From c9cc80455ff29fd2c8622c9487ec9c57ade6ea30 Mon Sep 17 00:00:00 2001 From: Aron Virginas-Tar Date: Thu, 1 Nov 2018 16:15:57 +0000 Subject: IVGCVSW-1946: Remove armnn/src from the include paths Change-Id: I663a0a0fccb43ee960ec070121a59df9db0bb04e --- .../backendsCommon/test/ActivationFixture.hpp | 61 + .../backendsCommon/test/ActivationTestImpl.hpp | 560 ++ .../backendsCommon/test/BackendIdTests.cpp | 28 + .../backendsCommon/test/BackendRegistryTests.cpp | 103 + .../backendsCommon/test/BatchNormTestImpl.hpp | 186 + src/backends/backendsCommon/test/CMakeLists.txt | 41 + .../backendsCommon/test/Conv2dTestImpl.hpp | 1240 ++++ .../test/ConvertFp16ToFp32TestImpl.hpp | 54 + .../test/ConvertFp32ToFp16TestImpl.hpp | 55 + .../backendsCommon/test/EndToEndTestImpl.hpp | 102 + .../backendsCommon/test/FullyConnectedTestImpl.hpp | 287 + .../test/IsLayerSupportedTestImpl.hpp | 565 ++ .../backendsCommon/test/JsonPrinterTestImpl.hpp | 355 ++ .../test/LayerReleaseConstantDataTest.cpp | 213 + src/backends/backendsCommon/test/LayerTests.cpp | 6125 ++++++++++++++++++++ src/backends/backendsCommon/test/LayerTests.hpp | 416 ++ src/backends/backendsCommon/test/LstmTestImpl.hpp | 1150 ++++ src/backends/backendsCommon/test/NormTestImpl.hpp | 343 ++ .../backendsCommon/test/OptimizedNetworkTests.cpp | 330 ++ .../backendsCommon/test/PermuteTestImpl.hpp | 225 + .../backendsCommon/test/Pooling2dTestImpl.hpp | 1240 ++++ .../backendsCommon/test/QuantizeHelper.hpp | 91 + .../backendsCommon/test/ReshapeTestImpl.hpp | 177 + .../backendsCommon/test/RuntimeTestImpl.hpp | 43 + .../backendsCommon/test/SoftmaxTestImpl.hpp | 153 + .../backendsCommon/test/SplitterTestImpl.hpp | 304 + .../backendsCommon/test/TensorCopyUtils.cpp | 161 + .../backendsCommon/test/TensorCopyUtils.hpp | 15 + .../backendsCommon/test/WorkloadDataValidation.cpp | 474 ++ .../backendsCommon/test/WorkloadTestUtils.hpp | 56 + 30 files changed, 15153 insertions(+) create mode 100644 src/backends/backendsCommon/test/ActivationFixture.hpp create mode 100644 src/backends/backendsCommon/test/ActivationTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/BackendIdTests.cpp create mode 100644 src/backends/backendsCommon/test/BackendRegistryTests.cpp create mode 100644 src/backends/backendsCommon/test/BatchNormTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/CMakeLists.txt create mode 100755 src/backends/backendsCommon/test/Conv2dTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/ConvertFp16ToFp32TestImpl.hpp create mode 100644 src/backends/backendsCommon/test/ConvertFp32ToFp16TestImpl.hpp create mode 100644 src/backends/backendsCommon/test/EndToEndTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/FullyConnectedTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/JsonPrinterTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp create mode 100755 src/backends/backendsCommon/test/LayerTests.cpp create mode 100644 src/backends/backendsCommon/test/LayerTests.hpp create mode 100644 src/backends/backendsCommon/test/LstmTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/NormTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/OptimizedNetworkTests.cpp create mode 100644 src/backends/backendsCommon/test/PermuteTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/Pooling2dTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/QuantizeHelper.hpp create mode 100644 src/backends/backendsCommon/test/ReshapeTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/RuntimeTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/SoftmaxTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/SplitterTestImpl.hpp create mode 100644 src/backends/backendsCommon/test/TensorCopyUtils.cpp create mode 100644 src/backends/backendsCommon/test/TensorCopyUtils.hpp create mode 100644 src/backends/backendsCommon/test/WorkloadDataValidation.cpp create mode 100644 src/backends/backendsCommon/test/WorkloadTestUtils.hpp (limited to 'src/backends/backendsCommon/test') diff --git a/src/backends/backendsCommon/test/ActivationFixture.hpp b/src/backends/backendsCommon/test/ActivationFixture.hpp new file mode 100644 index 0000000000..8ff77f6c5d --- /dev/null +++ b/src/backends/backendsCommon/test/ActivationFixture.hpp @@ -0,0 +1,61 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "TensorCopyUtils.hpp" +#include "WorkloadTestUtils.hpp" + +#include + +#include +#include + +struct ActivationFixture +{ + ActivationFixture() + { + auto boostArrayExtents = boost::extents + [boost::numeric_cast(batchSize)] + [boost::numeric_cast(channels)] + [boost::numeric_cast(height)] + [boost::numeric_cast(width)]; + output.resize(boostArrayExtents); + outputExpected.resize(boostArrayExtents); + input.resize(boostArrayExtents); + + unsigned int inputShape[] = { batchSize, channels, height, width }; + unsigned int outputShape[] = { batchSize, channels, height, width }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + input = MakeRandomTensor(inputTensorInfo, 21453); + } + + unsigned int width = 17; + unsigned int height = 29; + unsigned int channels = 2; + unsigned int batchSize = 5; + + boost::multi_array output; + boost::multi_array outputExpected; + boost::multi_array input; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + // Parameters used by some of the activation functions. + float a = 0.234f; + float b = -12.345f; +}; + + +struct PositiveActivationFixture : public ActivationFixture +{ + PositiveActivationFixture() + { + input = MakeRandomTensor(inputTensorInfo, 2342423, 0.0f, 1.0f); + } +}; \ No newline at end of file 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 +#include +#include + +#include +#include + +#include + +#include + +template +LayerTestResult BoundedReLuTestCommon(armnn::IWorkloadFactory& workloadFactory, + float upperBound, float lowerBound, + float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset, + const std::vector& inputData, const std::vector& 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()); + + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, + armnn::GetDataType()); + + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(inputScale); + inputTensorInfo.SetQuantizationOffset(inputOffset); + + outputTensorInfo.SetQuantizationScale(outputScale); + outputTensorInfo.SetQuantizationOffset(outputOffset); + } + + LayerTestResult result(inputTensorInfo); + + auto input = MakeTensor(inputTensorInfo, inputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr 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 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(outputTensorInfo, outputExpectedData); + + return result; +} + +LayerTestResult BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int inputWidth = 4u; + unsigned int inputHeight = 5u; + unsigned int inputChannels = 1u; + unsigned int inputBatchSize = 1; + + std::vector input = std::vector{ + -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 output = std::vector{ + -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 BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int inputWidth = 4u; + unsigned int inputHeight = 5u; + unsigned int inputChannels = 1u; + unsigned int inputBatchSize = 1; + + std::vector input = std::vector{ + -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 output = std::vector{ + 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 BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int inputWidth = 3u; + unsigned int inputHeight = 2u; + unsigned int inputChannels = 1u; + unsigned int inputBatchSize = 1; + + std::vector input = std::vector{ + 51, 124, 28, + 251, 8, 92 + }; + + // Calculated manually. + std::vector output = std::vector{ + 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 BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int inputWidth = 3u; + unsigned int inputHeight = 2u; + unsigned int inputChannels = 1u; + unsigned int inputBatchSize = 1; + + std::vector input = std::vector{ + 51, 230, 28, + 251, 8, 92 + }; + + // Calculated manually. + std::vector output = std::vector{ + 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 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 output(GetTensorShapeAsArray<4>(outputTensorInfo)); + + // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu + // range [lowerBound, upperBound]. + auto input = MakeRandomTensor(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr 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 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 CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& otherWorkloadFactory, + float upperBound, + float lowerBound) +{ + LayerTestResult 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 +LayerTestResult 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()); + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + LayerTestResult ret(outputTensorInfo); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr 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 workload = workloadFactory.CreateActivation(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + boost::multi_array input = MakeRandomTensor(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 ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory) +{ + return ConstantLinearActivationTestCommon(workloadFactory); +} + +LayerTestResult ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return ConstantLinearActivationTestCommon(workloadFactory, 4.0f, 3); +} + +template +LayerTestResult SimpleActivationTest(armnn::IWorkloadFactory& workloadFactory, + armnn::ActivationFunction activationFunction, + float activationParameterA, + float activationParameterB, + float qScale, + int32_t qOffset, + const std::vector& inputData, + const std::vector& 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()); + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, + armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + LayerTestResult result(inputTensorInfo); + + auto input = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, inputData)); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr 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 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(outputTensorInfo, QuantizedVector(qScale, qOffset, outputExpectedData)); + + return result; +} + +template +LayerTestResult SimpleSigmoidTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) +{ + std::vector 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 outputExpectedData(inputData.size()); + std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + + return SimpleActivationTest(workloadFactory, + armnn::ActivationFunction::Sigmoid, + 0.f, + 0.f, + qScale, + qOffset, + inputData, + outputExpectedData); +} + +LayerTestResult SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory) +{ + return SimpleSigmoidTestCommon(workloadFactory, 0.0f, 0); +} + +LayerTestResult SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return SimpleSigmoidTestCommon(workloadFactory, 0.1f, 50); +} + +template +LayerTestResult 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()); + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + 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 input = MakeRandomTensor(inputTensorInfo, 21453, minVal, 10.f); + + + LayerTestResult ret(outputTensorInfo); + auto boostArrayExtents = boost::extents + [boost::numeric_cast(batchSize)] + [boost::numeric_cast(channels)] + [boost::numeric_cast(height)] + [boost::numeric_cast(width)]; + ret.output.resize(boostArrayExtents); + ret.outputExpected.resize(boostArrayExtents); + + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr 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 workload = workloadFactory.CreateActivation(data, info); + BOOST_ASSERT(workload != nullptr); + std::unique_ptr 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 CompareActivationTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::ActivationFunction f, + unsigned int batchSize) +{ + return CompareActivationTestImpl(workloadFactory, refWorkloadFactory, f, batchSize); +} + +LayerTestResult CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::ActivationFunction f) +{ + return CompareActivationTestImpl(workloadFactory, refWorkloadFactory, f, 5, 0.1f, 50); +} diff --git a/src/backends/backendsCommon/test/BackendIdTests.cpp b/src/backends/backendsCommon/test/BackendIdTests.cpp new file mode 100644 index 0000000000..e11c13e6ae --- /dev/null +++ b/src/backends/backendsCommon/test/BackendIdTests.cpp @@ -0,0 +1,28 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include +#include + +#include + +using namespace armnn; + +BOOST_AUTO_TEST_SUITE(BackendIdTests) + +BOOST_AUTO_TEST_CASE(CreateBackendIdFromCompute) +{ + BackendId fromCompute{Compute::GpuAcc}; + BOOST_TEST(fromCompute.Get() == GetComputeDeviceAsCString(Compute::GpuAcc)); +} + +BOOST_AUTO_TEST_CASE(CreateBackendIdVectorFromCompute) +{ + std::vector fromComputes = {Compute::GpuAcc, Compute::CpuRef}; + BOOST_TEST(fromComputes[0].Get() == GetComputeDeviceAsCString(Compute::GpuAcc)); + BOOST_TEST(fromComputes[1].Get() == GetComputeDeviceAsCString(Compute::CpuRef)); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/backendsCommon/test/BackendRegistryTests.cpp b/src/backends/backendsCommon/test/BackendRegistryTests.cpp new file mode 100644 index 0000000000..4afe273de4 --- /dev/null +++ b/src/backends/backendsCommon/test/BackendRegistryTests.cpp @@ -0,0 +1,103 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include + +#include + +#include + +namespace +{ + +class SwapRegistryStorage : public armnn::BackendRegistry +{ +public: + SwapRegistryStorage() : armnn::BackendRegistry() + { + Swap(armnn::BackendRegistryInstance(), m_TempStorage); + } + + ~SwapRegistryStorage() + { + Swap(armnn::BackendRegistryInstance(),m_TempStorage); + } + +private: + FactoryStorage m_TempStorage; +}; + +} + +BOOST_AUTO_TEST_SUITE(BackendRegistryTests) + +BOOST_AUTO_TEST_CASE(SwapRegistry) +{ + using namespace armnn; + auto nFactories = BackendRegistryInstance().Size(); + { + SwapRegistryStorage helper; + BOOST_TEST(BackendRegistryInstance().Size() == 0); + } + BOOST_TEST(BackendRegistryInstance().Size() == nFactories); +} + +BOOST_AUTO_TEST_CASE(TestRegistryHelper) +{ + using namespace armnn; + SwapRegistryStorage helper; + + bool called = false; + + StaticRegistryInitializer factoryHelper( + BackendRegistryInstance(), + "HelloWorld", + [&called](const EmptyInitializer&) + { + called = true; + return armnn::IBackendInternalUniquePtr(nullptr); + } + ); + + // sanity check: the factory has not been called yet + BOOST_TEST(called == false); + + auto factoryFunction = BackendRegistryInstance().GetFactory("HelloWorld"); + + // sanity check: the factory still not called + BOOST_TEST(called == false); + + factoryFunction(EmptyInitializer()); + BOOST_TEST(called == true); +} + +BOOST_AUTO_TEST_CASE(TestDirectCallToRegistry) +{ + using namespace armnn; + SwapRegistryStorage helper; + + bool called = false; + BackendRegistryInstance().Register( + "HelloWorld", + [&called](const EmptyInitializer&) + { + called = true; + return armnn::IBackendInternalUniquePtr(nullptr); + } + ); + + // sanity check: the factory has not been called yet + BOOST_TEST(called == false); + + auto factoryFunction = BackendRegistryInstance().GetFactory("HelloWorld"); + + // sanity check: the factory still not called + BOOST_TEST(called == false); + + factoryFunction(EmptyInitializer()); + BOOST_TEST(called == true); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/backendsCommon/test/BatchNormTestImpl.hpp b/src/backends/backendsCommon/test/BatchNormTestImpl.hpp new file mode 100644 index 0000000000..2360fd56e2 --- /dev/null +++ b/src/backends/backendsCommon/test/BatchNormTestImpl.hpp @@ -0,0 +1,186 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include +#include + +#include +#include +#include + +#include + +template +LayerTestResult BatchNormTestImpl(armnn::IWorkloadFactory& workloadFactory, + const armnn::TensorShape& inputOutputTensorShape, + const std::vector& inputValues, + const std::vector& expectedOutputValues, + float qScale, + int32_t qOffset, + armnn::DataLayout dataLayout) +{ + armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::GetDataType()); + + armnn::DataLayoutIndexed dataLayoutIndexed(dataLayout); + + armnn::TensorInfo tensorInfo({ inputOutputTensorShape[dataLayoutIndexed.GetChannelsIndex()] }, + armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if (armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + tensorInfo.SetQuantizationScale(qScale); + tensorInfo.SetQuantizationOffset(qOffset); + } + + auto inputTensor = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, inputValues)); + + // These values are per-channel of the input. + auto mean = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {3, -2})); + auto variance = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {4, 9})); + auto beta = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {3, 2})); + auto gamma = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {2, 1})); + + LayerTestResult result(outputTensorInfo); + + result.outputExpected = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, expectedOutputValues)); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ScopedCpuTensorHandle meanTensor(tensorInfo); + armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo); + armnn::ScopedCpuTensorHandle betaTensor(tensorInfo); + armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo); + + armnn::BatchNormalizationQueueDescriptor descriptor; + descriptor.m_Mean = &meanTensor; + descriptor.m_Variance = &varianceTensor; + descriptor.m_Beta = &betaTensor; + descriptor.m_Gamma = &gammaTensor; + descriptor.m_Parameters.m_Eps = 0.0f; + descriptor.m_Parameters.m_DataLayout = dataLayout; + armnn::WorkloadInfo info; + + AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]); + AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]); + AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]); + AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]); + + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateBatchNormalization(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + + return result; +} + + +template +LayerTestResult BatchNormTestNhwcImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset) +{ + const unsigned int width = 2; + const unsigned int height = 3; + const unsigned int channels = 2; + const unsigned int num = 1; + + armnn::TensorInfo inputTensorInfo({num, height, width, channels}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({num, height, width, channels}, armnn::GetDataType()); + armnn::TensorInfo tensorInfo({channels}, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + tensorInfo.SetQuantizationScale(qScale); + tensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, + { + 1.f, 1.f, 4.f, 1.f, + 4.f, 4.f, 2.f, 1.f, + 1.f, -2.f, 6.f, 4.f + })); + // These values are per-channel of the input. + auto mean = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {3, -2})); + auto variance = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {4, 9})); + auto beta = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {3, 2})); + auto gamma = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, {2, 1})); + LayerTestResult ret(outputTensorInfo); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::BatchNormalizationQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle meanTensor(tensorInfo); + armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo); + armnn::ScopedCpuTensorHandle betaTensor(tensorInfo); + armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo); + + AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]); + AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]); + AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]); + AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]); + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Mean = &meanTensor; + data.m_Variance = &varianceTensor; + data.m_Beta = &betaTensor; + data.m_Gamma = &gammaTensor; + data.m_Parameters.m_Eps = 0.0f; + data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC; + + // For each channel: + // substract mean, divide by standard deviation (with an epsilon to avoid div by 0), + // multiply by gamma and add beta + ret.outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, + { + 1.f, 3.f, 4.f, 3.f, + 4.f, 4.f, 2.f, 3.f, + 1.f, 2.f, 6.f, 4.f + })); + + std::unique_ptr workload = workloadFactory.CreateBatchNormalization(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} \ No newline at end of file diff --git a/src/backends/backendsCommon/test/CMakeLists.txt b/src/backends/backendsCommon/test/CMakeLists.txt new file mode 100644 index 0000000000..ae94ad5462 --- /dev/null +++ b/src/backends/backendsCommon/test/CMakeLists.txt @@ -0,0 +1,41 @@ +# +# Copyright © 2017 Arm Ltd. All rights reserved. +# SPDX-License-Identifier: MIT +# + +list(APPEND armnnBackendsCommonUnitTests_sources + ActivationFixture.hpp + ActivationTestImpl.hpp + BackendIdTests.cpp + BackendRegistryTests.cpp + BatchNormTestImpl.hpp + Conv2dTestImpl.hpp + ConvertFp16ToFp32TestImpl.hpp + ConvertFp32ToFp16TestImpl.hpp + EndToEndTestImpl.hpp + FullyConnectedTestImpl.hpp + IsLayerSupportedTestImpl.hpp + JsonPrinterTestImpl.hpp + LayerReleaseConstantDataTest.cpp + LayerTests.cpp + LayerTests.hpp + LstmTestImpl.hpp + NormTestImpl.hpp + OptimizedNetworkTests.cpp + PermuteTestImpl.hpp + Pooling2dTestImpl.hpp + QuantizeHelper.hpp + ReshapeTestImpl.hpp + RuntimeTestImpl.hpp + SoftmaxTestImpl.hpp + SplitterTestImpl.hpp + TensorCopyUtils.cpp + TensorCopyUtils.hpp + WorkloadDataValidation.cpp + WorkloadTestUtils.hpp +) + +add_library(armnnBackendsCommonUnitTests OBJECT ${armnnBackendsCommonUnitTests_sources}) +target_include_directories(armnnBackendsCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/armnn) +target_include_directories(armnnBackendsCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/armnnUtils) +target_include_directories(armnnBackendsCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/backends) \ No newline at end of file diff --git a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp new file mode 100755 index 0000000000..aa3a44db5d --- /dev/null +++ b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp @@ -0,0 +1,1240 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include +#include +#include +#include + +#include +#include "QuantizeHelper.hpp" + +#include +#include +#include "Permute.hpp" +#include + +// Mapping from input type to bias type for fully connected layers. +// float => float, uint8_t => int32_t +template +struct FullyConnectedBiasTypeForInputType; + +template<> +struct FullyConnectedBiasTypeForInputType +{ + using Type = float; +}; + +template<> +struct FullyConnectedBiasTypeForInputType +{ + using Type = int32_t; +}; + +// Modifies a std::vector in-place using a specified bias. +template +void ApplyBias(std::vector& v, float vScale, int32_t vOffset, + const std::vector& bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h) +{ + BOOST_ASSERT_MSG((armnn::IsQuantizedType() && vScale != 0.0f) || (!armnn::IsQuantizedType()), + "Invalid type and parameter combination."); + BOOST_ASSERT_MSG((armnn::IsQuantizedType() && bScale != 0.0f) || (!armnn::IsQuantizedType()), + "Invalid type and parameter combination."); + + // Note we need to dequantize and re-quantize the image value and the bias. + for (uint32_t i = 0; i < bias.size(); ++i) + { + float dBias = SelectiveDequantize(bias[i], bScale, bOffset); + for (uint32_t y = 0; y < h; ++y) + { + for (uint32_t x = 0; x < w; ++x) + { + uint32_t offset = (i * h + y) * w + x; + BOOST_ASSERT(offset < v.size()); + T& outRef = v[offset]; + float dOutput = SelectiveDequantize(outRef, vScale, vOffset); + outRef = SelectiveQuantize(dOutput + dBias, vScale, vOffset); + } + } + } +} + +template +LayerTestResult SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, + const boost::multi_array& originalInput, + const boost::multi_array& originalKernel, + const boost::multi_array& bias, + const boost::multi_array& originalOutputExpected, + float qScale, + int32_t qOffset, + const armnn::DataLayoutIndexed& layout = armnn::DataLayout::NCHW, + uint32_t padLeft = 0, + uint32_t padTop = 0, + uint32_t padRight = 0, + uint32_t padBottom = 0) +{ + unsigned int inputHeight = boost::numeric_cast(originalInput.shape()[2]); + unsigned int inputWidth = boost::numeric_cast(originalInput.shape()[3]); + unsigned int inputChannels = boost::numeric_cast(originalInput.shape()[1]); + unsigned int inputNum = boost::numeric_cast(originalInput.shape()[0]); + + unsigned int outputHeight = boost::numeric_cast(originalOutputExpected.shape()[2]); + unsigned int outputWidth = boost::numeric_cast(originalOutputExpected.shape()[3]); + unsigned int outputChannels = boost::numeric_cast(originalOutputExpected.shape()[1]); + unsigned int outputNum = boost::numeric_cast(originalOutputExpected.shape()[0]); + + unsigned int kernelHeight = boost::numeric_cast(originalKernel.shape()[2]); + unsigned int kernelWidth = boost::numeric_cast(originalKernel.shape()[3]); + unsigned int kernelChannels = boost::numeric_cast(originalKernel.shape()[1]); + unsigned int kernelDepthMul = boost::numeric_cast(originalKernel.shape()[0]); + + bool biasEnabled = bias.size() > 0; + + // This function currently assumes 1 batch of input/output (and duplicates this into 2 batches). + BOOST_ASSERT(inputNum == 1); + BOOST_ASSERT(outputNum == 1); + + // If a bias is used, its size must equal the number of output channels. + BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels); + + + // Note these tensors will use two (identical) batches. + armnn::TensorInfo inputTensorInfo = GetTensorInfo(2*inputNum, inputChannels, inputHeight, inputWidth, layout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo( + 2*outputNum, outputChannels, outputHeight, outputWidth, layout); + armnn::TensorInfo kernelDesc = GetTensorInfo(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout); + armnn::TensorInfo biasDesc({static_cast(bias.size())}, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + kernelDesc.SetQuantizationScale(qScale); + kernelDesc.SetQuantizationOffset(qOffset); + biasDesc.SetQuantizationScale(qScale*qScale); + biasDesc.SetQuantizationOffset(0); + } + + LayerTestResult ret(outputTensorInfo); + + // Construct input data - two batches of the same input image. + std::vector inputImage; + inputImage.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth); + std::vector inputData; + inputData.insert(inputData.end(), inputImage.begin(), inputImage.end()); + inputData.insert(inputData.end(), inputImage.begin(), inputImage.end()); + + // at this point if we require it permute the input data + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + } + + auto batchedInput = MakeTensor(inputTensorInfo, inputData); + + std::vector outputImage; + outputImage.assign(originalOutputExpected.data(), + originalOutputExpected.data() + outputChannels*outputHeight*outputWidth); + + // Apply bias to output image if it is enabled. + if(biasEnabled) + { + std::vector biasV; + biasV.assign(bias.data(), bias.data() + outputChannels); + ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), + biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), + outputWidth, outputHeight); + } + + // Construct expected output data - two identical images. + std::vector outputData; + outputData.insert(outputData.end(), outputImage.begin(), outputImage.end()); + outputData.insert(outputData.end(), outputImage.begin(), outputImage.end()); + + // at this point if we require it permute the expected output + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data()); + outputData = tmp; + } + ret.outputExpected = MakeTensor(outputTensorInfo, outputData); + + // Todo: nontrivial padding and strides. + uint32_t strideX = 1; + uint32_t strideY = 1; + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::Convolution2dQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + armnn::ScopedCpuTensorHandle biasTensor(biasDesc); + // Permute the kernel if necessary + boost::multi_array kernel = boost::multi_array(originalKernel); + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data()); + } + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); + + if(biasEnabled) + { + AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); + } + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs. + data.m_Parameters.m_StrideX = strideX; + data.m_Parameters.m_StrideY = strideY; + data.m_Parameters.m_PadLeft = padLeft; + data.m_Parameters.m_PadRight = padRight; + data.m_Parameters.m_PadTop = padTop; + data.m_Parameters.m_PadBottom = padBottom; + data.m_Parameters.m_BiasEnabled = biasEnabled; + data.m_Parameters.m_DataLayout = layout.GetDataLayout(); + + std::unique_ptr workload = workloadFactory.CreateConvolution2d(data, info); + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template +LayerTestResult SimpleConvolution2dNhwcTestImpl(armnn::IWorkloadFactory& workloadFactory, + const boost::multi_array& input, + const boost::multi_array& kernel, + const boost::multi_array& bias, + const boost::multi_array& outputExpected, + armnn::DataLayout dataLayout, + float qScale, + int32_t qOffset, + uint32_t padLeft = 1, + uint32_t padTop = 1, + uint32_t padRight = 1, + uint32_t padBottom = 1, + uint32_t strideX = 1, + uint32_t strideY = 1) +{ + unsigned int inputNum = boost::numeric_cast(input.shape()[0]); + unsigned int inputChannels = boost::numeric_cast(input.shape()[3]); + unsigned int inputHeight = boost::numeric_cast(input.shape()[1]); + unsigned int inputWidth = boost::numeric_cast(input.shape()[2]); + + unsigned int kernelChanMul = boost::numeric_cast(kernel.shape()[0]); + unsigned int kernelChannels = boost::numeric_cast(kernel.shape()[3]); + unsigned int kernelHeight = boost::numeric_cast(kernel.shape()[1]); + unsigned int kernelWidth = boost::numeric_cast(kernel.shape()[2]); + + unsigned int outputNum = boost::numeric_cast(outputExpected.shape()[0]); + unsigned int outputChannels = boost::numeric_cast(outputExpected.shape()[3]); + unsigned int outputHeight = boost::numeric_cast(outputExpected.shape()[1]); + unsigned int outputWidth = boost::numeric_cast(outputExpected.shape()[2]); + + bool biasEnabled = bias.size() > 0; + + // Creates the tensors. + armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels}, + armnn::GetDataType()); + armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, armnn::GetDataType()); + armnn::TensorInfo biasDesc({static_cast(bias.size())}, armnn::GetDataType()); + + // Construct the input data. + std::vector inputData; + inputData.assign(input.data(), input.data() + inputHeight*inputWidth*inputChannels); + auto batchedInput = MakeTensor(inputTensorInfo, inputData); + + // Construct the output data, with bias applied, as appropriate. + std::vector outputData; + outputData.assign(outputExpected.data(), outputExpected.data() + outputHeight*outputWidth*outputChannels); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, outputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); + + armnn::ScopedCpuTensorHandle biasTensor(biasDesc); + + armnn::Convolution2dQueueDescriptor data; + + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs. + data.m_Parameters.m_StrideX = strideX; + data.m_Parameters.m_StrideY = strideY; + data.m_Parameters.m_PadLeft = padLeft; + data.m_Parameters.m_PadRight = padRight; + data.m_Parameters.m_PadTop = padTop; + data.m_Parameters.m_PadBottom = padBottom; + data.m_Parameters.m_BiasEnabled = biasEnabled; + data.m_Parameters.m_DataLayout = dataLayout; + + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateConvolution2d(data, info); + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template +LayerTestResult DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadFactory& workloadFactory, + const boost::multi_array& input, + const boost::multi_array& originalKernel, + const boost::multi_array& bias, + const boost::multi_array& outputExpected, + float qScale, + int32_t qOffset, + const armnn::DataLayoutIndexed& layout, + uint32_t padLeft = 0, + uint32_t padTop = 0, + uint32_t padRight = 0, + uint32_t padBottom = 0, + uint32_t strideX = 1, + uint32_t strideY = 1) +{ + unsigned int inputNum = boost::numeric_cast(input.shape()[0]); + unsigned int inputChannels = boost::numeric_cast(input.shape()[1]); + unsigned int inputHeight = boost::numeric_cast(input.shape()[2]); + unsigned int inputWidth = boost::numeric_cast(input.shape()[3]); + unsigned int kernelChanMul = boost::numeric_cast(originalKernel.shape()[0]); + unsigned int kernelChannels = boost::numeric_cast(originalKernel.shape()[1]); + unsigned int kernelHeight = boost::numeric_cast(originalKernel.shape()[2]); + unsigned int kernelWidth = boost::numeric_cast(originalKernel.shape()[3]); + unsigned int outputNum = boost::numeric_cast(outputExpected.shape()[0]); + unsigned int outputChannels = boost::numeric_cast(outputExpected.shape()[1]); + unsigned int outputHeight = boost::numeric_cast(outputExpected.shape()[2]); + unsigned int outputWidth = boost::numeric_cast(outputExpected.shape()[3]); + + // If a bias is used, its size must equal the number of output channels. + bool biasEnabled = bias.size() > 0; + BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels); + + // Creates the tensors. + armnn::TensorInfo inputTensorInfo = GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout); + armnn::TensorInfo kernelDesc = GetTensorInfo(kernelChanMul, kernelChannels, kernelHeight, kernelWidth, layout); + armnn::TensorInfo biasDesc({static_cast(bias.size())}, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if (armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + kernelDesc.SetQuantizationScale(qScale); + kernelDesc.SetQuantizationOffset(qOffset); + biasDesc.SetQuantizationScale(qScale*qScale); + biasDesc.SetQuantizationOffset(0); + } + + // Construct the input data. + std::vector inputData; + inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth); + + // At this point if we require it permute the input data + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + } + + auto batchedInput = MakeTensor(inputTensorInfo, inputData); + + // Construct the output data, with bias applied, as appropriate. + std::vector outputData; + outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth); + if (biasEnabled) + { + std::vector biasV; + biasV.assign(bias.data(), bias.data() + outputChannels); + ApplyBias(outputData, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), + biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), + outputWidth, outputHeight); + } + + LayerTestResult ret(outputTensorInfo); + + // At this point if we require it permute the expected output + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data()); + outputData = tmp; + } + + ret.outputExpected = MakeTensor(outputTensorInfo, outputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + + // Permute the kernel if necessary + boost::multi_array kernel = boost::multi_array(originalKernel); + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data()); + } + + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); + + armnn::ScopedCpuTensorHandle biasTensor(biasDesc); + if (biasEnabled) + { + AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); + } + + armnn::DepthwiseConvolution2dQueueDescriptor data; + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs. + data.m_Parameters.m_StrideX = strideX; + data.m_Parameters.m_StrideY = strideY; + data.m_Parameters.m_PadLeft = padLeft; + data.m_Parameters.m_PadRight = padRight; + data.m_Parameters.m_PadTop = padTop; + data.m_Parameters.m_PadBottom = padBottom; + data.m_Parameters.m_BiasEnabled = biasEnabled; + data.m_Parameters.m_DataLayout = layout.GetDataLayout(); + + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template +LayerTestResult DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + unsigned int inputHeight = 3; + unsigned int inputWidth = 3; + unsigned int inputChannels = 2; + unsigned int inputNum = 1; + + unsigned int kernelHeight = 3; + unsigned int kernelWidth = 3; + unsigned int kernelChannels = inputChannels; + + unsigned int outputHeight = 1; + unsigned int outputWidth = 1; + unsigned int outputChannels = kernelChannels; + unsigned int outputNum = inputNum; + + armnn::TensorInfo inputTensorInfo = GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout); + armnn::TensorInfo kernelDesc = GetTensorInfo(1, outputChannels, kernelHeight, kernelWidth, layout); + armnn::TensorInfo biasDesc({ outputChannels }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + kernelDesc.SetQuantizationScale(qScale); + kernelDesc.SetQuantizationOffset(qOffset); + biasDesc.SetQuantizationScale(qScale*qScale); + biasDesc.SetQuantizationOffset(0); + } + std::vector inputData = std::vector( + QuantizedVector(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { + 1.f, 2.f, 1.f, + 2.f, 1.f, 2.f, + 1.f, 2.f, 1.f, + + 1.f, 2.f, 1.f, + 2.f, 1.f, 2.f, + 1.f, 2.f, 1.f, + })); + // at this point if we require it permute the input data + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + } + auto input = MakeTensor(inputTensorInfo, inputData); + + std::vector biasV(QuantizedVector(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), + {0, 2})); + auto bias = MakeTensor(biasDesc, biasV); + + std::vector kernelData = std::vector( + QuantizedVector(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { + 1.f, 0.f, 1.f, + 0.f, 0.f, 0.f, + -1.f, 0.f, -1.f, + + 1.f, 0.f, 1.f, + 0.f, 0.f, 0.f, + -1.f, 0.f, -1.f, + })); + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(kernelData.size()); + armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, kernelData.data(), tmp.data()); + kernelData = tmp; + } + auto kernel = MakeTensor(kernelDesc, kernelData); + + // Manually calculated. + std::vector outputImage( + QuantizedVector(outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset(), + {0.f, 0.f}) + ); + + // Optionally apply bias to output image. + if(biasEnabled) + { + ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), + biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), + outputWidth, outputHeight); + } + + LayerTestResult ret(outputTensorInfo); + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(outputImage.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputImage.data(), tmp.data()); + outputImage = tmp; + } + + ret.outputExpected = MakeTensor(outputTensorInfo, outputImage); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::DepthwiseConvolution2dQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + armnn::ScopedCpuTensorHandle biasTensor(biasDesc); + + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); + AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled. + data.m_Parameters.m_StrideX = 1; + data.m_Parameters.m_StrideY = 1; + data.m_Parameters.m_PadLeft = 0; + data.m_Parameters.m_PadRight = 0; + data.m_Parameters.m_PadTop = 0; + data.m_Parameters.m_PadBottom = 0; + data.m_Parameters.m_BiasEnabled = biasEnabled; + data.m_Parameters.m_DataLayout = layout.GetDataLayout(); + + std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template +LayerTestResult DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + unsigned int depthMultiplier = 2; + + unsigned int inputHeight = 8; + unsigned int inputWidth = 16; + unsigned int inputChannels = 2; + unsigned int inputBatchSize = 1; + + unsigned int kernelHeight = 5; + unsigned int kernelWidth = 3; + + unsigned int outputHeight = inputHeight - kernelHeight + 1 + 2; + unsigned int outputWidth = (inputWidth - kernelWidth + 1)/2; + unsigned int outputChannels = inputChannels * depthMultiplier; + unsigned int outputBatchSize = inputBatchSize; + + armnn::TensorInfo inputTensorInfo = GetTensorInfo( + inputBatchSize, inputChannels, inputHeight, inputWidth, layout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo( + outputBatchSize, outputChannels, outputHeight, outputWidth, layout); + armnn::TensorInfo kernelDesc = GetTensorInfo( + depthMultiplier, inputChannels, kernelHeight, kernelWidth, layout); + armnn::TensorInfo biasDesc({outputChannels}, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + kernelDesc.SetQuantizationScale(qScale); + kernelDesc.SetQuantizationOffset(qOffset); + biasDesc.SetQuantizationScale(qScale*qScale); + biasDesc.SetQuantizationOffset(0); + } + + // NOTE: originalInputData is in NCHW format + std::vector originalInputData = std::vector( + QuantizedVector(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + })); + std::vector inputData = originalInputData; + // at this point if we require it permute the input data + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, originalInputData.data(), inputData.data()); + } + auto input = MakeTensor(inputTensorInfo, inputData); + + std::vector biasV(QuantizedVector(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), + {0, 2, 1, -1})); + auto bias = MakeTensor(biasDesc, biasV); + + std::vector originalKernelData = std::vector( + QuantizedVector(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { + 1, 1, 1, + 1, -1, 1, + 1, 1, 1, + 1, 1, 1, + 1, 1, 1, + + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + + 0, 0, 0, + 0, -1, 0, + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + + 0, 0, 0, + 0, 0, 0, + 0, 1, 0, + 0, 0, 0, + 0, 0, 0 + })); + std::vector kernelData = originalKernelData; + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernelData.data(), kernelData.data()); + } + auto kernel = MakeTensor(kernelDesc, kernelData); + + // Manually calculated. + std::vector originalOutputImage = std::vector( + QuantizedVector(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { + 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, + 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, + 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, + 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, + 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, + 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, + + -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, + -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, + -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, + -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, + + 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f + })); + + // Optionally apply bias to output image. + if(biasEnabled) + { + ApplyBias(originalOutputImage, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset(), + biasV, + biasDesc.GetQuantizationScale(), + biasDesc.GetQuantizationOffset(), + outputWidth, + outputHeight); + } + + LayerTestResult ret(outputTensorInfo); + std::vector outputImage = originalOutputImage; + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, originalOutputImage.data(), outputImage.data()); + } + + ret.outputExpected = MakeTensor(outputTensorInfo, outputImage); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::DepthwiseConvolution2dQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + armnn::ScopedCpuTensorHandle biasTensor(biasDesc); + + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); + AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled. + data.m_Parameters.m_StrideX = 2; + data.m_Parameters.m_StrideY = 1; + data.m_Parameters.m_PadLeft = 0; + data.m_Parameters.m_PadRight = 0; + data.m_Parameters.m_PadTop = 1; + data.m_Parameters.m_PadBottom = 1; + data.m_Parameters.m_BiasEnabled = biasEnabled; + data.m_Parameters.m_DataLayout = layout.GetDataLayout(); + + std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template +LayerTestResult DepthwiseConvolution2dNhwcTestImpl(armnn::IWorkloadFactory& workloadFactory, + const boost::multi_array& input, + const boost::multi_array& kernel, + const boost::multi_array& bias, + const boost::multi_array& outputExpected, + float qScale, + int32_t qOffset, + uint32_t padLeft = 0, + uint32_t padTop = 0, + uint32_t padRight = 0, + uint32_t padBottom = 0, + uint32_t strideX = 1, + uint32_t strideY = 1) +{ + unsigned int inputNum = boost::numeric_cast(input.shape()[0]); + unsigned int inputChannels = boost::numeric_cast(input.shape()[3]); + unsigned int inputHeight = boost::numeric_cast(input.shape()[1]); + unsigned int inputWidth = boost::numeric_cast(input.shape()[2]); + + unsigned int kernelChanMul = boost::numeric_cast(kernel.shape()[0]); + unsigned int kernelChannels = boost::numeric_cast(kernel.shape()[3]); + unsigned int kernelHeight = boost::numeric_cast(kernel.shape()[1]); + unsigned int kernelWidth = boost::numeric_cast(kernel.shape()[2]); + + unsigned int outputNum = boost::numeric_cast(outputExpected.shape()[0]); + unsigned int outputChannels = boost::numeric_cast(outputExpected.shape()[3]); + unsigned int outputHeight = boost::numeric_cast(outputExpected.shape()[1]); + unsigned int outputWidth = boost::numeric_cast(outputExpected.shape()[2]); + + // Creates the tensors. + armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels}, + armnn::GetDataType()); + armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, armnn::GetDataType()); + armnn::TensorInfo biasDesc({static_cast(bias.size())}, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if (armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + kernelDesc.SetQuantizationScale(qScale); + kernelDesc.SetQuantizationOffset(qOffset); + biasDesc.SetQuantizationScale(qScale*qScale); + biasDesc.SetQuantizationOffset(0); + } + + // Construct the input data. + std::vector inputData; + inputData.assign(input.data(), input.data() + inputHeight*inputWidth*inputChannels); + auto batchedInput = MakeTensor(inputTensorInfo, inputData); + + // Construct the output data, with bias applied, as appropriate. + std::vector outputData; + outputData.assign(outputExpected.data(), outputExpected.data() + outputHeight*outputWidth*outputChannels); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, outputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); + + armnn::ScopedCpuTensorHandle biasTensor(biasDesc); + + armnn::DepthwiseConvolution2dQueueDescriptor data; + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs. + data.m_Parameters.m_StrideX = strideX; + data.m_Parameters.m_StrideY = strideY; + data.m_Parameters.m_PadLeft = padLeft; + data.m_Parameters.m_PadRight = padRight; + data.m_Parameters.m_PadTop = padTop; + data.m_Parameters.m_PadBottom = padBottom; + data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC; + + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template +LayerTestResult Convolution1dTestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset, + bool biasEnabled) +{ + using B = typename