From 00d306e4db5153a4f4d280de4d4cf3e03788fefb Mon Sep 17 00:00:00 2001 From: Aron Virginas-Tar Date: Wed, 28 Aug 2019 18:08:46 +0100 Subject: IVGCVSW-3381 Break up LayerTests.hpp into more manageable files Signed-off-by: Aron Virginas-Tar Change-Id: Icf39434f09fd340ad664cb3b97b8bee6d9da4838 --- .../test/layerTests/SoftmaxTestImpl.cpp | 682 +++++++++++++++++++++ 1 file changed, 682 insertions(+) create mode 100644 src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp (limited to 'src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp') diff --git a/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp new file mode 100644 index 0000000000..49184edde9 --- /dev/null +++ b/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp @@ -0,0 +1,682 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "SoftmaxTestImpl.hpp" + +#include + +#include + +#include + +#include +#include +#include + +#include + +#include + +namespace +{ + +struct Simple3dSoftmaxOutputData +{ + const std::vector outputData = + { + 0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f, + 0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f + }; + + const armnn::TensorShape inputShape{ 1, 8, 1 }; + + const std::vector inputData = + { + 0.0f, 1.0f, 0.0f, 0.0f, + 0.5f, 0.0f, 0.0f, 0.0f, + }; +}; + +struct Simple4dSoftmaxData +{ + const armnn::TensorShape inputShape{ 1, 8, 1, 1 }; + + const std::vector outputData = + { + 0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f, + 0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f + }; + + const std::vector inputData = + { + 0.0f, 1.0f, 0.0f, 0.0f, + 0.5f, 0.0f, 0.0f, 0.0f + }; +}; + +template> +LayerTestResult SimpleSoftmaxBaseTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta, + const armnn::TensorShape& inputShape, + const std::vector& outputData, + const std::vector& inputData, + int axis = 1) +{ + using std::exp; + + const float qScale = 1.f / 256.f; + const int qOffset = 0; + + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + inputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType); + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + + outputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + + LayerTestResult ret(outputTensorInfo); + + // Each row is independently softmax'd. + auto input = MakeTensor(inputTensorInfo, std::vector( + QuantizedVector(qScale, qOffset, inputData))); + + std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::SoftmaxQueueDescriptor data; + data.m_Parameters.m_Beta = beta; + data.m_Parameters.m_Axis = axis; + + 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.origin()); + + BOOST_ASSERT(workload); + + ExecuteWorkload(*workload, memoryManager); + + CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get()); + + std::vector expectedOutput = std::vector( + QuantizedVector(qScale, qOffset, outputData)); + ret.outputExpected = MakeTensor(outputTensorInfo, expectedOutput); + + return ret; +} + +template> +LayerTestResult SimpleSoftmaxTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + using std::exp; + const armnn::TensorShape inputShape{ 2, 4 }; + + 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]; + + const std::vector outputData = { x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0, + x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 }; + + const std::vector inputData = + { + 0.f, 1.f, 0.f, 0.f, + .5f, 0.f, 0.f, 0.f, + }; + + return SimpleSoftmaxBaseTestImpl(workloadFactory, memoryManager, beta, + inputShape, outputData, inputData); +} + +template> +LayerTestResult SimpleSoftmaxTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta, + int axis) +{ + armnn::TensorShape inputShape; + std::vector inputData; + std::vector outputData; + switch (axis) + { + case -2: + case 0: + { + inputShape = {5, 2}; + + inputData = + { + 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f, + 0.087144312427294f, + 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, + 7.246299848982885e-08f + }; + break; + } + case -1: + case 1: + { + inputShape = {2, 5}; + + inputData = + { + 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f + }; + break; + } + } + return SimpleSoftmaxBaseTestImpl(workloadFactory, memoryManager, beta, + inputShape, outputData, inputData, axis); +} + +template> +LayerTestResult Simple3dSoftmaxTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta, + const armnn::TensorShape& inputShape, + const std::vector& outputData, + const std::vector& inputData, + int axis = 1) +{ + return SimpleSoftmaxBaseTestImpl(workloadFactory, memoryManager, beta, + inputShape, outputData, inputData, axis); +} + +template> +LayerTestResult Simple4dSoftmaxTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta, + const armnn::TensorShape& inputShape, + const std::vector& outputData, + const std::vector& inputData, + int axis = 1) +{ + + return SimpleSoftmaxBaseTestImpl(workloadFactory, memoryManager, beta, + inputShape, outputData, inputData, axis); +} + +template> +LayerTestResult CompareSoftmaxTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + 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, ArmnnType); + outputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType); + 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]); + + ExecuteWorkload(*workload, memoryManager); + + workloadRef->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); + CopyDataFromITensorHandle(&ret.outputExpected[0][0], outputHandleRef.get()); + + return ret; +} + +} // anonymous namespace + +LayerTestResult SimpleSoftmaxTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + return SimpleSoftmaxTestImpl(workloadFactory, memoryManager, beta); +} + +LayerTestResult SimpleAxisSoftmaxTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta, + int axis) +{ + return SimpleSoftmaxTestImpl(workloadFactory, memoryManager, beta, axis); +} + +LayerTestResult Simple3dSoftmaxTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + Simple3dSoftmaxOutputData data; + return Simple3dSoftmaxTestImpl(workloadFactory, memoryManager, beta, + data.inputShape, data.outputData, data.inputData); +} + +LayerTestResult Simple3dAxisSoftmaxTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta, + int axis) +{ + armnn::TensorShape inputShape; + std::vector inputData; + std::vector outputData; + switch (axis) + { + case -3: + case 0: + { + inputShape = {5, 2, 2}; + + inputData = + { + 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f, + + 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, + 0.236882800924671f, + 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f, + 0.087144312427294f, + + 0.087144312427294f, 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, + 0.032058600957022f, + 