| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "SoftmaxTestImpl.hpp" |
| |
| #include <ResolveType.hpp> |
| |
| #include <armnn/ArmNN.hpp> |
| |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| |
| #include <backendsCommon/test/QuantizeHelper.hpp> |
| #include <backendsCommon/test/TensorCopyUtils.hpp> |
| #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| |
| #include <algorithm> |
| |
| namespace |
| { |
| |
| struct Simple3dSoftmaxOutputData |
| { |
| const std::vector<float> 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<float> 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<float> outputData = |
| { |
| 0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f, |
| 0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f |
| }; |
| |
| const std::vector<float> inputData = |
| { |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.5f, 0.0f, 0.0f, 0.0f |
| }; |
| }; |
| |
| template<armnn::DataType ArmnnType, std::size_t n, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, n> SimpleSoftmaxBaseTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta, |
| const armnn::TensorShape& inputShape, |
| const std::vector<float>& outputData, |
| const std::vector<float>& 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<T, n> ret(outputTensorInfo); |
| |
| // Each row is independently softmax'd. |
| auto input = MakeTensor<T, n>(inputTensorInfo, std::vector<T>( |
| QuantizedVector<T>(qScale, qOffset, inputData))); |
| |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> 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<armnn::IWorkload> 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<T> expectedOutput = std::vector<T>( |
| QuantizedVector<T>(qScale, qOffset, outputData)); |
| ret.outputExpected = MakeTensor<T, n>(outputTensorInfo, expectedOutput); |
| |
| return ret; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 2> 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<float> 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<float> inputData = |
| { |
| 0.f, 1.f, 0.f, 0.f, |
| .5f, 0.f, 0.f, 0.f, |
| }; |
| |
| return SimpleSoftmaxBaseTestImpl<ArmnnType, 2>(workloadFactory, memoryManager, beta, |
| inputShape, outputData, inputData); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 2> SimpleSoftmaxTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta, |
| int axis) |
| { |
| armnn::TensorShape inputShape; |
| std::vector<float> inputData; |
| std::vector<float> 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<ArmnnType, 2>(workloadFactory, memoryManager, beta, |
| inputShape, outputData, inputData, axis); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 3> Simple3dSoftmaxTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta, |
| const armnn::TensorShape& inputShape, |
| const std::vector<float>& outputData, |
| const std::vector<float>& inputData, |
| int axis = 1) |
| { |
| return SimpleSoftmaxBaseTestImpl<ArmnnType, 3>(workloadFactory, memoryManager, beta, |
| inputShape, outputData, inputData, axis); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 4> Simple4dSoftmaxTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta, |
| const armnn::TensorShape& inputShape, |
| const std::vector<float>& outputData, |
| const std::vector<float>& inputData, |
| int axis = 1) |
| { |
| |
| return SimpleSoftmaxBaseTestImpl<ArmnnType, 4>(workloadFactory, memoryManager, beta, |
| inputShape, outputData, inputData, axis); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 2> 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<T, 2> ret(outputTensorInfo); |
| auto input = MakeRandomTensor<T, 2>(inputTensorInfo, 0xF00D, 0.0f, 1.0f); |
| |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> 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<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> 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<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info); |
| std::unique_ptr<armnn::IWorkload> 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<float,2> SimpleSoftmaxTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| return SimpleSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta); |
| } |
| |
| LayerTestResult<float,2> SimpleAxisSoftmaxTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta, |
| int axis) |
| { |
| return SimpleSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta, axis); |
| } |
| |
| LayerTestResult<float,3> Simple3dSoftmaxTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| Simple3dSoftmaxOutputData data; |
| return Simple3dSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta, |
| data.inputShape, data.outputData, data.inputData); |
| } |
| |
| LayerTestResult<float,3> Simple3dAxisSoftmaxTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta, |
| int axis) |
| { |
| armnn::TensorShape inputShape; |
| std::vector<float> inputData; |
| std::vector<float> 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<armnn::DataType::Float32>(workloadFactory, memoryManager, beta, |
| inputShape, outputData, inputData, axis); |
| } |
| |
| LayerTestResult<float,4> Simple4dSoftmaxTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| Simple4dSoftmaxData data; |
| return Simple4dSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta, data.inputShape, |
| data.outputData, data.inputData); |
| } |
| |
| LayerTestResult<float,4> Simple4dAxisSoftmaxTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta, |
| int axis) |
| { |
| armnn::TensorShape inputShape; |
| std::vector<float> inputData; |
| std::vector<float> 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<armnn::DataType::Float32>( |
| workloadFactory, |
| memoryManager, |
| beta, |
| inputShape, |
| outputData, |
| inputData, |
| axis); |
| } |
| |
| LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| return SimpleSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, beta); |
| } |
| |
| LayerTestResult<uint8_t,3> Simple3dSoftmaxUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| Simple3dSoftmaxOutputData data; |
| return Simple3dSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>( |
| workloadFactory, |
| memoryManager, |
| beta, |
| data.inputShape, |
| data.outputData, |
| data.inputData); |
| } |
| |
| LayerTestResult<uint8_t,4> Simple4dSoftmaxUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| Simple4dSoftmaxData data; |
| |
| return Simple4dSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, beta, |
| data.inputShape, data.outputData, data.inputData); |
| } |
| |
| LayerTestResult<armnn::Half,2> SimpleSoftmaxFloat16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| return SimpleSoftmaxTestImpl<armnn::DataType::Float16>(workloadFactory, memoryManager, beta); |
| } |
| |
| LayerTestResult<armnn::Half,3> Simple3dSoftmaxFloat16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| Simple3dSoftmaxOutputData data; |
| return Simple3dSoftmaxTestImpl<armnn::DataType::Float16>(workloadFactory, memoryManager, beta, |
| data.inputShape, data.outputData, data.inputData); |
| } |
| |
| LayerTestResult<armnn::Half,4> Simple4dSoftmaxFloat16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| Simple4dSoftmaxData data; |
| return Simple4dSoftmaxTestImpl<armnn::DataType::Float16>(workloadFactory, memoryManager, beta, |
| data.inputShape, data.outputData, data.inputData); |
| } |
| |
| LayerTestResult<int16_t,2> SimpleSoftmaxUint16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| return SimpleSoftmaxTestImpl<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, beta); |
| } |
| |
| LayerTestResult<int16_t,3> Simple3dSoftmaxUint16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| Simple3dSoftmaxOutputData data; |
| return Simple3dSoftmaxTestImpl<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, beta, |
| data.inputShape, data.outputData, data.inputData); |
| } |
| |
| LayerTestResult<int16_t,4> Simple4dSoftmaxUint16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| float beta) |
| { |
| Simple4dSoftmaxData data; |
| |
| return Simple4dSoftmaxTestImpl<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, beta, |
| data.inputShape, data.outputData, data.inputData); |
| } |
| |
| LayerTestResult<float,2> CompareSoftmaxTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::IWorkloadFactory& refWorkloadFactory, |
| float beta) |
| { |
| return CompareSoftmaxTestImpl<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, refWorkloadFactory, beta); |
| } |
| |
| LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::IWorkloadFactory& refWorkloadFactory, |
| float beta) |
| { |
| return CompareSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>( |
| workloadFactory, memoryManager, refWorkloadFactory, beta); |
| } |