FullyConnectedBiasTypeForInputType::Type; + + // Until we have a specialist 1D convolution layer, we can fake one using + // 2D convolution with the final dimension set to 1. + // I don't anticipate this being particularly slow, given that convolution is implemented + // as a matrix multiplication, at which point dimension doesn't matter. + + unsigned int batchSize = 1; + unsigned int inputChannels = 2; + unsigned int outputChannels = 3; + unsigned int inputSize = 5; // The 1D size (could view as 'width' or 'height'). + unsigned int kernelSize = 3; + unsigned int padSize = 2; + unsigned int stride = 1; + unsigned int outputSize = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride. + + armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, armnn::GetDataType()); + armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, armnn::GetDataType()); + armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, armnn::GetDataType()); + armnn::TensorInfo biasInfo({outputChannels}, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputInfo.SetQuantizationScale(qScale); + inputInfo.SetQuantizationOffset(qOffset); + outputInfo.SetQuantizationScale(qScale); + outputInfo.SetQuantizationOffset(qOffset); + kernelInfo.SetQuantizationScale(qScale); + kernelInfo.SetQuantizationOffset(qOffset); + biasInfo.SetQuantizationScale(inputInfo.GetQuantizationScale()*kernelInfo.GetQuantizationScale()); + biasInfo.SetQuantizationOffset(0); + } + + std::vector inputData( + QuantizedVector(inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), { + 5.0f, -2.0f, 2.5f, 0.0f, 1.0f, + -3.0f, 3.2f, 5.0f, 2.0f, 3.0f, + })); + + std::vector kernelData( + QuantizedVector(kernelInfo.GetQuantizationScale(), kernelInfo.GetQuantizationOffset(), { + 1.0f, 0.0f, 0.0f, + 0.0f, 2.0f, -1.5f, + + 0.0f, 0.0f, 0.0f, + 0.2f, 0.2f, 0.2f, + + 0.5f, 0.0f, 0.5f, + 0.0f, -1.0f, 0.0f + })); + + std::vector biasData( + QuantizedVector(biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), { + 1.0f, 0.0f, 0.0f + })); + + std::vector outputData( + QuantizedVector(outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), { + 4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f, + -0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f, + 2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f + })); + + // Optionally apply bias to output image. + if(biasEnabled) + { + ApplyBias(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), + biasData, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), + 1, outputSize); + } + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputInfo); + + armnn::Convolution2dQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle weightsTensor(kernelInfo); + armnn::ScopedCpuTensorHandle biasTensor(biasInfo); + + AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data()); + AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data()); + + AddInputToWorkload(data, info, inputInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputInfo, outputHandle.get()); + + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; + data.m_Parameters.m_StrideX = 1; + data.m_Parameters.m_StrideY = stride; + data.m_Parameters.m_PadLeft = 0; + data.m_Parameters.m_PadRight = 0; + data.m_Parameters.m_PadTop = padSize; + data.m_Parameters.m_PadBottom = padSize; + data.m_Parameters.m_BiasEnabled = biasEnabled; + + std::unique_ptr workload = workloadFactory.CreateConvolution2d(data, info); + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), inputData.data()); + + workloadFactory.Finalize(); + workload->Execute(); + + // Output + LayerTestResult ret(outputInfo); + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + ret.outputExpected = MakeTensor(outputInfo, outputData); + return ret; +} + + + +template +LayerTestResult CompareConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory) +{ + unsigned int inputHeight = 8; + unsigned int inputWidth = 16; + unsigned int inputChannels = 3; + unsigned int inputNum = 5; + + unsigned int kernelHeight = 3; + unsigned int kernelWidth = 3; + + unsigned int strideX = 2; + unsigned int strideY = 3; + unsigned int padX = 1; + unsigned int padY = 1; + + unsigned int outputNum = inputNum; + unsigned int outputChannels = 2; + unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY; + unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + armnn::TensorInfo kernelDesc; + armnn::TensorInfo biasDesc; + + unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; + unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; + unsigned int kernelShape[] = {outputChannels, inputChannels, kernelHeight, kernelWidth}; + unsigned int biasShape[] = {outputChannels}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType()); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType()); + kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType()); + biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType()); + + LayerTestResult ret(outputTensorInfo); + + auto input = MakeRandomTensor(inputTensorInfo, 124908); + auto kernel = MakeRandomTensor(kernelDesc, 891234); + auto bias = MakeRandomTensor(biasDesc, 1028); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::Convolution2dQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + armnn::ScopedCpuTensorHandle biasTensor(biasDesc); + + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); + AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; + data.m_Parameters.m_StrideX = strideX; + data.m_Parameters.m_StrideY = strideY; + data.m_Parameters.m_PadLeft = padX; + data.m_Parameters.m_PadRight = padX; + data.m_Parameters.m_PadTop = padY; + data.m_Parameters.m_PadBottom = padY; + data.m_Parameters.m_BiasEnabled = true; + + std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); + + armnn::Convolution2dQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + std::unique_ptr workload = workloadFactory.CreateConvolution2d(data, info); + std::unique_ptr workloadRef = refWorkloadFactory.CreateConvolution2d(refData, refInfo); + + outputHandleRef->Allocate(); + inputHandleRef->Allocate(); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + refWorkloadFactory.Finalize(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); + + return ret; +} + +template +LayerTestResult CompareDepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + const armnn::DataLayoutIndexed& layout) +{ + unsigned int inputHeight = 8; + unsigned int inputWidth = 16; + unsigned int inputChannels = 3; + unsigned int inputNum = 5; + + unsigned int kernelHeight = 3; + unsigned int kernelWidth = 3; + unsigned int channelMultiplier = 1; + + unsigned int strideX = 2; + unsigned int strideY = 3; + unsigned int padX = 1; + unsigned int padY = 1; + + unsigned int outputNum = inputNum; + unsigned int outputChannels = inputChannels * channelMultiplier; + unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY; + unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + armnn::TensorInfo kernelDesc; + armnn::TensorInfo biasDesc; + + + std::vector inputShape; + std::vector outputShape; + std::vector kernelShape; + std::vector biasShape= { outputChannels }; + switch (layout.GetDataLayout()) + { + case armnn::DataLayout::NCHW: + inputShape = { inputNum, inputChannels, inputHeight, inputWidth }; + outputShape = { outputNum, outputChannels, outputHeight, outputWidth }; + kernelShape = { channelMultiplier, inputChannels, kernelHeight, kernelWidth }; + break; + case armnn::DataLayout ::NHWC: + inputShape = { inputNum, inputHeight, inputWidth, inputChannels }; + outputShape = { outputNum, outputHeight, outputWidth, outputChannels }; + kernelShape = { channelMultiplier, kernelHeight, kernelWidth, inputChannels }; + break; + default: + throw armnn::InvalidArgumentException("unknown data layout [" + + std::to_string(static_cast(layout.GetDataLayout())) + "]"); + } + + float inputsQScale = armnn::IsQuantizedType() ? 1.0f : 0; + float outputQScale = armnn::IsQuantizedType() ? 2.0f : 0; + int32_t qOffset = 0; + + inputTensorInfo = armnn::TensorInfo(4, inputShape.data(), armnn::GetDataType(), inputsQScale, qOffset); + outputTensorInfo = armnn::TensorInfo(4, outputShape.data(), armnn::GetDataType(), outputQScale, qOffset); + kernelDesc = armnn::TensorInfo(4, kernelShape.data(), armnn::GetDataType(), inputsQScale, qOffset); + biasDesc = armnn::TensorInfo( + 1, biasShape.data(), armnn::GetBiasDataType(armnn::GetDataType()), inputsQScale, qOffset); + + LayerTestResult ret(outputTensorInfo); + + auto input = MakeRandomTensor(inputTensorInfo, 124908, 0.0f, 255.0f); + auto kernel = MakeRandomTensor(kernelDesc, 891234, 0.0f, 255.0f); + auto bias = MakeRandomTensor::Type, 1>( + biasDesc, 1028, 0.0f, 255.0f); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::DepthwiseConvolution2dQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + armnn::ScopedCpuTensorHandle biasTensor(biasDesc); + + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); + AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; + data.m_Parameters.m_StrideX = strideX; + data.m_Parameters.m_StrideY = strideY; + data.m_Parameters.m_PadLeft = padX; + data.m_Parameters.m_PadRight = padX; + data.m_Parameters.m_PadTop = padY; + data.m_Parameters.m_PadBottom = padY; + data.m_Parameters.m_BiasEnabled = true; + data.m_Parameters.m_DataLayout = layout.GetDataLayout(); + + std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); + + armnn::DepthwiseConvolution2dQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + std::unique_ptr workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); + std::unique_ptr workloadRef = refWorkloadFactory.CreateDepthwiseConvolution2d(refData, refInfo); + + outputHandleRef->Allocate(); + inputHandleRef->Allocate(); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + refWorkloadFactory.Finalize(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); + + return ret; +} diff --git a/src/backends/backendsCommon/test/ConvertFp16ToFp32TestImpl.hpp b/src/backends/backendsCommon/test/ConvertFp16ToFp32TestImpl.hpp new file mode 100644 index 0000000000..a63f0cbd1d --- /dev/null +++ b/src/backends/backendsCommon/test/ConvertFp16ToFp32TestImpl.hpp @@ -0,0 +1,54 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include +#include +#include + +#include + +#include + +#include + +LayerTestResult SimpleConvertFp16ToFp32Test(armnn::IWorkloadFactory& workloadFactory) +{ + using namespace half_float::literal; + + const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16); + const armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); + + auto input = MakeTensor(inputTensorInfo, + { -37.5_h, -15.2_h, -8.76_h, -2.0_h, -1.5_h, -1.3_h, -0.5_h, -0.4_h, 0.0_h, + 1.0_h, 0.4_h, 0.5_h, 1.3_h, 1.5_h, 2.0_h, 8.76_h, 15.2_h, 37.5_h }); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, + { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, + 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f }); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ConvertFp16ToFp32QueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateConvertFp16ToFp32(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} diff --git a/src/backends/backendsCommon/test/ConvertFp32ToFp16TestImpl.hpp b/src/backends/backendsCommon/test/ConvertFp32ToFp16TestImpl.hpp new file mode 100644 index 0000000000..3513823b4e --- /dev/null +++ b/src/backends/backendsCommon/test/ConvertFp32ToFp16TestImpl.hpp @@ -0,0 +1,55 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include + +#include +#include +#include + +#include + +#include + + +LayerTestResult SimpleConvertFp32ToFp16Test(armnn::IWorkloadFactory& workloadFactory) +{ + using namespace half_float::literal; + + const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); + const armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16); + + auto input = MakeTensor(inputTensorInfo, + { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, + 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f }); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, + { -37.5_h, -15.2_h, -8.76_h, -2.0_h, -1.5_h, -1.3_h, -0.5_h, -0.4_h, 0.0_h, + 1.0_h, 0.4_h, 0.5_h, 1.3_h, 1.5_h, 2.0_h, 8.76_h, 15.2_h, 37.5_h }); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ConvertFp32ToFp16QueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateConvertFp32ToFp16(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} diff --git a/src/backends/backendsCommon/test/EndToEndTestImpl.hpp b/src/backends/backendsCommon/test/EndToEndTestImpl.hpp new file mode 100644 index 0000000000..e16116ee10 --- /dev/null +++ b/src/backends/backendsCommon/test/EndToEndTestImpl.hpp @@ -0,0 +1,102 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include + +#include + +#include + +namespace +{ + +using namespace armnn; + +template +bool ConstantUsageTest(const std::vector& computeDevice, + const TensorInfo& commonTensorInfo, + const std::vector& inputData, + const std::vector& constantData, + const std::vector& expectedOutputData) +{ + // Create runtime in which test will run + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input = net->AddInputLayer(0); + IConnectableLayer* constant = net->AddConstantLayer(ConstTensor(commonTensorInfo, constantData)); + IConnectableLayer* add = net->AddAdditionLayer(); + IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + constant->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + // Sets the tensors in the network. + input->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); + constant->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); + add->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); + + // optimize the network + IOptimizedNetworkPtr optNet = Optimize(*net, computeDevice, runtime->GetDeviceSpec()); + + // Loads it into the runtime. + NetworkId netId; + runtime->LoadNetwork(netId, std::move(optNet)); + + // Creates structures for input & output. + std::vector outputData(inputData.size()); + + InputTensors inputTensors + { + {0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} + }; + OutputTensors outputTensors + { + {0, Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} + }; + + // Does the inference. + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Checks the results. + return outputData == expectedOutputData; +} + +inline bool ConstantUsageFloat32Test(const std::vector& backends) +{ + const TensorInfo commonTensorInfo({ 2, 3 }, DataType::Float32); + + return ConstantUsageTest(backends, + commonTensorInfo, + std::vector{ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, // Input. + std::vector{ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, // Const input. + std::vector{ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f } // Expected output. + ); +} + +inline bool ConstantUsageUint8Test(const std::vector& backends) +{ + TensorInfo commonTensorInfo({ 2, 3 }, DataType::QuantisedAsymm8); + + const float scale = 0.023529f; + const int8_t offset = -43; + + commonTensorInfo.SetQuantizationScale(scale); + commonTensorInfo.SetQuantizationOffset(offset); + + return ConstantUsageTest(backends, + commonTensorInfo, + QuantizedVector(scale, offset, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }), // Input. + QuantizedVector(scale, offset, { 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }), // Const input. + QuantizedVector(scale, offset, { 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }) // Expected output. + ); +} + +} // anonymous namespace \ No newline at end of file diff --git a/src/backends/backendsCommon/test/FullyConnectedTestImpl.hpp b/src/backends/backendsCommon/test/FullyConnectedTestImpl.hpp new file mode 100644 index 0000000000..125b7e62b1 --- /dev/null +++ b/src/backends/backendsCommon/test/FullyConnectedTestImpl.hpp @@ -0,0 +1,287 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +template +LayerTestResult SimpleFullyConnectedTestImpl( + armnn::IWorkloadFactory& workloadFactory, + armnn::TensorInfo inputTensorInfo, + armnn::TensorInfo outputTensorInfo, + armnn::TensorInfo weightsDesc, + armnn::TensorInfo biasesDesc, + boost::multi_array& weights, + boost::multi_array& bias, + boost::multi_array& input, + bool biasEnabled, + bool transposeWeights) +{ + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::FullyConnectedQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle weightsTensor(weightsDesc); + armnn::ScopedCpuTensorHandle biasTensor(biasesDesc); + + AllocateAndCopyDataToITensorHandle(&weightsTensor, &weights[0][0]); + AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Weight = &weightsTensor; + data.m_Bias = &biasTensor; + data.m_Parameters.m_BiasEnabled = biasEnabled; + data.m_Parameters.m_TransposeWeightMatrix = transposeWeights; + + std::unique_ptr workload = workloadFactory.CreateFullyConnected(data, info); + LayerTestResult result(outputTensorInfo); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get()); + + return result; +} + +LayerTestResult FullyConnectedFloat32Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled, + bool transposeWeights) +{ + unsigned int inputWidth = 1; + unsigned int inputHeight = 1; + unsigned int inputChannels = 5; + unsigned int inputNum = 2; + + unsigned int outputChannels = 3; + unsigned int outputNum = 2; + + // Define the tensor descriptors. + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + armnn::TensorInfo weightsDesc; + armnn::TensorInfo biasesDesc; + + unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; + unsigned int outputShape[] = { outputNum, outputChannels }; + unsigned int weightsShape[] = { inputChannels, outputChannels }; + if (transposeWeights) + { + std::swap(weightsShape[0], weightsShape[1]); + } + unsigned int biasShape[] = { outputChannels }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32); + weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32); + biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32); + + LayerTestResult result(outputTensorInfo); + + boost::multi_array input = MakeTensor(inputTensorInfo, std::vector( + { + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, + + 5.0f, 4.0f, 3.0f, 2.0f, 1.0f + }) + ); + + boost::multi_array weights = MakeTensor(weightsDesc, std::vector( + { + .5f, 2.f, .5f, + .5f, 2.f, 1.f, + .5f, 2.f, 2.f, + .5f, 2.f, 3.f, + .5f, 2.f, 4.f + })); + + if (transposeWeights) + { + weights = MakeTensor(weightsDesc, std::vector( + { + .5f, .5f, .5f, .5f, .5f, + 2.f, 2.f, 2.f, 2.f, 2.f, + .5f, 1.f, 2.f, 3.f, 4.f + })); + } + + + std::vector biasValues({0.f, 0.f, 0.f}); + if (biasEnabled) + { + biasValues = std::vector({10.f, 20.f, 30.f}); + } + boost::multi_array bias = MakeTensor(biasesDesc, biasValues); + + result = SimpleFullyConnectedTestImpl( + workloadFactory, + inputTensorInfo, outputTensorInfo, + weightsDesc, biasesDesc, + weights, bias, input, + biasEnabled, transposeWeights + ); + + result.outputExpected = MakeTensor(outputTensorInfo, std::vector( + { + 0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0], + 2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1], + 0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2], + + 2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0], + 10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1], + 2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2] + }) + ); + + return result; +} + +LayerTestResult FullyConnectedUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) +{ + constexpr static unsigned int inputWidth = 3u; + constexpr static unsigned int inputHeight = 2u; + constexpr static unsigned int inputChannels = 1u; + + constexpr static unsigned int inputSize = inputWidth * inputHeight * inputChannels; + + constexpr static unsigned int outputChannels = 2u; + + armnn::TensorInfo inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, armnn::DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationScale(0.1f); + inputTensorInfo.SetQuantizationOffset(63); + + armnn::TensorInfo outputTensorInfo({ 1, outputChannels }, armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(5.f); + outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10); + + armnn::TensorInfo weightsDesc({ outputChannels, inputSize }, armnn::DataType::QuantisedAsymm8); + weightsDesc.SetQuantizationScale(0.2f); + weightsDesc.SetQuantizationOffset(93); + + armnn::TensorInfo biasesDesc({ outputChannels }, armnn::DataType::Signed32); + biasesDesc.SetQuantizationScale(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale()); + biasesDesc.SetQuantizationOffset(0); + + LayerTestResult result(outputTensorInfo); + + auto input = MakeTensor(inputTensorInfo, std::vector{51, 124, 28, + 251, 8, 92}); + + auto weights = MakeTensor(weightsDesc, std::vector{51, 193, 42, 53, 175, 34, + 210, 145, 23, 74, 34, 150}); + + // scale = 0.02 + // offset = 0 + auto bias = MakeTensor(biasesDesc, std::vector{9250, 67500}); + + result = SimpleFullyConnectedTestImpl( + workloadFactory, + inputTensorInfo, outputTensorInfo, + weightsDesc, biasesDesc, + weights, bias, input, + biasEnabled, true + ); + + // Manually calculated. + // Note one of these values has been clamped to 0. + if (biasEnabled) + { + result.outputExpected = MakeTensor(outputTensorInfo, std::vector{0, 242}); + } + else + { + result.outputExpected = MakeTensor(outputTensorInfo, std::vector{0, 32}); + } + + return result; +} + + + +// +// ArmNN variant of the AndroidNN fully_connected_float_large test. +// +// Tests the fully connected layer with large values, optionally transposing weights. +// Note this is templated for consistency, but the nature of this tests makes it unlikely to be useful in Uint8 mode. +// +template +LayerTestResult FullyConnectedLargeTestCommon(armnn::IWorkloadFactory& workloadFactory, + bool transposeWeights, + float qScale = 0.0f, + int32_t qOffset = 0) +{ + unsigned int inputWidth = 1; + unsigned int inputHeight = 1; + unsigned int inputChannels = 5; + unsigned int inputNum = 1; + + unsigned int outputChannels = 1; + unsigned int outputNum = 1; + + // Define the tensor descriptors. + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + armnn::TensorInfo weightsDesc; + armnn::TensorInfo biasesDesc; + + unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; + unsigned int outputShape[] = { outputNum, outputChannels }; + unsigned int weightsShape[] = { inputChannels, outputChannels }; + if (transposeWeights) + { + std::swap(weightsShape[0], weightsShape[1]); + } + + unsigned int biasShape[] = { outputChannels }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType()); + outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::GetDataType()); + weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::GetDataType()); + biasesDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + LayerTestResult result(outputTensorInfo); + + boost::multi_array input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f, + }) + ); + + boost::multi_array weights = MakeTensor(weightsDesc, + QuantizedVector(qScale, qOffset, { + 2.0f, 3.0f, 4.0f, 5.0f, 6.0f + }) + ); + + std::vector biasValues({900000.f}); + boost::multi_array bias = MakeTensor(biasesDesc, biasValues); + + result = SimpleFullyConnectedTestImpl( + workloadFactory, + inputTensorInfo, outputTensorInfo, + weightsDesc, biasesDesc, + weights, bias, input, + true, transposeWeights + ); + + result.outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 965432.0f, + }) + ); + + return result; +} diff --git a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp new file mode 100644 index 0000000000..2c992bc10b --- /dev/null +++ b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp @@ -0,0 +1,565 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include + +#include + +#include + +namespace +{ +armnn::Graph dummyGraph; + +// Make a dummy TensorInfo object. +template +armnn::TensorInfo MakeDummyTensorInfo() +{ + return armnn::TensorInfo({2,2,2,2}, DataType); +} + + +// Make a dummy WorkloadInfo using a dummy TensorInfo. +template +armnn::WorkloadInfo MakeDummyWorkloadInfo(unsigned int numInputs, unsigned int numOutputs) +{ + armnn::WorkloadInfo info; + for (unsigned int i=0; i < numInputs; i++) + { + info.m_InputTensorInfos.push_back(MakeDummyTensorInfo()); + } + for (unsigned int o=0; o < numOutputs; o++) + { + info.m_OutputTensorInfos.push_back(MakeDummyTensorInfo()); + } + return info; +} + +// Template class to create a dummy layer (2 parameters). +template +struct DummyLayer +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer(DescType(), ""); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + LayerType* m_Layer; +}; + +// Template class to create a dummy layer (1 parameter). +template +struct DummyLayer +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer(""); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + LayerType* m_Layer; +}; + +template<> +struct DummyLayer +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer(armnn::BatchNormalizationDescriptor(), ""); + m_Layer->m_Mean = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_Variance = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_Beta = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_Gamma = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::BatchNormalizationLayer* m_Layer; + +}; + +template<> +struct DummyLayer +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer(""); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::ConstantLayer* m_Layer; +}; + +template<> +struct DummyLayer +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer(armnn::LayerBindingId(), ""); + + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::InputLayer* m_Layer; +}; + +template<> +struct DummyLayer +{ + DummyLayer() + { + armnn::OriginsDescriptor desc(2); + m_Layer = dummyGraph.AddLayer(desc, ""); + + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::MergerLayer* m_Layer; +}; + +template<> +struct DummyLayer +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer(armnn::LayerBindingId(), ""); + + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::OutputLayer* m_Layer; +}; + +template<> +struct DummyLayer +{ + DummyLayer() + { + armnn::ViewsDescriptor desc(1); + m_Layer = dummyGraph.AddLayer(desc, ""); + + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::SplitterLayer* m_Layer; +}; + +template +struct DummyConvolutionLayer +{ + DummyConvolutionLayer() + { + typename ConvolutionLayerType::DescriptorType desc; + m_Layer = dummyGraph.AddLayer(desc, ""); + m_Layer->m_Weight = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_Bias = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + } + ~DummyConvolutionLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + ConvolutionLayerType* m_Layer; +}; + +template<> +struct DummyLayer + : public DummyConvolutionLayer +{ +}; + +template<> +struct DummyLayer + : public DummyConvolutionLayer +{ +}; + +template +struct DummyLstmLayer +{ + DummyLstmLayer() + { + typename LstmLayerType::DescriptorType desc; + desc.m_CifgEnabled = false; + + m_Layer = dummyGraph.AddLayer(armnn::LstmDescriptor(), ""); + m_Layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_InputToCellWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_ForgetGateBias = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_CellBias = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_OutputGateBias = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + + m_Layer->m_CifgParameters.m_InputToInputWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_CifgParameters.m_CellToInputWeights = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_CifgParameters.m_InputGateBias = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + } + ~DummyLstmLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::LstmLayer* m_Layer; +}; + +template<> +struct DummyLayer + : public DummyLstmLayer +{ +}; + +template<> +struct DummyLayer +{ + DummyLayer() + { + armnn::FullyConnectedLayer::DescriptorType desc; + m_Layer = dummyGraph.AddLayer(desc, ""); + m_Layer->m_Weight = std::make_unique( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::FullyConnectedLayer* m_Layer; +}; + +// Tag for giving LayerType entries a unique strong type each. +template +struct Tag{}; + +#define DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, descType) \ +template \ +struct LayerTypePolicy \ +{ \ + using Type = armnn::name##Layer; \ + using Desc = descType; \ + using QueueDesc = armnn::name##QueueDescriptor; \ + constexpr static const char* NameStr = #name; \ + \ + static std::unique_ptr MakeDummyWorkload(armnn::IWorkloadFactory *factory, \ + unsigned int nIn, unsigned int nOut) \ + { \ + QueueDesc desc; \ + armnn::WorkloadInfo info = MakeDummyWorkloadInfo(nIn, nOut); \ + return factory->Create##name(desc, info); \ + } \ +}; + +// Define a layer policy specialization for use with the IsLayerSupported tests. +// Use this version for layers whose constructor takes 1 parameter(name). +#define DECLARE_LAYER_POLICY_1_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, void) + +// Define a layer policy specialization for use with the IsLayerSupported tests. +// Use this version for layers whose constructor takes 2 parameters(descriptor and name). +#define DECLARE_LAYER_POLICY_2_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, armnn::name##Descriptor) + +// Layer policy template. +template +struct LayerTypePolicy; + +// Every entry in the armnn::LayerType enum must be accounted for below. +DECLARE_LAYER_POLICY_2_PARAM(Activation) + +DECLARE_LAYER_POLICY_1_PARAM(Addition) + +DECLARE_LAYER_POLICY_2_PARAM(BatchNormalization) + +DECLARE_LAYER_POLICY_1_PARAM(Constant) + +DECLARE_LAYER_POLICY_1_PARAM(ConvertFp16ToFp32) + +DECLARE_LAYER_POLICY_1_PARAM(ConvertFp32ToFp16) + +DECLARE_LAYER_POLICY_2_PARAM(Convolution2d) + +DECLARE_LAYER_POLICY_1_PARAM(MemCopy) + +DECLARE_LAYER_POLICY_2_PARAM(DepthwiseConvolution2d) + +DECLARE_LAYER_POLICY_2_PARAM(FakeQuantization) + +DECLARE_LAYER_POLICY_1_PARAM(Floor) + +DECLARE_LAYER_POLICY_2_PARAM(FullyConnected) + +DECLARE_LAYER_POLICY_CUSTOM_PARAM(Input, armnn::LayerBindingId) + +DECLARE_LAYER_POLICY_2_PARAM(L2Normalization) + +DECLARE_LAYER_POLICY_2_PARAM(Lstm) + +DECLARE_LAYER_POLICY_2_PARAM(Mean) + +DECLARE_LAYER_POLICY_2_PARAM(Merger) + +DECLARE_LAYER_POLICY_1_PARAM(Multiplication) + +DECLARE_LAYER_POLICY_2_PARAM(Normalization) + +DECLARE_LAYER_POLICY_CUSTOM_PARAM(Output, armnn::LayerBindingId) + +DECLARE_LAYER_POLICY_2_PARAM(Pad) + +DECLARE_LAYER_POLICY_2_PARAM(Permute) + +DECLARE_LAYER_POLICY_2_PARAM(Pooling2d) + +DECLARE_LAYER_POLICY_1_PARAM(Division) + +DECLARE_LAYER_POLICY_2_PARAM(ResizeBilinear) + +DECLARE_LAYER_POLICY_2_PARAM(Reshape) + +DECLARE_LAYER_POLICY_2_PARAM(Softmax) + +DECLARE_LAYER_POLICY_2_PARAM(SpaceToBatchNd) + +DECLARE_LAYER_POLICY_2_PARAM(Splitter) + +DECLARE_LAYER_POLICY_1_PARAM(Subtraction) + + +// Generic implementation to get the number of input slots for a given layer type; +template +unsigned int GetNumInputs(const armnn::Layer& layer) +{ + return layer.GetNumInputSlots(); +} + +// Generic implementation to get the number of output slots for a given layer type; +template +unsigned int GetNumOutputs(const armnn::Layer& layer) +{ + return layer.GetNumOutputSlots(); +} + +template<> +unsigned int GetNumInputs(const armnn::Layer& layer) +{ + boost::ignore_unused(layer); + return 2; +} + +// Tests that the IsLayerSupported() function returns the correct value. +// We determined the correct value by *trying* to create the relevant workload and seeing if it matches what we expect. +// Returns true if expectations are met, otherwise returns false. +template +bool IsLayerSupportedTest(FactoryType *factory, Tag) +{ + using LayerPolicy = LayerTypePolicy; + using LayerType = typename LayerPolicy::Type; + using LayerDesc = typename LayerPolicy::Desc; + DummyLayer layer; + + unsigned int numIn = GetNumInputs(*layer.m_Layer); + unsigned int numOut = GetNumOutputs(*layer.m_Layer); + + // Make another dummy layer just to make IsLayerSupported have valid inputs. + DummyLayer previousLayer; + // Set output of the previous layer to a dummy tensor. + armnn::TensorInfo output = MakeDummyTensorInfo(); + previousLayer.m_Layer->GetOutputSlot(0).SetTensorInfo(output); + // Connect all outputs of the previous layer to inputs of tested layer. + for (unsigned int i = 0; i < numIn; i++) + { + armnn::IOutputSlot& previousLayerOutputSlot = previousLayer.m_Layer->GetOutputSlot(0); + armnn::IInputSlot& layerInputSlot = layer.m_Layer->GetInputSlot(i); + previousLayerOutputSlot.Connect(layerInputSlot); + } + // Set outputs of tested layer to a dummy tensor. + for (unsigned int i = 0; i < numOut; i++) + { + layer.m_Layer->GetOutputSlot(0).SetTensorInfo(output); + } + + std::string layerName = LayerPolicy::NameStr; + std::string reasonIfUnsupported; + if (FactoryType::IsLayerSupported(*layer.m_Layer, DataType, reasonIfUnsupported)) + { + std::string errorMsg = " layer expected support but found none."; + try + { + bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() != nullptr; + BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg); + return retVal; + } + catch(const armnn::InvalidArgumentException& e) + { + boost::ignore_unused(e); + // This is ok since we throw InvalidArgumentException when creating the dummy workload. + return true; + } + catch(const std::exception& e) + { + errorMsg = e.what(); + BOOST_TEST_ERROR(layerName << ": " << errorMsg); + return false; + } + catch(...) + { + errorMsg = "Unexpected error while testing support for "; + BOOST_TEST_ERROR(errorMsg << layerName); + return false; + } + } + else + { + std::string errorMsg = "layer expected no support (giving reason: " + reasonIfUnsupported + ") but found some."; + try + { + bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() == nullptr; + BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg); + return retVal; + } + // These two exceptions are ok: For workloads that are partially supported, attempting to instantiate them + // using parameters that make IsLayerSupported() return false should throw an + // InvalidArgumentException or UnimplementedException. + catch(const armnn::InvalidArgumentException& e) + { + boost::ignore_unused(e); + return true; + } + catch(const armnn::UnimplementedException& e) + { + boost::ignore_unused(e); + return true; + } + catch(const std::exception& e) + { + errorMsg = e.what(); + BOOST_TEST_ERROR(layerName << ": " << errorMsg); + return false; + } + catch(...) + { + errorMsg = "Unexpected error while testing support for "; + BOOST_TEST_ERROR(errorMsg << layerName); + return false; + } + } +} + +// Helper function to compute the next type in the LayerType enum. +constexpr armnn::LayerType NextType(armnn::LayerType type) +{ + return static_cast(static_cast(type)+1); +} + +// Termination function for determining the end of the LayerType enumeration. +template +bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag) +{ + return IsLayerSupportedTest(factory, Tag()); +}; + +// Recursive function to test and enter in the LayerType enum and then iterate on the next entry. +template +bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag) +{ + bool v = IsLayerSupportedTest(factory, Tag()); + + return v && + IsLayerSupportedTestsImpl + (factory, Tag()); +}; + +// Helper function to pass through to the test framework. +template +bool IsLayerSupportedTests(FactoryType *factory) +{ + return IsLayerSupportedTestsImpl(factory, Tag()); +}; + +template +bool TestLayerTypeMatches() +{ + using LayerPolicy = LayerTypePolicy; + using LayerType = typename LayerPolicy::Type; + using LayerDesc = typename LayerPolicy::Desc; + DummyLayer layer; + + std::stringstream ss; + ss << LayerPolicy::NameStr << " layer type mismatches expected layer type value."; + bool v = Type == layer.m_Layer->GetType(); + BOOST_CHECK_MESSAGE(v, ss.str()); + return v; +}; + +template +bool LayerTypeMatchesTestImpl(Tag) +{ + return TestLayerTypeMatches(); +}; + +template +bool LayerTypeMatchesTestImpl(Tag) +{ + return TestLayerTypeMatches() && + LayerTypeMatchesTestImpl(Tag()); +}; + +template +bool IsConvertLayerSupportedTests(std::string& reasonIfUnsupported) +{ + armnn::Graph graph; + LayerType* const layer = graph.AddLayer("LayerName"); + + armnn::Layer* const input = graph.AddLayer(0, "input"); + armnn::Layer* const output = graph.AddLayer(0, "output"); + + armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, InputDataType); + armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, OutputDataType); + + input->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + input->GetOutputHandler(0).SetTensorInfo(inputTensorInfo); + layer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + layer->GetOutputHandler(0).SetTensorInfo(outputTensorInfo); + + bool result = FactoryType::IsLayerSupported(*layer, InputDataType, reasonIfUnsupported); + + return result; +}; + +} //namespace diff --git a/src/backends/backendsCommon/test/JsonPrinterTestImpl.hpp b/src/backends/backendsCommon/test/JsonPrinterTestImpl.hpp new file mode 100644 index 0000000000..a286b28307 --- /dev/null +++ b/src/backends/backendsCommon/test/JsonPrinterTestImpl.hpp @@ -0,0 +1,355 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include + +#include +#include +#include + +#include +#include +#include + +#include +#include +#include +#include + +inline bool AreMatchingPair(const char opening, const char closing) +{ + return (opening == '{' && closing == '}') || (opening == '[' && closing == ']'); +} + +inline bool AreParenthesesMatching(const std::string& exp) +{ + std::stack expStack; + for (size_t i = 0; i < exp.length(); ++i) + { + if (exp[i] == '{' || exp[i] == '[') + { + expStack.push(exp[i]); + } + else if (exp[i] == '}' || exp[i] == ']') + { + if (expStack.empty() || !AreMatchingPair(expStack.top(), exp[i])) + { + return false; + } + else + { + expStack.pop(); + } + } + } + return expStack.empty(); +} + +inline std::vector ExtractMeasurements(const std::string& exp) +{ + std::vector numbers; + bool inArray = false; + std::string numberString; + for (size_t i = 0; i < exp.size(); ++i) + { + if (exp[i] == '[') + { + inArray = true; + } + else if (exp[i] == ']' && inArray) + { + try + { + boost::trim_if(numberString, boost::is_any_of("\t,\n")); + numbers.push_back(std::stod(numberString)); + } + catch (std::invalid_argument const& e) + { + BOOST_FAIL("Could not convert measurements to double: " + numberString); + } + + numberString.clear(); + inArray = false; + } + else if (exp[i] == ',' && inArray) + { + try + { + boost::trim_if(numberString, boost::is_any_of("\t,\n")); + numbers.push_back(std::stod(numberString)); + } + catch (std::invalid_argument const& e) + { + BOOST_FAIL("Could not convert measurements to double: " + numberString); + } + numberString.clear(); + } + else if (exp[i] != '[' && inArray && exp[i] != ',' && exp[i] != ' ') + { + numberString += exp[i]; + } + } + return numbers; +} + +inline std::vector ExtractSections(const std::string& exp) +{ + std::vector sections; + + std::stack s; + for (size_t i = 0; i < exp.size(); i++) + { + if (exp.at(i) == '{') + { + s.push(i); + } + else if (exp.at(i) == '}') + { + size_t from = s.top(); + s.pop(); + sections.push_back(exp.substr(from, i - from + 1)); + } + } + + return sections; +} + +inline std::string SoftmaxProfilerTestSetupHelper(const std::vector& backends) +{ + using namespace armnn; + + BOOST_CHECK(!backends.empty()); + + ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); + + // Create runtime in which test will run + IRuntime::CreationOptions options; + options.m_EnableGpuProfiling = backends.front() == armnn::Compute::GpuAcc; + IRuntimePtr runtime(IRuntime::Create(options)); + + // build up the structure of the network + INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input = net->AddInputLayer(0, "input"); + IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax"); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + + input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); + softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + // set the tensors in the network + TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationOffset(100); + inputTensorInfo.SetQuantizationScale(10000.0f); + input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + + TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationOffset(0); + outputTensorInfo.SetQuantizationScale(1.0f / 256.0f); + softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + // optimize the network + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + if(!optNet) + { + BOOST_FAIL("Error occurred during Optimization, Optimize() returned nullptr."); + } + // load it into the runtime + NetworkId netId; + auto error = runtime->LoadNetwork(netId, std::move(optNet)); + BOOST_TEST(error == Status::Success); + + // create structures for input & output + std::vector inputData + { + 1, 10, 3, 200, 5 + // one of inputs is sufficiently larger than the others to saturate softmax + }; + std::vector outputData(5); + + armnn::InputTensors inputTensors + { + {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} + }; + armnn::OutputTensors outputTensors + { + {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} + }; + + runtime->GetProfiler(netId)->EnableProfiling(true); + + // do the inferences + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // retrieve the Profiler.Print() output + std::stringstream ss; + profilerManager.GetProfiler()->Print(ss); + + return ss.str(); +} + +inline void SoftmaxProfilerTestValidationHelper(std::string& result, const std::string& testData) +{ + // ensure all measurements are greater than zero + std::vector measurementsVector = ExtractMeasurements(result); + BOOST_CHECK(!measurementsVector.empty()); + + // check sections contain raw and unit tags + // first ensure Parenthesis are balanced + if (AreParenthesesMatching(result)) + { + // remove parent sections that will not have raw or unit tag + std::vector sectionVector = ExtractSections(result); + for (size_t i = 0; i < sectionVector.size(); ++i) + { + if (boost::contains(sectionVector[i], "\"ArmNN\":") + || boost::contains(sectionVector[i], "\"inference_measurements\":")) + { + sectionVector.erase(sectionVector.begin() + static_cast(i)); + } + } + BOOST_CHECK(!sectionVector.empty()); + + BOOST_CHECK(std::all_of(sectionVector.begin(), sectionVector.end(), + [](std::string i) { return boost::contains(i, "\"raw\":"); })); + + BOOST_CHECK(std::all_of(sectionVector.begin(), sectionVector.end(), + [](std::string i) { return boost::contains(i, "\"unit\":"); })); + } + + // remove the time measurements as they vary from test to test + result.erase(std::remove_if (result.begin(),result.end(), + [](char c) { return c == '.'; }), result.end()); + result.erase(std::remove_if (result.begin(), result.end(), &isdigit), result.end()); + result.erase(std::remove_if (result.begin(),result.end(), + [](char c) { return c == '\t'; }), result.end()); + + BOOST_CHECK(boost::contains(result, "ArmNN")); + BOOST_CHECK(boost::contains(result, "inference_measurements")); + BOOST_CHECK(boost::contains(result, "layer_measurements")); + BOOST_CHECK_EQUAL(result, testData); + + // ensure no spare parenthesis present in print output + BOOST_CHECK(AreParenthesesMatching(result)); +} + +inline void SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult( + const std::vector& backends) +{ + // setup the test fixture and obtain JSON Printer result + std::string result = SoftmaxProfilerTestSetupHelper(backends); + + std::string backend = "Ref"; + std::string changeLine31 = "\n},\n\"CopyMemGeneric_Execute\": {"; + std::string changeLine39 = "us\""; + std::string changeLine40; + std::string changeLine45; + + if (backends[0] == armnn::Compute::GpuAcc) { + backend = "Cl"; + changeLine31 = ",\n\"OpenClKernelTimer/: softmax_layer_max_shift_exp_sum_quantized_serial GWS[,,]\": {"; + changeLine39 = R"(us" +}, +"OpenClKernelTimer/: softmax_layer_norm_quantized GWS[,,]": { +"raw": [ +, +, + +], +"unit": "us")"; + + changeLine40 = R"( +}, +"CopyMemGeneric_Execute": { +"raw": [ +, +, + +], +"unit": "us")"; + changeLine45 = "}\n"; + } + else if (backends[0] == armnn::Compute::CpuAcc) + { + backend = "Neon"; + changeLine31 = ",\n\"NeonKernelTimer/: NEFillBorderKernel\": {"; + changeLine39 = R"(us" +}, +"NeonKernelTimer/: NELogitsDMaxKernel": { +"raw": [ +, +, + +], +"unit": "us" +}, +"NeonKernelTimer/: NELogitsDSoftmaxKernel": { +"raw": [ +, +, + +], +"unit": "us")"; + changeLine40 = R"( +}, +"CopyMemGeneric_Execute": { +"raw": [ +, +, + +], +"unit": "us")"; + changeLine45 = "}\n"; + } + + std::string testData = R"({ +"ArmNN": { +"inference_measurements": { +"raw": [ +, +, + +], +"unit": "us", +"layer_measurements": { +"raw": [ +, +, + +], +"unit": "us", +"CopyMemGeneric_Execute": { +"raw": [ +, +, + +], +"unit": "us" +}, +")" + backend + R"(SoftmaxUintWorkload_Execute": { +"raw": [ +, +, + +], +"unit": "us")" + changeLine31 + R"( +"raw": [ +, +, + +], +"unit": ")" + changeLine39 + R"( +})" + changeLine40 + R"( +} +} +} +} +)" + changeLine45 + R"()"; + + // validate the JSON Printer result + SoftmaxProfilerTestValidationHelper(result, testData); +} diff --git a/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp b/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp new file mode 100644 index 0000000000..fc32fdcd02 --- /dev/null +++ b/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp @@ -0,0 +1,213 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include + +#include +#include + +#include + +#include +#include + +#include + +using namespace armnn; +using namespace std; + +// connects two layers +void Connect(Layer* from, Layer* to, const TensorInfo& tensorInfo, unsigned int fromIndex = 0, unsigned int toIndex = 0) +{ + from->GetOutputSlot(fromIndex).Connect(to->GetInputSlot(toIndex)); + from->GetOutputHandler(fromIndex).SetTensorInfo(tensorInfo); +} + +///////////////////////////////////////////////////////////////////////////////////////////// +// The following test are created specifically to test ReleaseConstantData() method in the Layer +// They build very simple graphs including the layer will be checked. +// Checks weights and biases before the method called and after. +///////////////////////////////////////////////////////////////////////////////////////////// + +BOOST_AUTO_TEST_SUITE(LayerReleaseConstantDataTest) + +BOOST_AUTO_TEST_CASE(ReleaseBatchNormalizationLayerConstantDataTest) +{ + Graph graph; + ClWorkloadFactory factory; + + // create the layer we're testing + BatchNormalizationDescriptor layerDesc; + layerDesc.m_Eps = 0.05f; + BatchNormalizationLayer* const layer = graph.AddLayer(layerDesc, "layer"); + + armnn::TensorInfo weightInfo({3}, armnn::DataType::Float32); + layer->m_Mean = std::make_unique(weightInfo); + layer->m_Variance = std::make_unique(weightInfo); + layer->m_Beta = std::make_unique(weightInfo); + layer->m_Gamma = std::make_unique(weightInfo); + layer->m_Mean->Allocate(); + layer->m_Variance->Allocate(); + layer->m_Beta->Allocate(); + layer->m_Gamma->Allocate(); + + // create extra layers + Layer* const input = graph.AddLayer(0, "input"); + Layer* const output = graph.AddLayer(0, "output"); + + // connect up + armnn::TensorInfo tensorInfo({2, 3, 1, 1}, armnn::DataType::Float32); + Connect(input, layer, tensorInfo); + Connect(layer, output, tensorInfo); + + // check the constants that they are not NULL + BOOST_CHECK(layer->m_Mean != nullptr); + BOOST_CHECK(layer->m_Variance != nullptr); + BOOST_CHECK(layer->m_Beta != nullptr); + BOOST_CHECK(layer->m_Gamma != nullptr); + + // free up the constants.. + layer->ReleaseConstantData(); + + // check the constants that they are NULL now + BOOST_CHECK(layer->m_Mean == nullptr); + BOOST_CHECK(layer->m_Variance == nullptr); + BOOST_CHECK(layer->m_Beta == nullptr); + BOOST_CHECK(layer->m_Gamma == nullptr); + + } + + + BOOST_AUTO_TEST_CASE(ReleaseConvolution2dLayerConstantDataTest) + { + Graph graph; + ClWorkloadFactory factory; + + // create the layer we're testing + Convolution2dDescriptor layerDesc; + layerDesc.m_PadLeft = 3; + layerDesc.m_PadRight = 3; + layerDesc.m_PadTop = 1; + layerDesc.m_PadBottom = 1; + layerDesc.m_StrideX = 2; + layerDesc.m_StrideY = 4; + layerDesc.m_BiasEnabled = true; + + Convolution2dLayer* const layer = graph.AddLayer(layerDesc, "layer"); + + layer->m_Weight = std::make_unique(TensorInfo({2, 3, 5, 3}, + armnn::DataType::Float32)); + layer->m_Bias = std::make_unique + (TensorInfo({2}, GetBiasDataType(armnn::DataType::Float32))); + + layer->m_Weight->Allocate(); + layer->m_Bias->Allocate(); + + // create extra layers + Layer* const input = graph.AddLayer(0, "input"); + Layer* const output = graph.AddLayer(0, "output"); + + // connect up + Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32)); + Connect(layer, output, TensorInfo({2, 2, 2, 10}, armnn::DataType::Float32)); + + // check the constants that they are not NULL + BOOST_CHECK(layer->m_Weight != nullptr); + BOOST_CHECK(layer->m_Bias != nullptr); + + // free up the constants.. + layer->ReleaseConstantData(); + + // check the constants that they are NULL now + BOOST_CHECK(layer->m_Weight == nullptr); + BOOST_CHECK(layer->m_Bias == nullptr); +} + +BOOST_AUTO_TEST_CASE(ReleaseDepthwiseConvolution2dLayerConstantDataTest) +{ + Graph graph; + ClWorkloadFactory factory; + + // create the layer we're testing + DepthwiseConvolution2dDescriptor layerDesc; + layerDesc.m_PadLeft = 3; + layerDesc.m_PadRight = 3; + layerDesc.m_PadTop = 1; + layerDesc.m_PadBottom = 1; + layerDesc.m_StrideX = 2; + layerDesc.m_StrideY = 4; + layerDesc.m_BiasEnabled = true; + + DepthwiseConvolution2dLayer* const layer = graph.AddLayer(layerDesc, "layer"); + + layer->m_Weight = std::make_unique(TensorInfo({3, 3, 5, 3}, DataType::Float32)); + layer->m_Bias = std::make_unique(TensorInfo({9}, DataType::Float32)); + layer->m_Weight->Allocate(); + layer->m_Bias->Allocate(); + + // create extra layers + Layer* const input = graph.AddLayer(0, "input"); + Layer* const output = graph.AddLayer(0, "output"); + + // connect up + Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32)); + Connect(layer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32)); + + // check the constants that they are not NULL + BOOST_CHECK(layer->m_Weight != nullptr); + BOOST_CHECK(layer->m_Bias != nullptr); + + // free up the constants.. + layer->ReleaseConstantData(); + + // check the constants that they are NULL now + BOOST_CHECK(layer->m_Weight == nullptr); + BOOST_CHECK(layer->m_Bias == nullptr); +} + +BOOST_AUTO_TEST_CASE(ReleaseFullyConnectedLayerConstantDataTest) +{ + Graph graph; + ClWorkloadFactory factory; + + // create the layer we're testing + FullyConnectedDescriptor layerDesc; + layerDesc.m_BiasEnabled = true; + layerDesc.m_TransposeWeightMatrix = true; + + FullyConnectedLayer* const layer = graph.AddLayer(layerDesc, "layer"); + + float inputsQScale = 1.0f; + float outputQScale = 2.0f; + + layer->m_Weight = std::make_unique(TensorInfo({7, 20}, + DataType::QuantisedAsymm8, inputsQScale, 0)); + layer->m_Bias = std::make_unique(TensorInfo({7}, + GetBiasDataType(DataType::QuantisedAsymm8), inputsQScale)); + layer->m_Weight->Allocate(); + layer->m_Bias->Allocate(); + + // create extra layers + Layer* const input = graph.AddLayer(0, "input"); + Layer* const output = graph.AddLayer(0, "output"); + + // connect up + Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType::QuantisedAsymm8, inputsQScale)); + Connect(layer, output, TensorInfo({3, 7}, DataType::QuantisedAsymm8, outputQScale)); + + // check the constants that they are not NULL + BOOST_CHECK(layer->m_Weight != nullptr); + BOOST_CHECK(layer->m_Bias != nullptr); + + // free up the constants.. + layer->ReleaseConstantData(); + + // check the constants that they are NULL now + BOOST_CHECK(layer->m_Weight == nullptr); + BOOST_CHECK(layer->m_Bias == nullptr); +} + +BOOST_AUTO_TEST_SUITE_END() + diff --git a/src/backends/backendsCommon/test/LayerTests.cpp b/src/backends/backendsCommon/test/LayerTests.cpp new file mode 100755 index 0000000000..12a7063e22 --- /dev/null +++ b/src/backends/backendsCommon/test/LayerTests.cpp @@ -0,0 +1,6125 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#include "LayerTests.hpp" + +#include "test/TensorHelpers.hpp" +#include "TensorCopyUtils.hpp" +#include "Permute.hpp" + +#include +#include + +#include + +#include +#include + +#include +#include + +#include "WorkloadTestUtils.hpp" +#include "Conv2dTestImpl.hpp" +#include "BatchNormTestImpl.hpp" +#include "ActivationTestImpl.hpp" +#include "Pooling2dTestImpl.hpp" +#include "ReshapeTestImpl.hpp" +#include "FullyConnectedTestImpl.hpp" +#include "SplitterTestImpl.hpp" +#include "SoftmaxTestImpl.hpp" +#include "NormTestImpl.hpp" +#include "PermuteTestImpl.hpp" +#include "LstmTestImpl.hpp" +#include "ConvertFp16ToFp32TestImpl.hpp" +#include "ConvertFp32ToFp16TestImpl.hpp" + +// 3-channel 16x8 image used as common input data for a number of Conv2d tests. +static std::vector ConvInput3x8x16({ + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 +}); + +// 2-channel bias used by a number of Conv2d tests. +static std::vector Bias2({0, 2}); + +// Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled. +template +boost::multi_array GetBias2(bool biasEnabled, float qScale, int32_t qOffset) +{ + if(biasEnabled) + { + armnn::TensorInfo biasDesc({static_cast(Bias2.size())}, armnn::GetDataType()); + boost::multi_array bias = MakeTensor(biasDesc, QuantizedVector(qScale, qOffset, Bias2)); + return bias; + } + else + { + return boost::multi_array(); + } +} + +template +LayerTestResult SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + // Use common single-batch 3-channel 16x8 image. + armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType()); + boost::multi_array input = MakeTensor(inputDesc, QuantizedVector(qScale, qOffset, ConvInput3x8x16)); + + // Use a 2-element batch with 3-channel 3x5 kernels. + armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType()); + boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( + QuantizedVector(qScale, qOffset, { + 1, 1, 1, + 1, -1, 1, + 1, 1, 1, + 1, 1, 1, + 1, 1, 1, + + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + + + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + + 1, 1, 1, + 1, 1, 1, + 1, 1, 1, + 1, 1, 1, + 1, 1, 1, + + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + 0, 0, 0 + }))); + + // Expected output is 2 batch elements of a 1-channel 14x4 image. + armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType()); + boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( + QuantizedVector(qScale, qOffset, { + -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, + -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, + -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, + -23.5f, -23.5f, -23.5f, + -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, + -23.5f, -23.5f, -23.5f, + + 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }))); + + return SimpleConvolution2dTestImpl(workloadFactory, + input, + kernel, + GetBias2::Type>(biasEnabled, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + layout); +} + +template +LayerTestResult SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + // Use a 3x3 kernel, which exercises ArmCompute's direct convolution path. + + // Use common single-batch 3-channel 16x8 image. + armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType()); + boost::multi_array input = MakeTensor(inputDesc, QuantizedVector(qScale, qOffset, ConvInput3x8x16)); + + // Use a 2-element batch of 3-channel 3x3 kernels. + armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType()); + boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( + QuantizedVector(qScale, qOffset, { + 1, 1, 1, + 1, -1, 1, + 1, 1, 1, + + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + + + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + + 1, 1, 1, + 1, 1, 1, + 1, 1, 1, + + 0, 0, 0, + 0, 0, 0, + 0, 0, 0 + }))); + + // Expected output is 1 batch of a 2-channel 14x6 image. + armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType()); + boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( + QuantizedVector(qScale, qOffset, { + -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, + -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, + -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, + -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, + -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, + -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, + + 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + }))); + + return SimpleConvolution2dTestImpl(workloadFactory, + input, + kernel, + GetBias2::Type>(biasEnabled, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + layout); +} + +template +LayerTestResult SimpleConvolution2d3x3NhwcTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset, + bool biasEnabled, + armnn::DataLayout dataLayout) +{ + // Use common single-batch 5x5 image. + + armnn::TensorInfo inputDesc({1, 3, 4, 1}, armnn::GetDataType()); + boost::multi_array input = MakeTensor(inputDesc, + { + 1, 5, 2, 3, + 8, 7, 3, 6, + 3, 3, 9, 1 + }); + + + // Use a 2-element batch of 3-channel 3x3 kernels. + armnn::TensorInfo kernelDesc({1, 3, 3, 1}, armnn::GetDataType()); + boost::multi_array kernel = MakeTensor(kernelDesc, { + 4, 5, 6, + 0, 0, 0, + 3, 2, 1 + }); + + // Expected output is 1 batch of a 5x5 image. + armnn::TensorInfo outputDesc({1, 3, 4, 1}, armnn::GetDataType()); + + const std::vector outputData = + { + 23, 41, 33, 21, + 44, 65, 76, 52, + 82, 85, 79, 42 + }; + + boost::multi_array expectedOutput = MakeTensor(outputDesc, outputData); + + return SimpleConvolution2dNhwcTestImpl(workloadFactory, + input, + kernel, + boost::multi_array(), + expectedOutput, + dataLayout, + qScale, + qOffset); +} + +LayerTestResult SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return SimpleConvolution2d3x5TestCommon(workloadFactory, 0.f, 0, biasEnabled, layout); +} + +LayerTestResult SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return SimpleConvolution2d3x5TestCommon(workloadFactory, 0.5f, 50, biasEnabled, layout); +} + +LayerTestResult SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return SimpleConvolution2d3x3TestCommon(workloadFactory, 0.f, 0, biasEnabled, layout); +} + +LayerTestResult SimpleConvolution2d3x3NhwcTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled) +{ + return SimpleConvolution2d3x3NhwcTestCommon(workloadFactory, 0.f, 0, biasEnabled, armnn::DataLayout::NHWC); +} + +LayerTestResult SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return SimpleConvolution2d3x3TestCommon(workloadFactory, 0.5f, 50, biasEnabled, layout); +} + +template +LayerTestResult Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon( + armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& layout, + float qScale, + int32_t qOffset) +{ + // Use a single-batch 1-channel 3x3 image as input. + armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType()); + boost::multi_array input = MakeTensor(inputDesc, std::vector( + QuantizedVector(qScale, qOffset, { + 11,21,31, + 12,22,32, + 13,23,33 + }))); + + // Use 1 batch of a 1-channel 2x2 kernel. + armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType()); + boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( + QuantizedVector(qScale, qOffset, { + -11,-21, + -12,-22, + }))); + +// Expected output is 1 batch of a 1-channel 6x8 image. +// Manually calculated like this: +//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..] +//[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..] +//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..] +//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..] +//[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..] +//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..] +//[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..] + armnn::TensorInfo outputDesc({1, 1, 8, 6}, armnn::GetDataType()); + boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( + QuantizedVector(qScale, qOffset, { + 0, 0, 0, 0, 0, 0, + -242, -594, -934, -372, 0, 0, + -495, -1190, -1850, -725, 0, 0, + -538, -1256, -1916, -748, 0, 0, + -273, -626, -946, -363, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0 + }))); + + return SimpleConvolution2dTestImpl(workloadFactory, + input, + kernel, + GetBias2::Type>(false, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + layout, + 1, // Padding left. + 2, // Padding top. + 3, // Padding right. + 4); // Padding bottom. +} + +template +LayerTestResult SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& layout, + float qScale, + int32_t qOffset) +{ + // Use a single-batch 1-channel 5x5 image as input. + armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType()); + boost::multi_array input = MakeTensor(inputDesc, std::vector( + QuantizedVector(qScale, qOffset, { + 11,21,31,41,51, + 12,22,32,42,52, + 13,23,33,43,53, + 14,24,34,44,54, + 15,25,35,45,55, + }))); + + // Use 1 batch of a 1-channel 4x4 kernel. + armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType()); + boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( + QuantizedVector(qScale, qOffset, { + -11,-21,-31,-41, + -12,-22,-32,-42, + -13,-23,-33,-43, + -14,-24,-34,-44, + }))); + + // Expected output is 1 batch of a 1-channel 5x5 image. + armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType()); + std::vector myVec(outputDesc.GetNumElements(), 0); + boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( + QuantizedVector(qScale, qOffset, { + -7140, -10580, -13940, -9300, -5230, + -9590, -14120, -18520, -12290, -6860, + -9980, -14560, -18960, -12560, -7000, + -7518, -10904, -14144, -9318, -5152, + -5032, -7256, -9376, -6142, -3368, + }))); + + return SimpleConvolution2dTestImpl(workloadFactory, + input, + kernel, + GetBias2::Type>(false, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + layout, + 1, // Padding left. + 1, // Padding top. + 2, // Padding right. + 2); // Padding bottom. +} + +template +LayerTestResult DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + // Use a single-batch 2-channel 5x5 image as input. + armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType()); + auto input = MakeTensor(inputTensorInfo, std::vector( + QuantizedVector(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { + 0, 1, 2, 3, 4, + 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, + 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, + + 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, + 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49 + }))); + + // Use a depth multiplier of 1 on a 2-channel 4x4 kernel. + armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType()); + auto kernel = MakeTensor(kernelTensorInfo, std::vector( + QuantizedVector(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { + 32, 31, 30, 29, + 28, 27, 26, 25, + 24, 23, 22, 21, + 20, 19, 18, 17, + + 16, 15, 14, 13, + 12, 11, 10, 9, + 8, 7, 6, 5, + 4, 3, 2, 1 + }))); + + // Expected output is 1 batch of a 2-channel 5x5 image. + // Calculated using the python tensorflow library with strideX=1, strideY=1. + armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType()); + boost::multi_array expectedOutput = MakeTensor(outputTensorInfo, std::vector( + QuantizedVector(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { + 1062, 1580, 1850, 1530, 1117, + 2140, 3108, 3500, 2842, 2042, + 3580, 5068, 5460, 4342, 3062, + 3618, 5072, 5390, 4248, 2971, + 3074, 4282, 4510, 3533, 2457, + 1550, 2284, 2362, 1955, 1428, + 2910, 4206, 4342, 3528, 2536, + 3390, 4886, 5022, 4068, 2916, + 3566, 5056, 5182, 4133, 2922, + 3100, 4352, 4452, 3517, 2465 + }))); + + return DepthwiseConvolution2dAsymmetricTestImpl(workloadFactory, + input, + kernel, + GetBias2::Type>(biasEnabled, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + layout, + 1, // Padding left. + 1, // Padding top. + 2, // Padding right. + 2, // Padding bottom. + 1, // strideX + 1); // strideY +} + +template +LayerTestResult DepthwiseConvolution2dNhwcTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset, + bool biasEnabled) +{ + armnn::TensorInfo inputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType()); + auto input = MakeTensor(inputTensorInfo, std::vector( + QuantizedVector(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { + 0, 25, + 1, 26, + 2, 27, + 3, 28, + 4, 29, + + 5, 30, + 6, 31, + 7, 32, + 8, 33, + 9, 34, + + 10, 35, + 11, 36, + 12, 37, + 13, 38, + 14, 39, + + 15, 40, + 16, 41, + 17, 42, + 18, 43, + 19, 44, + + 20, 45, + 21, 46, + 22, 47, + 23, 48, + 24, 49 + }))); + + armnn::TensorInfo kernelTensorInfo({ 1, 4, 4, 2}, armnn::GetDataType()); + auto kernel = MakeTensor(kernelTensorInfo, std::vector( + QuantizedVector(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { + 32, 16, + 31, 15, + 30, 14, + 29, 13, + + 28, 12, + 27, 11, + 26, 10, + 25, 9, + + 24, 8, + 23, 7, + 22, 6, + 21, 5, + + 20, 4, + 19, 3, + 18, 2, + 17, 1 + }))); + + armnn::TensorInfo outputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType()); + boost::multi_array expectedOutput = MakeTensor(outputTensorInfo, std::vector( + QuantizedVector(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { + 1062, 1550, + 1580, 2284, + 1850, 2362, + 1530, 1955, + 1117, 1428, + + 2140, 2910, + 3108, 4206, + 3500, 4342, + 2842, 3528, + 2042, 2536, + + 3580, 3390, + 5068, 4886, + 5460, 5022, + 4342, 4068, + 3062, 2916, + + 3618, 3566, + 5072, 5056, + 5390, 5182, + 4248, 4133, + 2971, 2922, + + 3074, 3100, + 4282, 4352, + 4510, 4452, + 3533, 3517, + 2457, 2465 + }))); + + return DepthwiseConvolution2dNhwcTestImpl(workloadFactory, + input, + kernel, + GetBias2::Type>(biasEnabled, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + 1, // Padding left. + 1, // Padding top. + 2, // Padding right. + 2, // Padding bottom. + 1, // strideX + 1); // strideY +} + +LayerTestResult +Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& layout) +{ + return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(workloadFactory, layout, 0.0f, 0); +} + +LayerTestResult Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& layout) +{ + return SimpleConvolution2dAsymmetricPaddingTestCommon(workloadFactory, layout, 0.0f, 0); +} + +LayerTestResult DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return DepthwiseConvolution2dTestImpl(workloadFactory, 0.0f, 0, biasEnabled, layout); +} + +LayerTestResult DepthwiseConvolution2dDepthNhwcTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled) +{ + return DepthwiseConvolution2dNhwcTestCommon(workloadFactory, 0.0f, 0, biasEnabled); +} + +LayerTestResult DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return DepthwiseConvolution2dDepthMul1TestImpl(workloadFactory, 0.0f, 0, biasEnabled, layout); +} + +LayerTestResult DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return DepthwiseConvolution2dAsymmetricTestCommon(workloadFactory, 0.0f, 0, biasEnabled, layout); +} + +LayerTestResult DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return DepthwiseConvolution2dTestImpl(workloadFactory, 0.5f, 50, biasEnabled, layout); +} + +LayerTestResult DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) +{ + return DepthwiseConvolution2dDepthMul1TestImpl(workloadFactory, 0.5f, 50, biasEnabled, layout); +} + +LayerTestResult Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) +{ + return Convolution1dTestImpl(workloadFactory, 0.0f, 0, biasEnabled); +} + +LayerTestResult Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) +{ + return Convolution1dTestImpl(workloadFactory, 0.1f, 128, biasEnabled); +} + +LayerTestResult CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory) +{ + return CompareConvolution2dTestImpl(workloadFactory, refWorkloadFactory); +} + +template +LayerTestResult CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + const armnn::DataLayoutIndexed& layout) +{ + return CompareDepthwiseConvolution2dTestImpl(workloadFactory, refWorkloadFactory, layout); +} + +template LayerTestResult CompareDepthwiseConvolution2dTest( + armnn::IWorkloadFactory&, armnn::IWorkloadFactory&, const armnn::DataLayoutIndexed&); +template LayerTestResult CompareDepthwiseConvolution2dTest( + armnn::IWorkloadFactory&, armnn::IWorkloadFactory&, const armnn::DataLayoutIndexed&); + +LayerTestResult SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory) +{ + auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; + auto normChannel = armnn::NormalizationAlgorithmChannel::Across; + return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); +} + +LayerTestResult SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory) +{ + auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; + auto normChannel = armnn::NormalizationAlgorithmChannel::Within; + return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); +} + +LayerTestResult SimpleNormalizationAcrossNhwcTest(armnn::IWorkloadFactory& workloadFactory) +{ + auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; + auto normChannel = armnn::NormalizationAlgorithmChannel::Across; + return SimpleNormalizationNhwcTestImpl(workloadFactory, normChannel, normMethod); +} + +LayerTestResult SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta) +{ + return SimpleSoftmaxTestImpl(workloadFactory, beta); +} + +LayerTestResult SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta) +{ + return SimpleSoftmaxTestImpl(workloadFactory, beta); +} + +LayerTestResult CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::NormalizationAlgorithmChannel normChannel, + armnn::NormalizationAlgorithmMethod normMethod) +{ + return CompareNormalizationTestImpl(workloadFactory, refWorkloadFactory, normChannel, normMethod); +} + +LayerTestResult CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + float beta) +{ + return CompareSoftmaxTestImpl(workloadFactory, refWorkloadFactory, beta); +} + +LayerTestResult CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + float beta) +{ + return CompareSoftmaxTestImpl(workloadFactory, refWorkloadFactory, beta); +} + +std::vector> SplitterTest(armnn::IWorkloadFactory& workloadFactory) +{ + return SplitterTestCommon(workloadFactory); +} + +std::vector> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return SplitterTestCommon(workloadFactory, 1.0f, 0); +} + +LayerTestResult CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory) +{ + return CopyViaSplitterTestImpl(workloadFactory, 0.0f, 0); +} + +LayerTestResult CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return CopyViaSplitterTestImpl(workloadFactory, 1.0f, 0); +} + +LayerTestResult LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest( + armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType()); + boost::multi_array input = MakeTensor(inputDesc, std::vector( + { 2., 3., 3., 4. })); + + armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType()); + boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( + {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, + -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f})); + return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); +} + +LayerTestResult LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest( + armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType()); + boost::multi_array input = MakeTensor(inputDesc, std::vector( + {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, + 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f})); + + armnn::TensorInfo outputDesc({ 2, 16 }, armnn::GetDataType()); + boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( + {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, + -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, + -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, + 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, + -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, + 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, + 0.02168f})); + return LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(workloadFactory, input, expectedOutput); +} + +LayerTestResult LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType()); + boost::multi_array input = MakeTensor(inputDesc, std::vector( + {2., 3., 3., 4.})); + + + armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType()); + boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( + {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, + -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}})); + + return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); +} + +LayerTestResult MergerTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int outputWidth = 3; + unsigned int outputHeight = 6; + unsigned int outputChannels = 3; + + unsigned int inputWidth1 = 3; + unsigned int inputHeight1 = 6; + unsigned int inputChannels1 = 2; + + unsigned int inputWidth2 = 3; + unsigned int inputHeight2 = 6; + unsigned int inputChannels2 = 1; + + // Define the tensor descriptors. + armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32); + armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32); + armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32); + + LayerTestResult ret(outputTensorInfo); + + ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( + { + 1.0f, 2.0f, 3.0f, + 4.0f, 5.0f, 6.0f, + 7.0f, 8.0f, 9.0f, + 10.0f, 11.0f, 12.0f, + 13.0f, 14.0f, 15.0f, + 16.0f, 17.0f, 18.0f, + + 19.0f, 20.0f, 21.0f, + 22.0f, 23.0f, 24.0f, + 25.0f, 26.0f, 27.0f, + 28.0f, 29.0f, 30.0f, + 31.0f, 32.0f, 33.0f, + 34.0f, 35.0f, 36.0f, + + 37.0f, 38.0f, 39.0f, + 40.0f, 41.0f, 42.0f, + 43.0f, 44.0f, 45.0f, + 46.0f, 47.0f, 48.0f, + 49.0f, 50.0f, 51.0f, + 52.0f, 53.0f, 54.0f, + }) + ); + + auto input1 = MakeTensor(inputTensorInfo1, std::vector( + { + 1.0f, 2.0f, 3.0f, + 4.0f, 5.0f, 6.0f, + 7.0f, 8.0f, 9.0f, + 10.0f, 11.0f, 12.0f, + 13.0f, 14.0f, 15.0f, + 16.0f, 17.0f, 18.0f, + + 19.0f, 20.0f, 21.0f, + 22.0f, 23.0f, 24.0f, + 25.0f, 26.0f, 27.0f, + 28.0f, 29.0f, 30.0f, + 31.0f, 32.0f, 33.0f, + 34.0f, 35.0f, 36.0f, + }) + ); + + auto input2 = MakeTensor(inputTensorInfo2, std::vector( + { + 37.0f, 38.0f, 39.0f, + 40.0f, 41.0f, 42.0f, + 43.0f, 44.0f, 45.0f, + 46.0f, 47.0f, 48.0f, + 49.0f, 50.0f, 51.0f, + 52.0f, 53.0f, 54.0f, + }) + ); + + std::vector wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. + armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); + + std::vector wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. + armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); + + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + bool subTensorsSupported = workloadFactory.SupportsSubTensors(); + + std::unique_ptr inputHandle1 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : + workloadFactory.CreateTensorHandle(inputTensorInfo1); + + std::unique_ptr inputHandle2 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : + workloadFactory.CreateTensorHandle(inputTensorInfo2); + + armnn::MergerQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + data.m_ViewOrigins.push_back(window1); + data.m_ViewOrigins.push_back(window2); + + std::unique_ptr workload = workloadFactory.CreateMerger(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); + + return ret; +} + +LayerTestResult AdditionTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int batchSize = 2; + unsigned int channels = 2; + unsigned int height = 2; + unsigned int width = 3; + + armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; + armnn::TensorInfo outputTensorInfo; + + unsigned int shape[] = {batchSize, channels, height, width}; + + inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + + + auto input1 = MakeTensor(inputTensorInfo1, std::vector( + { + 0.0f, 2.0f, 1.0f, + 0.2f, 1.0f, 2.0f, + + 1.0f, 2.0f, 1.0f, + 0.2f, 1.0f, 2.0f, + + 0.0f, 2.0f, 1.0f, + 4.2f, 1.0f, 2.0f, + + 0.0f, 0.0f, 1.0f, + 0.2f, 1.0f, 2.0f, + })); + + auto input2 = MakeTensor(inputTensorInfo2, std::vector( + { + 1.0f, 2.0f, 1.0f, + 0.0f, 1.0f, 2.0f, + + 1.0f, 2.0f, -2.0f, + 0.2f, 1.0f, 2.0f, + + 0.0f, 2.0f, 1.0f, + 4.2f, 0.0f, -3.0f, + + 0.0f, 0.0f, 1.0f, + 0.7f, 1.0f, 5.0f, + })); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( + { + 1.0f, 4.0f, 2.0f, + 0.2f, 2.0f, 4.0f, + + 2.0f, 4.0f, -1.0f, + 0.4f, 2.0f, 4.0f, + + 0.0f, 4.0f, 2.0f, + 8.4f, 1.0f, -1.0f, + + 0.0f, 0.0f, 2.0f, + 0.9f, 2.0f, 7.0f, + })); + + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateAddition(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template +LayerTestResult AdditionBroadcastTestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset) +{ + armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, armnn::GetDataType()); + armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); + + if (armnn::IsQuantizedType()) + { + inputTensorInfo1.SetQuantizationScale(qScale); + inputTensorInfo1.SetQuantizationOffset(qOffset); + inputTensorInfo2.SetQuantizationScale(qScale); + inputTensorInfo2.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input1 = MakeTensor(inputTensorInfo1, QuantizedVector(qScale, qOffset, + { + 0.0f, + 1.0f, + + 2.0f, + 3.0f, + + 4.0f, + 5.0f, + })); + + auto input2 = MakeTensor(inputTensorInfo2, QuantizedVector(qScale, qOffset, + { + 0.5f, 1.5f, 2.5f, + 3.5f, 4.5f, 5.5f, + })); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, + { + 0.5f, 1.5f, 2.5f, + 4.5f, 5.5f, 6.5f, + + 2.5f, 3.5f, 4.5f, + 6.5f, 7.5f, 8.5f, + + 4.5f, 5.5f, 6.5f, + 8.5f, 9.5f, 10.5f, + })); + + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateAddition(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template +LayerTestResult AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset) +{ + armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); + armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); + + if (armnn::IsQuantizedType()) + { + inputTensorInfo1.SetQuantizationScale(qScale); + inputTensorInfo1.SetQuantizationOffset(qOffset); + inputTensorInfo2.SetQuantizationScale(qScale); + inputTensorInfo2.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input1 = MakeTensor(inputTensorInfo1, QuantizedVector(qScale, qOffset, + { + 0.0f, 1.0f, 2.0f, + 3.0f, 4.0f, 5.0f, + 6.0f, 7.0f, 8.0f, + 9.0f, 10.0f, 11.0f, + 12.0f, 13.0f, 14.0f, + 15.0f, 16.0f, 17.0f, + })); + + auto input2 = MakeTensor(inputTensorInfo2, QuantizedVector(qScale, qOffset, + { + 0.5f, + })); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, + { + 0.5f, 1.5f, 2.5f, + 3.5f, 4.5f, 5.5f, + 6.5f, 7.5f, 8.5f, + 9.5f, 10.5f, 11.5f, + 12.5f, 13.5f, 14.5f, + 15.5f, 16.5f, 17.5f, + })); + + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateAddition(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +LayerTestResult AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) +{ + return AdditionBroadcastTestImpl(workloadFactory, 0.0f, 0); +} + +LayerTestResult AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return AdditionBroadcastTestImpl(workloadFactory, 2.f, 0); +} + +LayerTestResult AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) +{ + return AdditionBroadcast1ElementTestImpl(workloadFactory, 0.0f, 0); +} + +LayerTestResult AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return AdditionBroadcast1ElementTestImpl(workloadFactory, 0.1333333f, 128); +} + +LayerTestResult CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory) +{ + unsigned int batchSize = 4; + unsigned int channels = 1; + unsigned int height = 2; + unsigned int width = 3; + + armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; + armnn::TensorInfo outputTensorInfo; + + unsigned int shape[] = {batchSize, channels, height, width}; + + inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + + auto input1 = MakeRandomTensor(inputTensorInfo1, 1232); + auto input2 = MakeRandomTensor(inputTensorInfo2, 456); + + LayerTestResult ret(outputTensorInfo); + + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + std::unique_ptr inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + armnn::AdditionQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get()); + SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + std::unique_ptr workload = workloadFactory.CreateAddition(data, info); + std::unique_ptr workloadRef = refWorkloadFactory.CreateAddition(refData, refInfo); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + inputHandle1Ref->Allocate(); + inputHandle2Ref->Allocate(); + outputHandleRef->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); + CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2Ref.get(), &input2[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + refWorkloadFactory.Finalize(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); + + return ret; +} + +namespace { +template +LayerTestResult DivisionTestHelper(armnn::IWorkloadFactory& workloadFactory, + const unsigned int shape0[4], + const std::vector& values0, + float scale0, + int32_t offset0, + const unsigned int shape1[4], + const std::vector & values1, + float scale1, + int32_t offset1, + const unsigned int outShape[4], + const std::vector & outValues, + float outScale, + int32_t outOffset) +{ + auto dataType = (std::is_same::value ? + armnn::DataType::QuantisedAsymm8 : + armnn::DataType::Float32); + + armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); + armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); + armnn::TensorInfo outputTensorInfo(4, outShape, dataType); + + inputTensorInfo0.SetQuantizationScale(scale0); + inputTensorInfo0.SetQuantizationOffset(offset0); + + inputTensorInfo1.SetQuantizationScale(scale1); + inputTensorInfo1.SetQuantizationOffset(offset1); + + outputTensorInfo.SetQuantizationScale(outScale); + outputTensorInfo.SetQuantizationOffset(outOffset); + + auto input0 = MakeTensor(inputTensorInfo0, values0); + auto input1 = MakeTensor(inputTensorInfo1, values1); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, outValues); + + std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::DivisionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateDivision(data, info); + + inputHandle0->Allocate(); + inputHandle1->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + + return result; +} +} // anonymous namespace + +LayerTestResult DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int width = 2; + const unsigned int height = 2; + const unsigned int channelCount = 2; + const unsigned int batchSize = 2; + + unsigned int shape[] = { batchSize, channelCount, height, width }; + + std::vector input0({ + 1.f, 1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f, + -1.f, -1.f, -1.f, -1.f, 5.f, 5.f, 5.f, 5.f }); + + std::vector input1({ + 0.f, 0.f, -0.f, -0.f, 0.f, 0.f, -0.f, -0.f, + 0.f, 0.f, -0.f, -0.f, 5.f, 5.f, 5.f, 5.f }); + + std::vector output({ + INFINITY, INFINITY, -INFINITY, -INFINITY, NAN, NAN, -NAN, -NAN, + -INFINITY, -INFINITY, INFINITY, INFINITY, 1, 1, 1, 1 }); + + return DivisionTestHelper(workloadFactory, + shape, input0, 1.0f, 0, + shape, input1, 1.0f, 0, + shape, output, 1.0f, 0); +} + +LayerTestResult DivisionTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int width = 2; + const unsigned int height = 2; + const unsigned int channelCount = 2; + const unsigned int batchSize = 2; + + unsigned int shape[] = { batchSize, channelCount, height, width }; + + std::vector input0({ + 2, 2, 2, 2, 3, 3, 3, 3, + 4, 4, 4, 4, 5, 5, 5, 5 }); + + std::vector input1({ + 1, 1, 1, 1, 2, 2, 2, 2, + 4, 4, 4, 4, 4, 4, 4, 4 }); + + std::vector output({ + 2, 2, 2, 2, 1.5, 1.5, 1.5, 1.5, + 1, 1, 1, 1, 1.25, 1.25, 1.25, 1.25 }); + + + return DivisionTestHelper(workloadFactory, + shape, input0, 1.0f, 0, + shape, input1, 1.0f, 0, + shape, output, 1.0f, 0); +} + +LayerTestResult DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int shape0[] = { 1, 2, 2, 2 }; + std::vector input0({ 2, 4, 6, 8, 10, 12, 14, 16}); + + unsigned int shape1[] = { 1, 1, 1, 1 }; + std::vector input1({ 2 }); + + std::vector output({ 1, 2, 3, 4, 5, 6, 7, 8}); + + + return DivisionTestHelper(workloadFactory, + shape0, input0, 1.0f, 0, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + +LayerTestResult DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int shape0[] = { 1, 3, 3, 2 }; + std::vector input0({ + 1, 4, 3, 8, 5, 12, + 7, 16, 9, 20, 11, 24, + 13, 28, 15, 32, 17, 36}); + + unsigned int shape1[] = { 1, 1, 1, 2 }; + std::vector input1({ 1, 2 }); + + std::vector output({ + 1, 2, 3, 4, 5, 6, + 7, 8, 9, 10, 11, 12, + 13, 14, 15, 16, 17, 18}); + + return DivisionTestHelper(workloadFactory, + shape0, input0, 1.0f, 0, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + + +LayerTestResult DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int width = 2; + const unsigned int height = 2; + const unsigned int channelCount = 2; + const unsigned int batchSize = 2; + + unsigned int shape[] = { batchSize, channelCount, height, width }; + + std::vector input0({2, 2, 2, 2, 3, 3, 3, 3, + 4, 4, 4, 4, 5, 5, 5, 5 }); + + std::vector input1({1, 1, 1, 1, 2, 2, 2, 2, + 4, 4, 4, 4, 4, 4, 4, 4 }); + + std::vector output({8, 8, 8, 8, 6, 6, 6, 6, + 4, 4, 4, 4, 5, 5, 5, 5}); + + + return DivisionTestHelper(workloadFactory, + shape, input0, 1.0f, 0, + shape, input1, 1.0f, 0, + shape, output, 0.25f, 0); +} + +LayerTestResult DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int shape0[] = { 1, 2, 2, 2 }; + std::vector input0({ 2, 4, 6, 8, 10, 12, 14, 16}); + + unsigned int shape1[] = { 1, 1, 1, 1 }; + std::vector input1({ 2 }); + + std::vector output({ 1, 2, 3, 4, 5, 6, 7, 8}); + + return DivisionTestHelper(workloadFactory, + shape0, input0, 1.0f, 0, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + +LayerTestResult DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int shape0[] = { 1, 3, 3, 2 }; + std::vector input0({1, 4, 3, 8, 5, 12, + 7, 16, 9, 20, 11, 24, + 13, 28, 15, 32, 17, 36}); + + unsigned int shape1[] = { 1, 1, 1, 2 }; + std::vector input1({ 1, 2 }); + + std::vector output({1, 2, 3, 4, 5, 6, + 7, 8, 9, 10, 11, 12, + 13, 14, 15, 16, 17, 18}); + + return DivisionTestHelper(workloadFactory, + shape0, input0, 1.0f, 0, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + +namespace { +LayerTestResult MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory, + const unsigned int shape0[4], + const std::vector & values0, + const unsigned int shape1[4], + const std::vector & values1, + const unsigned int outShape[4], + const std::vector & outValues) +{ + const size_t dimensionCount = 4; + armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32}; + armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32}; + armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32}; + + auto input0 = MakeTensor(inputTensorInfo0, values0); + auto input1 = MakeTensor(inputTensorInfo1, values1); + + LayerTestResult ret(outputTensorInfo); + + std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::MultiplicationQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateMultiplication(data, info); + + inputHandle0->Allocate(); + inputHandle1->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + ret.outputExpected = MakeTensor(outputTensorInfo, outValues); + return ret; +} +} // anonymous namespace + + +LayerTestResult MultiplicationTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int width = 2; + const unsigned int height = 2; + const unsigned int channelCount = 2; + const unsigned int batchSize = 2; + + unsigned int shape[] = { batchSize, channelCount, height, width }; + + std::vector input0({ + 1, 1, 1, 1, 2, 2, 2, 2, + 3, 3, 3, 3, 4, 4, 4, 4 }); + + std::vector input1({ + 2, 2, 2, 2, 3, 3, 3, 3, + 4, 4, 4, 4, 5, 5, 5, 5 }); + + std::vector output({ + 2, 2, 2, 2, 6, 6, 6, 6, + 12, 12, 12, 12, 20, 20, 20, 20 }); + + return MultiplicationTestHelper(workloadFactory, + shape, + input0, + shape, + input1, + shape, + output); +} + +LayerTestResult MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int shape0[] = { 1, 2, 2, 2 }; + std::vector input0({ 1, 2, 3, 4, 5, 6, 7, 8}); + + unsigned int shape1[] = { 1, 1, 1, 1 }; + std::vector input1({ 2 }); + + std::vector output({ 2, 4, 6, 8, 10, 12, 14, 16}); + + return MultiplicationTestHelper(workloadFactory, + shape0, + input0, + shape1, + input1, + shape0, + output); +} + +LayerTestResult MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int shape0[] = { 1, 3, 3, 2 }; + std::vector input0({ + 1, 2, 3, 4, 5, 6, + 7, 8, 9, 10, 11, 12, + 13, 14, 15, 16, 17, 18}); + + unsigned int shape1[] = { 1, 1, 1, 2 }; + std::vector input1({ 1, 2 }); + + std::vector output({ + 1, 4, 3, 8, 5, 12, + 7, 16, 9, 20, 11, 24, + 13, 28, 15, 32, 17, 36}); + + return MultiplicationTestHelper(workloadFactory, + shape0, + input0, + shape1, + input1, + shape0, + output); +} + +LayerTestResult CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory) +{ + const unsigned int width = 16; + const unsigned int height = 32; + const unsigned int channelCount = 2; + const unsigned int batchSize = 5; + + armnn::TensorInfo inputTensorInfo0; + armnn::TensorInfo inputTensorInfo1; + armnn::TensorInfo outputTensorInfo; + + constexpr unsigned int shape[] = { batchSize, channelCount, height, width }; + + inputTensorInfo0 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + + LayerTestResult comparisonResult(outputTensorInfo); + + auto input0 = MakeRandomTensor(inputTensorInfo0, 803506992); + auto input1 = MakeRandomTensor(inputTensorInfo1, 54902257); + + std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + std::unique_ptr inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0); + std::unique_ptr inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::MultiplicationQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + armnn::MultiplicationQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo0, inputHandle0Ref.get()); + SetWorkloadInput(refData, refInfo, 1, inputTensorInfo1, inputHandle1Ref.