| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "LayerTestResult.hpp" |
| |
| #include <QuantizeHelper.hpp> |
| #include <ResolveType.hpp> |
| |
| |
| #include <armnn/backends/IBackendInternal.hpp> |
| #include <backendsCommon/WorkloadFactory.hpp> |
| |
| #include <backendsCommon/test/TensorCopyUtils.hpp> |
| #include <backendsCommon/test/WorkloadFactoryHelper.hpp> |
| #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> PreluTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| IgnoreUnused(memoryManager); |
| |
| armnn::TensorInfo inputTensorInfo ({ 1, 2, 2, 3 }, ArmnnType); |
| armnn::TensorInfo alphaTensorInfo ({ 1, 1, 1, 3 }, ArmnnType); |
| armnn::TensorInfo outputTensorInfo({ 1, 2, 2, 3 }, ArmnnType); |
| |
| if (armnn::IsQuantizedType<T>()) |
| { |
| inputTensorInfo.SetQuantizationScale(0.25f); |
| inputTensorInfo.SetQuantizationOffset(128); |
| alphaTensorInfo.SetQuantizationScale(0.25f); |
| alphaTensorInfo.SetQuantizationOffset(50); |
| outputTensorInfo.SetQuantizationScale(0.5f); |
| outputTensorInfo.SetQuantizationOffset(120); |
| } |
| |
| std::vector<float> inputData |
| { |
| // Expected quantized values: |
| // 128, 128, 128, 132, 132, 132, 124, 124, 124, 120, 120, 120 |
| 0.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, -1.0f, -1.0f, -1.0f, -2.0f, -2.0f, -2.0f |
| }; |
| std::vector<float> alphaData |
| { |
| // Expected quantized values: |
| // 50, 54, 58 |
| 0.0f, 1.0f, 2.0f |
| }; |
| std::vector<float> outputExpectedData = |
| { |
| // Expected quantized values: |
| // 20, 120, 120, 122, 122, 122, 120, 118, 116, 120, 116, 112 |
| 0.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 0.0f, -1.0f, -2.0f, 0.0f, -2.0f, -4.0f |
| }; |
| |
| std::vector<T> input = armnnUtils::QuantizedVector<T>(inputData, |
| inputTensorInfo.GetQuantizationScale(), |
| inputTensorInfo.GetQuantizationOffset()); |
| |
| std::vector<T> alpha = armnnUtils::QuantizedVector<T>(alphaData, |
| alphaTensorInfo.GetQuantizationScale(), |
| alphaTensorInfo.GetQuantizationOffset()); |
| |
| std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); |
| std::vector<T> expectedOutput = armnnUtils::QuantizedVector<T>(outputExpectedData, |
| outputTensorInfo.GetQuantizationScale(), |
| outputTensorInfo.GetQuantizationOffset()); |
| |
| std::unique_ptr <armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr <armnn::ITensorHandle> alphaHandle = tensorHandleFactory.CreateTensorHandle(alphaTensorInfo); |
| std::unique_ptr <armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| |
| armnn::PreluQueueDescriptor descriptor; |
| armnn::WorkloadInfo info; |
| AddInputToWorkload (descriptor, info, inputTensorInfo, inputHandle.get()); |
| AddInputToWorkload (descriptor, info, alphaTensorInfo, alphaHandle.get()); |
| AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePrelu(descriptor, info); |
| |
| inputHandle->Allocate(); |
| alphaHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), input.data()); |
| CopyDataToITensorHandle(alphaHandle.get(), alpha.data()); |
| |
| workload->Execute(); |
| |
| CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| |
| return LayerTestResult<T, 4>(actualOutput, |
| expectedOutput, |
| outputHandle->GetShape(), |
| outputTensorInfo.GetShape()); |
| } |