| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "FullyConnectedTestImpl.hpp" |
| |
| #include <armnn/ArmNN.hpp> |
| |
| #include <QuantizeHelper.hpp> |
| |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| |
| #include <backendsCommon/test/DataTypeUtils.hpp> |
| #include <backendsCommon/test/TensorCopyUtils.hpp> |
| #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| |
| // |
| // Implementation templates |
| // |
| |
| template<typename T, typename B> |
| LayerTestResult<T, 2> SimpleFullyConnectedTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::TensorInfo inputTensorInfo, |
| armnn::TensorInfo outputTensorInfo, |
| armnn::TensorInfo weightsDesc, |
| armnn::TensorInfo biasesDesc, |
| boost::multi_array<T, 2>& weights, |
| boost::multi_array<B, 1>& bias, |
| boost::multi_array<T, 4>& input, |
| bool biasEnabled, |
| bool transposeWeights) |
| { |
| boost::ignore_unused(memoryManager); |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> 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<armnn::IWorkload> workload = workloadFactory.CreateFullyConnected(data, info); |
| LayerTestResult<T, 2> result(outputTensorInfo); |
| |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| |
| ExecuteWorkload(*workload, memoryManager); |
| |
| CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get()); |
| |
| return result; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T> |
| LayerTestResult<T, 2> FullyConnectedTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 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 }, ArmnnType); |
| inputTensorInfo.SetQuantizationScale(0.1f); |
| inputTensorInfo.SetQuantizationOffset(63); |
| |
| armnn::TensorInfo outputTensorInfo({ 1, outputChannels }, ArmnnType); |
| outputTensorInfo.SetQuantizationScale(5.f); |
| outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10); |
| |
| armnn::TensorInfo weightsDesc({ outputChannels, inputSize }, ArmnnType); |
| weightsDesc.SetQuantizationScale(0.2f); |
| weightsDesc.SetQuantizationOffset(93); |
| |
| armnn::TensorInfo biasesDesc({ outputChannels }, GetBiasTypeFromWeightsType(weightsDesc.GetDataType()).value()); |
| biasesDesc.SetQuantizationScale(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale()); |
| biasesDesc.SetQuantizationOffset(0); |
| |
| LayerTestResult<T, 2> result(outputTensorInfo); |
| |
| auto input = MakeTensor<T, 4>(inputTensorInfo, ConvertToDataType<ArmnnType>( |
| { |
| -1.2f, 6.1f, -3.5f, |
| 18.8f, -5.5f, 2.9f |
| }, |
| inputTensorInfo)); |
| |
| auto weights = MakeTensor<T, 2>(weightsDesc, ConvertToDataType<ArmnnType>( |
| { |
| -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f, |
| 23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f |
| }, |
| weightsDesc)); |
| |
| auto bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500}); |
| |
| result = SimpleFullyConnectedTestImpl<T>( |
| workloadFactory, |
| memoryManager, |
| inputTensorInfo, outputTensorInfo, |
| weightsDesc, biasesDesc, |
| weights, bias, input, |
| biasEnabled, true |
| ); |
| |
| if (biasEnabled) |
| { |
| result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, |
| ConvertToDataType<ArmnnType>({80.f, 1460.f}, outputTensorInfo)); |
| } |
| else |
| { |
| result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, |
| ConvertToDataType<ArmnnType>({-107.04f, 110.f}, outputTensorInfo)); |
| } |
| |
| 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<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 2> FullyConnectedLargeTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 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, ArmnnType); |
| outputTensorInfo = armnn::TensorInfo(2, outputShape, ArmnnType); |
| weightsDesc = armnn::TensorInfo(2, weightsShape, ArmnnType); |
| biasesDesc = armnn::TensorInfo(1, biasShape, ArmnnType); |
| |
| // Set quantization parameters if the requested type is a quantized type. |
| if(armnn::IsQuantizedType<T>()) |
| { |
| inputTensorInfo.SetQuantizationScale(qScale); |
| inputTensorInfo.SetQuantizationOffset(qOffset); |
| outputTensorInfo.SetQuantizationScale(qScale); |
| outputTensorInfo.SetQuantizationOffset(qOffset); |
| } |
| |
| LayerTestResult<T, 2> result(outputTensorInfo); |
| |
| boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo, |
| armnnUtils::QuantizedVector<T>({ |
| 1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f, |
| }, |
| qScale, qOffset) |
| ); |
| |
| boost::multi_array<T, 2> weights = MakeTensor<T, 2>(weightsDesc, |
| armnnUtils::QuantizedVector<T>({ |
| 2.0f, 3.0f, 4.0f, 5.0f, 6.0f |
| }, |
| qScale, qOffset) |
| ); |
| |
| std::vector<T> biasValues({900000.f}); |
| boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasesDesc, biasValues); |
| |
| result = SimpleFullyConnectedTestImpl<T>( |
| workloadFactory, |
| memoryManager, |
| inputTensorInfo, outputTensorInfo, |
| weightsDesc, biasesDesc, |
| weights, bias, input, |
| true, transposeWeights |
| ); |
| |
| result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, |
| armnnUtils::QuantizedVector<T>({ 965432.0f }, qScale, qOffset)); |
| |
| return result; |
| } |
| |
| // |
| // Explicit template specializations |
| // |
| |
| template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 2> |
| FullyConnectedTest<armnn::DataType::QuantisedAsymm8>( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| bool biasEnabled); |
| |
| template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 2> |
| FullyConnectedTest<armnn::DataType::QuantisedSymm16>( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| bool biasEnabled); |
| |
| // |
| // Implementation functions |
| // |
| |
| LayerTestResult<float, 2> FullyConnectedFloat32Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 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<float, 2> result(outputTensorInfo); |
| |
| boost::multi_array<float, 4> input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>( |
| { |
| 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, |
| |
| 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| }) |
| ); |
| |
| boost::multi_array<float, 2> weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>( |
| { |
| .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<float, 2>(weightsDesc, std::vector<float>( |
| { |
| .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<float> biasValues({0.f, 0.f, 0.f}); |
| if (biasEnabled) |
| { |
| biasValues = std::vector<float>({10.f, 20.f, 30.f}); |
| } |
| boost::multi_array<float, 1> bias = MakeTensor<float, 1>(biasesDesc, biasValues); |
| |
| result = SimpleFullyConnectedTestImpl<float>( |
| workloadFactory, |
| memoryManager, |
| inputTensorInfo, outputTensorInfo, |
| weightsDesc, biasesDesc, |
| weights, bias, input, |
| biasEnabled, transposeWeights |
| ); |
| |
| result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, std::vector<float>( |
| { |
| 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<float, 2> FullyConnectedLargeTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| bool transposeWeights) |
| { |
| return FullyConnectedLargeTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, transposeWeights); |
| } |