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + std::unique_ptr workload = workloadFactory.CreateMultiplication(data, info); + std::unique_ptr workloadRef = refWorkloadFactory.CreateMultiplication(refData, refInfo); + + inputHandle0->Allocate(); + inputHandle1->Allocate(); + outputHandle->Allocate(); + inputHandle0Ref->Allocate(); + inputHandle1Ref->Allocate(); + outputHandleRef->Allocate(); + + CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle0Ref.get(), &input0[0][0][0][0]); + CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + refWorkloadFactory.Finalize(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); + + return comparisonResult; +} + +LayerTestResult CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory) +{ + const unsigned int width = 2; + const unsigned int height = 3; + const unsigned int channels = 5; + const unsigned int batchSize = 3; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + armnn::TensorInfo tensorInfo; + + constexpr unsigned int shape[] = {batchSize, channels, height, width}; + constexpr unsigned int tensorShape[] = {channels}; + + inputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + tensorInfo = armnn::TensorInfo(1, tensorShape, armnn::DataType::Float32); + + auto input = MakeRandomTensor(inputTensorInfo, 21312); + + auto mean = MakeRandomTensor(tensorInfo, 123); + auto variance = MakeRandomTensor(tensorInfo, 234, 0.0f); + auto beta = MakeRandomTensor(tensorInfo, 123); + auto gamma = MakeRandomTensor(tensorInfo, 345); + + LayerTestResult ret(outputTensorInfo); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::BatchNormalizationQueueDescriptor data; + armnn::WorkloadInfo info; + armnn::ScopedCpuTensorHandle meanTensor(tensorInfo); + armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo); + armnn::ScopedCpuTensorHandle betaTensor(tensorInfo); + armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo); + + AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]); + AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]); + AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]); + AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]); + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Mean = &meanTensor; + data.m_Variance = &varianceTensor; + data.m_Beta = &betaTensor; + data.m_Gamma = &gammaTensor; + data.m_Parameters.m_Eps = 0.01f; + + armnn::BatchNormalizationQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + std::unique_ptr workload = workloadFactory.CreateBatchNormalization(data, info); + std::unique_ptr workloadRef = refWorkloadFactory.CreateBatchNormalization(refData, refInfo); + + inputHandle->Allocate(); + outputHandle->Allocate(); + inputHandleRef->Allocate(); + outputHandleRef->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + refWorkloadFactory.Finalize(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); + + return ret; +} + +template +void PermuteTensorData( + armnn::IWorkloadFactory& workloadFactory, + const armnn::PermutationVector& mappings, + armnn::TensorInfo & inputTensorInfo, + const T * inputData, + std::vector& outputData) +{ + BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null"); + if (inputData == nullptr) + { + // Nullptr is an error in the test. By returning without doing the concatenation + // I expect the caller to fail the test. It still makes sense to report this as + // an assert for Debug builds. + return; + } + + armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::PermuteQueueDescriptor queueDescriptor; + queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings}; + armnn::WorkloadInfo workloadInfo; + AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), inputData); + + workload->Execute(); + + outputData.resize(outputTensorInfo.GetNumElements()); + CopyDataFromITensorHandle(&outputData[0], outputHandle.get()); + inputTensorInfo = outputTensorInfo; +} + +armnn::OriginsDescriptor CreateMergerDescriptorForConcatenation( + const std::vector & inputTensorInfos, + unsigned int concatDim) +{ + std::vector shapes; + shapes.reserve(inputTensorInfos.size()); + for (const armnn::TensorInfo& it: inputTensorInfos) + { + shapes.push_back(it.GetShape()); + } + + return armnn::CreateMergerDescriptorForConcatenation(shapes.begin(), + shapes.end(), + concatDim); +} + +// +// Concatenation is only supported for N and C dimensions for NCHW. In case of +// <4 dimensions we need to make sure that the concat dimensions are at least +// the 3rd slowest iterating one. +// + +bool NeedPermuteForConcat( + const std::vector & inputTensorInfos, + unsigned int concatDim) +{ + // See note above. Additionally we expect the input shapes to have the + // same number of dimensions. + unsigned int nDimensions = 0; + + // Determine the number of dimensions as well as sanity check them + // agains test implementation issues. + for (auto && tensorInfo : inputTensorInfos) + { + if (!nDimensions) + { + nDimensions = tensorInfo.GetShape().GetNumDimensions(); + } + else + { + BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(), + "Input shapes must have the same number of dimensions"); + } + } + + return (nDimensions-concatDim) < 3; +} + +armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape) +{ + unsigned int numDims = inputShape.GetNumDimensions(); + if (numDims >= 3) + { + // Nothing to do if the inputShape has at least 3 dimensions. + return inputShape; + } + + std::vector newDims(size_t(3), 1u); + unsigned int expandedBy = 3 - numDims; + for (unsigned int i=0; i & permutations) +{ + BOOST_ASSERT_MSG(numDimensions <= 3, + "Only dimensions 1,2 and 3 are supported by this helper"); + + unsigned int expandedBy = 3 - numDimensions; + unsigned int expandedConcatAxis = concatDim + expandedBy; + + if (expandedConcatAxis == 2) + { + concatDim = 0; + armnn::PermutationVector forwardPermutation({1, 2, 0}); + armnn::PermutationVector reversePermutation({2, 0, 1}); + permutations = std::make_pair(forwardPermutation, reversePermutation); + } + else if (expandedConcatAxis == 1) + { + concatDim = 0; + armnn::PermutationVector forwardPermutation({2, 0, 1}); + armnn::PermutationVector reversePermutation({1, 2, 0}); + permutations = std::make_pair(forwardPermutation, reversePermutation); + } + else + { + BOOST_ASSERT(expandedConcatAxis == 0); + concatDim = 0; + } +} + +// +// Permute the input tensors so we can do a supported concatenation. +// Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions +// at the front. Finally this function tells what the output shape +// of the permuted concatenated tensor is going to be. +// +template +void PermuteInputsForConcat( + armnn::IWorkloadFactory& workloadFactory, + std::vector & inputTensorInfos, + std::vector & inputData, + std::vector> & inputDataStorage, + armnn::PermutationVector & permuteVector, + unsigned int & concatDim, + armnn::TensorInfo & outputTensorInfo) +{ + BOOST_ASSERT_MSG(inputTensorInfos.size() > 1, + "Expecting more than one tensor to be concatenated here"); + + unsigned int numDims = 0; + unsigned int nthInput = 0; + const armnn::PermutationVector identity({0, 1, 2}); + + std::pair permutations = + std::make_pair(identity, identity); + + inputDataStorage.resize(inputData.size()); + + for (auto && tensorInfo : inputTensorInfos) + { + if (numDims == 0) + { + numDims = tensorInfo.GetShape().GetNumDimensions(); + Generate3dPermuteVectorForConcat(numDims, concatDim, permutations); + // Store the reverese permutation. + permuteVector = permutations.second; + BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity), + "Test logic error, we don't need permutation, so we shouldn't arrive here"); + } + else + { + BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(), + "All inputs must have the same number of dimensions"); + } + + armnn::TensorInfo newTensorInfo = tensorInfo; + newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape())); + + PermuteTensorData(workloadFactory, + permutations.first, + newTensorInfo, + inputData[nthInput], + inputDataStorage[nthInput]); + + inputData[nthInput] = inputDataStorage[nthInput].data(); + inputTensorInfos[nthInput] = newTensorInfo; + + ++nthInput; + } + + outputTensorInfo.SetShape( + armnnUtils::Permuted( + ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()), + permutations.first)); +} + + +// +// This is the pair of PermuteInputsForConcat(...) which permutes back +// the output of the concatenation so we can check it against an expected +// output. +// +template +void PermuteOutputForConcat( + armnn::IWorkloadFactory& workloadFactory, + const armnn::TensorInfo & tensorInfo, + const armnn::PermutationVector & permuteVector, + std::unique_ptr && inputDataHandle, + T * data) +{ + BOOST_ASSERT_MSG(data != nullptr, "data must not be null"); + if (data == nullptr) + { + // Nullptr is an error in the test. By returning without doing the permutation + // I expect the caller to fail the test. It still makes sense to report this as + // an assert for Debug builds. + return; + } + + armnn::TensorInfo resultTensorInfo = tensorInfo; + std::vector inputData(tensorInfo.GetNumElements()); + std::vector outputData; + + CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get()); + + PermuteTensorData(workloadFactory, + permuteVector, + resultTensorInfo, + &inputData[0], + outputData); + + ::memcpy(data, &outputData[0], sizeof(T)*outputData.size()); +} + +template +void Concatenate(armnn::IWorkloadFactory& workloadFactory, + std::initializer_list inputTensorInfosOrig, + std::initializer_list inputsOrig, + const armnn::TensorInfo& outputTensorInfoOrig, + T * output, + unsigned int concatDim) +{ + BOOST_ASSERT_MSG(output != nullptr, "output must not be null"); + if (output == nullptr) + { + // Nullptr is an error in the test. By returning without doing the permutation + // I expect the caller to fail the test. It still makes sense to report this as + // an assert for Debug builds. + return; + } + + armnn::MergerQueueDescriptor queueDescriptor; + + // Saves a copy of the parameters which we might need to change. + std::vector inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); + std::vector inputs = inputsOrig; + armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig; + + armnn::PermutationVector permuteVector{0, 1, 2}; + + // Holds and automatically releases memory for the reshaped input data. + std::vector> tmpInputDataStorage; + + const size_t inputCount = inputTensorInfos.size(); + + bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim); + + if (needPermuteForConcat) + { + // + // We need to permute the inputs, because concatenation along + // the requested axis is not supported. + // + PermuteInputsForConcat(workloadFactory, + inputTensorInfos, + inputs, + tmpInputDataStorage, + permuteVector, + concatDim, + outputTensorInfo); + } + + armnn::OriginsDescriptor viewsDescriptor = CreateMergerDescriptorForConcatenation(inputTensorInfos, concatDim); + + queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews()); + for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i) + { + queueDescriptor.m_ViewOrigins.emplace_back(std::vector(viewsDescriptor.GetViewOrigin(i), + viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions())); + } + + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + std::vector> inputHandles; + inputHandles.reserve(inputCount); + + const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); + for (unsigned int i = 0; i < inputCount; ++i) + { + const armnn::TensorInfo& inputTensorInfo = inputTensorInfos[i]; + + std::unique_ptr inputHandle = subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo.GetShape(), + queueDescriptor.m_ViewOrigins[i].m_Origin.data()) + : workloadFactory.CreateTensorHandle(inputTensorInfo); + + inputHandles.emplace_back(std::move(inputHandle)); + } + + armnn::WorkloadInfo workloadInfo; + + for (unsigned int i = 0; i < inputCount; ++i) + { + AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get()); + } + + AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateMerger(queueDescriptor, workloadInfo); + + for (auto& inputHandle : inputHandles) + { + inputHandle->Allocate(); + } + + outputHandle->Allocate(); + + unsigned int nextInputId = 0; + for (auto& inputHandle : inputHandles) + { + CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]); + ++nextInputId; + } + + workloadFactory.Finalize(); + workload->Execute(); + + if (needPermuteForConcat) + { + PermuteOutputForConcat(workloadFactory, + outputTensorInfo, + permuteVector, + std::move(outputHandle), + output); + } + else + { + CopyDataFromITensorHandle(output, outputHandle.get()); + } +} + +template +LayerTestResult Concatenation1dTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) +{ + armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType()); + + auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 1.0f, 2.0f, 3.0f })); + auto input1 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 4.0f, 5.0f, 6.0f })); + auto input2 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 7.0f, 8.0f, 9.0f })); + + armnn::TensorInfo outputTensorInfo({ 9 }, armnn::GetDataType()); + + LayerTestResult result(outputTensorInfo); + + std::vector output; + output.resize(outputTensorInfo.GetNumElements()); + Concatenate(workloadFactory, + { inputTensorInfo, inputTensorInfo, inputTensorInfo }, + { input0.data(), input1.data(), input2.data() }, + outputTensorInfo, + output.data(), + 0); + + result.output = MakeTensor(outputTensorInfo, output); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f + })); + + return result; +} + +LayerTestResult Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation1dTestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation2dTestImpl(armnn::IWorkloadFactory& workloadFactory, + const armnn::TensorInfo& outputTensorInfo, + unsigned int dimension, + const float qScale, + const int32_t qOffset) +{ + armnn::TensorInfo inputTensorInfo({ 2, 3 }, armnn::GetDataType()); + + auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 1.0f, 2.0f, 3.0f, + + // Batch 1 + 10.0f, 11.0f, 12.0f, + })); + + auto input1 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 4.0f, 5.0f, 6.0f, + + // Batch 1 + 13.0f, 14.0f, 15.0f, + })); + + auto input2 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 7.0f, 8.0f, 9.0f, + + // Batch 1 + 16.0f, 17.0f, 18.0f, + })); + + LayerTestResult result(outputTensorInfo); + + std::vector output; + output.resize(outputTensorInfo.GetNumElements()); + Concatenate(workloadFactory, + { inputTensorInfo, inputTensorInfo, inputTensorInfo }, + { input0.data(), input1.data(), input2.data() }, + outputTensorInfo, + output.data(), + dimension); + + result.output = MakeTensor(outputTensorInfo, output); + return result; +} + +template +LayerTestResult Concatenation2dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, int32_t qOffset) +{ + armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType()); + + LayerTestResult result = Concatenation2dTestImpl(workloadFactory, outputTensorInfo, 0, qScale, qOffset); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 1.0f, 2.0f, 3.0f, + + // Batch 1 + 10.0f, 11.0f, 12.0f, + + // Batch 2 + 4.0f, 5.0f, 6.0f, + + // Batch 3 + 13.0f, 14.0f, 15.0f, + + // Batch 4 + 7.0f, 8.0f, 9.0f, + + // Batch 5 + 16.0f, 17.0f, 18.0f, + })); + + return result; +} + +LayerTestResult Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation2dDim0TestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation2dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, int32_t qOffset) +{ + armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType()); + + LayerTestResult result = Concatenation2dTestImpl(workloadFactory, outputTensorInfo, 1, qScale, qOffset); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, + + // Batch 1 + 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f + })); + + return result; +} + +LayerTestResult Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation2dDim1TestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation2dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, + int32_t qOffset) +{ + armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType()); + auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 1.0f, 2.0f, 3.0f, + + // Batch 1 + 10.0f, 11.0f, 12.0f, + })); + + armnn::TensorInfo input1TensorInfo({ 3, 3 }, armnn::GetDataType()); + auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 4.0f, 5.0f, 6.0f, + + // Batch 1 + 13.0f, 14.0f, 15.0f, + + // Batch 0 + 7.0f, 8.0f, 9.0f, + })); + + armnn::TensorInfo input2TensorInfo({ 1, 3 }, armnn::GetDataType()); + auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 1 + 16.0f, 17.0f, 18.0f, + })); + + armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType()); + LayerTestResult result(outputTensorInfo); + + std::vector output; + output.resize(outputTensorInfo.GetNumElements()); + Concatenate(workloadFactory, + { input0TensorInfo, input1TensorInfo, input2TensorInfo }, + { input0.data(), input1.data(), input2.data() }, + outputTensorInfo, + output.data(), + 0); + + result.output = MakeTensor(outputTensorInfo, output); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 1.0f, 2.0f, 3.0f, + + // Batch 1 + 10.0f, 11.0f, 12.0f, + + // Batch 2 + 4.0f, 5.0f, 6.0f, + + // Batch 3 + 13.0f, 14.0f, 15.0f, + + // Batch 4 + 7.0f, 8.0f, 9.0f, + + // Batch 5 + 16.0f, 17.0f, 18.0f, + })); + + return result; +} + +LayerTestResult Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation2dDim0DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation2dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, + int32_t qOffset) +{ + armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType()); + auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 1.0f, 2.0f, 3.0f, + + // Batch 1 + 10.0f, 11.0f, 12.0f, + })); + + armnn::TensorInfo input1TensorInfo({ 2, 5 }, armnn::GetDataType()); + auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, + + // Batch 1 + 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, + })); + + armnn::TensorInfo input2TensorInfo({ 2, 1 }, armnn::GetDataType()); + auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 9.0f, + + // Batch 1 + 18.0f + })); + + armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType()); + LayerTestResult result(outputTensorInfo); + + std::vector output; + output.resize(outputTensorInfo.GetNumElements()); + Concatenate(workloadFactory, + { input0TensorInfo, input1TensorInfo, input2TensorInfo }, + { input0.data(), input1.data(), input2.data() }, + outputTensorInfo, + output.data(), + 1); + + result.output = MakeTensor(outputTensorInfo, output); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0 + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, + + // Batch 1 + 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, + })); + + return result; +} + +LayerTestResult Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation2dDim1DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation3dTestImpl(armnn::IWorkloadFactory& workloadFactory, + const armnn::TensorInfo& outputTensorInfo, + unsigned int dimension, + float qScale, + int32_t qOffset) +{ + armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType()); + + auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f + })); + + auto input1 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 7.0f, 8.0f, + + // Batch 0, Channel 1 + 9.0f, 10.0f, + + // Batch 0, Channel 2 + 11.0f, 12.0f, + + // Batch 1, Channel 0 + 25.0f, 26.0f, + + // Batch 1, Channel 1 + 27.0f, 28.0f, + + // Batch 1, Channel 2 + 29.0f, 30.0f + })); + + auto input2 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 13.0f, 14.0f, + + // Batch 0, Channel 1 + 15.0f, 16.0f, + + // Batch 0, Channel 2 + 17.0f, 18.0f, + + // Batch 1, Channel 0 + 31.0f, 32.0f, + + // Batch 1, Channel 1 + 33.0f, 34.0f, + + // Batch 1, Channel 2 + 35.0f, 36.0f + })); + + LayerTestResult result(outputTensorInfo); + + std::vector output; + output.resize(outputTensorInfo.GetNumElements()); + Concatenate(workloadFactory, + { inputTensorInfo, inputTensorInfo, inputTensorInfo }, + { input0.data(), input1.data(), input2.data() }, + outputTensorInfo, + output.data(), + dimension); + + result.output = MakeTensor(outputTensorInfo, output); + return result; +} + +template +LayerTestResult Concatenation3dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, + int32_t qOffset) +{ + armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType()); + + LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 0, + qScale, qOffset); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f, + + // Batch 2, Channel 0 + 7.0f, 8.0f, + + // Batch 2, Channel 1 + 9.0f, 10.0f, + + // Batch 2, Channel 2 + 11.0f, 12.0f, + + // Batch 3, Channel 0 + 25.0f, 26.0f, + + // Batch 3, Channel 1 + 27.0f, 28.0f, + + // Batch 3, Channel 2 + 29.0f, 30.0f, + + // Batch 4, Channel 0 + 13.0f, 14.0f, + + // Batch 4, Channel 1 + 15.0f, 16.0f, + + // Batch 4, Channel 2 + 17.0f, 18.0f, + + // Batch 5, Channel 0 + 31.0f, 32.0f, + + // Batch 5, Channel 1 + 33.0f, 34.0f, + + // Batch 5, Channel 2 + 35.0f, 36.0f + })); + return result; +} + +LayerTestResult Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim0TestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation3dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, int32_t qOffset) +{ + armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType()); + + LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 1, qScale, qOffset); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, + + // Batch 0, Channel 3 + 7.0f, 8.0f, + + // Batch 0, Channel 4 + 9.0f, 10.0f, + + // Batch 0, Channel 5 + 11.0f, 12.0f, + + // Batch 0, Channel 6 + 13.0f, 14.0f, + + // Batch 0, Channel 7 + 15.0f, 16.0f, + + // Batch 0, Channel 8 + 17.0f, 18.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f, + + // Batch 1, Channel 3 + 25.0f, 26.0f, + + // Batch 1, Channel 4 + 27.0f, 28.0f, + + // Batch 1, Channel 5 + 29.0f, 30.0f, + + // Batch 1, Channel 6 + 31.0f, 32.0f, + + // Batch 1, Channel 7 + 33.0f, 34.0f, + + // Batch 1, Channel 8 + 35.0f, 36.0f + })); + + return result; +} + +LayerTestResult Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim1TestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation3dDim2TestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, int32_t qOffset) +{ + armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType()); + + LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 2, qScale, qOffset); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f, + })); + + return result; +} + +LayerTestResult Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim2TestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation3dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, + int32_t qOffset) +{ + armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); + auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f + })); + + armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, armnn::GetDataType()); + auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 7.0f, 8.0f, + + // Batch 0, Channel 1 + 9.0f, 10.0f, + + // Batch 0, Channel 2 + 11.0f, 12.0f, + })); + + armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, armnn::GetDataType()); + auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 25.0f, 26.0f, + + // Batch 0, Channel 1 + 27.0f, 28.0f, + + // Batch 0, Channel 2 + 29.0f, 30.0f, + + // Batch 1, Channel 0 + 13.0f, 14.0f, + + // Batch 1, Channel 1 + 15.0f, 16.0f, + + // Batch 1, Channel 2 + 17.0f, 18.0f, + + // Batch 2, Channel 0 + 31.0f, 32.0f, + + // Batch 2, Channel 1 + 33.0f, 34.0f, + + // Batch 2, Channel 2 + 35.0f, 36.0f + })); + + armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType()); + LayerTestResult result(outputTensorInfo); + + std::vector output; + output.resize(outputTensorInfo.GetNumElements()); + Concatenate(workloadFactory, + { input0TensorInfo, input1TensorInfo, input2TensorInfo }, + { input0.data(), input1.data(), input2.data() }, + outputTensorInfo, + output.data(), + 0); + + result.output = MakeTensor(outputTensorInfo, output); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f, + + // Batch 2, Channel 0 + 7.0f, 8.0f, + + // Batch 2, Channel 1 + 9.0f, 10.0f, + + // Batch 2, Channel 2 + 11.0f, 12.0f, + + // Batch 3, Channel 0 + 25.0f, 26.0f, + + // Batch 3, Channel 1 + 27.0f, 28.0f, + + // Batch 3, Channel 2 + 29.0f, 30.0f, + + // Batch 4, Channel 0 + 13.0f, 14.0f, + + // Batch 4, Channel 1 + 15.0f, 16.0f, + + // Batch 4, Channel 2 + 17.0f, 18.0f, + + // Batch 5, Channel 0 + 31.0f, 32.0f, + + // Batch 5, Channel 1 + 33.0f, 34.0f, + + // Batch 5, Channel 2 + 35.0f, 36.0f + })); + + return result; +} + +LayerTestResult Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim0DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation3dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, + int32_t qOffset) +{ + armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); + auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f + })); + + armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, armnn::GetDataType()); + auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 7.0f, 8.0f, + + // Batch 0, Channel 1 + 9.0f, 10.0f, + + // Batch 0, Channel 2 + 11.0f, 12.0f, + + // Batch 0, Channel 3 + 25.0f, 26.0f, + + // Batch 1, Channel 0 + 27.0f, 28.0f, + + // Batch 1, Channel 1 + 29.0f, 30.0f, + + // Batch 1, Channel 2 + 13.0f, 14.0f, + + // Batch 1, Channel 3 + 15.0f, 16.0f, + })); + + armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, armnn::GetDataType()); + auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 17.0f, 18.0f, + + // Batch 1, Channel 0 + 31.0f, 32.0f, + })); + + armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, armnn::GetDataType()); + LayerTestResult result(outputTensorInfo); + + std::vector output; + output.resize(outputTensorInfo.GetNumElements()); + Concatenate(workloadFactory, + { input0TensorInfo, input1TensorInfo, input2TensorInfo }, + { input0.data(), input1.data(), input2.data() }, + outputTensorInfo, + output.data(), + 1); + + result.output = MakeTensor(outputTensorInfo, output); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, + + // Batch 0, Channel 3 + 7.0f, 8.0f, + + // Batch 0, Channel 4 + 9.0f, 10.0f, + + // Batch 0, Channel 5 + 11.0f, 12.0f, + + // Batch 0, Channel 6 + 25.0f, 26.0f, + + // Batch 0, Channel 7 + 17.0f, 18.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f, + + // Batch 1, Channel 3 + 27.0f, 28.0f, + + // Batch 1, Channel 4 + 29.0f, 30.0f, + + // Batch 1, Channel 5 + 13.0f, 14.0f, + + // Batch 1, Channel 6 + 15.0f, 16.0f, + + // Batch 1, Channel 7 + 31.0f, 32.0f, + })); + + return result; +} + +LayerTestResult Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim1DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); +} + +template +LayerTestResult Concatenation3dDim2DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, + int32_t qOffset) +{ + armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); + auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f + })); + + armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, armnn::GetDataType()); + auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 7.0f, + + // Batch 0, Channel 1 + 9.0f, + + // Batch 0, Channel 2 + 11.0f, + + // Batch 1, Channel 0 + 25.0f, + + // Batch 1, Channel 1 + 27.0f, + + // Batch 1, Channel 2 + 29.0f + })); + + armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, armnn::GetDataType()); + auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 13.0f, 14.0f, 50.0f, + + // Batch 0, Channel 1 + 15.0f, 16.0f, 51.0f, + + // Batch 0, Channel 2 + 17.0f, 18.0f, 52.0f, + + // Batch 1, Channel 0 + 31.0f, 32.0f, 53.0f, + + // Batch 1, Channel 1 + 33.0f, 34.0f, 54.0f, + + // Batch 1, Channel 2 + 35.0f, 36.0f, 55.0f, + })); + + armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType()); + LayerTestResult result(outputTensorInfo); + + std::vector output; + output.resize(outputTensorInfo.GetNumElements()); + Concatenate(workloadFactory, + { input0TensorInfo, input1TensorInfo, input2TensorInfo }, + { input0.data(), input1.data(), input2.data() }, + outputTensorInfo, + output.data(), + 2); + + result.output = MakeTensor(outputTensorInfo, output); + result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f, + + // Batch 0, Channel 1 + 3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f, + + // Batch 0, Channel 2 + 5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f, + + // Batch 1, Channel 0 + 19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f, + + // Batch 1, Channel 1 + 21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f, + + // Batch 1, Channel 2 + 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f, + })); + + return result; +} + +LayerTestResult Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim2DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); +} + +LayerTestResult ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + const armnn::TensorInfo inputTensorInfo = GetTensorInfo(1, 2, 4, 4, dataLayout); + const armnn::TensorInfo outputTensorInfo = GetTensorInfo(1, 2, 4, 4, dataLayout); + + std::vector inputData({ + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f, + + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f + }); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + } + + auto input = MakeTensor(inputTensorInfo, inputData); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = input; + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + descriptor.m_Parameters.m_DataLayout = dataLayout; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + const armnn::TensorInfo inputTensorInfo = GetTensorInfo(1, 2, 2, 2, dataLayout); + const armnn::TensorInfo outputTensorInfo = GetTensorInfo(1, 2, 1, 1, dataLayout); + + std::vector inputData({ + 1.0f, 255.0f, + 200.0f, 250.0f, + + 250.0f, 200.0f, + 250.0f, 1.0f + }); + + // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, + // then figures out the interpolants and weights. Note this is different to projecting the centre of the + // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as + // its single element, the value that was at position (0,0) of the input matrix (rather than an average, + // which we would expect if projecting the centre). + + std::vector outputData({ + 1.0f, + + 250.0f + }); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor(inputTensorInfo, inputData); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, outputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + descriptor.m_Parameters.m_DataLayout = dataLayout; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + const armnn::TensorInfo inputTensorInfo = GetTensorInfo(1, 2, 4, 4, dataLayout); + const armnn::TensorInfo outputTensorInfo = GetTensorInfo(1, 2, 2, 2, dataLayout); + + std::vector inputData({ + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f, + + 7.0f, 6.0f, 5.0f, 4.0f, + 6.0f, 5.0f, 4.0f, 3.0f, + 5.0f, 4.0f, 3.0f, 2.0f, + 4.0f, 3.0f, 2.0f, 1.0f + }); + + std::vector outputData({ + 1.0f, 3.0f, + 3.0f, 5.0f, + + 7.0f, 5.0f, + 5.0f, 3.0f + }); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor(inputTensorInfo, inputData); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, outputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + descriptor.m_Parameters.m_DataLayout = dataLayout; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + const armnn::TensorInfo inputTensorInfo = GetTensorInfo(1, 2, 3, 5, dataLayout); + const armnn::TensorInfo outputTensorInfo = GetTensorInfo(1, 2, 2, 3, dataLayout); + + std::vector inputData({ + 1.0f, 2.0f, 3.0f, 5.0f, 8.0f, + 13.0f, 21.0f, 34.0f, 55.0f, 89.0f, + 144.0f, 233.0f, 377.0f, 610.0f, 987.0f, + + 987.0f, 610.0f, 377.0f, 233.0f, 144.0f, + 89.0f, 55.0f, 34.0f, 21.0f, 13.0f, + 8.0f, 5.0f, 3.0f, 2.0f, 1.0f + }); + + std::vector outputData({ + 1.0f, 2.6666f, 6.00f, + 78.5f, 179.3333f, 401.00f, + + 987.0f, 454.6670f, 203.33f, + 48.5f, 22.3333f, 10.00f + }); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor(inputTensorInfo, inputData); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, outputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + descriptor.m_Parameters.m_DataLayout = dataLayout; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + const armnn::TensorInfo inputTensorInfo = GetTensorInfo(1, 2, 3, 2, dataLayout); + const armnn::TensorInfo outputTensorInfo = GetTensorInfo(1, 2, 3, 5, dataLayout); + + std::vector inputData({ + 1.0f, 2.0f, + 13.0f, 21.0f, + 144.0f, 233.0f, + + 233.0f, 144.0f, + 21.0f, 13.0f, + 2.0f, 1.0f + }); + + std::vector outputData({ + 1.0f, 1.4f, 1.8f, 2.0f, 2.0f, + 13.0f, 16.2f, 19.4f, 21.0f, 21.0f, + 144.0f, 179.6f, 215.2f, 233.0f, 233.0f, + + 233.0f, 197.4f, 161.8f, 144.0f, 144.0f, + 21.0f, 17.8f, 14.6f, 13.0f, 13.0f, + 2.0f, 1.6f, 1.2f, 1.0f, 1.0f + }); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor(inputTensorInfo, inputData); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, outputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + descriptor.m_Parameters.m_DataLayout = dataLayout; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory) +{ + constexpr unsigned int width = 2; + constexpr unsigned int height = 3; + + const armnn::TensorInfo tensorInfo({height, width }, + armnn::DataType::Float32); + auto input = MakeTensor(tensorInfo, std::vector({ + -10.0f, -5.0f, + 0.0f, 5.0f, + 10.0f, 10.0f + })); + + LayerTestResult ret(tensorInfo); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(tensorInfo); + + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(tensorInfo); + + armnn::FakeQuantizationQueueDescriptor data; + armnn::WorkloadInfo info; + + AddInputToWorkload(data, info, tensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, tensorInfo, outputHandle.get()); + float min = -10.f; + float max = 10.f; + + data.m_Parameters.m_Min = min; + data.m_Parameters.m_Max = max; + + armnn::PassthroughCpuTensorHandle refHandle(tensorInfo, &ret.outputExpected[0][0]); + armnn::FakeQuantizationQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadOutput(refData, refInfo, 0, tensorInfo, &refHandle); + + std::unique_ptr workload = workloadFactory.CreateFakeQuantization(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); + + ret.outputExpected = MakeTensor(tensorInfo, std::vector({ + 0.0f, 63.0f, + 128.0f, 191.0f, + 255.0f, 255.0f + })); + return ret; +} + +namespace +{ + +LayerTestResult L2NormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, + const armnn::TensorShape& inputOutputTensorShape, + const std::vector& inputValues, + const std::vector& expectedOutputValues, + armnn::DataLayout dataLayout) +{ + const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); + const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); + + auto inputTensor = MakeTensor(inputTensorInfo, std::vector(inputValues)); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(inputTensorInfo, std::vector(expectedOutputValues)); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::L2NormalizationQueueDescriptor descriptor; + descriptor.m_Parameters.m_DataLayout = dataLayout; + armnn::WorkloadInfo info; + + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateL2Normalization(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + + return result; +} + +float CalcInvL2Norm(std::initializer_list elements) +{ + const float reduction = std::accumulate(elements.begin(), elements.end(), 0.0f, + [](float acc, float element) { return acc + element * element; }); + return 1.0f / sqrtf(reduction); +} + +} // anonymous namespace + +template +LayerTestResult Pad2dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) +{ + const armnn::TensorShape inputShape{ 3, 3 }; + const armnn::TensorShape outputShape{ 7, 7 }; + + const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType()); + const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType()); + + std::vector inputValues( + QuantizedVector(qScale, qOffset, + { + // Height (3) x Width (3) + 4, 8, 6, + 7, 4, 4, + 3, 2, 4 + })); + + std::vector expectedOutputValues( + QuantizedVector(qScale, qOffset, + { + 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, + 0, 0, 4, 8, 6, 0, 0, + 0, 0, 7, 4, 4, 0, 0, + 0, 0, 3, 2, 4, 0, 0, + 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0 + })); + + auto inputTensor = MakeTensor(inputTensorInfo, std::vector(inputValues)); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, std::vector(expectedOutputValues)); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::PadQueueDescriptor descriptor; + + std::vector> PadList; + PadList.push_back(std::pair(2,2)); + PadList.push_back(std::pair(2,2)); + + descriptor.m_Parameters.m_PadList = PadList; + armnn::WorkloadInfo info; + + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreatePad(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get()); + + return result; +} + +template +LayerTestResult Pad3dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) +{ + const armnn::TensorShape inputShape{ 2, 2, 2 }; + const armnn::TensorShape outputShape{ 3, 5, 6 }; + + const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType()); + const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType()); + + std::vector inputValues( + QuantizedVector(qScale,qOffset, + { + // Channel 0, Height (2) x Width (2) + 0, 4, + 2, 5, + + // Channel 1, Height (2) x Width (2) + 6, 1, + 5, 2 + })); + + std::vector expectedOutputValues( + QuantizedVector(qScale,qOffset, + { + + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 4, 0, 0, + 0, 0, 2, 5, 0, 0, + 0, 0, 0, 0, 0, 0, + + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 6, 1, 0, 0, + 0, 0, 5, 2, 0, 0, + 0, 0, 0, 0, 0, 0, + + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0 + + })); + + auto inputTensor = MakeTensor(inputTensorInfo, std::vector(inputValues)); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, std::vector(expectedOutputValues)); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::PadQueueDescriptor descriptor; + + std::vector> PadList; + PadList.push_back(std::pair(0,1)); + PadList.push_back(std::pair(2,1)); + PadList.push_back(std::pair(2,2)); + + descriptor.m_Parameters.m_PadList = PadList; + armnn::WorkloadInfo info; + + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreatePad(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0], outputHandle.get()); + + return result; +} + +template +LayerTestResult Pad4dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) +{ + const armnn::TensorShape inputShape{ 2, 2, 3, 2 }; + const armnn::TensorShape outputShape{ 4, 5, 7, 4 }; + + const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType()); + const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType()); + + std::vector inputValues( + QuantizedVector(qScale,qOffset, + { + // Batch 0, Channel 0, Height (3) x Width (2) + 0, 1, + 2, 3, + 4, 5, + + // Batch 0, Channel 1, Height (3) x Width (2) + 6, 7, + 8, 9, + 10, 11, + + // Batch 1, Channel 0, Height (3) x Width (2) + 12, 13, + 14, 15, + 16, 17, + + // Batch 1, Channel 1, Height (3) x Width (2) + 18, 19, + 20, 21, + 22, 23 + })); + + std::vector expectedOutputValues( + QuantizedVector(qScale,qOffset, + { + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 1, 0, + 0, 2, 3, 0, + 0, 4, 5, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 6, 7, 0, + 0, 8, 9, 0, + 0, 10, 11, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 12, 13, 0, + 0, 14, 15, 0, + 0, 16, 17, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 18, 19, 0, + 0, 20, 21, 0, + 0, 22, 23, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0 + })); + + auto inputTensor = MakeTensor(inputTensorInfo, std::vector(inputValues)); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, std::vector(expectedOutputValues)); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::PadQueueDescriptor descriptor; + + std::vector> PadList; + PadList.push_back(std::pair(1,1)); + PadList.push_back(std::pair(2,1)); + PadList.push_back(std::pair(3,1)); + PadList.push_back(std::pair(1,1)); + + descriptor.m_Parameters.m_PadList = PadList; + armnn::WorkloadInfo info; + + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreatePad(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]); + + workloadFactory.Finalize(); + + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + + return result; +} + +LayerTestResult PadUint82dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Pad2dTestCommon(workloadFactory, 1.0f, 0); +} + +LayerTestResult PadUint83dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Pad3dTestCommon(workloadFactory, 1.0f, 0); +} + +LayerTestResult PadUint84dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Pad4dTestCommon(workloadFactory, 1.0f, 0); +} + +LayerTestResult PadFloat322dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Pad2dTestCommon(workloadFactory, 0.0f, 0); +} + +LayerTestResult PadFloat323dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Pad3dTestCommon(workloadFactory, 0.0f, 0); +} + +LayerTestResult PadFloat324dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return Pad4dTestCommon(workloadFactory, 0.0f, 0); +} + +LayerTestResult L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Width: 1 + // Height: 1 + // Channels: 10 + // BatchSize: 1 + + const armnn::TensorShape inputOutputShape{ 1, 10, 1, 1 }; + std::vector inputValues + { + // Batch 0, Channel 0, Height (1) x Width (1) + 1.0f, + + // Batch 0, Channel 1, Height (1) x Width (1) + 2.0f, + + // Batch 0, Channel 2, Height (1) x Width (1) + 3.0f, + + // Batch 0, Channel 3, Height (1) x Width (1) + 4.0f, + + // Batch 0, Channel 4, Height (1) x Width (1) + 5.0f, + + // Batch 0, Channel 5, Height (1) x Width (1) + 6.0f, + + // Batch 0, Channel 6, Height (1) x Width (1) + 7.0f, + + // Batch 0, Channel 7, Height (1) x Width (1) + 8.0f, + + // Batch 0, Channel 8, Height (1) x Width (1) + 9.0f, + + // Batch 0, Channel 9, Height (1) x Width (1) + 10.0f + }; + const float approxInvL2Norm = 0.050964719f; + std::vector expectedOutputValues + { + // Batch 0, Channel 0, Height (1) x Width (1) + 1.0f * approxInvL2Norm, + 2.0f * approxInvL2Norm, + 3.0f * approxInvL2Norm, + 4.0f * approxInvL2Norm, + 5.0f * approxInvL2Norm, + 6.0f * approxInvL2Norm, + 7.0f * approxInvL2Norm, + 8.0f * approxInvL2Norm, + 9.0f * approxInvL2Norm, + 10.0f * approxInvL2Norm + }; + + return L2NormalizationTestImpl(workloadFactory, inputOutputShape, + inputValues, expectedOutputValues, armnn::DataLayout::NCHW); +} + +LayerTestResult L2Normalization1dNhwcTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Width: 1 + // Height: 1 + // Channels: 10 + // BatchSize: 1 + + const armnn::TensorShape inputOutputShape{ 1, 1, 1, 10 }; + std::vector inputValues + { + // Batch 0, Height 0, Width (1) x Channel (10) + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f + }; + const float approxInvL2Norm = 0.050964719f; + std::vector expectedOutputValues + { + // Batch 0, Height 0, Width (1) x Channel (10) + 1.0f * approxInvL2Norm, + 2.0f * approxInvL2Norm, + 3.0f * approxInvL2Norm, + 4.0f * approxInvL2Norm, + 5.0f * approxInvL2Norm, + 6.0f * approxInvL2Norm, + 7.0f * approxInvL2Norm, + 8.0f * approxInvL2Norm, + 9.0f * approxInvL2Norm, + 10.0f * approxInvL2Norm + }; + + return L2NormalizationTestImpl(workloadFactory, inputOutputShape, + inputValues, expectedOutputValues, armnn::DataLayout::NHWC); +} + +LayerTestResult L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Width: 5 + // Height: 1 + // Channels: 2 + // BatchSize: 1 + + const armnn::TensorShape inputOutputShape{ 1, 2, 1, 5 }; + std::vector inputValues + { + // Batch 0, Channel 0, Height (1) x Width (5) + 1.0f, 3.0f, 5.0f, 7.0f, 9.0f, + + // Batch 0, Channel 1, Height (1) x Width (5) + 2.0f, 4.0f, 6.0f, 8.0f, 10.0f + }; + std::vector expectedOutputValues + { + // Batch 0, Channel 0, Height (1) x Width (5) + 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }), + 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }), + 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }), + 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }), + 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }), + + // Batch 0, Channel 1, Height (1) x Width (5) + 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }), + 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }), + 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }), + 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }), + 10.0f * CalcInvL2Norm({ 9.0f, 10.0f }) + }; + + return L2NormalizationTestImpl(workloadFactory, inputOutputShape, + inputValues, expectedOutputValues, armnn::DataLayout::NCHW); +} + +LayerTestResult L2Normalization2dNhwcTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Width: 5 + // Height: 1 + // Channels: 2 + // BatchSize: 1 + + const armnn::TensorShape inputOutputShape{ 1, 1, 5, 2 }; + std::vector inputValues + { + // Batch 0, Height 0, Width (5) x Channel (2) + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f + }; + std::vector expectedOutputValues + { + // Batch 0, Height 0, Width (5) x Channel (2) + 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }), + 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }), + 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }), + 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }), + 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }), + 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }), + 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }), + 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }), + 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }), + 10.0f * CalcInvL2Norm({ 9.0f, 10.0f }) + }; + + return L2NormalizationTestImpl(workloadFactory, inputOutputShape, + inputValues, expectedOutputValues, armnn::DataLayout::NHWC); +} + +LayerTestResult L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Width: 3 + // Height: 4 + // Channels: 2 + // BatchSize: 1 + + const armnn::TensorShape inputOutputShape{ 1, 2, 4, 3 }; + std::vector inputValues + { + // Batch 0, Channel 0, Height (4) x Width (3) + 119.0f, 21.0f, 150.0f, + 149.0f, 32.0f, 179.0f, + 15.0f, 227.0f, 141.0f, + 147.0f, 199.0f, 220.0f, + + // Batch 0, Channel 1, Height (4) x Width (3) + 110.0f, 140.0f, 73.0f, + 211.0f, 212.0f, 89.0f, + 24.0f, 138.0f, 188.0f, + 162.0f, 12.0f, 161.0f + }; + std::vector expectedOutputValues + { + // Batch 0, Channel 0, Height (4) x Width (3) + 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }), + 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }), + 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }), + 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }), + 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }), + 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }), + 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }), + 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }), + 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }), + 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }), + 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }), + 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }), + + // Batch 0, Channel 1, Height (4) x Width (3) + 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }), + 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }), + 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }), + 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }), + 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }), + 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }), + 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }), + 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }), + 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }), + 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }), + 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }), + 161.0f * CalcInvL2Norm({ 220.0f, 161.0f }) + }; + + return L2NormalizationTestImpl(workloadFactory, inputOutputShape, + inputValues, expectedOutputValues, armnn::DataLayout::NCHW); +} + +LayerTestResult L2Normalization3dNhwcTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Width: 3 + // Height: 4 + // Channels: 2 + // BatchSize: 1 + + const armnn::TensorShape inputOutputShape{ 1, 4, 3, 2 }; + std::vector inputValues + { + // Batch 0, Height 0, Width (3) x Channel (2) + 119.0f, 110.0f, + 21.0f, 140.0f, + 150.0f, 73.0f, + + // Batch 0, Height 1, Width (3) x Channel (2) + 149.0f, 211.0f, + 32.0f, 212.0f, + 179.0f, 89.0f, + + // Batch 0, Height 2, Width (3) x Channel (2) + 15.0f, 24.0f, + 227.0f, 138.0f, + 141.0f, 188.0f, + + // Batch 0, Height 3, Width (3) x Channel (2) + 147.0f, 162.0f, + 199.0f, 12.0f, + 220.0f, 161.0f + }; + std::vector expectedOutputValues + { + // Batch 0, Height 0, Width (3) x Channel (2) + 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }), + 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }), + 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }), + 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }), + 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }), + 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }), + + // Batch 0, Height 1, Width (3) x Channel (2) + 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }), + 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }), + 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }), + 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }), + 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }), + 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }), + + // Batch 0, Height 2, Width (3) x Channel (2) + 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }), + 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }), + 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }), + 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }), + 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }), + 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }), + + // Batch 0, Height 3, Width (3) x Channel (2) + 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }), + 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }), + 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }), + 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }), + 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }), + 161.0f * CalcInvL2Norm({ 220.0f, 161.0f }) + }; + + return L2NormalizationTestImpl(workloadFactory, inputOutputShape, + inputValues, expectedOutputValues, armnn::DataLayout::NHWC); +} + +LayerTestResult