| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include <armnn/ArmNN.hpp> |
| #include <armnn/Tensor.hpp> |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| #include "QuantizeHelper.hpp" |
| |
| #include <backends/CpuTensorHandle.hpp> |
| #include <backends/WorkloadFactory.hpp> |
| #include "ActivationFixture.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); |
| } |
