diff options
Diffstat (limited to 'src/backends/backendsCommon/test/LayerTests.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/LayerTests.hpp | 145 |
1 files changed, 144 insertions, 1 deletions
diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp index 9e57ec58a3..f8f50366eb 100644 --- a/src/backends/backendsCommon/test/LayerTests.hpp +++ b/src/backends/backendsCommon/test/LayerTests.hpp @@ -8,9 +8,15 @@ #include <armnn/Tensor.hpp> #include <Half.hpp> +#include "TensorCopyUtils.hpp" +#include "WorkloadTestUtils.hpp" +#include <backendsCommon/CpuTensorHandle.hpp> #include <backendsCommon/IBackendInternal.hpp> #include <backendsCommon/IMemoryManager.hpp> +#include <reference/workloads/Decoders.hpp> +#include <reference/workloads/Encoders.hpp> +#include <test/TensorHelpers.hpp> #include <boost/multi_array.hpp> #include <boost/assert.hpp> @@ -793,7 +799,8 @@ LayerTestResult<uint8_t, 4> BoundedReLuUint8Test( float upperBound, float lowerBound); -LayerTestResult<uint8_t, 2> FullyConnectedUint8Test( +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 2> FullyConnectedTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool biasEnabled); @@ -1638,3 +1645,139 @@ LayerTestResult<uint8_t, 4> QuantizeClampUint8Test( LayerTestResult<int16_t, 4> QuantizeClampInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); + +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) +{ + 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 = armnn::ResolveType<ArmnnType>> +std::vector<T> ConvertToDataType(const std::vector<float>& input, + const armnn::TensorInfo& inputTensorInfo) +{ + std::vector<T> output(input.size()); + auto outputTensorInfo = inputTensorInfo; + outputTensorInfo.SetDataType(ArmnnType); + + std::unique_ptr<armnn::Encoder<float>> pOutputEncoder = armnn::MakeEncoder<float>(outputTensorInfo, output.data()); + armnn::Encoder<float>& rOutputEncoder = *pOutputEncoder; + + for (auto it = input.begin(); it != input.end(); ++it) + { + rOutputEncoder.Set(*it); + ++rOutputEncoder; + } + return output; +} + +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; +}
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