L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Width: 3 + // Height: 4 + // Channels: 3 + // BatchSize: 2 + + const armnn::TensorShape inputOutputShape{ 2, 3, 4, 3 }; + std::vector inputValues + { + // Batch 0, Channel 0, Height (4) x Width (3) + 235.0f, 46.0f, 178.0f, + 100.0f, 123.0f, 19.0f, + 172.0f, 74.0f, 250.0f, + 6.0f, 195.0f, 80.0f, + + // Batch 0, Channel 1, Height (4) x Width (3) + 113.0f, 95.0f, 202.0f, + 77.0f, 114.0f, 71.0f, + 122.0f, 246.0f, 166.0f, + 82.0f, 28.0f, 37.0f, + + // Batch 0, Channel 2, Height (4) x Width (3) + 56.0f, 170.0f, 162.0f, + 194.0f, 89.0f, 254.0f, + 12.0f, 209.0f, 200.0f, + 1.0f, 64.0f, 54.0f, + + // Batch 1, Channel 0, Height (4) x Width (3) + 67.0f, 90.0f, 49.0f, + 7.0f, 163.0f, 18.0f, + 25.0f, 117.0f, 103.0f, + 247.0f, 59.0f, 189.0f, + + // Batch 1, Channel 1, Height (4) x Width (3) + 239.0f, 104.0f, 199.0f, + 17.0f, 124.0f, 153.0f, + 222.0f, 217.0f, 75.0f, + 32.0f, 126.0f, 21.0f, + + // Batch 1, Channel 2, Height (4) x Width (3) + 97.0f, 145.0f, 215.0f, + 115.0f, 116.0f, 238.0f, + 226.0f, 16.0f, 132.0f, + 92.0f, 125.0f, 88.0f + }; + std::vector expectedOutputValues + { + // Batch 0, Channel 0, Height (4) x Width (3) + 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), + 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), + 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), + 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), + 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), + 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), + 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), + 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), + 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), + 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), + 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), + 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), + + // Batch 0, Channel 1, Height (4) x Width (3) + 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), + 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), + 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), + 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), + 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), + 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), + 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), + 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), + 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), + 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), + 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), + 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), + + // Batch 0, Channel 2, Height (4) x Width (3) + 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), + 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), + 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), + 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), + 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), + 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), + 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), + 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), + 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), + 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), + 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), + 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), + + // Batch 1, Channel 0, Height (4) x Width (3) + 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), + 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), + 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), + 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), + 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), + 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), + 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), + 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), + 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), + 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), + 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), + 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), + + // Batch 1, Channel 1, Height (4) x Width (3) + 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), + 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), + 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), + 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), + 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), + 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), + 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), + 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), + 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), + 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), + 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), + 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), + + // Batch 1, Channel 2, Height (4) x Width (3) + 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), + 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), + 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), + 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), + 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), + 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), + 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), + 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), + 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), + 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), + 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), + 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }) + }; + + return L2NormalizationTestImpl(workloadFactory, inputOutputShape, + inputValues, expectedOutputValues, armnn::DataLayout::NCHW); +} + +LayerTestResult L2Normalization4dNhwcTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Width: 3 + // Height: 4 + // Channels: 3 + // BatchSize: 2 + + const armnn::TensorShape inputOutputShape{ 2, 4, 3, 3 }; + std::vector inputValues + { + // Batch 0, Height 0, Width (3) x Channel (3) + 235.0f, 113.0f, 56.0f, + 46.0f, 95.0f, 170.0f, + 178.0f, 202.0f, 162.0f, + + // Batch 0, Height 1, Width (3) x Channel (3) + 100.0f, 77.0f, 194.0f, + 123.0f, 114.0f, 89.0f, + 19.0f, 71.0f, 254.0f, + + // Batch 0, Height 2, Width (3) x Channel (3) + 172.0f, 122.0f, 12.0f, + 74.0f, 246.0f, 209.0f, + 250.0f, 166.0f, 200.0f, + + // Batch 0, Height 3, Width (3) x Channel (3) + 6.0f, 82.0f, 1.0f, + 195.0f, 28.0f, 64.0f, + 80.0f, 37.0f, 54.0f, + + // Batch 1, Height 0, Width (3) x Channel (3) + 67.0f, 239.0f, 97.0f, + 90.0f, 104.0f, 145.0f, + 49.0f, 199.0f, 215.0f, + + // Batch 1, Height 1, Width (3) x Channel (3) + 7.0f, 17.0f, 115.0f, + 163.0f, 124.0f, 116.0f, + 18.0f, 153.0f, 238.0f, + + // Batch 1, Height 2, Width (3) x Channel (3) + 25.0f, 222.0f, 226.0f, + 117.0f, 217.0f, 16.0f, + 103.0f, 75.0f, 132.0f, + + // Batch 1, Height 3, Width (3) x Channel (3) + 247.0f, 32.0f, 92.0f, + 59.0f, 126.0f, 125.0f, + 189.0f, 21.0f, 88.0f + }; + std::vector expectedOutputValues + { + // Batch 0, Height 0, Width (3) x Channel (3) + 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), + 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), + 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), + 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), + 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), + 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), + 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), + 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), + 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), + + // Batch 0, Height 1, Width (3) x Channel (3) + 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), + 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), + 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), + 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), + 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), + 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), + 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), + 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), + 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), + + // Batch 0, Height 2, Width (3) x Channel (3) + 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), + 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), + 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), + 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), + 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), + 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), + 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), + 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), + 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), + + // Batch 0, Height 3, Width (3) x Channel (3) + 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), + 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), + 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), + 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), + 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), + 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), + 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), + 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), + 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), + + // Batch 1, Height 0, Width (3) x Channel (3) + 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), + 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), + 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), + 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), + 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), + 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), + 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), + 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), + 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), + + // Batch 1, Height 1, Width (3) x Channel (3) + 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), + 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), + 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), + 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), + 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), + 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), + 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), + 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), + 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), + + // Batch 1, Height 2, Width (3) x Channel (3) + 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), + 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), + 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), + 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), + 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), + 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), + 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), + 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), + 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), + + // Batch 1, Height 3, Width (3) x Channel (3) + 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), + 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), + 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), + 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), + 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), + 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), + 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), + 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), + 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }) + }; + + return L2NormalizationTestImpl(workloadFactory, inputOutputShape, + inputValues, expectedOutputValues, armnn::DataLayout::NHWC); +} + +template +LayerTestResult ConstantTestImpl(armnn::IWorkloadFactory& workloadFactory, + float qScale, + int32_t qOffset) +{ + constexpr unsigned int inputWidth = 3; + constexpr unsigned int inputHeight = 4; + constexpr unsigned int inputChannels = 3; + constexpr unsigned int inputBatchSize = 2; + + constexpr unsigned int outputWidth = inputWidth; + constexpr unsigned int outputHeight = inputHeight; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputBatchSize = inputBatchSize; + + armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, + armnn::GetDataType()); + + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, + armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, std::vector( + QuantizedVector(qScale, qOffset, { + // Batch 0, Channel 0 + 235.0f, 46.0f, 178.0f, + 100.0f, 123.0f, 19.0f, + 172.0f, 74.0f, 250.0f, + 6.0f, 195.0f, 80.0f, + + // Batch 0, Channel 1 + 113.0f, 95.0f, 202.0f, + 77.0f, 114.0f, 71.0f, + 122.0f, 246.0f, 166.0f, + 82.0f, 28.0f, 37.0f, + + // Batch 0, Channel 2 + 56.0f, 170.0f, 162.0f, + 194.0f, 89.0f, 254.0f, + 12.0f, 209.0f, 200.0f, + 1.0f, 64.0f, 54.0f, + + // Batch 1, Channel 0 + 67.0f, 90.0f, 49.0f, + 7.0f, 163.0f, 18.0f, + 25.0f, 117.0f, 103.0f, + 247.0f, 59.0f, 189.0f, + + // Batch 1, Channel 1 + 239.0f, 104.0f, 199.0f, + 17.0f, 124.0f, 153.0f, + 222.0f, 217.0f, 75.0f, + 32.0f, 126.0f, 21.0f, + + // Batch 1, Channel 2 + 97.0f, 145.0f, 215.0f, + 115.0f, 116.0f, 238.0f, + 226.0f, 16.0f, 132.0f, + 92.0f, 125.0f, 88.0f, + }))); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = input; + + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ScopedCpuTensorHandle constantTensor(inputTensorInfo); + AllocateAndCopyDataToITensorHandle(&constantTensor, &input[0][0][0][0]); + + armnn::ConstantQueueDescriptor descriptor; + descriptor.m_LayerOutput = &constantTensor; + + armnn::WorkloadInfo info; + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateConstant(descriptor, info); + + outputHandle->Allocate(); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult ConstantTest(armnn::IWorkloadFactory& workloadFactory) +{ + return ConstantTestImpl(workloadFactory, 0.0f, 0); +} + +LayerTestResult ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory) +{ + return ConstantTestImpl(workloadFactory, 1.0f, 0); +} + +LayerTestResult MergerUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int outputWidth = 3; + unsigned int outputHeight = 6; + unsigned int outputChannels = 3; + + unsigned int inputWidth1 = 3; + unsigned int inputHeight1 = 6; + unsigned int inputChannels1 = 2; + + unsigned int inputWidth2 = 3; + unsigned int inputHeight2 = 6; + unsigned int inputChannels2 = 1; + + // Defines the tensor descriptors. + armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8); + armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8); + armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8); + + // Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize them. + const float scale = 0.13497836f; + const int32_t offset = -7; + + outputTensorInfo.SetQuantizationScale(scale); + outputTensorInfo.SetQuantizationOffset(offset); + inputTensorInfo1.SetQuantizationScale(scale); + inputTensorInfo1.SetQuantizationOffset(offset); + inputTensorInfo2.SetQuantizationScale(scale); + inputTensorInfo2.SetQuantizationOffset(offset); + + LayerTestResult ret(outputTensorInfo); + + ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( + { + 1, 2, 3, + 4, 5, 6, + 7, 8, 9, + 10, 11, 12, + 13, 14, 15, + 16, 17, 18, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + 28, 29, 30, + 31, 32, 33, + 34, 35, 36, + + 37, 38, 39, + 40, 41, 42, + 43, 44, 45, + 46, 47, 48, + 49, 50, 51, + 52, 53, 54, + }) + ); + + auto input1 = MakeTensor(inputTensorInfo1, std::vector( + { + 1, 2, 3, + 4, 5, 6, + 7, 8, 9, + 10, 11, 12, + 13, 14, 15, + 16, 17, 18, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + 28, 29, 30, + 31, 32, 33, + 34, 35, 36, + }) + ); + + auto input2 = MakeTensor(inputTensorInfo2, std::vector( + { + 37, 38, 39, + 40, 41, 42, + 43, 44, 45, + 46, 47, 48, + 49, 50, 51, + 52, 53, 54, + }) + ); + + std::vector wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. + armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); + + std::vector wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. + armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); + + + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + bool subTensorsSupported = workloadFactory.SupportsSubTensors(); + + std::unique_ptr inputHandle1 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : + workloadFactory.CreateTensorHandle(inputTensorInfo1); + + std::unique_ptr inputHandle2 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : + workloadFactory.CreateTensorHandle(inputTensorInfo2); + + + armnn::MergerQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + data.m_ViewOrigins.push_back(window1); + data.m_ViewOrigins.push_back(window2); + + std::unique_ptr workload = workloadFactory.CreateMerger(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); + + return ret; +} + +LayerTestResult AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int batchSize = 1; + unsigned int channels = 2; + unsigned int height = 2; + unsigned int width = 3; + + const float scale = 7.0f; + const int32_t offset = 3; + + armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; + armnn::TensorInfo outputTensorInfo; + + const unsigned int shape[] = { batchSize, channels, height, width }; + inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); + inputTensorInfo1.SetQuantizationScale(scale); + inputTensorInfo1.SetQuantizationOffset(offset); + + inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); + inputTensorInfo2.SetQuantizationScale(scale); + inputTensorInfo2.SetQuantizationOffset(offset); + + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(scale); + outputTensorInfo.SetQuantizationOffset(offset); + + // See dequantized values to the right. + auto input1 = MakeTensor(inputTensorInfo1, std::vector( + { + 63, 35, 77, 70, 56, 112, // 420, 224, 518, 469, 371, 763 + 203, 28, 252, 168, 245, 91 // 1400, 175, 1743, 1155, 1694, 616 + })); + + // See dequantized values to the right. + auto input2 = MakeTensor(inputTensorInfo1, std::vector( + { + 21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449 + 126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861 + })); + + // See dequantized values to the right. + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, std::vector( + { + 81, 39, 249, 255, 228, 255, // 546, 252, 1722, 2065(clamped), 1575, 2212(clamped) + 255, 186, 255, 186, 255, 214, // 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477 + })); + + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateAddition(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + + return result; +} + +namespace +{ +LayerTestResult MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory, + const unsigned int shape0[4], + const std::vector & values0, + float scale0, + int32_t offset0, + const unsigned int shape1[4], + const std::vector & values1, + float scale1, + int32_t offset1, + const unsigned int outShape[4], + const std::vector & outValues, + float outScale, + int32_t outOffset) +{ + armnn::TensorInfo inputTensorInfo0(4, shape0, armnn::DataType::QuantisedAsymm8); + armnn::TensorInfo inputTensorInfo1(4, shape1, armnn::DataType::QuantisedAsymm8); + armnn::TensorInfo outputTensorInfo(4, outShape, armnn::DataType::QuantisedAsymm8); + + inputTensorInfo0.SetQuantizationScale(scale0); + inputTensorInfo0.SetQuantizationOffset(offset0); + + inputTensorInfo1.SetQuantizationScale(scale1); + inputTensorInfo1.SetQuantizationOffset(offset1); + + outputTensorInfo.SetQuantizationScale(outScale); + outputTensorInfo.SetQuantizationOffset(outOffset); + + auto input0 = MakeTensor(inputTensorInfo0, values0); + auto input1 = MakeTensor(inputTensorInfo1, values1); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, outValues); + + std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::MultiplicationQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateMultiplication(data, info); + + inputHandle0->Allocate(); + inputHandle1->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + + return result; +} +} // anonymous namespace + +LayerTestResult MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int batchSize = 1; + unsigned int channels = 2; + unsigned int height = 2; + unsigned int width = 3; + const unsigned int shape[] = { batchSize, channels, height, width }; + + // See dequantized values to the right. + std::vector input0({ + 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440, + 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120 + }); + + // See dequantized values to the right. + std::vector input1({ + 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747, + 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297 + }); + + // See dequantized values to the right. + std::vector output( + { + 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680, + 77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640 + }); + + return MultiplicationUint8TestHelper(workloadFactory, + shape, + input0, + 4.0f, + 1, + shape, + input1, + 3.0f, + -2, + shape, + output, + 1366.255f, // Scale/offset chosen to have output values out of range. + -5); +} + +LayerTestResult MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 2, 2, 3 }; + const unsigned int shape1[] = { 1, 1, 1, 1 }; + + std::vector input0({ + 1, 2, 3, 4, 5, 6, + 7, 8, 9, 10, 11, 12 + }); + + std::vector input1({2}); + + std::vector output({ + 2, 4, 6, 8, 10, 12, + 14, 16, 18, 20, 22, 24 + }); + + return MultiplicationUint8TestHelper(workloadFactory, + shape0, + input0, + 1.0f, + 0, + shape1, + input1, + 1.0f, + 0, + shape0, + output, + 1.0f, + 0); +} + +LayerTestResult MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 2, 2, 3 }; + const unsigned int shape1[] = { 1, 1, 1, 3 }; + + std::vector input0({ + 1, 2, 3, 4, 5, 6, + 7, 8, 9, 10, 11, 12 + }); + + std::vector input1({1, 2, 3}); + + std::vector output({ + 1, 4, 9, 4, 10, 18, + 7, 16, 27, 10, 22, 36 + }); + + return MultiplicationUint8TestHelper(workloadFactory, + shape0, + input0, + 1.0f, + 0, + shape1, + input1, + 1.0f, + 0, + shape0, + output, + 1.0f, + 0); +} + +namespace +{ +template +LayerTestResult SubtractionTestHelper(armnn::IWorkloadFactory& workloadFactory, + const unsigned int shape0[4], + const std::vector& values0, + float scale0, + int32_t offset0, + const unsigned int shape1[4], + const std::vector & values1, + float scale1, + int32_t offset1, + const unsigned int outShape[4], + const std::vector & outValues, + float outScale, + int32_t outOffset) +{ + auto dataType = (std::is_same::value ? + armnn::DataType::QuantisedAsymm8 : + armnn::DataType::Float32); + + armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); + armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); + armnn::TensorInfo outputTensorInfo(4, outShape, dataType); + + inputTensorInfo0.SetQuantizationScale(scale0); + inputTensorInfo0.SetQuantizationOffset(offset0); + + inputTensorInfo1.SetQuantizationScale(scale1); + inputTensorInfo1.SetQuantizationOffset(offset1); + + outputTensorInfo.SetQuantizationScale(outScale); + outputTensorInfo.SetQuantizationOffset(outOffset); + + auto input0 = MakeTensor(inputTensorInfo0, values0); + auto input1 = MakeTensor(inputTensorInfo1, values1); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, outValues); + + std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); + std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::SubtractionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateSubtraction(data, info); + + inputHandle0->Allocate(); + inputHandle1->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + + return result; +} +} // anonymous namespace + +LayerTestResult SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 1, 2, 2 }; + const unsigned int shape1[] = { 1, 1, 2, 2 }; + + std::vector input0({ 10, 12, 14, 16 }); + std::vector input1({ 1, 2, 1, 2 }); + std::vector output({ 3, 3, 5, 5 }); + + return SubtractionTestHelper(workloadFactory, + shape0, input0, 0.5f, 2, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + +LayerTestResult SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 1, 2, 2 }; + const unsigned int shape1[] = { 1, 1, 1, 1 }; + + std::vector input0({ 10, 12, 14, 16 }); + std::vector input1({ 2 }); + std::vector output({ 5, 6, 7, 8 }); + + return SubtractionTestHelper(workloadFactory, + shape0, input0, 0.5f, 2, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 3); +} + +LayerTestResult SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 1, 2, 2 }; + const unsigned int shape1[] = { 1, 1, 2, 1 }; + + std::vector input0({ 10, 12, 14, 16 }); + std::vector input1({ 2, 1 }); + std::vector output({ 8, 11, 12, 15 }); + + return SubtractionTestHelper(workloadFactory, + shape0, input0, 1.0f, 0, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + +LayerTestResult SubtractionTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 1, 2, 2 }; + const unsigned int shape1[] = { 1, 1, 2, 2 }; + + std::vector input0({ 1, 2, 3, 4 }); + std::vector input1({ 1, -1, 0, 2 }); + std::vector output({ 0, 3, 3, 2 }); + + return SubtractionTestHelper(workloadFactory, + shape0, input0, 1.0f, 0, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + +LayerTestResult SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 1, 2, 2 }; + const unsigned int shape1[] = { 1, 1, 1, 1 }; + + std::vector input0({ 1, 2, 3, 4 }); + std::vector input1({ 10 }); + std::vector output({ -9, -8, -7, -6 }); + + return SubtractionTestHelper(workloadFactory, + shape0, input0, 1.0f, 0, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + +LayerTestResult SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 1, 2, 2 }; + const unsigned int shape1[] = { 1, 1, 1, 2 }; + + std::vector input0({ 1, 2, 3, 4 }); + std::vector input1({ 10, -5 }); + std::vector output({ -9, 7, -7, 9 }); + + return SubtractionTestHelper(workloadFactory, + shape0, input0, 1.0f, 0, + shape1, input1, 1.0f, 0, + shape0, output, 1.0f, 0); +} + +LayerTestResult ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + constexpr unsigned int inputWidth = 4; + constexpr unsigned int inputHeight = 4; + constexpr unsigned int inputChannels = 1; + constexpr unsigned int inputBatchSize = 1; + + constexpr unsigned int outputWidth = inputWidth; + constexpr unsigned int outputHeight = inputHeight; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputBatchSize = inputBatchSize; + + armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, + armnn::DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationScale(1.5f); + inputTensorInfo.SetQuantizationOffset(-3); + + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, + armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(1.5f); + outputTensorInfo.SetQuantizationOffset(-3); + + auto input = MakeTensor(inputTensorInfo, std::vector({ + 1, 2, 3, 4, + 2, 3, 4, 5, + 3, 4, 5, 6, + 4, 5, 6, 7 + })); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = input; + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + constexpr unsigned int inputWidth = 2; + constexpr unsigned int inputHeight = 2; + constexpr unsigned int inputChannels = 1; + constexpr unsigned int inputBatchSize = 1; + + constexpr unsigned int outputWidth = inputWidth / 2; + constexpr unsigned int outputHeight = inputHeight / 2; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputBatchSize = inputBatchSize; + + armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, + armnn::DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationScale(0.1567f); + inputTensorInfo.SetQuantizationOffset(1); + + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, + armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(0.1567f); + outputTensorInfo.SetQuantizationOffset(1); + + auto input = MakeTensor(inputTensorInfo, std::vector({ + 1, 255, + 200, 250 + })); + + // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, + // then figures out the interpolants and weights. Note this is different to projecting the centre of the + // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value + // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting + // the centre). + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ + 1 + })); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + constexpr unsigned int inputWidth = 4; + constexpr unsigned int inputHeight = 4; + constexpr unsigned int inputChannels = 1; + constexpr unsigned int inputBatchSize = 1; + + constexpr unsigned int outputWidth = inputWidth / 2; + constexpr unsigned int outputHeight = inputHeight / 2; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputBatchSize = inputBatchSize; + + armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, + armnn::DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationScale(3.141592f); + inputTensorInfo.SetQuantizationOffset(3); + + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, + armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(3.141592f); + outputTensorInfo.SetQuantizationOffset(3); + + auto input = MakeTensor(inputTensorInfo, std::vector({ + 1, 2, 3, 4, + 2, 3, 4, 5, + 3, 4, 5, 6, + 4, 5, 6, 7 + })); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ + 1, 3, + 3, 5 + })); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + constexpr unsigned int inputWidth = 3; + constexpr unsigned int inputHeight = 2; + constexpr unsigned int inputChannels = 1; + constexpr unsigned int inputBatchSize = 1; + + constexpr unsigned int outputWidth = 2; + constexpr unsigned int outputHeight = 1; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputBatchSize = inputBatchSize; + + armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, + armnn::DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationScale(1.5f); + inputTensorInfo.SetQuantizationOffset(-1); + + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, + armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(1.5f); + outputTensorInfo.SetQuantizationOffset(-1); + + auto input = MakeTensor(inputTensorInfo, std::vector({ + 1, 2, 3, // 3.0, 4.5, 6.0 + 5, 8, 13 // 9.0, 13.5, 21.0 + })); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ + 1, 3 // 3.0, 5.25 + })); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + constexpr unsigned int inputWidth = 2; + constexpr unsigned int inputHeight = 3; + constexpr unsigned int inputChannels = 1; + constexpr unsigned int inputBatchSize = 1; + + constexpr unsigned int outputWidth = 5; + constexpr unsigned int outputHeight = 3; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputBatchSize = inputBatchSize; + + armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, + armnn::DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationScale(0.010765f); + inputTensorInfo.SetQuantizationOffset(7); + + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, + armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(0.010132f); + outputTensorInfo.SetQuantizationOffset(-18); + + auto input = MakeTensor(inputTensorInfo, std::vector({ + 24, 228, // 0.183005, 2.379065, + 105, 128, // 1.05497, 1.302565 + 230, 71 // 2.400595, 0.68896 + })); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ + 0, 87, 173, 217, 217, // 0.18300501, 1.06142902, 1.93985295, 2.37906504, 2.37906504 + 86, 96, 106, 111, 111, // 1.05497003, 1.15400803, 1.25304604, 1.30256498, 1.30256498 + 219, 151, 84, 50, 50 // 2.40059495, 1.71594095, 1.03128707, 0.68896002, 0.68896002 + })); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ResizeBilinearQueueDescriptor descriptor; + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateResizeBilinear(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); + return result; +} + +LayerTestResult BatchNormTest(armnn::IWorkloadFactory& workloadFactory) +{ + // BatchSize: 1 + // Channels: 2 + // Height: 3 + // Width: 2 + + const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 }; + std::vector inputValues + { + // Batch 0, Channel 0, Height (3) x Width (2) + 1.f, 4.f, + 4.f, 2.f, + 1.f, 6.f, + + // Batch 0, Channel 1, Height (3) x Width (2) + 1.f, 1.f, + 4.f, 1.f, + -2.f, 4.f + }; + std::vector expectedOutputValues + { + // Batch 0, Channel 0, Height (3) x Width (2) + 1.f, 4.f, + 4.f, 2.f, + 1.f, 6.f, + + // Batch 0, Channel 1, Height (3) x Width (2) + 3.f, 3.f, + 4.f, 3.f, + 2.f, 4.f + }; + + return BatchNormTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, + 0.f, 0, armnn::DataLayout::NCHW); +} + +LayerTestResult BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory) +{ + // BatchSize: 1 + // Height: 3 + // Width: 2 + // Channels: 2 + + const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 }; + std::vector inputValues + { + // Batch 0, Height 0, Width (2) x Channel (2) + 1.f, 1.f, + 4.f, 1.f, + + // Batch 0, Height 1, Width (2) x Channel (2) + 4.f, 4.f, + 2.f, 1.f, + + // Batch 0, Height 2, Width (2) x Channel (2) + 1.f, -2.f, + 6.f, 4.f + }; + std::vector expectedOutputValues + { + // Batch 0, Height 0, Width (2) x Channel (2) + 1.f, 3.f, + 4.f, 3.f, + + // Batch 0, Height 1, Width (2) x Channel (2) + 4.f, 4.f, + 2.f, 3.f, + + // Batch 0, Height 2, Width (2) x Channel (2) + 1.f, 2.f, + 6.f, 4.f + }; + + return BatchNormTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, + 0.f, 0, armnn::DataLayout::NHWC); +} + +LayerTestResult BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + // BatchSize: 1 + // Channels: 2 + // Height: 3 + // Width: 2 + + const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 }; + std::vector inputValues + { + // Batch 0, Channel 0, Height (3) x Width (2) + 1.f, 4.f, + 4.f, 2.f, + 1.f, 6.f, + + // Batch 0, Channel 1, Height (3) x Width (2) + 1.f, 1.f, + 4.f, 1.f, + -2.f, 4.f + }; + std::vector expectedOutputValues + { + // Batch 0, Channel 0, Height (3) x Width (2) + 1.f, 4.f, + 4.f, 2.f, + 1.f, 6.f, + + // Batch 0, Channel 1, Height (3) x Width (2) + 3.f, 3.f, + 4.f, 3.f, + 2.f, 4.f + }; + + return BatchNormTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, + 1.f/20.f, 50, armnn::DataLayout::NCHW); +} + +LayerTestResult BatchNormUint8NhwcTest(armnn::IWorkloadFactory& workloadFactory) +{ + // BatchSize: 1 + // Height: 3 + // Width: 2 + // Channels: 2 + + const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 }; + std::vector inputValues + { + // Batch 0, Height 0, Width (2) x Channel (2) + 1.f, 1.f, + 4.f, 1.f, + + // Batch 0, Height 1, Width (2) x Channel (2) + 4.f, 4.f, + 2.f, 1.f, + + // Batch 0, Height 2, Width (2) x Channel (2) + 1.f, -2.f, + 6.f, 4.f + }; + std::vector expectedOutputValues + { + // Batch 0, Height 0, Width (2) x Channel (2) + 1.f, 3.f, + 4.f, 3.f, + + // Batch 0, Height 1, Width (2) x Channel (2) + 4.f, 4.f, + 2.f, 3.f, + + // Batch 0, Height 2, Width (2) x Channel (2) + 1.f, 2.f, + 6.f, 4.f + }; + + return BatchNormTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, + 1.f/20.f, 50, armnn::DataLayout::NHWC); +} + +LayerTestResult ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return ConstantTestImpl(workloadFactory, 2e-6f, 1); +} + +LayerTestResult Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation1dTestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation2dDim0TestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation2dDim1TestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation2dDim0DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation2dDim1DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim0TestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim1TestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim2TestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim0TestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim1DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return Concatenation3dDim2DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); +} + +LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding) +{ + return SimpleMaxPooling2dSize2x2Stride2x2TestCommon(workloadFactory, forceNoPadding); +} + +LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding) +{ + return SimpleMaxPooling2dSize2x2Stride2x2TestCommon(workloadFactory, forceNoPadding, 3.0f, -5); +} + +LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding) +{ + return SimpleMaxPooling2dSize3x3Stride2x4TestCommon(workloadFactory, forceNoPadding); +} + +LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding) +{ + return SimpleMaxPooling2dSize3x3Stride2x4TestCommon(workloadFactory, forceNoPadding, 0.1f, 128); +} + +LayerTestResult SimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + return SimpleMaxPooling2dTestCommon(workloadFactory, dataLayout); +} + +LayerTestResult SimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + return SimpleMaxPooling2dTestCommon(workloadFactory, dataLayout); +} + +LayerTestResult SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + return SimpleAveragePooling2dTestCommon(workloadFactory, dataLayout); +} + +LayerTestResult SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + return SimpleAveragePooling2dTestCommon(workloadFactory, dataLayout, 0.5, -1); +} + +LayerTestResult IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding) +{ + return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon(workloadFactory, forceNoPadding); +} + +LayerTestResult LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return LargeTensorsAveragePooling2dTestCommon(workloadFactory); +} + +LayerTestResult LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return LargeTensorsAveragePooling2dTestCommon(workloadFactory, 0.5, -1); +} + +LayerTestResult SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + return SimpleL2Pooling2dTestCommon(workloadFactory, dataLayout); +} + +LayerTestResult SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout) +{ + return SimpleL2Pooling2dTestCommon(workloadFactory, dataLayout); +} + +LayerTestResult L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize3Stride1TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize3Stride1TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize3Stride3TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize3Stride3TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize3Stride4TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize3Stride4TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize7TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize7TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize9TestCommon(workloadFactory); +} + +LayerTestResult L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return L2Pooling2dSize9TestCommon(workloadFactory); +} + +LayerTestResult AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return AsymmetricNonSquarePooling2dTestCommon(workloadFactory); +} + +LayerTestResult AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return AsymmetricNonSquarePooling2dTestCommon(workloadFactory); +} + +LayerTestResult ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::PoolingAlgorithm poolingType) +{ + return ComparePooling2dTestCommon(workloadFactory, refWorkloadFactory, poolingType); +} + +LayerTestResult ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::PoolingAlgorithm poolingType) +{ + return ComparePooling2dTestCommon(workloadFactory, refWorkloadFactory, poolingType, 0.1f, 128); +} + +LayerTestResult FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory, + bool transposeWeights) +{ + return FullyConnectedLargeTestCommon(workloadFactory, transposeWeights); +} + +LayerTestResult IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingSimpleMaxPooling2dTestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingSimpleMaxPooling2dTestCommon(workloadFactory, 1.0f, -5); +} + +LayerTestResult IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingMaxPooling2dSize3TestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingMaxPooling2dSize3TestCommon(workloadFactory, 1.0f, -5); +} + +LayerTestResult IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingSimpleAveragePooling2dTestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingSimpleAveragePooling2dTestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test( + armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingAveragePooling2dSize3TestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingAveragePooling2dSize3TestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingSimpleL2Pooling2dTestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingSimpleL2Pooling2dTestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingL2Pooling2dSize3TestCommon(workloadFactory); +} + +LayerTestResult IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return IgnorePaddingL2Pooling2dSize3TestCommon(workloadFactory); +} + +LayerTestResult SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory) +{ + return SimplePermuteFloat32TestCommon(workloadFactory); +}; + +LayerTestResult SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + return SimplePermuteUint8TestCommon(workloadFactory); +}; + +LayerTestResult PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory) +{ + return PermuteFloat32ValueSet1TestCommon(workloadFactory); +}; + +LayerTestResult PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory) +{ + return PermuteFloat32ValueSet2TestCommon(workloadFactory); +}; + +LayerTestResult PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory) +{ + return PermuteFloat32ValueSet3TestCommon(workloadFactory); +}; + +namespace +{ + +template +LayerTestResult MeanTestHelper(armnn::IWorkloadFactory& workloadFactory, + const unsigned int* inputShape, + const std::vector& inputData, + const std::vector& axis, + bool keepDims, + const unsigned int* outputShape, + const std::vector& outputData, + float scale = 1.0f, + int32_t offset = 0) +{ + auto dataType = (std::is_same::value ? armnn::DataType::QuantisedAsymm8 : armnn::DataType::Float32); + + armnn::TensorInfo inputTensorInfo(InputDim, inputShape, dataType); + armnn::TensorInfo outputTensorInfo(OutputDim, outputShape, dataType); + + inputTensorInfo.SetQuantizationScale(scale); + inputTensorInfo.SetQuantizationOffset(offset); + + outputTensorInfo.SetQuantizationScale(scale); + outputTensorInfo.SetQuantizationOffset(offset); + + auto input = MakeTensor(inputTensorInfo, inputData); + + LayerTestResult result(outputTensorInfo); + result.outputExpected = MakeTensor(outputTensorInfo, outputData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::MeanQueueDescriptor data; + data.m_Parameters.m_Axis = axis; + data.m_Parameters.m_KeepDims = keepDims; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateMean(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), input.origin()); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(result.output.origin(), outputHandle.get()); + + return result; +} + +} // anonymous namespace + +LayerTestResult MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 3, 2 }; + const unsigned int outputShape[] = { 1 }; + + std::vector input({ 1, 1, 2, 2, 3, 3 }); + std::vector output({ 2 }); + + return MeanTestHelper(workloadFactory, inputShape, input, {}, false, outputShape, output); +} + +LayerTestResult MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 1, 1, 3, 2 }; + const unsigned int outputShape[] = { 1, 1, 2 }; + + std::vector input({ 1, 1, 2, 2, 3, 3 }); + std::vector output({ 2, 2 }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 2 }, false, outputShape, output); +} + +LayerTestResult MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 1, 1, 3, 2 }; + const unsigned int outputShape[] = { 1, 1, 1, 2 }; + + std::vector input({ 1, 1, 2, 2, 3, 3 }); + std::vector output({ 2, 2 }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 2 }, true, outputShape, output); +} + +LayerTestResult MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 2, 3, 1, 2 }; + const unsigned int outputShape[] = { 1, 3, 1, 1 }; + + std::vector input({ 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6 }); + std::vector output({ 1, 3, 5 }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output); +} + +LayerTestResult MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 4, 3, 2 }; + const unsigned int outputShape[] = { 2 }; + + std::vector input({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, + 24 }); + std::vector output({ 12, 13 }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape, + output, 0.8f, 5); +} + +LayerTestResult MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 3, 2 }; + const unsigned int outputShape[] = { 1 }; + + std::vector input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f }); + std::vector output({ 2.0f }); + + return MeanTestHelper(workloadFactory, inputShape, input, {}, false, outputShape, output); +} + +LayerTestResult MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 2, 3, 1, 2 }; + const unsigned int outputShape[] = { 3, 1, 2 }; + + std::vector input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); + std::vector output({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 0 }, false, outputShape, output); +} + +LayerTestResult MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 1, 1, 3, 2 }; + const unsigned int outputShape[] = { 1, 1, 1, 2 }; + + std::vector input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f }); + std::vector output({ 2.0f, 2.0f }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 2 }, true, outputShape, output); +} + +LayerTestResult MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 2, 3, 1, 2 }; + const unsigned int outputShape[] = { 1, 3, 1, 1 }; + + std::vector input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); + std::vector output({ 1.5f, 3.5f, 5.5f }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output); +} + +LayerTestResult MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 4, 3, 2 }; + const unsigned int outputShape[] = { 2 }; + + std::vector input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, + 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f }); + std::vector output({ 12.0f, 13.0f }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape, output); +} + +LayerTestResult MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 4, 3, 2 }; + const unsigned int outputShape[] = { 1, 3, 1 }; + + std::vector input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, + 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f }); + std::vector output({ 10.5f, 12.5f, 14.5f }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 2 }, true, outputShape, output); +} + +LayerTestResult MeanVtsFloat3Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 1, 2, 2, 1 }; + const unsigned int outputShape[] = { 1, 2, 1 }; + + std::vector input({ 1.0f, 2.0f, 3.0f, 4.0f }); + std::vector output({ 1.5f, 3.5f }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 2 }, false, outputShape, output); +} + +LayerTestResult AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory) +{ + // Create Initial Tensor + // 1, 2, 3 + // 4, 5, 6 + // 7, 8, 9 + + armnn::TensorInfo poolingInputTensorInfo({ 1, 1, 3, 3}, armnn::GetDataType()); + armnn::TensorInfo poolingOutputTensorInfo({ 1, 1, 2, 2}, armnn::GetDataType()); + + boost::multi_array poolingInput = MakeTensor(poolingInputTensorInfo, + {1, 2, 3, + 4, 5, 6, + 7, 8, 9 + }); + + std::unique_ptr poolingInputHandle = + workloadFactory.CreateTensorHandle(poolingInputTensorInfo); + std::unique_ptr poolingOutputHandle = + workloadFactory.CreateTensorHandle(poolingOutputTensorInfo); + + // Apply MaxPool poolSize = 1x1, stride=2x2 + // Result = + // 1, 3 + // 7, 9 + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolHeight = 1; + descriptor.m_PoolWidth = 1; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + + armnn::Pooling2dQueueDescriptor queueDescriptor; + queueDescriptor.m_Parameters = descriptor; + armnn::WorkloadInfo workloadInfo; + AddInputToWorkload(queueDescriptor, workloadInfo, poolingInputTensorInfo, poolingInputHandle.get()); + AddOutputToWorkload(queueDescriptor, workloadInfo, poolingOutputTensorInfo, poolingOutputHandle.get()); + + // Create the MaxPool + std::unique_ptr workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); + + //LayerTestResult result(poolingOutputTensorInfo); + auto shape( GetTensorShapeAsArray<4>(poolingOutputTensorInfo)); + boost::multi_array resultMaxPool; + resultMaxPool.resize(shape); + + + // Create addition with another tensor the same size + // This would be the result to apply a Conv2d with kernel ones(2) and stride 1x1 + // with the initial tensor. + // 12, 16 + // 24, 28 + + armnn::TensorInfo addInputTensorInfo({ 1,1,2,2}, armnn::GetDataType()); + armnn::TensorInfo addOutputTensorInfo({ 1,1,2,2}, armnn::GetDataType()); + + boost::multi_array addInput = MakeTensor(addInputTensorInfo, + {12, 16, + 24, 28, + }); + + // Expected output tensor after MaxPool and Addition. + LayerTestResult addRet(addOutputTensorInfo); + addRet.outputExpected = MakeTensor(addOutputTensorInfo, std::vector( + { + 13, 19, + 31, 37 + })); + + std::unique_ptr addInputHandle = workloadFactory.CreateTensorHandle(addInputTensorInfo); + std::unique_ptr addOutputHandle = workloadFactory.CreateTensorHandle(addOutputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + + // Add the output of the MaxPool and the new tensor + AddInputToWorkload(data, info, poolingOutputTensorInfo, poolingOutputHandle.get()); + AddInputToWorkload(data, info, addInputTensorInfo, addInputHandle.get()); + AddOutputToWorkload(data, info, addOutputTensorInfo, addOutputHandle.get()); + + std::unique_ptr addWorkload = workloadFactory.CreateAddition(data, info); + + poolingInputHandle->Allocate(); + poolingOutputHandle->Allocate(); + addInputHandle->Allocate(); + addOutputHandle->Allocate(); + + CopyDataToITensorHandle(poolingInputHandle.get(), &poolingInput[0][0][0][0]); + CopyDataFromITensorHandle(&resultMaxPool[0][0][0][0], poolingOutputHandle.get()); + + CopyDataToITensorHandle(poolingOutputHandle.get(), &resultMaxPool[0][0][0][0]); + CopyDataToITensorHandle(addInputHandle.get(), &addInput[0][0][0][0]); + + workload->Execute(); + addWorkload->Execute(); + + CopyDataFromITensorHandle(&addRet.output[0][0][0][0], addOutputHandle.get()); + + workloadFactory.Finalize(); + + return addRet; +} diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp new file mode 100644 index 0000000000..57383d3276 --- /dev/null +++ b/src/backends/backendsCommon/test/LayerTests.hpp @@ -0,0 +1,416 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include +#include + +#include + +#include +#include + +#include + +// Layer callables. + +namespace armnn +{ +class IWorkloadFactory; +} + +template +boost::array GetTensorShapeAsArray(const armnn::TensorInfo& tensorInfo) +{ + BOOST_ASSERT_MSG(n == tensorInfo.GetNumDimensions(), + "Attempting to construct a shape array of mismatching size"); + + boost::array shape; + for (unsigned int i = 0; i < n; i++) + { + shape[i] = tensorInfo.GetShape()[i]; + } + return shape; +} + +template +struct LayerTestResult +{ + LayerTestResult(const armnn::TensorInfo& outputInfo) + { + auto shape( GetTensorShapeAsArray(outputInfo) ); + output.resize(shape); + outputExpected.resize(shape); + supported = true; + } + + boost::multi_array output; + boost::multi_array outputExpected; + bool supported; +}; + +LayerTestResult SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult SimpleConvolution2d3x3NhwcTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled); + +LayerTestResult +Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& layout); +LayerTestResult Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& layout); + + +LayerTestResult Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled); +LayerTestResult Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled); + +LayerTestResult DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult DepthwiseConvolution2dDepthNhwcTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled); + +LayerTestResult DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding); +LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding); +LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding); +LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding ); +LayerTestResult IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); +LayerTestResult SimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); + +LayerTestResult SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); +LayerTestResult SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); + +LayerTestResult IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding); +LayerTestResult IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test( + armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); +LayerTestResult SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); + +LayerTestResult L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::PoolingAlgorithm poolingType); +LayerTestResult ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::PoolingAlgorithm poolingType); + +LayerTestResult ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SimpleNormalizationAcrossNhwcTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta); +LayerTestResult SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta); + +LayerTestResult SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleReshapeFloat32Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SimpleReshapeUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleFloorTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory); + +template +LayerTestResult CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::NormalizationAlgorithmChannel normChannel, + armnn::NormalizationAlgorithmMethod normMethod); + +LayerTestResult CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, float beta); + +LayerTestResult FullyConnectedFloat32Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + bool transposeWeights); + +std::vector> SplitterTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult MergerTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult AdditionTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory); + +LayerTestResult SubtractionTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult CompareActivationTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::ActivationFunction f, + unsigned int batchSize); + +LayerTestResult DivisionTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult MultiplicationTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory); + +LayerTestResult BatchNormTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory); + +LayerTestResult BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + float upperBound, + float lowerBound); + +// Tests that the output should be identical to the input when the output dimensions match the input ones. +LayerTestResult ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); + +// Tests the behaviour of the resize bilinear operation when rescaling a 2x2 image into a 1x1 image. +LayerTestResult SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); + +// Tests the resize bilinear for minification of a square input matrix (also: input dimensions are a +// multiple of output dimensions). +LayerTestResult ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); + +// Tests the resize bilinear for minification (output dimensions smaller than input dimensions). +LayerTestResult ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); + +// Tests the resize bilinear for magnification (output dimensions bigger than input dimensions). +LayerTestResult ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout); + +LayerTestResult BatchNormTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult L2Normalization1dNhwcTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Normalization2dNhwcTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Normalization3dNhwcTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult L2Normalization4dNhwcTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult ConstantTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult BoundedReLuUint8Test(armnn::IWorkloadFactory& workloadFactory, float upperBound); +LayerTestResult BoundedReLuUint8Test(armnn::IWorkloadFactory& workloadFactory, + float upperBound, + float lowerBound); + +LayerTestResult FullyConnectedUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled); + +std::vector> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult MergerUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::ActivationFunction f); + +LayerTestResult CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + float beta); + +LayerTestResult MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory, + bool biasEnabled, + const armnn::DataLayoutIndexed& layout); + +LayerTestResult ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult BatchNormUint8NhwcTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory); + + +LayerTestResult FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory, + bool transposeWeights); +LayerTestResult SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult PadUint82dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult PadUint83dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult PadUint84dTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult PadFloat322dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult PadFloat323dTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult PadFloat324dTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest + (armnn::IWorkloadFactory& workloadFactory); +LayerTestResult + LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult +LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult SimpleConvertFp16ToFp32Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult SimpleConvertFp32ToFp16Test(armnn::IWorkloadFactory& workloadFactory); + + +LayerTestResult MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanVtsFloat3Test(armnn::IWorkloadFactory& workloadFactory); + +LayerTestResult AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory); diff --git a/src/backends/backendsCommon/test/LstmTestImpl.hpp b/src/backends/backendsCommon/test/LstmTestImpl.hpp new file mode 100644 index 0000000000..758f294d48 --- /dev/null +++ b/src/backends/backendsCommon/test/LstmTestImpl.hpp @@ -0,0 +1,1150 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "QuantizeHelper.hpp" + +#include +#include +#include + +#include + +#include +#include + +LayerTestResult LstmNoCifgNoPeepholeNoProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory, + const boost::multi_array& input, + const boost::multi_array& outputExpected) +{ + unsigned int batchSize = boost::numeric_cast(input.shape()[0]); + unsigned int inputSize = boost::numeric_cast(input.shape()[1]); + unsigned int outputSize = boost::numeric_cast(outputExpected.shape()[1]); + // cellSize and outputSize have the same size when there is no projection. + unsigned numUnits = outputSize; + + + armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType()); + armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::GetDataType()); + armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::GetDataType()); + + + armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, armnn::GetDataType()); + armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, armnn::GetDataType()); + armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType()); + + + LayerTestResult ret(outputTensorInfo); + + std::vector inputVector; + inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); + auto inputTensor = MakeTensor(inputTensorInfo, inputVector); + + std::vector cellStateInVector(batchSize * numUnits, 0.f); + auto cellStateInTensor = MakeTensor(cellStateInTensorInfo, cellStateInVector); + + std::vector outputStateInVector(batchSize * outputSize, 0.f); + auto outputStateInTensor = MakeTensor(outputStateInTensorInfo, outputStateInVector); + + std::vector scratchBufferVector(batchSize * numUnits * 3, 0.f); + auto scratchBufferTensor = MakeTensor(scratchBufferTensorInfo, scratchBufferVector); + + std::vector outputStateOutVector(batchSize * outputSize, 0.f); + auto outputStateOutTensor = MakeTensor(outputStateOutTensorInfo, outputStateOutVector); + + std::vector cellStateOutVector(batchSize * numUnits, 0.f); + auto cellStateOutTensor = MakeTensor(cellStateOutTensorInfo, cellStateOutVector); + + std::vector outputVector; + outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); + ret.outputExpected = MakeTensor(outputTensorInfo, outputVector); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr cellStateInHandle = + workloadFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr outputStateInHandle = + workloadFactory.CreateTensorHandle(outputStateInTensorInfo); + + std::unique_ptr scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo); + std::unique_ptr outputStateOutHandle = + workloadFactory.CreateTensorHandle(outputStateOutTensorInfo); + std::unique_ptr cellStateOutHandle = + workloadFactory.CreateTensorHandle(cellStateOutTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + + armnn::LstmQueueDescriptor data; + armnn::WorkloadInfo info; + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); + AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); + + AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get()); + AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); + AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo4({numUnits}, armnn::GetDataType()); + armnn::TensorInfo tensorInfo8({numUnits, 2}, armnn::GetDataType()); + armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::GetDataType()); + + auto inputToInputWeights = MakeTensor(tensorInfo8, {-0.45018822f, -0.02338299f, -0.0870589f, + -0.34550029f, 0.04266912f, -0.15680569f, + -0.34856534f, 0.43890524f}); + + auto inputToForgetWeights = MakeTensor(tensorInfo8, {0.09701663f, 0.20334584f, -0.50592935f, + -0.31343272f, -0.40032279f, 0.44781327f, + 0.01387155f, -0.35593212f}); + + auto inputToCellWeights = MakeTensor(tensorInfo8, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, + -0.20583314f, 0.44344562f, 0.22077113f, + -0.29909778f}); + + auto inputToOutputWeights = MakeTensor(tensorInfo8, {-0.25065863f, -0.28290087f, 0.04613829f, + 0.40525138f, 0.44272184f, 0.03897077f, + -0.1556896f, 0.19487578f}); + + auto recurrentToInputWeights = MakeTensor(tensorInfo16, {-0.0063535f, -0.2042388f, 0.31454784f, + -0.35746509f, 0.28902304f, 0.08183324f, + -0.16555229f, 0.02286911f, -0.13566875f, + 0.03034258f, 0.48091322f, -0.12528998f, + 0.24077177f, -0.51332325f, -0.33502164f, + 0.10629296f}); + + auto recurrentToForgetWeights = MakeTensor(tensorInfo16, {-0.48684245f, -0.06655136f, 0.42224967f, + 0.2112639f, 0.27654213f, 0.20864892f, + -0.07646349f, 0.45877004f, 0.00141793f, + -0.14609534f, 0.36447752f, 0.09196436f, + 0.28053468f, 0.01560611f, -0.20127171f, + -0.01140004f}); + + auto recurrentToCellWeights = MakeTensor(tensorInfo16, {-0.3407414f, 0.24443203f, -0.2078532f, + 0.26320225f, 0.05695659f, -0.00123841f, + -0.4744786f, -0.35869038f, -0.06418842f, + -0.13502428f, -0.501764f, 0.22830659f, + -0.46367589f, 0.26016325f, -0.03894562f, + -0.16368064f}); + + auto recurrentToOutputWeights = MakeTensor(tensorInfo16, {0.43385774f, -0.17194885f, 0.2718237f, + 0.09215671f, 0.24107647f, -0.39835793f, + 0.18212086f, 0.01301402f, 0.48572797f, + -0.50656658f, 0.20047462f, -0.20607421f, + -0.51818722f, -0.15390486f, 0.0468148f, + 0.39922136f}); + + auto cellToInputWeights = MakeTensor(tensorInfo4, {0., 0., 0., 0.}); + + auto inputGateBias = MakeTensor(tensorInfo4, {0., 0., 0., 0.}); + + auto forgetGateBias = MakeTensor(tensorInfo4, {1., 1., 1., 1.}); + + auto cellBias = MakeTensor(tensorInfo4, {0., 0., 0., 0.}); + + auto outputGateBias = MakeTensor(tensorInfo4, {0., 0., 0., 0.}); + + armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo8); + armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo8); + armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo8); + armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo8); + armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); + armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo16); + armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo16); + armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); + armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo4); + + AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]); + AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]); + AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]); + AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]); + AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]); + + data.m_InputToInputWeights = &inputToInputWeightsTensor; + data.m_InputToForgetWeights = &inputToForgetWeightsTensor; + data.m_InputToCellWeights = &inputToCellWeightsTensor; + data.m_InputToOutputWeights = &inputToOutputWeightsTensor; + data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; + data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; + data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; + data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; + data.m_CellToInputWeights = &cellToInputWeightsTensor; + data.m_InputGateBias = &inputGateBiasTensor; + data.m_ForgetGateBias = &forgetGateBiasTensor; + data.m_CellBias = &cellBiasTensor; + data.m_OutputGateBias = &outputGateBiasTensor; + + + // Flags to set test configuration + data.m_Parameters.m_ActivationFunc = 4; + data.m_Parameters.m_CifgEnabled = false; + data.m_Parameters.m_PeepholeEnabled = false; + data.m_Parameters.m_ProjectionEnabled = false; + + + std::unique_ptr workload = workloadFactory.CreateLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->Allocate(); + + scratchHandle->Allocate(); + outputStateOutHandle->Allocate(); + cellStateOutHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); + CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]); + CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); + + return ret; +} + + +LayerTestResult +LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory, + const boost::multi_array& input, + const boost::multi_array& outputExpected) { + + unsigned int batchSize = 2; + unsigned int outputSize = 16; + unsigned int inputSize = 5; + unsigned numUnits = 20; + + armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType()); + armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::GetDataType()); + armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::GetDataType()); + + // Scratch buffer size without CIFG [batchSize, numUnits * 3] + armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, armnn::GetDataType()); + armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, armnn::GetDataType()); + armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType()); + + LayerTestResult ret(outputTensorInfo); + + std::vector inputVector; + inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); + auto inputTensor = MakeTensor(inputTensorInfo, inputVector); + + std::vector cellStateInVector(batchSize * numUnits, 0.f); + auto cellStateInTensor = MakeTensor(cellStateInTensorInfo, cellStateInVector); + + std::vector outputStateInVector(batchSize * outputSize, 0.f); + auto outputStateInTensor = MakeTensor(outputStateInTensorInfo, outputStateInVector); + + std::vector scratchBufferVector(batchSize * numUnits * 3, 0.f); + auto scratchBufferTensor = MakeTensor(scratchBufferTensorInfo, scratchBufferVector); + + std::vector outputStateOutVector(batchSize * outputSize, 0.f); + auto outputStateOutTensor = MakeTensor(outputStateOutTensorInfo, outputStateOutVector); + + std::vector cellStateOutVector(batchSize * numUnits, 0.f); + auto cellStateOutTensor = MakeTensor(cellStateOutTensorInfo, cellStateOutVector); + + std::vector outputVector; + outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); + ret.outputExpected = MakeTensor(outputTensorInfo, outputVector); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr cellStateInHandle = + workloadFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr outputStateInHandle = + workloadFactory.CreateTensorHandle(outputStateInTensorInfo); + + std::unique_ptr scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo); + std::unique_ptr outputStateOutHandle = + workloadFactory.CreateTensorHandle(outputStateOutTensorInfo); + std::unique_ptr cellStateOutHandle = + workloadFactory.CreateTensorHandle(cellStateOutTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::LstmQueueDescriptor data; + armnn::WorkloadInfo info; + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); + AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); + + AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get()); + AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); + AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo16({outputSize}, armnn::GetDataType()); + armnn::TensorInfo tensorInfo20({numUnits}, armnn::GetDataType()); + armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::GetDataType()); + armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::GetDataType()); + armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::GetDataType()); + + auto inputToInputWeights = + MakeTensor(tensorInfo20x5, {0.021393683f,0.06124551f, 0.046905167f,-0.014657677f,-0.03149463f, + 0.09171803f, 0.14647801f,0.10797193f, -0.0057968358f,0.0019193048f, + -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, + -0.022599787f,-0.09121155f, -0.008675967f, -0.045206103f,-0.0821282f, + -0.008045952f,0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, + -0.06453243f,0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, + -0.1671009f, -0.15519552f, -0.16819797f,-0.13971269f,-0.11953059f, + 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, + 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, + 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, + -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, + 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, + -0.028545704f,0.024939531f,0.050929718f,0.0076203286f,-0.0029723682f, + -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f,-0.16327988f, + -0.2273378f, -0.0993647f, -0.017155107f,0.0023917493f,0.049272764f, + 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, + -0.04544234f, -0.0497073f,-0.07135631f, -0.048929106f,-0.004042012f, + -0.009284026f, 0.018042054f, 0.0036860977f,-0.07427302f, -0.11434604f, + -0.018995456f, 0.031487543f, 0.012834908f,0.019977754f,0.044256654f, + -0.39292613f, -0.18519334f, -0.11651281f,-0.06809892f, 0.011373677f + }); + + auto inputToForgetWeights = + MakeTensor(tensorInfo20x5, {-0.0018401089f, -0.004852237f,0.03698424f, 0.014181704f,0.028273236f, + -0.016726194f, -0.05249759f,-0.10204261f, 0.00861066f,-0.040979505f, + -0.009899187f,0.01923892f,-0.028177269f, -0.08535103f,-0.14585495f, + 0.10662567f,-0.01909731f,-0.017883534f,-0.0047269356f,-0.045103323f, + 0.0030784295f,0.076784775f,0.07463696f, 0.094531395f,0.0814421f, + -0.12257899f, -0.033945758f,-0.031303465f, 0.045630626f,0.06843887f, + -0.13492945f, -0.012480007f,-0.0811829f, -0.07224499f,-0.09628791f, + 0.045100946f,0.0012300825f, 0.013964662f, 0.099372394f,0.02543059f, + 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f,0.052625068f, + 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f,0.07241905f, + 0.018668644f, -0.037377294f,-0.06277783f,-0.08833636f,-0.040120605f, + -0.011405586f,-0.007808335f,-0.010301386f,-0.005102167f,0.027717464f, + 0.05483423f, 0.11449111f, 0.11289652f,0.10939839f, 0.13396506f, + -0.08402166f,-0.01901462f, -0.044678304f,-0.07720565f,0.014350063f, + -0.11757958f, -0.0652038f, -0.08185733f,-0.076754324f,-0.092614375f, + 0.10405491f, 0.052960336f, 0.035755895f,0.035839386f,-0.012540553f, + 0.036881298f, 0.02913376f, 0.03420159f,0.05448447f,-0.054523353f, + 0.02582715f, 0.02327355f, -0.011857179f,-0.0011980024f,-0.034641717f, + -0.026125094f,-0.17582615f,-0.15923657f,-0.27486774f,-0.0006143371f, + 0.0001771948f, -8.470171e-05f, 0.02651807f,0.045790765f,0.06956496f + }); + + auto inputToCellWeights = + MakeTensor(tensorInfo20x5, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, + -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, + -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, + -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, + -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, + 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f, + -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f, + 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f, + -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f, + -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f, + -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f, + 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f, + 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f, + 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f, + -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f, + -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f, + -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f, + -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f, + -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f, + -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f, + 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f, + 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f, + 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f, + 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f, + 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f + }); + + auto inputToOutputWeights = + MakeTensor(tensorInfo20x5, {-0.0998932f, -0.07201956f, -0.052803773f,-0.15629593f,-0.15001918f, + -0.07650751f,0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f, + 0.029517552f, -0.04751393f, 0.010350531f,-0.02664851f, -0.016839722f, + -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f,-0.0129761f, + -0.021737747f,-0.038305793f,-0.06870586f, -0.01481247f,-0.001285394f, + 0.10124236f, 0.083122835f, 0.053313006f,-0.062235646f,-0.075637154f, + -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f, + -0.086665764f,-0.037162706f,-0.038880914f,-0.035832845f,-0.014481564f, + -0.09825003f,-0.12048569f,-0.097665586f,-0.05287633f, -0.0964047f, + -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f,0.050649304f, + 0.05798951f, -0.021852335f,-0.099848844f,0.014740475f,-0.078897946f, + 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f, + 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f,0.06945813f, + -0.078907564f,-0.06707616f, -0.11844508f, -0.09986688f,-0.07509403f, + 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f,0.14639415f, + 0.0015017595f, -0.014267382f, -0.03417257f,0.012711468f,0.0028300495f, + -0.024758482f, -0.05098548f,-0.0821182f, 0.014225672f, 0.021544158f, + 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f,0.06476295f, + -0.022907063f,0.027562456f,0.040185735f, 0.019567577f,-0.015598739f, + -0.049097303f, -0.017121866f, -0.083368234f,-0.02332002f,-0.0840956f + }); + + auto inputGateBias = + MakeTensor(tensorInfo20, {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, + -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, + -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, + 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f + }); + + auto forgetGateBias = + MakeTensor(tensorInfo20, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, + 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f, + 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f, + -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f, + 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f + }); + + auto cellBias = + MakeTensor(tensorInfo20, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, + -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f, + -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f, + -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f, + 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f + }); + + auto outputGateBias = + MakeTensor(tensorInfo20, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, + 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, + 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, + -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f + }); + + auto recurrentToInputWeights = + MakeTensor(tensorInfo20x16, {-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, + -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, + -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, + -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, + 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, + 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, + -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, + 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f, + -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f, + 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f, + -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f, + 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f, + -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f, + 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f, + -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f, + -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f, + -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f, + -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f, + -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f, + 0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f, + 0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f, + 0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f, + 0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f, + 0.012092817f, -0.04989123f, -0.086576f, -0.003399834f, + -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f, + -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f, + 0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f, + -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f, + -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f, + -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f, + -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f, + -0.011337001f, 0.035530265f, -0.010912711f, 0.0706555f, + -0.005894094f, 0.051841937f, -0.1401738f, -0.02351249f, + 0.0365468f, 0.07590991f, 0.08838724f, 0.021681072f, + -0.10086113f, 0.019608743f, -0.06195883f, 0.077335775f, + 0.023646897f, -0.095322326f, 0.02233014f, 0.09756986f, + -0.048691444f, -0.009579111f, 0.07595467f, 0.11480546f, + -0.09801813f, 0.019894179f, 0.08502348f, 0.004032281f, + 0.037211012f, 0.068537936f, -0.048005626f, -0.091520436f, + -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f, + -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f, + 0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f, + -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f, + 0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f, + 0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f, + 0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f, + 0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f, + 0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f, + 0.05052217f, -0.058925f, -0.011402121f, -0.024987547f, + -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f, + -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f, + -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f, + -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f, + -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f, + 0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f, + 0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f, + -0.017081132f, 0.019358726f, 0.0027079724f, 0.004635139f, + 0.062634714f, -0.02338735f, -0.039547626f, -0.02050681f, + 0.03385117f, -0.083611414f, 0.002862572f, -0.09421313f, + 0.058618143f, -0.08598433f, 0.00972939f, 0.023867095f, + -0.053934585f, -0.023203006f, 0.07452513f, -0.048767887f, + -0.07314807f, -0.056307215f, -0.10433547f, -0.06440842f, + 0.04328182f, 0.04389765f, -0.020006588f, -0.09076438f, + -0.11652589f, -0.021705797f, 0.03345259f, -0.010329105f, + -0.025767034f, 0.013057034f, -0.07316461f, -0.10145612f, + 0.06358255f, 0.18531723f, 0.07759293f, 0.12006465f, + 0.1305557f, 0.058638252f, -0.03393652f, 0.09622831f, + -0.16253184f, -2.4580743e-06f, 0.079869635f, -0.070196845f, + -0.005644518f, 0.06857898f, -0.12598175f, -0.035084512f, + 0.03156317f, -0.12794146f, -0.031963028f, 0.04692781f, + 0.030070418f, 0.0071660685f, -0.095516115f, -0.004643372f, + 0.040170413f, -0.062104587f, -0.0037324072f, 0.0554317f, + 0.08184801f, -0.019164372f, 0.06791302f, 0.034257166f, + -0.10307039f, 0.021943003f, 0.046745934f, 0.0790918f, + -0.0265588f, -0.007824208f, 0.042546265f, -0.00977924f, + -0.0002440307f, -0.017384544f, -0.017990116f, 0.12252321f, + -0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f, + 0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f, + -0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f, + -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f + }); + + auto recurrentToForgetWeights = + MakeTensor(tensorInfo20x16, {-0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, + 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, + -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, + 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, + 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, + -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, + -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, + 0.061878487f, -0.04729229f, 0.034919553f, -0.07585433f, + -0.04421272f, -0.044019096f, 0.085488975f, 0.04058006f, + -0.06890133f, -0.030951202f, -0.024628663f, -0.07672815f, + 0.034293607f, 0.08556707f, -0.05293577f, -0.033561368f, + -0.04899627f, 0.0241671f, 0.015736353f, -0.095442444f, + -0.029564252f, 0.016493602f, -0.035026584f, 0.022337519f, + -0.026871363f, 0.004780428f, 0.0077918363f, 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0.10053256f, -0.026142767f, -0.08271222f, -0.0030240538f, + -0.016368777f, 0.1070414f, 0.042672627f, 0.013456989f, + -0.0437609f, -0.022309763f, 0.11576483f, 0.04108048f, + 0.061026827f, -0.0190714f, -0.0869359f, 0.037901703f, 0.0610107f, + 0.07202949f, 0.01675338f, 0.086139716f, -0.08795751f, + -0.014898893f, -0.023771819f, -0.01965048f, 0.007955471f, + -0.043740474f, 0.03346837f, -0.10549954f, 0.090567775f, + 0.042013682f, -0.03176985f, 0.12569028f, -0.02421228f, + -0.029526481f, 0.023851605f, 0.031539805f, 0.05292009f, + -0.02344001f, -0.07811758f, -0.08834428f, 0.10094801f, + 0.16594367f, -0.06861939f, -0.021256343f, -0.041093912f, + -0.06669611f, 0.035498552f, 0.021757556f, -0.09302526f, + -0.015403468f, -0.06614931f, -0.051798206f, -0.013874718f, + 0.03630673f, 0.010412845f, -0.08077351f, 0.046185967f, + 0.0035662893f, 0.03541868f, -0.094149634f, -0.034814864f, + 0.003128424f, -0.020674974f, -0.03944324f, -0.008110165f, + -0.11113267f, 0.08484226f, 0.043586485f, 0.040582247f, + 0.0968012f, -0.065249965f, -0.028036479f, 0.0050708856f, + 0.0017462453f, 0.0326779f, 0.041296225f, 0.09164146f, + -0.047743853f, -0.015952192f, -0.034451712f, 0.084197424f, + -0.05347844f, -0.11768019f, 0.085926116f, -0.08251791f, + -0.045081906f, 0.0948852f, 0.068401024f, 0.024856757f, + 0.06978981f, -0.057309967f, -0.012775832f, -0.0032452994f, + 0.01977615f, -0.041040014f, -0.024264973f,0.063464895f, 0.05431621f + }); + + auto cellToInputWeights = + MakeTensor(tensorInfo20, {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, + -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, + -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f,-0.052169047f, + 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f + }); + + + auto cellToForgetWeights = + MakeTensor(tensorInfo20, {-0.01998659f,-0.15568835f,-0.24248174f, -0.012770197f, 0.041331276f, + -0.072311886f, -0.052123554f,-0.0066330447f,-0.043891653f,0.036225766f, + -0.047248036f, 0.021479502f,0.033189066f, 0.11952997f, -0.020432774f, + 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f + }); + + auto cellToOutputWeights = + MakeTensor(tensorInfo20, {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, + -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, + -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, + 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f + }); + + auto projectionWeights = + MakeTensor(tensorInfo16x20, + {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, + 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, + -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, + -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, + 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, + 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, + 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, + 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, + -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, + -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, + -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, + 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, + 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, + 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, + 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, + 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, + -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, + 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, + -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, + 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, + -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, + -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, + 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, + -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, + 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, + -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, + -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, + 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, + -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, + -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, + -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, + 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, + 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, + -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, + 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, + 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, + 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, + 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, + 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, + -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, + -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, + 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, + -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, + -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, + 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, + 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, + 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, + -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, + -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, + -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, + 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, + -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, + 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, + 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, + -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, + -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, + -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, + 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, + -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, + -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, + -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, + 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, + 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, + 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f + }); + + std::vector projectionBiasVector(outputSize, 0.f); + auto projectionBias = MakeTensor(tensorInfo16, projectionBiasVector); + + armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo20x5); + armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo20x5); + armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo20x5); + armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo20x5); + armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo20x16); + armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo20x16); + armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo20x16); + armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo20x16); + armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo20); + armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo20); + armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo20); + armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo20); + armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo20); + armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfo20); + armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfo20); + armnn::ScopedCpuTensorHandle projectionWeightsTensor(tensorInfo16x20); + armnn::ScopedCpuTensorHandle projectionBiasTensor(tensorInfo16); + + AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]); + AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]); + AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]); + AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]); + AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]); + AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]); + AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]); + AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, &projectionBias[0]); + + data.m_InputToInputWeights = &inputToInputWeightsTensor; + data.m_InputToForgetWeights = &inputToForgetWeightsTensor; + data.m_InputToCellWeights = &inputToCellWeightsTensor; + data.m_InputToOutputWeights = &inputToOutputWeightsTensor; + data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; + data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; + data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; + data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; + data.m_CellToInputWeights = &cellToInputWeightsTensor; + data.m_InputGateBias = &inputGateBiasTensor; + data.m_ForgetGateBias = &forgetGateBiasTensor; + data.m_CellBias = &cellBiasTensor; + data.m_OutputGateBias = &outputGateBiasTensor; + data.m_CellToForgetWeights = &cellToForgetWeightsTensor; + data.m_CellToOutputWeights = &cellToOutputWeightsTensor; + data.m_ProjectionWeights = &projectionWeightsTensor; + data.m_ProjectionBias = &projectionBiasTensor; + + // Flags to set test configuration + data.m_Parameters.m_ActivationFunc = 4; + data.m_Parameters.m_CifgEnabled = false; + data.m_Parameters.m_PeepholeEnabled = true; + data.m_Parameters.m_ProjectionEnabled = true; + + + std::unique_ptr workload = workloadFactory.CreateLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->Allocate(); + + scratchHandle->Allocate(); + outputStateOutHandle->Allocate(); + cellStateOutHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); + CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]); + CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); + + return ret; + +} + + +LayerTestResult LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory, + const boost::multi_array& input, + const boost::multi_array& outputExpected) +{ + bool cifgEnabled = true; + bool peepholeEnabled = true; + bool projectionEnabled = false; + // These are not the input and the output of Lstm yet + unsigned int batchSize = boost::numeric_cast(input.shape()[0]); + unsigned int inputSize = boost::numeric_cast(input.shape()[1]); + + unsigned int outputSize = boost::numeric_cast(outputExpected.shape()[1]); + + const unsigned int cellSize = outputSize; + + // Decide the shape of all input tensors + armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType()); + armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::GetDataType()); + armnn::TensorInfo cellStateInTensorInfo({batchSize, cellSize}, armnn::GetDataType()); + + unsigned int scratchBufferSize = cifgEnabled ? cellSize * 4 : cellSize * 3; + armnn::TensorInfo scratchBufferTensorInfo({batchSize, scratchBufferSize}, armnn::GetDataType()); + armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType()); + armnn::TensorInfo cellStateOutTensorInfo({batchSize, cellSize}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType()); + + // List of inputs + std::vector inputData; + inputData.assign(input.data(), input.data() + batchSize*inputSize); + auto inputTensor = MakeTensor(inputTensorInfo, inputData); + + std::vector outputStateInVector(batchSize * outputSize, 0.f); + auto outputStateInTensor = MakeTensor(outputStateInTensorInfo, outputStateInVector); + + std::vector cellStateInVector(batchSize * cellSize, 0.f); + auto cellStateInTensor = MakeTensor(cellStateInTensorInfo, cellStateInVector); + + + // Prepare all the weights in the descriptor for LSTM + armnn::LstmQueueDescriptor data; + armnn::TensorInfo tensorInfoInput({cellSize, inputSize}, armnn::GetDataType()); + armnn::TensorInfo tensorInfoOutput({cellSize, outputSize}, armnn::GetDataType()); + armnn::TensorInfo tensorInfoNumUnits({cellSize}, armnn::GetDataType()); + + auto inputToCellWeights = MakeTensor(tensorInfoInput, + {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, + 