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Diffstat (limited to 'src/armnn/backends/test/ActivationTestImpl.hpp')
-rw-r--r-- | src/armnn/backends/test/ActivationTestImpl.hpp | 560 |
1 files changed, 0 insertions, 560 deletions
diff --git a/src/armnn/backends/test/ActivationTestImpl.hpp b/src/armnn/backends/test/ActivationTestImpl.hpp deleted file mode 100644 index a5d327c287..0000000000 --- a/src/armnn/backends/test/ActivationTestImpl.hpp +++ /dev/null @@ -1,560 +0,0 @@ -// -// 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 <backends/WorkloadInfo.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); -} |