0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f, + 7.246299848982885e-08f + }; + break; + } + case -2: + case 1: + { + inputShape = {2, 5, 2}; + + inputData = + { + 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f, + + 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f, + 0.087144312427294f, + 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, + 7.246299848982885e-08f, + + 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f, + 0.087144312427294f, + 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, + 7.246299848982885e-08f + }; + break; + } + case -1: + case 2: + { + inputShape = {2, 2, 5}; + + inputData = + { + 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f, + 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f + }; + break; + } + } + + return Simple3dSoftmaxTestImpl(workloadFactory, memoryManager, beta, + inputShape, outputData, inputData, axis); +} + +LayerTestResult Simple4dSoftmaxTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + Simple4dSoftmaxData data; + return Simple4dSoftmaxTestImpl(workloadFactory, memoryManager, beta, data.inputShape, + data.outputData, data.inputData); +} + +LayerTestResult Simple4dAxisSoftmaxTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta, + int axis) +{ + armnn::TensorShape inputShape; + std::vector inputData; + std::vector outputData; + switch (axis) + { + case -4: + case 0: + { + inputShape = {5, 2, 2, 2}; + + inputData = + { + 17.0f, -1.0f, 17.0f, -1.0f, 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, + 16.0f, -2.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f, 15.0f, -3.0f, + 15.0f, -3.0f, 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 14.0f, -4.0f, + 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f, 1.0f, -17.0f, 1.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, + 0.643914213228014f, + 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, + 0.236882800924671f, + 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, + 0.236882800924671f, + 0.236882800924671f, 0.087144312427294f, 0.087144312427294f, 0.087144312427294f, + 0.087144312427294f, + + 0.087144312427294f, 0.087144312427294f, 0.087144312427294f, 0.087144312427294f, + 0.032058600957022f, + 0.032058600957022f, 0.032058600957022f, 0.032058600957022f, 0.032058600957022f, + 0.032058600957022f, + 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, + 7.246299848982885e-08f, + 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f, + 7.246299848982885e-08f, 7.246299848982885e-08f + }; + break; + } + case -3: + case 1: + { + inputShape = {2, 5, 2, 2}; + + inputData = + { + 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f, + 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f, + 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f, + 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, + 0.236882800924671f, + 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f, + 0.087144312427294f, + 0.087144312427294f, 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, + 0.032058600957022f, + 0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f, + 7.246299848982885e-08f, + + + 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, + 0.236882800924671f, + 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f, + 0.087144312427294f, + 0.087144312427294f, 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, + 0.032058600957022f, + 0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f, + 7.246299848982885e-08f + }; + break; + } + case -2: + case 2: + { + inputShape = {2, 2, 5, 2}; + + inputData = + { + 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f, + 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f, + 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f, + 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f, + 0.087144312427294f, + 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f, + 0.087144312427294f, + 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, + 7.246299848982885e-08f, + + 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f, + 0.087144312427294f, + 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f, + 0.087144312427294f, + 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, + 7.246299848982885e-08f + }; + break; + } + case -1: + case 3: + { + inputShape = {2, 2, 2, 5}; + + inputData = + { + 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f, + 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f, + 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f, + 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f + }; + + outputData = + { + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f, + 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f, + 7.246299848982885e-08f + }; + break; + } + } + + return Simple4dSoftmaxTestImpl( + workloadFactory, + memoryManager, + beta, + inputShape, + outputData, + inputData, + axis); +} + +LayerTestResult SimpleSoftmaxUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + return SimpleSoftmaxTestImpl(workloadFactory, memoryManager, beta); +} + +LayerTestResult Simple3dSoftmaxUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + Simple3dSoftmaxOutputData data; + return Simple3dSoftmaxTestImpl( + workloadFactory, + memoryManager, + beta, + data.inputShape, + data.outputData, + data.inputData); +} + +LayerTestResult Simple4dSoftmaxUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + Simple4dSoftmaxData data; + + return Simple4dSoftmaxTestImpl(workloadFactory, memoryManager, beta, + data.inputShape, data.outputData, data.inputData); +} + +LayerTestResult SimpleSoftmaxUint16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + return SimpleSoftmaxTestImpl(workloadFactory, memoryManager, beta); +} + +LayerTestResult Simple3dSoftmaxUint16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + Simple3dSoftmaxOutputData data; + return Simple3dSoftmaxTestImpl(workloadFactory, memoryManager, beta, + data.inputShape, data.outputData, data.inputData); +} + +LayerTestResult Simple4dSoftmaxUint16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float beta) +{ + Simple4dSoftmaxData data; + + return Simple4dSoftmaxTestImpl(workloadFactory, memoryManager, beta, + data.inputShape, data.outputData, data.inputData); +} + +LayerTestResult CompareSoftmaxTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + armnn::IWorkloadFactory& refWorkloadFactory, + float beta) +{ + return CompareSoftmaxTestImpl( + workloadFactory, memoryManager, refWorkloadFactory, beta); +} + +LayerTestResult CompareSoftmaxUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + armnn::IWorkloadFactory& refWorkloadFactory, + float beta) +{ + return CompareSoftmaxTestImpl( + workloadFactory, memoryManager, refWorkloadFactory, beta); +} -- cgit v1.2.1