0.04717243f, 0.48944736f, -0.38535351f, + -0.17212132f}); + auto inputToForgetWeights = MakeTensor(tensorInfoInput, + {-0.55291498f, -0.42866567f, 0.13056988f, + -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, + 0.33826375f}); + auto inputToOutputWeights = MakeTensor(tensorInfoInput, + {0.10725588f, -0.02335852f, -0.55932593f, + -0.09426838f, -0.44257352f, 0.54939759f, + 0.01533556f, 0.42751634f}); + auto cellBias = MakeTensor(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f}); + auto forgetGateBias = MakeTensor(tensorInfoNumUnits, {1.f, 1.f, 1.f, 1.f}); + auto outputGateBias = MakeTensor(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f}); + + auto recurrentToCellWeights = MakeTensor(tensorInfoOutput, + {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, + 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, + 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, + 0.21193194f}); + auto recurrentToForgetWeights = MakeTensor(tensorInfoOutput, + {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, + 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, + -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}); + + auto recurrentToOutputWeights = MakeTensor(tensorInfoOutput, + {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, + -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, + 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}); + + auto cellToForgetWeights = MakeTensor(tensorInfoNumUnits, + {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}); + auto cellToOutputWeights = MakeTensor(tensorInfoNumUnits, + {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}); + + armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfoInput); + armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfoInput); + armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfoInput); + + armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfoNumUnits); + armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfoNumUnits); + armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfoNumUnits); + + armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfoOutput); + armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfoOutput); + armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfoOutput); + + + armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfoNumUnits); + armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfoNumUnits); + + AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]); + + AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]); + AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]); + AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]); + + AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]); + + AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]); + AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]); + + + data.m_InputToCellWeights = &inputToCellWeightsTensor; + data.m_InputToForgetWeights = &inputToForgetWeightsTensor; + data.m_InputToOutputWeights = &inputToOutputWeightsTensor; + + data.m_CellBias = &cellBiasTensor; + data.m_ForgetGateBias = &forgetGateBiasTensor; + data.m_OutputGateBias = &outputGateBiasTensor; + + data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; + data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; + data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; + + data.m_CellToForgetWeights = &cellToForgetWeightsTensor; + data.m_CellToOutputWeights = &cellToOutputWeightsTensor; + + // other parameters for the descriptor + data.m_Parameters.m_CifgEnabled = cifgEnabled; + data.m_Parameters.m_ProjectionEnabled = projectionEnabled; + data.m_Parameters.m_PeepholeEnabled = peepholeEnabled; + + data.m_Parameters.m_ActivationFunc = 4; + data.m_Parameters.m_ClippingThresProj = 0.0; + data.m_Parameters.m_ClippingThresCell = 0.0; + + + // List of outputs + std::vector scratchBufferVector(batchSize * scratchBufferSize, 0.f); + auto scratchBufferTensor = MakeTensor(scratchBufferTensorInfo, scratchBufferVector); + LayerTestResult ret0(scratchBufferTensorInfo); + + // Output state for a certain time step + std::vector outputStateOutVector(batchSize * outputSize, 0.f); + auto outputStateOutTensor = MakeTensor(outputStateOutTensorInfo, outputStateOutVector); + LayerTestResult ret1(outputStateOutTensorInfo); + + // Cell state for a certain time step + std::vector cellStateOutVector(batchSize * cellSize, 0.f); + auto cellStateOutTensor = MakeTensor(cellStateOutTensorInfo, cellStateOutVector); + LayerTestResult ret2(cellStateOutTensorInfo); + + // Output for a certain time step + std::vector outputVector(batchSize * outputSize, 0.f); + auto outputTensor = MakeTensor(outputTensorInfo, outputVector); + std::vector outputData; + outputData.assign(outputExpected.data(), outputExpected.data() + batchSize*outputSize); + LayerTestResult ret3(outputTensorInfo); + ret3.outputExpected = MakeTensor(outputTensorInfo, outputData); + + // Prepare the inputs and outputs for the workload + std::unique_ptr inputHandle = + workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputStateInHandle = + workloadFactory.CreateTensorHandle(outputStateInTensorInfo); + std::unique_ptr cellStateInHandle = + workloadFactory.CreateTensorHandle(cellStateInTensorInfo); + + std::unique_ptr scratchBufferHandle = + workloadFactory.CreateTensorHandle(scratchBufferTensorInfo); + std::unique_ptr outputStateOutHandle = + workloadFactory.CreateTensorHandle(outputStateOutTensorInfo); + std::unique_ptr cellStateOutHandle = + workloadFactory.CreateTensorHandle(cellStateOutTensorInfo); + std::unique_ptr outputHandle = + workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); + AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); + + AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchBufferHandle.get()); + AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); + AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateLstm(data, info); + + + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->Allocate(); + + scratchBufferHandle->Allocate(); + outputStateOutHandle->Allocate(); + cellStateOutHandle->Allocate(); + outputHandle->Allocate(); + + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); + CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]); + CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]); + + CopyDataToITensorHandle(scratchBufferHandle.get(), &scratchBufferTensor[0][0]); + CopyDataToITensorHandle(outputStateOutHandle.get(), &outputStateOutTensor[0][0]); + CopyDataToITensorHandle(cellStateOutHandle.get(), &cellStateOutTensor[0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret0.output[0][0], scratchBufferHandle.get()); + CopyDataFromITensorHandle(&ret1.output[0][0], outputStateOutHandle.get()); + CopyDataFromITensorHandle(&ret2.output[0][0], cellStateOutHandle.get()); + CopyDataFromITensorHandle(&ret3.output[0][0], outputHandle.get()); + + return ret3; +} diff --git a/src/backends/backendsCommon/test/NormTestImpl.hpp b/src/backends/backendsCommon/test/NormTestImpl.hpp new file mode 100644 index 0000000000..0d8d4340a8 --- /dev/null +++ b/src/backends/backendsCommon/test/NormTestImpl.hpp @@ -0,0 +1,343 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include +#include +#include "armnn/Types.hpp" + +#include +#include + +LayerTestResult SimpleNormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, + armnn::NormalizationAlgorithmChannel normChannel, + armnn::NormalizationAlgorithmMethod normMethod) +{ + const unsigned int inputHeight = 2; + const unsigned int inputWidth = 2; + const unsigned int inputChannels = 1; + const unsigned int inputNum = 2; + + unsigned int outputHeight = inputHeight; + unsigned int outputWidth = inputWidth; + unsigned int outputChannels = inputChannels; + unsigned int outputNum = inputNum; + + unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; + unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; + + auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + LayerTestResult ret(outputTensorInfo); + + auto input = MakeTensor(inputTensorInfo, std::vector({ + // Batch #0 + 1.0f, 2.0f, + 3.0f, 4.0f, + // Batch #1 + 5.0f, 6.0f, + 7.0f, 8.0f + })); + + float alpha = 1.f; + float beta = 1.f; + float kappa = 1.f; + uint32_t normSize = 3; + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::NormalizationQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Parameters.m_NormChannelType = normChannel; + data.m_Parameters.m_NormMethodType = normMethod; + data.m_Parameters.m_NormSize = normSize; + data.m_Parameters.m_Alpha = alpha; + data.m_Parameters.m_Beta = beta; + data.m_Parameters.m_K = kappa; + data.m_Parameters.m_DataLayout = armnn::DataLayout::NCHW; + + armnn::PassthroughCpuTensorHandle refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]); + armnn::NormalizationQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); + + std::unique_ptr workload = workloadFactory.CreateNormalization(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + switch (normMethod) + { + case armnn::NormalizationAlgorithmMethod::LocalBrightness: + { + switch (normChannel) + { + case armnn::NormalizationAlgorithmChannel::Within: + { + // When normalising within channels, the 3x3 kernel covers the entire 2x2 input at every index. + // Therefore, all output values should equal the inputs, but divided by: + // pow((kappa + (accumulatedScale * alpha)), beta) + // ...where accumulatedScale is the sum of every element squared. + float divisor[inputNum]; + for(int i = 0; i < boost::numeric_cast(inputNum); i++) + { + float accumulatedScale = input[i][0][0][0]*input[i][0][0][0] + + input[i][0][0][1]*input[i][0][0][1] + + input[i][0][1][0]*input[i][0][1][0] + + input[i][0][1][1]*input[i][0][1][1]; + divisor[i] = powf((kappa + accumulatedScale * alpha), beta); + } + ret.outputExpected = MakeTensor(outputTensorInfo, + std::vector({input[0][0][0][0]/divisor[0], + input[0][0][0][1]/divisor[0], + input[0][0][1][0]/divisor[0], + input[0][0][1][1]/divisor[0], + input[1][0][0][0]/divisor[1], + input[1][0][0][1]/divisor[1], + input[1][0][1][0]/divisor[1], + input[1][0][1][1]/divisor[1]})); + break; + } + case armnn::NormalizationAlgorithmChannel::Across: + { + // When normalising across channels, all output values should equal the inputs, but multiplied by: + // pow((kappa + (accumulatedScale * alpha)), -beta) + // ...where accumulatedScale is the sum of the inputs for adjacent channels for this element squared + // ...where adjacent channels means within half the normSize for the channel + // The test data has only one channel, so this is simplified below. + std::vector outputVector; + for (int n = 0; n < boost::numeric_cast(inputNum); ++n) + { + for (int h = 0; h < boost::numeric_cast(inputHeight); ++h) + { + for (int w = 0; w < boost::numeric_cast(inputWidth); ++w) + { + float accumulatedScale = input[n][0][h][w]*input[n][0][h][w]; + float scale = powf((kappa + accumulatedScale * alpha), -beta); + outputVector.push_back(input[n][0][h][w] * scale); + } + } + } + ret.outputExpected = MakeTensor(outputTensorInfo, outputVector); + break; + } + default: + { + throw armnn::UnimplementedException("Unsupported normalisation channel type, " + "only Across and Within are supported"); + } + } + break; + } + case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. + default: + { + throw armnn::UnimplementedException("Unsupported normalisation method type, " + "only LocalBrightness is supported"); + } + } + + return ret; +} + +LayerTestResult SimpleNormalizationNhwcTestImpl(armnn::IWorkloadFactory& workloadFactory, + armnn::NormalizationAlgorithmChannel normChannel, + armnn::NormalizationAlgorithmMethod normMethod) +{ + const unsigned int inputHeight = 2; + const unsigned int inputWidth = 2; + const unsigned int inputChannels = 1; + const unsigned int inputNum = 2; + + unsigned int outputHeight = inputHeight; + unsigned int outputWidth = inputWidth; + unsigned int outputChannels = inputChannels; + unsigned int outputNum = inputNum; + + unsigned int inputShape[] = { inputNum, inputHeight, inputWidth, inputChannels }; + unsigned int outputShape[] = { outputNum, outputHeight, outputWidth, outputChannels }; + + auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + LayerTestResult ret(outputTensorInfo); + + auto input = MakeTensor(inputTensorInfo, std::vector({ + // Batch #0 + 1.0f, 2.0f, + 3.0f, 4.0f, + // Batch #1 + 5.0f, 6.0f, + 7.0f, 8.0f + })); + + float alpha = 1.f; + float beta = 1.f; + float kappa = 1.f; + uint32_t normSize = 3; + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::NormalizationQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Parameters.m_NormChannelType = normChannel; + data.m_Parameters.m_NormMethodType = normMethod; + data.m_Parameters.m_NormSize = normSize; + data.m_Parameters.m_Alpha = alpha; + data.m_Parameters.m_Beta = beta; + data.m_Parameters.m_K = kappa; + data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC; + + armnn::PassthroughCpuTensorHandle refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]); + armnn::NormalizationQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); + + std::unique_ptr workload = workloadFactory.CreateNormalization(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + switch (normMethod) + { + case armnn::NormalizationAlgorithmMethod::LocalBrightness: + { + switch (normChannel) + { + case armnn::NormalizationAlgorithmChannel::Across: + { + std::vector expectedOutput{ 0.5f, 0.400000006f, 0.300000012f, 0.235294119f, + 0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f }; + ret.outputExpected = MakeTensor(outputTensorInfo, expectedOutput); + break; + } + default: + { + throw armnn::UnimplementedException("Unsupported normalisation channel type, " + "Only Cross-map is supported for NHWC layout"); + } + } + break; + } + case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. + default: + { + throw armnn::UnimplementedException("Unsupported normalisation method type, " + "only LocalBrightness is supported"); + } + } + + return ret; +} + +LayerTestResult CompareNormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::NormalizationAlgorithmChannel normChannel, + armnn::NormalizationAlgorithmMethod normMethod) +{ + constexpr unsigned int inputNum = 5; + constexpr unsigned int inputChannels = 3; + constexpr unsigned int inputHeight = 32; + constexpr unsigned int inputWidth = 24; + + constexpr unsigned int outputNum = inputNum; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputHeight = inputHeight; + constexpr unsigned int outputWidth = inputWidth; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; + unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + LayerTestResult ret(outputTensorInfo); + + auto input = MakeRandomTensor(inputTensorInfo, 111234); + + constexpr float alpha = 1.f; + constexpr float beta = 1.f; + constexpr float kappa = 1.f; + constexpr uint32_t normSize = 5; + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::NormalizationQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Parameters.m_NormChannelType = normChannel; + data.m_Parameters.m_NormMethodType = normMethod; + data.m_Parameters.m_NormSize = normSize; + data.m_Parameters.m_Alpha = alpha; + data.m_Parameters.m_Beta = beta; + data.m_Parameters.m_K = kappa; + + std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); + + armnn::NormalizationQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + // Don't execute if Normalization is not supported for the method and channel types, as an exception will be raised. + armnn::BackendId backend = workloadFactory.GetBackendId(); + const size_t reasonIfUnsupportedMaxLen = 255; + char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; + ret.supported = armnn::IsNormalizationSupported(backend, inputTensorInfo, outputTensorInfo, data.m_Parameters, + reasonIfUnsupported, reasonIfUnsupportedMaxLen); + if (!ret.supported) + { + return ret; + } + + std::unique_ptr workload = workloadFactory.CreateNormalization(data, info); + std::unique_ptr workloadRef = refWorkloadFactory.CreateNormalization(refData, refInfo); + + outputHandleRef->Allocate(); + inputHandleRef->Allocate(); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + refWorkloadFactory.Finalize(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); + + return ret; +} + diff --git a/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp b/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp new file mode 100644 index 0000000000..8d88241000 --- /dev/null +++ b/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp @@ -0,0 +1,330 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include + +#include +#include + +#include + +#include + +BOOST_AUTO_TEST_SUITE(OptimizedNetwork) + +BOOST_AUTO_TEST_CASE(SerializeToDot) +{ + armnn::Network net; + + //Defines layers. + auto input = net.AddInputLayer(0); + auto add = net.AddAdditionLayer(); + auto output = net.AddOutputLayer(0); + + // Connects layers. + input->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + input->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + armnn::TensorShape shape({4}); + armnn::TensorInfo info(shape, armnn::DataType::Float32); + input->GetOutputSlot(0).SetTensorInfo(info); + add->GetOutputSlot(0).SetTensorInfo(info); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector backends = {armnn::Compute::CpuRef}; + armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); + + std::ostringstream ss; + optimizedNet->SerializeToDot(ss); + + auto inputId = input->GetGuid(); + auto addId = add->GetGuid(); + auto outputId = output->GetGuid(); + + std::stringstream expected; + expected << + "digraph Optimized {\n" + " node [shape=\"record\"];\n" + " edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n" + " " << inputId << " [label=\"{Input}\"];\n" + " " << addId << " [label=\"{Addition}\"];\n" + " " << outputId << " [label=\"{Output}\"];\n" + " " << inputId << " -> " << addId << " [label=< [4] >];\n" + " " << inputId << " -> " << addId << " [label=< [4] >];\n" + " " << addId << " -> " << outputId << " [label=< [4] >];\n" + "}\n"; + + BOOST_TEST(ss.str() == expected.str()); +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerNoFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + // This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null. + armnn::NormalizationDescriptor descriptor; + armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); + normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector backends = { armnn::Compute::CpuAcc }; + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(!optNet); +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerWithFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef. + armnn::NormalizationDescriptor descriptor; + armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); + normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector backends = { armnn::Compute::CpuAcc, armnn::Compute::CpuRef }; + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_REQUIRE(optNet); + + for (auto&& layer : static_cast(optNet.get())->GetGraph()) + { + // If NEON is enabled, Input and Output layers are supported by CpuAcc, + // the other layers are supported by CpuRef. + // If NEON is not enabled, all layers are supported by CpuRef. +#if ARMCOMPUTENEON_ENABLED + if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc); + } + else if (layer->GetType() == armnn::LayerType::Normalization) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + } +#else + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); +#endif + } +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDevice) +{ + const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32); + + armnn::Network net; + + armnn::NormalizationDescriptor nmDesc; + armnn::ActivationDescriptor acDesc; + + // in + // | + // nm + // / | + // ac | + // \ | + // ml + // | + // sm + // | + // ot + armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in"); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm"); + + layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0)); + normLayer->GetOutputSlot(0).SetTensorInfo(desc); + + layer = net.AddActivationLayer(acDesc, "ac"); + + normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + armnn::IConnectableLayer* prevLayer = layer; + layer = net.AddMultiplicationLayer("ml"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + prevLayer = layer; + armnn::SoftmaxDescriptor softmaxDescriptor; + layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + prevLayer = layer; + layer = net.AddOutputLayer(0, "ot"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector backends = { armnn::Compute::Undefined }; + + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(!optNet); + +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDeviceWithFallback) +{ + const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32); + + armnn::Network net; + + armnn::NormalizationDescriptor nmDesc; + armnn::ActivationDescriptor acDesc; + + // in + // | + // nm + // / | + // ac | + // \ | + // ml + // | + // sm + // | + // ot + armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in"); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm"); + + layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0)); + normLayer->GetOutputSlot(0).SetTensorInfo(desc); + + layer = net.AddActivationLayer(acDesc, "ac"); + + normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + armnn::IConnectableLayer* prevLayer = layer; + layer = net.AddMultiplicationLayer("ml"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + prevLayer = layer; + armnn::SoftmaxDescriptor softmaxDescriptor; + layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + prevLayer = layer; + layer = net.AddOutputLayer(0, "ot"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector backends = { armnn::Compute::Undefined, armnn::Compute::CpuRef }; + + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(optNet); + + // validate workloads + armnn::RefWorkloadFactory fact; + for (auto&& layer : static_cast(optNet.get())->GetGraph()) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + BOOST_CHECK_NO_THROW( + layer->CreateWorkload(static_cast(optNet.get())->GetGraph(), fact)); + } +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsDuplicateComputeDeviceWithFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef. + armnn::NormalizationDescriptor descriptor; + armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); + normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector backends = { armnn::Compute::CpuAcc, + armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_REQUIRE(optNet); + + for (auto&& layer : static_cast(optNet.get())->GetGraph()) + { + // If NEON is enabled, Input and Output layers are supported by CpuAcc, + // the other layers are supported by CpuRef. + // If only CL is enabled, Input and Output layers are supported by GpuAcc, + // the other layers are supported by CpuRef. + // If neither NEON, nor CL is enabled, all layers are supported by CpuRef. +#if ARMCOMPUTENEON_ENABLED + if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc); + } + else if (layer->GetType() == armnn::LayerType::Normalization) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + } +#elif ARMCOMPUTECL_ENABLED + if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc); + } + else if (layer->GetType() == armnn::LayerType::Normalization) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + } +#else + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); +#endif + } +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/backendsCommon/test/PermuteTestImpl.hpp b/src/backends/backendsCommon/test/PermuteTestImpl.hpp new file mode 100644 index 0000000000..529f9d34e0 --- /dev/null +++ b/src/backends/backendsCommon/test/PermuteTestImpl.hpp @@ -0,0 +1,225 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "QuantizeHelper.hpp" + +#include +#include +#include + +#include + +#include +#include + +template +LayerTestResult SimplePermuteTestImpl( + armnn::IWorkloadFactory& workloadFactory, + armnn::PermuteDescriptor descriptor, + armnn::TensorInfo inputTensorInfo, + armnn::TensorInfo outputTensorInfo, + const std::vector& inputData, + const std::vector& outputExpectedData) +{ + auto input = MakeTensor(inputTensorInfo, inputData); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, outputExpectedData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::PermuteQueueDescriptor data; + data.m_Parameters = descriptor; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreatePermute(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +LayerTestResult SimplePermuteFloat32TestCommon(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 2, 2, 2 }; + unsigned int outputShape[] = { 1, 2, 2, 2 }; + + armnn::PermuteDescriptor descriptor; + descriptor.m_DimMappings = {0U, 3U, 1U, 2U}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + std::vector input = std::vector( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + + 5.0f, 6.0f, + 7.0f, 8.0f + }); + + std::vector outputExpected = std::vector( + { + 1.0f, 5.0f, 2.0f, 6.0f, + 3.0f, 7.0f, 4.0f, 8.0f + }); + + return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, + outputTensorInfo, input, outputExpected); +} + +LayerTestResult SimplePermuteUint8TestCommon(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 2, 2, 2 }; + unsigned int outputShape[] = { 1, 2, 2, 2 }; + + armnn::PermuteDescriptor descriptor; + descriptor.m_DimMappings = {0U, 3U, 1U, 2U}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationScale(1.0f); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(1.0f); + + std::vector input = std::vector( + { + 1, 2, + 3, 4, + + 5, 6, + 7, 8 + }); + + std::vector outputExpected = std::vector( + { + 1, 5, 2, 6, + 3, 7, 4, 8 + }); + + return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, + outputTensorInfo, input, outputExpected); +} + +LayerTestResult +PermuteFloat32ValueSet1TestCommon(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 2, 2, 3 }; + unsigned int outputShape[] = { 1, 3, 2, 2 }; + + armnn::PermuteDescriptor descriptor; + descriptor.m_DimMappings = {0U, 2U, 3U, 1U}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + std::vector input = std::vector( + { + 1.0f, 2.0f, 3.0f, + 11.0f, 12.0f, 13.0f, + 21.0f, 22.0f, 23.0f, + 31.0f, 32.0f, 33.0f, + }); + + std::vector outputExpected = std::vector( + { + 1.0f, 11.0f, 21.0f, 31.0f, + 2.0f, 12.0f, 22.0f, 32.0f, + 3.0f, 13.0f, 23.0f, 33.0f, + }); + + return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, + outputTensorInfo, input, outputExpected); +} + +LayerTestResult +PermuteFloat32ValueSet2TestCommon(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 3, 2, 2 }; + unsigned int outputShape[] = { 1, 2, 2, 3 }; + + armnn::PermuteDescriptor descriptor; + descriptor.m_DimMappings = {0U, 3U, 1U, 2U}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + std::vector input = std::vector( + { + 1.0f, 11.0f, 21.0f, 31.0f, + 2.0f, 12.0f, 22.0f, 32.0f, + 3.0f, 13.0f, 23.0f, 33.0f, + }); + + std::vector outputExpected = std::vector( + { + 1.0f, 2.0f, 3.0f, + 11.0f, 12.0f, 13.0f, + 21.0f, 22.0f, 23.0f, + 31.0f, 32.0f, 33.0f, + }); + + return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, + outputTensorInfo, input, outputExpected); +} + +LayerTestResult +PermuteFloat32ValueSet3TestCommon(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 2, 3, 3 }; + unsigned int outputShape[] = { 1, 3, 2, 3 }; + + armnn::PermuteDescriptor descriptor; + descriptor.m_DimMappings = {0U, 2U, 3U, 1U}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + std::vector input = std::vector( + { + 1.0f, 2.0f, 3.0f, + 11.0f, 12.0f, 13.0f, + 21.0f, 22.0f, 23.0f, + 31.0f, 32.0f, 33.0f, + 41.0f, 42.0f, 43.0f, + 51.0f, 52.0f, 53.0f, + }); + + std::vector outputExpected = std::vector( + { + 1.0f, 11.0f, 21.0f, 31.0f, 41.0f, 51.0f, + 2.0f, 12.0f, 22.0f, 32.0f, 42.0f, 52.0f, + 3.0f, 13.0f, 23.0f, 33.0f, 43.0f, 53.0f, + }); + + return SimplePermuteTestImpl(workloadFactory, descriptor, inputTensorInfo, + outputTensorInfo, input, outputExpected); +} diff --git a/src/backends/backendsCommon/test/Pooling2dTestImpl.hpp b/src/backends/backendsCommon/test/Pooling2dTestImpl.hpp new file mode 100644 index 0000000000..ded45ab999 --- /dev/null +++ b/src/backends/backendsCommon/test/Pooling2dTestImpl.hpp @@ -0,0 +1,1240 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "QuantizeHelper.hpp" + +#include + +#include + +#include +#include +#include + +#include + +#include + +#include +#include + +template +LayerTestResult SimplePooling2dTestImpl(armnn::IWorkloadFactory& workloadFactory, + armnn::Pooling2dDescriptor descriptor, + float qScale, + int32_t qOffset, + const boost::multi_array& input, + const boost::multi_array& outputExpected) +{ + const armnn::DataLayoutIndexed dataLayout = descriptor.m_DataLayout; + auto heightIndex = dataLayout.GetHeightIndex(); + auto widthIndex = dataLayout.GetWidthIndex(); + auto channelsIndex = dataLayout.GetChannelsIndex(); + + unsigned int inputHeight = boost::numeric_cast(input.shape()[heightIndex]); + unsigned int inputWidth = boost::numeric_cast(input.shape()[widthIndex]); + unsigned int inputChannels = boost::numeric_cast(input.shape()[channelsIndex]); + unsigned int inputBatchSize = boost::numeric_cast(input.shape()[0]); + + unsigned int outputHeight = boost::numeric_cast(outputExpected.shape()[heightIndex]); + unsigned int outputWidth = boost::numeric_cast(outputExpected.shape()[widthIndex]); + unsigned int outputChannels = boost::numeric_cast(outputExpected.shape()[channelsIndex]); + unsigned int outputBatchSize = boost::numeric_cast(outputExpected.shape()[0]); + + armnn::TensorInfo inputTensorInfo = GetTensorInfo(inputBatchSize, inputChannels, inputHeight, + inputWidth, dataLayout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo(outputBatchSize, outputChannels, outputHeight, + outputWidth, dataLayout); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + LayerTestResult result(outputTensorInfo); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::Pooling2dQueueDescriptor queueDescriptor; + queueDescriptor.m_Parameters = descriptor; + queueDescriptor.m_Parameters.m_DataLayout = dataLayout; + + armnn::WorkloadInfo workloadInfo; + AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); + + // Don't execute if Pooling is not supported, as an exception will be raised. + armnn::BackendId backend = workloadFactory.GetBackendId(); + const size_t reasonIfUnsupportedMaxLen = 255; + char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; + result.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, + queueDescriptor.m_Parameters, + reasonIfUnsupported, reasonIfUnsupportedMaxLen); + if (!result.supported) + { + return result; + } + + std::unique_ptr workload = workloadFactory.CreatePooling2d(queueDescriptor, 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 = outputExpected; + + return result; +} + +// +// Tests max pooling with the following parameters: +// +// Pooling size: 3x3 +// Stride: (2,4) +// input size: 8x13 +// channels: 2 +// batch size: 2 +// +template +LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4TestCommon(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 4; + // forceNoPadding is mainly used for compatibility with ARM Compute. + // As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size. + descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; + descriptor.m_PadTop = descriptor.m_PadBottom = 0; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + unsigned int inputWidth = 8; + unsigned int inputHeight = 13; + unsigned int outputWidth = + (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / + descriptor.m_StrideX; + unsigned int outputHeight = + (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / + descriptor.m_StrideY; + unsigned int channels = 2; + unsigned int batchSize = 2; + + armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + std::vector singleChannelData({ + 0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f, + 1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f, + 8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f, + 8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f, + 5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f, + 1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f, + 9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f, + 1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f, + 6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f, + 8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f, + 7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f, + 4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f, + 3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f, + }); + + // Constructs input data. + std::vector inputData; + auto negator = [](float f) { return -f; }; + + // First image (two channels where the second channel is the negative of the first one). + inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); + std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); + + // Second image (same as first image). + inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); + std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); + + auto input = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, inputData)); + + // These were calculated manually. + auto shape(GetTensorShapeAsArray<4>(outputTensorInfo)); + boost::multi_array outputExpected(shape); + if (forceNoPadding) + { + outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 8.0f, 8.0f, 8.0f, + 9.0f, 7.0f, 9.0f, + 9.0f, 9.0f, 9.0f, + + 0.0f, 0.0f, -3.0f, + -1.0f, 0.0f, 0.0f, + -1.0f, -1.0f, -1.0f, + + 8.0f, 8.0f, 8.0f, + 9.0f, 7.0f, 9.0f, + 9.0f, 9.0f, 9.0f, + + 0.0f, 0.0f, -3.0f, + -1.0f, 0.0f, 0.0f, + -1.0f, -1.0f, -1.0f + })); + } + else + { + outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, + 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, + 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, + + 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, + 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f, + + 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, + 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, + 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, + + 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, + 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f + })); + } + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult SimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout = armnn::DataLayout::NCHW, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = descriptor.m_StrideY = 2; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; + + armnn::TensorInfo inputTensorInfo = GetTensorInfo(1, 2, 4, 4, dataLayout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo(1, 2, 2, 2, dataLayout); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + std::vector inputData( + QuantizedVector(qScale, qOffset, { + 1.0f, 2.0f, 5.0f, 6.0f, + 3.0f, 4.0f, 7.0f, 8.0f, + 9.0f, 10.0f, 13.0f, 14.0f, + 11.0f, 12.0f, 15.0f, 16.0f, + + 17.0f, 18.0f, 21.0f, 22.0f, + 19.0f, 20.0f, 23.0f, 24.0f, + 25.0f, 26.0f, 29.0f, 30.0f, + 27.0f, 28.0f, 31.0f, 32.0f, + })); + + std::vector outputData( + QuantizedVector(qScale, qOffset, { + 4.0f, 8.0f, + 12.0f, 16.0f, + + 20.0f, 24.0f, + 28.0f, 32.0f, + })); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor(inputTensorInfo, inputData); + + auto outputExpected = MakeTensor(outputTensorInfo, outputData); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = descriptor.m_StrideY = 2; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; + + armnn::TensorInfo inputTensorInfo = GetTensorInfo(1, 2, 4, 4, dataLayout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo(1, 2, 2, 2, dataLayout); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + std::vector inputData( + QuantizedVector(qScale, qOffset, { + 2.0f, 2.0f, 6.0f, 6.0f, + 4.0f, 4.0f, 8.0f, 8.0f, + 10.0f, 12.0f, 14.0f, 16.0f, + 10.0f, 12.0f, 16.0f, 14.0f, + + 18.0f, 20.0f, 24.0f, 22.0f, + 20.0f, 18.0f, 22.0f, 24.0f, + 26.0f, 28.0f, 0.0f, 0.0f, + 26.0f, 28.0f, 0.0f, 0.0f, + })); + + std::vector outputData( + QuantizedVector(qScale, qOffset, { + 3.0f, 7.0f, + 11.0f, 15.0f, + + 19.0f, 23.0f, + 27.0f, 0.0f, + })); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor(inputTensorInfo, inputData); + + auto outputExpected = MakeTensor(outputTensorInfo, outputData); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 100; + descriptor.m_StrideX = descriptor.m_StrideY = 5; + descriptor.m_PadLeft = 50; + descriptor.m_PadRight = 50; + descriptor.m_PadTop = 50; + descriptor.m_PadBottom = 50; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + std::vector inputVec; + + for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i) + { + inputVec.push_back(1); + } + + auto input = MakeTensor(inputTensorInfo, inputVec); + + std::vector outputVec; + + for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i) + { + outputVec.push_back(1); + } + + auto outputExpected = MakeTensor(outputTensorInfo, outputVec); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult SimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = descriptor.m_StrideY = 2; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; + + armnn::TensorInfo inputTensorInfo = GetTensorInfo(1, 2, 4, 4, dataLayout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo(1, 2, 2, 2, dataLayout); + + std::vector inputData( + QuantizedVector(qScale, qOffset, { + 1.0f, 7.0f, 5.0f, 5.0f, + 1.0f, 7.0f, 5.0f, 5.0f, + 3.0f, 3.0f, 1.0f, 1.0f, + 3.0f, 3.0f, 1.0f, 1.0f, + + 1.0f, 7.0f, 0.0f, 0.0f, + 1.0f, 7.0f, 2.0f, 0.0f, + 0.0f, 2.0f, 1.0f, 1.0f, + 0.0f, 0.0f, 1.0f, 1.0f, + })); + + std::vector outputData( + QuantizedVector(qScale, qOffset, { + 5.0f, 5.0f, + 3.0f, 1.0f, + + 5.0f, 1.0f, + 1.0f, 1.0f, + })); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor(inputTensorInfo, inputData); + + auto outputExpected = MakeTensor(outputTensorInfo, outputData); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult L2Pooling2dSize3Stride1TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = descriptor.m_StrideY = 1; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 2.0f, 1.0f, 5.0f, 2.0f, + 1.0f, 2.0f, 2.0f, 1.0f, + 5.0f, 4.0f, 1.0f, 5.0f, + 2.0f, 1.0f, 5.0f, 2.0f, + })); + + armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 3.0f, 3.0f, + 3.0f, 3.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult L2Pooling2dSize3Stride3TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = descriptor.m_StrideY = 3; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType()); + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, + 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, + 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, + 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, + 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, + 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, + 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, + 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, + 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, + })); + + armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 3.0f, 3.0f, 3.0f, + 3.0f, 3.0f, 3.0f, + 3.0f, 3.0f, 3.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult L2Pooling2dSize3Stride4TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = descriptor.m_StrideY = 4; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType()); + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, + 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, + 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, + 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, + 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, + })); + + armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 3.0f, 3.0f, + 3.0f, 3.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult L2Pooling2dSize7TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 7; + descriptor.m_StrideX = descriptor.m_StrideY = 7; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType()); + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f, + 8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f, + 0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + })); + + armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType()); + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 3.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult L2Pooling2dSize9TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 9; + descriptor.m_StrideX = descriptor.m_StrideY = 9; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType()); + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, + 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, + 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, + 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, + 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, + 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, + 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, + 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, + 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, + })); + + armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType()); + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 3.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult AsymmetricNonSquarePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); + + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + descriptor.m_PoolWidth = 2; + descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 1; + descriptor.m_PadLeft = 2; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 2; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + // Construct input data. + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 1.0f, 3.0f, 4.0f, + })); + + // These were calculated manually. + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 0.0f, 3.0f, 0.0f, 3.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult ComparePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + armnn::PoolingAlgorithm poolingType, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + const unsigned int inputWidth = 16; + const unsigned int inputHeight = 32; + const unsigned int channelCount = 2; + const unsigned int batchSize = 5; + + const unsigned int poolSize = 3; + const unsigned int strideX = 2; + const unsigned int strideY = 4; + const unsigned int padX = 0; + const unsigned int padY = 0; + + const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX; + const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth }; + unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType()); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + boost::multi_array input = MakeRandomTensor(inputTensorInfo, 81715); + + LayerTestResult comparisonResult(outputTensorInfo); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::Pooling2dQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + data.m_Parameters.m_PoolType = poolingType; + data.m_Parameters.m_PoolWidth = poolSize; + data.m_Parameters.m_PoolHeight = poolSize; + data.m_Parameters.m_StrideX = strideX; + data.m_Parameters.m_StrideY = strideY; + data.m_Parameters.m_PadLeft = padX; + data.m_Parameters.m_PadRight = padX; + data.m_Parameters.m_PadTop = padY; + data.m_Parameters.m_PadBottom = padY; + data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + + std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); + + // Don't execute if Pooling is not supported, as an exception will be raised. + armnn::BackendId backend = workloadFactory.GetBackendId(); + const size_t reasonIfUnsupportedMaxLen = 255; + char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; + comparisonResult.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, + data.m_Parameters, + reasonIfUnsupported, reasonIfUnsupportedMaxLen); + if (!comparisonResult.supported) + { + return comparisonResult; + } + + armnn::Pooling2dQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + std::unique_ptr workload = workloadFactory.CreatePooling2d(data, info); + std::unique_ptr workloadRef = refWorkloadFactory.CreatePooling2d(refData, refInfo); + + outputHandleRef->Allocate(); + inputHandleRef->Allocate(); + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); + + workload->Execute(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); + + return comparisonResult; +} + +// +// Tests max pooling with the following parameters: +// +// Pooling size: 2x2 +// Stride: (2,2) +// input size: 4x4 +// channels: 1 +// batch size: 1 +// +template +LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2TestCommon(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; + descriptor.m_PadTop = descriptor.m_PadBottom = 0; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + + unsigned int inputWidth = 4; + unsigned int inputHeight = 4; + unsigned int outputWidth = + (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / + descriptor.m_StrideX; + unsigned int outputHeight = + (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / + descriptor.m_StrideY; + unsigned int channels = 1; + unsigned int batchSize = 1; + + std::vector inputData = { + 510.0f, 222.0f, 780.0f, 654.0f, + 141.0f, 276.0f, 15.0f, 546.0f, + 303.0f, 618.0f, 582.0f, 339.0f, + 438.0f, 564.0f, 573.0f, 402.0f + }; + + // Note that left and right edges will be 0.f, due to the 2x2 max pooling only accessing zeros here. + std::vector expectedOutputDataWithPadding = { + 0.0f, 510.0f, 780.0f, 654.0f, 0.0f, + 0.0f, 438.0f, 618.0f, 402.0f, 0.0f + }; + + std::vector expectedOutputDataNoPadding = { + 510.0f, 780.0f, + 618.0f, 582.0f + }; + + armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType()); + + // Scale and offset should match input - we're just calculating maximum values. + armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, inputData)); + + auto outputExpected = MakeTensor(outputTensorInfo, + forceNoPadding ? QuantizedVector(qScale, qOffset, expectedOutputDataNoPadding) : + QuantizedVector(qScale, qOffset, expectedOutputDataWithPadding)); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +// +// Tests max pooling with the following parameters: +// +// Pooling size: 3x2 +// Stride: (2,2) +// input size: 3x2 +// channels: 1 +// batch size: 1 +// +template +LayerTestResult IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( + armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = 3; + descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1; + descriptor.m_PadRight = descriptor.m_PadLeft; + descriptor.m_PadTop = 0; + descriptor.m_PadBottom = 0; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + + unsigned int inputWidth = 3; + unsigned int inputHeight = 2; + unsigned int outputWidth = + (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / + descriptor.m_StrideX; + unsigned int outputHeight = + (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / + descriptor.m_StrideY; + unsigned int channels = 1; + unsigned int batchSize = 1; + + std::vector inputData = { + 3.0f, 6.0f, 9.0f, + 12.0f, 15.0f, 18.0f, + }; + + std::vector expectedOutputDataWithPadding = { + 6.0f, 8.0f, + }; + + std::vector expectedOutputDataNoPadding = { + 10.5f, + }; + + armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType()); + + // Scale and offset should match input - we're just calculating average values. + armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, inputData)); + + auto outputExpected = MakeTensor(outputTensorInfo, + forceNoPadding ? QuantizedVector(qScale, qOffset, expectedOutputDataNoPadding) : + QuantizedVector(qScale, qOffset, expectedOutputDataWithPadding)); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + + +template +LayerTestResult IgnorePaddingSimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = descriptor.m_StrideY = 2; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + -1.0f, -2.0f, 3.0f, 4.0f, + -1.0f, -2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, -3.0f, -4.0f, + 1.0f, 2.0f, -3.0f, -4.0f, + })); + + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + -1.0f, 3.0f, 4.0f, + 1.0f, 3.0f, 4.0f, + 1.0f, 2.0f, -4.