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Diffstat (limited to 'src/armnn/backends/test/FullyConnectedTestImpl.hpp')
| -rw-r--r-- | src/armnn/backends/test/FullyConnectedTestImpl.hpp | 287 |
1 files changed, 0 insertions, 287 deletions
diff --git a/src/armnn/backends/test/FullyConnectedTestImpl.hpp b/src/armnn/backends/test/FullyConnectedTestImpl.hpp deleted file mode 100644 index 125b7e62b1..0000000000 --- a/src/armnn/backends/test/FullyConnectedTestImpl.hpp +++ /dev/null @@ -1,287 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -template<typename T, typename B> -LayerTestResult<T, 2> SimpleFullyConnectedTestImpl( - armnn::IWorkloadFactory& workloadFactory, - 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]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get()); - - return result; -} - -LayerTestResult<float, 2> 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<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, - 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<uint8_t, 2> 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<uint8_t, 2> result(outputTensorInfo); - - auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>{51, 124, 28, - 251, 8, 92}); - - auto weights = MakeTensor<uint8_t, 2>(weightsDesc, std::vector<uint8_t>{51, 193, 42, 53, 175, 34, - 210, 145, 23, 74, 34, 150}); - - // scale = 0.02 - // offset = 0 - auto bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500}); - - result = SimpleFullyConnectedTestImpl<uint8_t>( - 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<uint8_t, 2>(outputTensorInfo, std::vector<uint8_t>{0, 242}); - } - else - { - result.outputExpected = MakeTensor<uint8_t, 2>(outputTensorInfo, std::vector<uint8_t>{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<typename T> -LayerTestResult<T, 2> 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<T>()); - outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::GetDataType<T>()); - weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::GetDataType<T>()); - biasesDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType<T>()); - - // 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, - QuantizedVector<T>(qScale, qOffset, { - 1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f, - }) - ); - - boost::multi_array<T, 2> weights = MakeTensor<T, 2>(weightsDesc, - QuantizedVector<T>(qScale, qOffset, { - 2.0f, 3.0f, 4.0f, 5.0f, 6.0f - }) - ); - - std::vector<T> biasValues({900000.f}); - boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasesDesc, biasValues); - - result = SimpleFullyConnectedTestImpl<T>( - workloadFactory, - inputTensorInfo, outputTensorInfo, - weightsDesc, biasesDesc, - weights, bias, input, - true, transposeWeights - ); - - result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 965432.0f, - }) - ); - - return result; -} |