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Diffstat (limited to 'src/backends/backendsCommon/test/ActivationTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/ActivationTestImpl.hpp | 560 |
1 files changed, 560 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/ActivationTestImpl.hpp b/src/backends/backendsCommon/test/ActivationTestImpl.hpp new file mode 100644 index 0000000000..3b3ee9361c --- /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); +} |