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult IgnorePaddingMaxPooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = descriptor.m_StrideY = 1; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + -1.0f, -2.0f, 3.0f, 4.0f, + -1.0f, -2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, -3.0f, -4.0f, + 1.0f, 2.0f, -3.0f, -4.0f, + })); + + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + -1.0f, 3.0f, 4.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 4.0f, + 2.0f, 2.0f, 2.0f, -3.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult IgnorePaddingSimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = descriptor.m_StrideY = 2; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 12.0f, 20.0f, 32.0f, 40.0f, + 12.0f, 20.0f, 32.0f, 40.0f, + 12.0f, 20.0f, 32.0f, 40.0f, + 12.0f, 20.0f, 32.0f, 40.0f, + })); + + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 3.0f, 13.0f, 10.0f, + 6.0f, 26.0f, 20.0f, + 3.0f, 13.0f, 10.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = descriptor.m_StrideY = 2; + descriptor.m_PadLeft = 0; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 0; + descriptor.m_PadBottom = 0; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4}, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 1.0f, 2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, 3.0f, 4.0f, + })); + + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 2.0f, 3.5f, + 2.0f, 3.5f + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult IgnorePaddingAveragePooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = descriptor.m_StrideY = 1; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 9.0f, 27.0f, 18.0f, 36.0f, + 18.0f, 9.0f, 18.0f, 9.0f, + 27.0f, 18.0f, 9.0f, 27.0f, + 9.0f, 27.0f, 9.0f, 18.0f, + })); + + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 7.0f, 11.0f, 13.0f, 9.0f, + 12.0f, 17.0f, 19.0f, 13.0f, + 12.0f, 16.0f, 16.0f, 10.0f, + 9.0f, 11.0f, 12.0f, 7.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult IgnorePaddingSimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = descriptor.m_StrideY = 2; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 2.0f, 4.0f, 8.0f, 16.0f, + 4.0f, 2.0f, 2.0f, 4.0f, + 8.0f, 2.0f, 4.0f, 2.0f, + 16.0f, 2.0f, 2.0f, 8.0f, + })); + + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 1.0f, 4.4721f, 8.0f, + 4.4721f, 2.6457f, 2.236f, + 8.0f, 1.4142f, 4.0f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + +template +LayerTestResult IgnorePaddingL2Pooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; + descriptor.m_StrideX = descriptor.m_StrideY = 1; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + + armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor(inputTensorInfo, + QuantizedVector(qScale, qOffset, { + 1.0f, 2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, 3.0f, 4.0f, + })); + + auto outputExpected = MakeTensor(outputTensorInfo, + QuantizedVector(qScale, qOffset, { + 1.0540f, 1.7638f, 2.5385f, 2.3570f, + 1.2909f, 2.1602f, 3.1091f, 2.8867f, + 1.2909f, 2.1602f, 3.1091f, 2.8867f, + 1.0540f, 1.7638f, 2.5385f, 2.3570f, + })); + + return SimplePooling2dTestImpl(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} diff --git a/src/backends/backendsCommon/test/QuantizeHelper.hpp b/src/backends/backendsCommon/test/QuantizeHelper.hpp new file mode 100644 index 0000000000..bb4e561d59 --- /dev/null +++ b/src/backends/backendsCommon/test/QuantizeHelper.hpp @@ -0,0 +1,91 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include +#include + +#include +#include +#include +#include + +template +struct SelectiveQuantizer +{ + static T Quantize(float value, float scale, int32_t offset) + { + return armnn::Quantize(value, scale, offset); + } + + static float Dequantize(T value, float scale, int32_t offset) + { + return armnn::Dequantize(value, scale, offset); + } +}; + +template +struct SelectiveQuantizer +{ + static T Quantize(float value, float scale, int32_t offset) + { + boost::ignore_unused(scale, offset); + return value; + } + + static float Dequantize(T value, float scale, int32_t offset) + { + boost::ignore_unused(scale, offset); + return value; + } +}; + +template +T SelectiveQuantize(float value, float scale, int32_t offset) +{ + return SelectiveQuantizer()>::Quantize(value, scale, offset); +}; + +template +float SelectiveDequantize(T value, float scale, int32_t offset) +{ + return SelectiveQuantizer()>::Dequantize(value, scale, offset); +}; + +template +struct IsFloatingPointIterator +{ + static constexpr bool value=std::is_floating_point::value_type>::value; +}; + +template ::value, int>::type=0 // Makes sure fp iterator is valid. +> +std::vector QuantizedVector(float qScale, int32_t qOffset, FloatIt first, FloatIt last) +{ + std::vector quantized; + quantized.reserve(boost::numeric_cast(std::distance(first, last))); + + for (auto it = first; it != last; ++it) + { + auto f = *it; + T q =SelectiveQuantize(f, qScale, qOffset); + quantized.push_back(q); + } + + return quantized; +} + +template +std::vector QuantizedVector(float qScale, int32_t qOffset, const std::vector& array) +{ + return QuantizedVector(qScale, qOffset, array.begin(), array.end()); +} + +template +std::vector QuantizedVector(float qScale, int32_t qOffset, std::initializer_list array) +{ + return QuantizedVector(qScale, qOffset, array.begin(), array.end()); +} diff --git a/src/backends/backendsCommon/test/ReshapeTestImpl.hpp b/src/backends/backendsCommon/test/ReshapeTestImpl.hpp new file mode 100644 index 0000000000..fee992deb6 --- /dev/null +++ b/src/backends/backendsCommon/test/ReshapeTestImpl.hpp @@ -0,0 +1,177 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "QuantizeHelper.hpp" + +#include +#include +#include + +#include +#include + +#include + +template +LayerTestResult SimpleReshapeTestImpl( + armnn::IWorkloadFactory& workloadFactory, + armnn::TensorInfo inputTensorInfo, + armnn::TensorInfo outputTensorInfo, + const std::vector& inputData, + const std::vector& outputExpectedData) +{ + auto input = MakeTensor(inputTensorInfo, inputData); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, outputExpectedData); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ReshapeQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateReshape(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +LayerTestResult SimpleReshapeFloat32Test(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 2, 2, 3, 3 }; + unsigned int outputShape[] = { 2, 2, 9, 1 }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + std::vector input = std::vector( + { + 0.0f, 1.0f, 2.0f, + 3.0f, 4.0f, 5.0f, + 6.0f, 7.0f, 8.0f, + + 9.0f, 10.0f, 11.0f, + 12.0f, 13.0f, 14.0f, + 15.0f, 16.0f, 17.0f, + + 18.0f, 19.0f, 20.0f, + 21.0f, 22.0f, 23.0f, + 24.0f, 25.0f, 26.0f, + + 27.0f, 28.0f, 29.0f, + 30.0f, 31.0f, 32.0f, + 33.0f, 34.0f, 35.0f, + }); + + std::vector outputExpected = std::vector( + { + 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, + + 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, + + 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, + + 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, + }); + + return SimpleReshapeTestImpl(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected); +} + +LayerTestResult SimpleFloorTest(armnn::IWorkloadFactory& workloadFactory) +{ + const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); + const armnn::TensorInfo outputTensorInfo(inputTensorInfo); + + auto input = MakeTensor(inputTensorInfo, + { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, + 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f }); + + LayerTestResult ret(outputTensorInfo); + ret.outputExpected = MakeTensor(outputTensorInfo, + { -38.0f, -16.0f, -9.0f, -2.0f, -2.0f, -2.0f, -1.0f, -1.0f, 0.0f, + 1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 2.0f, 8.0f, 15.0f, 37.0f }); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::FloorQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateFloor(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +LayerTestResult SimpleReshapeUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 2, 2, 3, 3 }; + unsigned int outputShape[] = { 2, 2, 9, 1 }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationScale(1.0f); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationScale(1.0f); + + std::vector input = std::vector( + { + 0, 1, 2, + 3, 4, 5, + 6, 7, 8, + + 9, 10, 11, + 12, 13, 14, + 15, 16, 17, + + 18, 19, 20, + 21, 22, 23, + 24, 25, 26, + + 27, 28, 29, + 30, 31, 32, + 33, 34, 35, + }); + + std::vector outputExpected = std::vector( + { + 0, 1, 2, 3, 4, 5, 6, 7, 8, + + 9, 10, 11, 12, 13, 14, 15, 16, 17, + + 18, 19, 20, 21, 22, 23, 24, 25, 26, + + 27, 28, 29, 30, 31, 32, 33, 34, 35, + }); + + return SimpleReshapeTestImpl(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected); +} diff --git a/src/backends/backendsCommon/test/RuntimeTestImpl.hpp b/src/backends/backendsCommon/test/RuntimeTestImpl.hpp new file mode 100644 index 0000000000..b446fc48c3 --- /dev/null +++ b/src/backends/backendsCommon/test/RuntimeTestImpl.hpp @@ -0,0 +1,43 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include + +#include + +namespace +{ + +inline void CreateAndDropDummyNetwork(const std::vector& backends, armnn::Runtime& runtime) +{ + armnn::NetworkId networkIdentifier; + { + armnn::TensorInfo inputTensorInfo(armnn::TensorShape({ 7, 7 }), armnn::DataType::Float32); + armnn::TensorInfo outputTensorInfo(armnn::TensorShape({ 7, 7 }), armnn::DataType::Float32); + + armnn::INetworkPtr network(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = network->AddInputLayer(0, "input"); + armnn::IConnectableLayer* layer = network->AddActivationLayer(armnn::ActivationDescriptor(), "test"); + armnn::IConnectableLayer* output = network->AddOutputLayer(0, "output"); + + input->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + layer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + // Sets the tensors in the network. + input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + // optimize the network + armnn::IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime.GetDeviceSpec()); + + runtime.LoadNetwork(networkIdentifier, std::move(optNet)); + } + + runtime.UnloadNetwork(networkIdentifier); +} + +} // anonymous namespace diff --git a/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp b/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp new file mode 100644 index 0000000000..1e145a1a2e --- /dev/null +++ b/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp @@ -0,0 +1,153 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "QuantizeHelper.hpp" + +#include +#include +#include + +#include +#include + +#include + +#include + +template +LayerTestResult SimpleSoftmaxTestImpl(armnn::IWorkloadFactory& workloadFactory, float beta) +{ + using std::exp; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 2, 4 }; + + inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType()); + float qScale = 1.f / 256.f; + int qOffset = 0; + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + + outputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType()); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + + LayerTestResult ret(outputTensorInfo); + + // Each row is independently softmax'd. + auto input = MakeTensor(inputTensorInfo, std::vector( + QuantizedVector(qScale, 0, { + 0.f, 1.f, 0.f, 0.f, + .5f, 0.f, 0.f, 0.f, + }))); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::SoftmaxQueueDescriptor data; + data.m_Parameters.m_Beta = beta; + + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr workload = workloadFactory.CreateSoftmax(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); + + float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta), + exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) }; + float sum0 = x0[0] + x0[1] + x0[2] + x0[3]; + float x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta), + exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) }; + float sum1 = x1[0] + x1[1] + x1[2] + x1[3]; + + ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( + QuantizedVector(qScale, qOffset, { + x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0, + x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 + }))); + + return ret; +} + +template +LayerTestResult CompareSoftmaxTestImpl(armnn::IWorkloadFactory& workloadFactory, + armnn::IWorkloadFactory& refWorkloadFactory, + float beta) +{ + + const int batchSize = 20; + const int channels = 30; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { batchSize, channels }; + + inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType()); + outputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType()); + float qScale = 1.f / 256.f; + int qOffset = 0; + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + + + LayerTestResult ret(outputTensorInfo); + auto input = MakeRandomTensor(inputTensorInfo, 0xF00D, 0.0f, 1.0f); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::SoftmaxQueueDescriptor data; + data.m_Parameters.m_Beta = beta; + + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); + + + armnn::SoftmaxQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + std::unique_ptr workload = workloadFactory.CreateSoftmax(data, info); + std::unique_ptr workloadRef = refWorkloadFactory.CreateSoftmax(refData, refInfo); + + outputHandleRef->Allocate(); + inputHandleRef->Allocate(); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); + CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0]); + + workloadFactory.Finalize(); + workload->Execute(); + refWorkloadFactory.Finalize(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); + CopyDataFromITensorHandle(&ret.outputExpected[0][0], outputHandleRef.get()); + + return ret; +} diff --git a/src/backends/backendsCommon/test/SplitterTestImpl.hpp b/src/backends/backendsCommon/test/SplitterTestImpl.hpp new file mode 100644 index 0000000000..677950cf2d --- /dev/null +++ b/src/backends/backendsCommon/test/SplitterTestImpl.hpp @@ -0,0 +1,304 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include +#include + +#include +#include +#include + +#include + +template +std::vector> SplitterTestCommon(armnn::IWorkloadFactory& workloadFactory, + float qScale = 0.0f, + int32_t qOffset = 0) +{ + unsigned int inputWidth = 5; + unsigned int inputHeight = 6; + unsigned int inputChannels = 3; + + // NOTE: Compute Library imposes a restriction that the x and y dimension (input height and width) + // cannot be split. + // For the reasons for this, see first comment on https://jira.arm.com/browse/IVGCVSW-1239 + // + // This test has therefore been recast to split the channels, then split the resulting subtensor. + + // To take channel 0 of original output + // and channel 0 and channel 1 of the split subtensor. + unsigned int outputWidth1 = inputWidth; + unsigned int outputHeight1 = inputHeight; + unsigned int outputChannels1 = 1; + + // To take channel 1 and 2 of the original output. + unsigned int outputWidth2 = inputWidth; + unsigned int outputHeight2 = inputHeight; + unsigned int outputChannels2 = 2; + + + // Define the tensor descriptors. + armnn::TensorInfo inputTensorInfo({ inputChannels, inputHeight, inputWidth }, armnn::GetDataType()); + + // Outputs of the original split. + armnn::TensorInfo outputTensorInfo1({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo2({ outputChannels2, outputHeight2, outputWidth2 }, armnn::GetDataType()); + + // Outputs of the subsequent subtensor split. + armnn::TensorInfo outputTensorInfo3({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType()); + armnn::TensorInfo outputTensorInfo4({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType()); + + // Set quantization parameters if the requested type is a quantized type. + // The quantization doesn't really matter as the splitter operator doesn't dequantize/quantize. + if(armnn::IsQuantizedType()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo1.SetQuantizationScale(qScale); + outputTensorInfo1.SetQuantizationOffset(qOffset); + outputTensorInfo2.SetQuantizationScale(qScale); + outputTensorInfo2.SetQuantizationOffset(qOffset); + outputTensorInfo3.SetQuantizationScale(qScale); + outputTensorInfo3.SetQuantizationOffset(qOffset); + outputTensorInfo4.SetQuantizationScale(qScale); + outputTensorInfo4.SetQuantizationOffset(qOffset); + } + + LayerTestResult ret1(outputTensorInfo1); + LayerTestResult ret2(outputTensorInfo2); + LayerTestResult ret3(outputTensorInfo3); + LayerTestResult ret4(outputTensorInfo4); + + auto input = MakeTensor(inputTensorInfo, std::vector( + QuantizedVector(qScale, qOffset, { + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, + 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, + 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, + 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, + 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, + 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, + + 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, + 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, + 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, + 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, + 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, + 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, + + 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, + 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, + 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, + 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, + 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, + 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, + }) + )); + + // Channel 0 of the original input. + ret1.outputExpected = MakeTensor(outputTensorInfo1, std::vector( + QuantizedVector(qScale, qOffset, { + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, + 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, + 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, + 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, + 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, + 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, + }) + )); + + // Channel 1 & 2 of the original input. + ret2.outputExpected = MakeTensor(outputTensorInfo2, std::vector( + QuantizedVector(qScale, qOffset, { + 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, + 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, + 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, + 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, + 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, + 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, + + 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, + 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, + 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, + 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, + 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, + 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, + }) + )); + + // Channel 0 of return 2 (i.e. channels 1 and 2 of the original input). + ret3.outputExpected = MakeTensor(outputTensorInfo3, std::vector( + QuantizedVector(qScale, qOffset, { + 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, + 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, + 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, + 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, + 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, + 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, + }) + )); + + // Channel 1 of return 2. + ret4.outputExpected = MakeTensor(outputTensorInfo4, std::vector( + QuantizedVector(qScale, qOffset, { + 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, + 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, + 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, + 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, + 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, + 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, + }) + )); + + // NOTE: as a corollary of the splitting of x and y restriction the x and y values of the view origins + // have to be zero, the co-ordinates are as per the tensor info above channels, height/y, width/x + // note that under the hood the compute engine reverses these i.e. its coordinate system is x, y, channels. + std::vector wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of output[0]. + armnn::SplitterQueueDescriptor::ViewOrigin window1(wOrigin1); + + std::vector wOrigin2 = {1, 0, 0}; //Extent of the window is defined by size of output[1]. + armnn::SplitterQueueDescriptor::ViewOrigin window2(wOrigin2); + + std::vector wOrigin3 = {0, 0, 0}; //Extent of the window is defined by size of output[2]. + armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3); + + std::vector wOrigin4 = {1, 0, 0}; //Extent of the window is defined by size of output[3]. + armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4); + + bool subTensorsSupported = workloadFactory.SupportsSubTensors(); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + + std::unique_ptr outputHandle1 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo1.GetShape(), wOrigin1.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo1); + + std::unique_ptr outputHandle2 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo2.GetShape(), wOrigin2.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo2); + + std::unique_ptr outputHandle3 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo3.GetShape(), wOrigin3.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo3); + + std::unique_ptr outputHandle4 = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo4.GetShape(), wOrigin4.data()) : + workloadFactory.CreateTensorHandle(outputTensorInfo4); + + // Do the first split + armnn::SplitterQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo1, outputHandle1.get()); + AddOutputToWorkload(data, info, outputTensorInfo2, outputHandle2.get()); + + data.m_ViewOrigins.push_back(window1); + data.m_ViewOrigins.push_back(window2); + + std::unique_ptr workload = workloadFactory.CreateSplitter(data, info); + + inputHandle->Allocate(); + outputHandle1->Allocate(); + outputHandle2->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0]); + + workload->Execute(); + + CopyDataFromITensorHandle(&ret1.output[0][0][0], outputHandle1.get()); + CopyDataFromITensorHandle(&ret2.output[0][0][0], outputHandle2.get()); + +// // Do the second split. + armnn::SplitterQueueDescriptor data2; + armnn::WorkloadInfo info2; + AddInputToWorkload(data2, info2, outputTensorInfo2, outputHandle2.get()); + AddOutputToWorkload(data2, info2, outputTensorInfo3, outputHandle3.get()); + AddOutputToWorkload(data2, info2, outputTensorInfo4, outputHandle4.get()); + + data2.m_ViewOrigins.push_back(window3); + data2.m_ViewOrigins.push_back(window4); + + std::unique_ptr workload2 = workloadFactory.CreateSplitter(data2, info2); + + outputHandle3->Allocate(); + outputHandle4->Allocate(); + + workload2->Execute(); + + CopyDataFromITensorHandle(&ret3.output[0][0][0], outputHandle3.get()); + CopyDataFromITensorHandle(&ret4.output[0][0][0], outputHandle4.get()); + + std::vector> ret = {ret1, ret2, ret3, ret4,}; + + return ret; +} + + +template +LayerTestResult CopyViaSplitterTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) +{ + const armnn::TensorInfo tensorInfo({ 3, 6, 5 }, armnn::GetDataType()); + auto input = MakeTensor(tensorInfo, QuantizedVector(qScale, qOffset, + { + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, + 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, + 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, + 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, + 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, + 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, + + 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, + 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, + 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, + 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, + 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, + 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, + + 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, + 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, + 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, + 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, + 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, + 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, + })); + + std::vector origin = { 0, 0, 0 }; + armnn::SplitterQueueDescriptor::ViewOrigin window(origin); + + const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(tensorInfo); + + std::unique_ptr outputHandle = + subTensorsSupported ? + workloadFactory.CreateSubTensorHandle(*inputHandle, tensorInfo.GetShape(), origin.data()) : + workloadFactory.CreateTensorHandle(tensorInfo); + + armnn::SplitterQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, tensorInfo, inputHandle.get()); + AddOutputToWorkload(data, info, tensorInfo, outputHandle.get()); + + data.m_ViewOrigins.push_back(window); + + std::unique_ptr workload = workloadFactory.CreateSplitter(data, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0]); + + workload->Execute(); + + LayerTestResult ret(tensorInfo); + CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); + ret.outputExpected = input; + + return ret; +} diff --git a/src/backends/backendsCommon/test/TensorCopyUtils.cpp b/src/backends/backendsCommon/test/TensorCopyUtils.cpp new file mode 100644 index 0000000000..acc28c9c03 --- /dev/null +++ b/src/backends/backendsCommon/test/TensorCopyUtils.cpp @@ -0,0 +1,161 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "TensorCopyUtils.hpp" + +#include + +#ifdef ARMCOMPUTECL_ENABLED +#include +#endif + +#if ARMCOMPUTENEON_ENABLED +#include +#endif + +#if ARMCOMPUTECLENABLED || ARMCOMPUTENEON_ENABLED +#include +#endif + +#include + +#include + +#include +#include + +void CopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem) +{ + switch (tensorHandle->GetType()) + { + case armnn::ITensorHandle::Cpu: + { + auto handle = boost::polymorphic_downcast(tensorHandle); + memcpy(handle->GetTensor(), mem, handle->GetTensorInfo().GetNumBytes()); + break; + } +#ifdef ARMCOMPUTECL_ENABLED + case armnn::ITensorHandle::CL: + { + using armnn::armcomputetensorutils::CopyArmComputeITensorData; + auto handle = boost::polymorphic_downcast(tensorHandle); + handle->Map(true); + switch(handle->GetDataType()) + { + case arm_compute::DataType::F32: + CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); + break; + case arm_compute::DataType::QASYMM8: + CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); + break; + case arm_compute::DataType::F16: + CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); + break; + default: + { + throw armnn::UnimplementedException(); + } + } + handle->Unmap(); + break; + } +#endif +#if ARMCOMPUTENEON_ENABLED + case armnn::ITensorHandle::Neon: + { + using armnn::armcomputetensorutils::CopyArmComputeITensorData; + auto handle = boost::polymorphic_downcast(tensorHandle); + switch (handle->GetDataType()) + { + case arm_compute::DataType::F32: + CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); + break; + case arm_compute::DataType::QASYMM8: + CopyArmComputeITensorData(static_cast(mem), handle->GetTensor()); + break; + default: + { + throw armnn::UnimplementedException(); + } + } + break; + } +#endif + default: + { + throw armnn::UnimplementedException(); + } + } +} + +void CopyDataFromITensorHandle(void* mem, const armnn::ITensorHandle* tensorHandle) +{ + switch (tensorHandle->GetType()) + { + case armnn::ITensorHandle::Cpu: + { + auto handle = boost::polymorphic_downcast(tensorHandle); + memcpy(mem, handle->GetTensor(), handle->GetTensorInfo().GetNumBytes()); + break; + } +#ifdef ARMCOMPUTECL_ENABLED + case armnn::ITensorHandle::CL: + { + using armnn::armcomputetensorutils::CopyArmComputeITensorData; + auto handle = boost::polymorphic_downcast(tensorHandle); + const_cast(handle)->Map(true); + switch(handle->GetDataType()) + { + case arm_compute::DataType::F32: + CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); + break; + case arm_compute::DataType::QASYMM8: + CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); + break; + case arm_compute::DataType::F16: + CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); + break; + default: + { + throw armnn::UnimplementedException(); + } + } + const_cast(handle)->Unmap(); + break; + } +#endif +#if ARMCOMPUTENEON_ENABLED + case armnn::ITensorHandle::Neon: + { + using armnn::armcomputetensorutils::CopyArmComputeITensorData; + auto handle = boost::polymorphic_downcast(tensorHandle); + switch (handle->GetDataType()) + { + case arm_compute::DataType::F32: + CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); + break; + case arm_compute::DataType::QASYMM8: + CopyArmComputeITensorData(handle->GetTensor(), static_cast(mem)); + break; + default: + { + throw armnn::UnimplementedException(); + } + } + break; + } +#endif + default: + { + throw armnn::UnimplementedException(); + } + } +} + +void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem) +{ + tensorHandle->Allocate(); + CopyDataToITensorHandle(tensorHandle, mem); +} diff --git a/src/backends/backendsCommon/test/TensorCopyUtils.hpp b/src/backends/backendsCommon/test/TensorCopyUtils.hpp new file mode 100644 index 0000000000..2187523edb --- /dev/null +++ b/src/backends/backendsCommon/test/TensorCopyUtils.hpp @@ -0,0 +1,15 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include + +#include + +void CopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem); + +void CopyDataFromITensorHandle(void* mem, const armnn::ITensorHandle* tensorHandle); + +void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem); \ No newline at end of file diff --git a/src/backends/backendsCommon/test/WorkloadDataValidation.cpp b/src/backends/backendsCommon/test/WorkloadDataValidation.cpp new file mode 100644 index 0000000000..3664d56c28 --- /dev/null +++ b/src/backends/backendsCommon/test/WorkloadDataValidation.cpp @@ -0,0 +1,474 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "WorkloadTestUtils.hpp" + +#include + +#include +#include + +#include +#include + +#include + +using namespace armnn; + +BOOST_AUTO_TEST_SUITE(WorkloadInfoValidation) + + + +BOOST_AUTO_TEST_CASE(QueueDescriptor_Validate_WrongNumOfInputsOutputs) +{ + InputQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + //Invalid argument exception is expected, because no inputs and no outputs were defined. + BOOST_CHECK_THROW(RefWorkloadFactory().CreateInput(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + +BOOST_AUTO_TEST_CASE(RefPooling2dFloat32Workload_Validate_WrongDimTensor) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = {2, 3, 4}; // <- Invalid - input tensor has to be 4D. + unsigned int outputShape[] = {2, 3, 4, 5}; + + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + inputTensorInfo = armnn::TensorInfo(3, inputShape, armnn::DataType::Float32); + + Pooling2dQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); + + // Invalid argument exception is expected, input tensor has to be 4D. + BOOST_CHECK_THROW(RefPooling2dFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + +BOOST_AUTO_TEST_CASE(SoftmaxQueueDescriptor_Validate_WrongInputHeight) +{ + unsigned int inputHeight = 1; + unsigned int inputWidth = 1; + unsigned int inputChannels = 4; + unsigned int inputNum = 2; + + unsigned int outputChannels = inputChannels; + unsigned int outputHeight = inputHeight + 1; //Makes data invalid - Softmax expects height and width to be 1. + unsigned int outputWidth = inputWidth; + unsigned int outputNum = inputNum; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; + unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + SoftmaxQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + + //Invalid argument exception is expected, because height != 1. + BOOST_CHECK_THROW(RefSoftmaxFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + +BOOST_AUTO_TEST_CASE(FullyConnectedQueueDescriptor_Validate_RequiredDataMissing) +{ + unsigned int inputWidth = 1; + unsigned int inputHeight = 1; + unsigned int inputChannels = 5; + unsigned int inputNum = 2; + + unsigned int outputWidth = 1; + unsigned int outputHeight = 1; + unsigned int outputChannels = 3; + unsigned int outputNum = 2; + + // Define the tensor descriptors. + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + armnn::TensorInfo weightsDesc; + armnn::TensorInfo biasesDesc; + + unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; + unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; + unsigned int weightsShape[] = { 1, 1, inputChannels, outputChannels }; + unsigned int biasShape[] = { 1, outputChannels, outputHeight, outputWidth }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + weightsDesc = armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32); + biasesDesc = armnn::TensorInfo(4, biasShape, armnn::DataType::Float32); + + FullyConnectedQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + ScopedCpuTensorHandle weightTensor(weightsDesc); + ScopedCpuTensorHandle biasTensor(biasesDesc); + + AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + invalidData.m_Weight = &weightTensor; + invalidData.m_Bias = &biasTensor; + invalidData.m_Parameters.m_BiasEnabled = true; + invalidData.m_Parameters.m_TransposeWeightMatrix = false; + + + //Invalid argument exception is expected, because not all required fields have been provided. + //In particular inputsData[0], outputsData[0] and weightsData can not be null. + BOOST_CHECK_THROW(RefFullyConnectedFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + + +BOOST_AUTO_TEST_CASE(NormalizationQueueDescriptor_Validate_WrongInputHeight) +{ + constexpr unsigned int inputNum = 5; + constexpr unsigned int inputHeight = 32; + constexpr unsigned int inputWidth = 24; + constexpr unsigned int inputChannels = 3; + + constexpr unsigned int outputNum = inputNum; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputHeight = inputHeight + 1; //Makes data invalid - normalization requires. + //Input and output to have the same dimensions. + constexpr unsigned int outputWidth = inputWidth; + + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; + unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + + armnn::NormalizationAlgorithmMethod normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; + armnn::NormalizationAlgorithmChannel normChannel = armnn::NormalizationAlgorithmChannel::Across; + float alpha = 1.f; + float beta = 1.f; + float kappa = 1.f; + uint32_t normSize = 5; + + NormalizationQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + invalidData.m_Parameters.m_NormChannelType = normChannel; + invalidData.m_Parameters.m_NormMethodType = normMethod; + invalidData.m_Parameters.m_NormSize = normSize; + invalidData.m_Parameters.m_Alpha = alpha; + invalidData.m_Parameters.m_Beta = beta; + invalidData.m_Parameters.m_K = kappa; + + //Invalid argument exception is expected, because input height != output height. + BOOST_CHECK_THROW(RefNormalizationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + +BOOST_AUTO_TEST_CASE(SplitterQueueDescriptor_Validate_WrongWindow) +{ + constexpr unsigned int inputNum = 1; + constexpr unsigned int inputHeight = 32; + constexpr unsigned int inputWidth = 24; + constexpr unsigned int inputChannels = 3; + + constexpr unsigned int outputNum = inputNum; + constexpr unsigned int outputChannels = inputChannels; + constexpr unsigned int outputHeight = 18; + constexpr unsigned int outputWidth = inputWidth; + + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; + unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + SplitterQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + + // Invalid, since it has only 3 dimensions while the input tensor is 4d. + std::vector wOrigin = {0, 0, 0}; + armnn::SplitterQueueDescriptor::ViewOrigin window(wOrigin); + invalidData.m_ViewOrigins.push_back(window); + + BOOST_TEST_INFO("Invalid argument exception is expected, because split window dimensionality does not " + "match input."); + BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + + // Invalid, since window extends past the boundary of input tensor. + std::vector wOrigin3 = {0, 0, 15, 0}; + armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3); + invalidData.m_ViewOrigins[0] = window3; + BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ outputHeight > inputHeight"); + BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + + + std::vector wOrigin4 = {0, 0, 0, 0}; + armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4); + invalidData.m_ViewOrigins[0] = window4; + + std::vector wOrigin5 = {1, 16, 20, 2}; + armnn::SplitterQueueDescriptor::ViewOrigin window5(wOrigin4); + invalidData.m_ViewOrigins.push_back(window5); + + BOOST_TEST_INFO("Invalid exception due to number of split windows not matching number of outputs."); + BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + + +BOOST_AUTO_TEST_CASE(MergerQueueDescriptor_Validate_WrongWindow) +{ + constexpr unsigned int inputNum = 1; + constexpr unsigned int inputChannels = 3; + constexpr unsigned int inputHeight = 32; + constexpr unsigned int inputWidth = 24; + + constexpr unsigned int outputNum = 1; + constexpr unsigned int outputChannels = 3; + constexpr unsigned int outputHeight = 32; + constexpr unsigned int outputWidth = 24; + + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; + unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + MergerQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + + // Invalid, since it has only 3 dimensions while the input tensor is 4d. + std::vector wOrigin = {0, 0, 0}; + armnn::MergerQueueDescriptor::ViewOrigin window(wOrigin); + invalidData.m_ViewOrigins.push_back(window); + + BOOST_TEST_INFO("Invalid argument exception is expected, because merge window dimensionality does not " + "match input."); + BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + + // Invalid, since window extends past the boundary of output tensor. + std::vector wOrigin3 = {0, 0, 15, 0}; + armnn::MergerQueueDescriptor::ViewOrigin window3(wOrigin3); + invalidData.m_ViewOrigins[0] = window3; + BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ inputHeight > outputHeight"); + BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + + + std::vector wOrigin4 = {0, 0, 0, 0}; + armnn::MergerQueueDescriptor::ViewOrigin window4(wOrigin4); + invalidData.m_ViewOrigins[0] = window4; + + std::vector wOrigin5 = {1, 16, 20, 2}; + armnn::MergerQueueDescriptor::ViewOrigin window5(wOrigin4); + invalidData.m_ViewOrigins.push_back(window5); + + BOOST_TEST_INFO("Invalid exception due to number of merge windows not matching number of inputs."); + BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + +BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputNumbers) +{ + armnn::TensorInfo input1TensorInfo; + armnn::TensorInfo input2TensorInfo; + armnn::TensorInfo input3TensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int shape[] = {1, 1, 1, 1}; + + input1TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + input2TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + input3TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + + AdditionQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + + // Too few inputs. + BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + + AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); + + // Correct. + BOOST_CHECK_NO_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo)); + + AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, nullptr); + + // Too many inputs. + BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + +BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputShapes) +{ + armnn::TensorInfo input1TensorInfo; + armnn::TensorInfo input2TensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int shape1[] = {1, 1, 2, 1}; + unsigned int shape2[] = {1, 1, 3, 2}; + + // Incompatible shapes even with broadcasting. + { + input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); + input2TensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); + + AdditionQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); + AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + + BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + } + + // Output size not compatible with input sizes. + { + input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); + input2TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32); + + AdditionQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); + AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + + // Output differs. + BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + } +} + +BOOST_AUTO_TEST_CASE(MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch) +{ + armnn::TensorInfo input0TensorInfo; + armnn::TensorInfo input1TensorInfo; + armnn::TensorInfo outputTensorInfo; + + constexpr unsigned int input0Shape[] = { 2, 2, 4, 4 }; + constexpr std::size_t dimensionCount = std::extent::value; + + // Checks dimension consistency for input tensors. + for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex) + { + unsigned int input1Shape[dimensionCount]; + for (unsigned int i = 0; i < dimensionCount; ++i) + { + input1Shape[i] = input0Shape[i]; + } + + ++input1Shape[dimIndex]; + + input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); + input1TensorInfo = armnn::TensorInfo(dimensionCount, input1Shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); + + MultiplicationQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr); + AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); + + BOOST_CHECK_THROW(RefMultiplicationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + } + + // Checks dimension consistency for input and output tensors. + for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex) + { + unsigned int outputShape[dimensionCount]; + for (unsigned int i = 0; i < dimensionCount; ++i) + { + outputShape[i] = input0Shape[i]; + } + + ++outputShape[dimIndex]; + + input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); + input1TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(dimensionCount, outputShape, armnn::DataType::Float32); + + MultiplicationQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr); + AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); + + BOOST_CHECK_THROW(RefMultiplicationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); + } +} + +BOOST_AUTO_TEST_CASE(ReshapeQueueDescriptor_Validate_MismatchingNumElements) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + // The input and output shapes should have the same number of elements, but these don't. + unsigned int inputShape[] = { 1, 1, 2, 3 }; + unsigned int outputShape[] = { 1, 1, 1, 2 }; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + ReshapeQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + + // InvalidArgumentException is expected, because the number of elements don't match. + BOOST_CHECK_THROW(RefReshapeFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); +} + + +BOOST_AUTO_TEST_CASE(LstmQueueDescriptor_Validate) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 2 }; + unsigned int outputShape[] = { 1 }; + + inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(1, outputShape, armnn::DataType::Float32); + + LstmQueueDescriptor invalidData; + WorkloadInfo invalidInfo; + + AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); + AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); + + BOOST_CHECK_THROW(invalidData.Validate(invalidInfo), armnn::InvalidArgumentException); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/backendsCommon/test/WorkloadTestUtils.hpp b/src/backends/backendsCommon/test/WorkloadTestUtils.hpp new file mode 100644 index 0000000000..05f6dde35f --- /dev/null +++ b/src/backends/backendsCommon/test/WorkloadTestUtils.hpp @@ -0,0 +1,56 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include + +#include + +namespace armnn +{ +class ITensorHandle; +} + +template +void AddInputToWorkload(QueueDescriptor& descriptor, + armnn::WorkloadInfo& info, + const armnn::TensorInfo& tensorInfo, + armnn::ITensorHandle* tensorHandle) +{ + descriptor.m_Inputs.push_back(tensorHandle); + info.m_InputTensorInfos.push_back(tensorInfo); +} + +template +void AddOutputToWorkload(QueueDescriptor& descriptor, + armnn::WorkloadInfo& info, + const armnn::TensorInfo& tensorInfo, + armnn::ITensorHandle* tensorHandle) +{ + descriptor.m_Outputs.push_back(tensorHandle); + info.m_OutputTensorInfos.push_back(tensorInfo); +} + +template +void SetWorkloadInput(QueueDescriptor& descriptor, + armnn::WorkloadInfo& info, + unsigned int index, + const armnn::TensorInfo& tensorInfo, + armnn::ITensorHandle* tensorHandle) +{ + descriptor.m_Inputs[index] = tensorHandle; + info.m_InputTensorInfos[index] = tensorInfo; +} + +template +void SetWorkloadOutput(QueueDescriptor& descriptor, + armnn::WorkloadInfo& info, + unsigned int index, + const armnn::TensorInfo& tensorInfo, + armnn::ITensorHandle* tensorHandle) +{ + descriptor.m_Outputs[index] = tensorHandle; + info.m_OutputTensorInfos[index] = tensorInfo; +} \ No newline at end of file -- cgit v1.2.1