activationDescriptor;
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get());
+
+ return output;
+}
+
+} // namespace
+
+LayerTestResult<float, 4> CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& otherWorkloadFactory,
+ float upperBound,
+ float lowerBound)
+{
+ LayerTestResult<float, 4> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo());
+
+ armnn::ActivationDescriptor activationDescriptor;
+ activationDescriptor.m_Function = armnn::ActivationFunction::BoundedReLu;
+ activationDescriptor.m_A = upperBound;
+ activationDescriptor.m_B = lowerBound;
+
+ result.output = BoundedReLuRandomInputTest(workloadFactory, 0.0f, upperBound, activationDescriptor);
+ result.outputExpected = BoundedReLuRandomInputTest(otherWorkloadFactory, 0.0f, upperBound, activationDescriptor);
+
+ return result;
+}
+
+template<typename T>
+LayerTestResult<T,4> ConstantLinearActivationTestCommon(armnn::IWorkloadFactory& workloadFactory,
+ float qScale = 0.0f,
+ int32_t qOffset = 0)
+{
+ unsigned int inputHeight = 20;
+ unsigned int inputWidth = 17;
+ unsigned int inputChannels = 3;
+ unsigned int batchSize = 5;
+
+ armnn::TensorInfo inputTensorInfo;
+ armnn::TensorInfo outputTensorInfo;
+
+ unsigned int shape[] = {batchSize, inputChannels, inputHeight, inputWidth};
+
+ inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
+ outputTensorInfo = armnn::TensorInfo(4, shape, 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);
+ }
+
+ LayerTestResult<T, 4> ret(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ // Do linear activation that should leave the tensor unchanged.
+ armnn::ActivationQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+ data.m_Parameters.m_A = 1.0f;
+ data.m_Parameters.m_B = 0.0f;
+ data.m_Parameters.m_Function = armnn::ActivationFunction::Linear;
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561);
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+ // Ensure output equals input.
+ ret.outputExpected = input;
+
+ return ret;
+}
+
+LayerTestResult<float, 4> ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return ConstantLinearActivationTestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return ConstantLinearActivationTestCommon<uint8_t>(workloadFactory, 4.0f, 3);
+}
+
+template<typename T>
+LayerTestResult<T, 4> SimpleActivationTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::ActivationFunction activationFunction,
+ float activationParameterA,
+ float activationParameterB,
+ float qScale,
+ int32_t qOffset,
+ const std::vector<float>& inputData,
+ const std::vector<float>& outputExpectedData)
+{
+ constexpr static unsigned int inputWidth = 16u;
+ constexpr static unsigned int inputHeight = 1u;
+ constexpr static unsigned int inputChannels = 1u;
+ constexpr static unsigned int inputBatchSize = 1u;
+
+ constexpr static unsigned int outputWidth = inputWidth;
+ constexpr static unsigned int outputHeight = inputHeight;
+ constexpr static unsigned int outputChannels = inputChannels;
+ constexpr static unsigned int outputBatchSize = inputBatchSize;
+
+ armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::GetDataType<T>());
+ armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ 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);
+ }
+
+ LayerTestResult<T, 4> result(inputTensorInfo);
+
+ auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ // Setup bounded ReLu.
+ armnn::ActivationQueueDescriptor descriptor;
+ armnn::WorkloadInfo workloadInfo;
+ AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
+
+ descriptor.m_Parameters.m_Function = activationFunction;
+ descriptor.m_Parameters.m_A = activationParameterA;
+ descriptor.m_Parameters.m_B = activationParameterB;
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+ // Calculated manually.
+ result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData));
+
+ return result;
+}
+
+template<typename T>
+LayerTestResult<T, 4> SimpleSigmoidTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
+{
+ std::vector<float> inputData = {
+ -0.1f, -0.2f, -0.3f, -0.4f,
+ 0.1f, 0.2f, 0.3f, 0.4f,
+ -1.0f, -2.0f, -3.0f, -4.0f,
+ 1.0f, 2.0f, 3.0f, 4.0f
+ };
+
+ // Calculate output values for input.
+ auto f = [](float value)
+ {
+ return 1.0f / (1.0f + std::exp(-value));
+ };
+ std::vector<float> outputExpectedData(inputData.size());
+ std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+ return SimpleActivationTest<T>(workloadFactory,
+ armnn::ActivationFunction::Sigmoid,
+ 0.f,
+ 0.f,
+ qScale,
+ qOffset,
+ inputData,
+ outputExpectedData);
+}
+
+LayerTestResult<float, 4> SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return SimpleSigmoidTestCommon<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return SimpleSigmoidTestCommon<uint8_t>(workloadFactory, 0.1f, 50);
+}
+
+template<typename T>
+LayerTestResult<T,4> CompareActivationTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ armnn::ActivationFunction f,
+ unsigned int batchSize = 5,
+ float qScale = 0.0f,
+ int32_t qOffset = 0)
+{
+ unsigned int width = 17;
+ unsigned int height = 29;
+ unsigned int channels = 2;
+
+ float a = 0.234f;
+ float b = -12.345f;
+
+ armnn::TensorInfo inputTensorInfo;
+ armnn::TensorInfo outputTensorInfo;
+
+ unsigned int shape[] = {batchSize, channels, height, width};
+
+ inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
+ outputTensorInfo = armnn::TensorInfo(4, shape, 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);
+ }
+
+ float minVal = -10.f;
+ if (f == armnn::ActivationFunction::Sqrt)
+ {
+ minVal = 0.f;
+ }
+
+ boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f);
+
+
+ LayerTestResult<T,4> ret(outputTensorInfo);
+ auto boostArrayExtents = boost::extents
+ [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)]
+ [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)]
+ [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)]
+ [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)];
+ ret.output.resize(boostArrayExtents);
+ ret.outputExpected.resize(boostArrayExtents);
+
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ActivationQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+ data.m_Parameters.m_A = a;
+ data.m_Parameters.m_B = b;
+ data.m_Parameters.m_Function = f;
+
+ armnn::ActivationQueueDescriptor refData = data;
+ armnn::WorkloadInfo refInfo = info;
+ SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
+ SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info);
+ BOOST_ASSERT(workload != nullptr);
+ std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateActivation(refData, refInfo);
+ BOOST_ASSERT(workloadRef != nullptr);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ inputHandleRef->Allocate();
+ outputHandleRef->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
+
+ workload->Execute();
+ workloadRef->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+ CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
+
+ return ret;
+}
+
+LayerTestResult<float,4> CompareActivationTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ armnn::ActivationFunction f,
+ unsigned int batchSize)
+{
+ return CompareActivationTestImpl<float>(workloadFactory, refWorkloadFactory, f, batchSize);
+}
+
+LayerTestResult<uint8_t,4> CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ armnn::ActivationFunction f)
+{
+ return CompareActivationTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, f, 5, 0.1f, 50);
+}