diff options
Diffstat (limited to 'src/armnn/test/OptimizerTests.cpp')
| -rw-r--r-- | src/armnn/test/OptimizerTests.cpp | 308 |
1 files changed, 299 insertions, 9 deletions
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp index 3af50ecf3a..879905bda8 100644 --- a/src/armnn/test/OptimizerTests.cpp +++ b/src/armnn/test/OptimizerTests.cpp @@ -597,11 +597,11 @@ BOOST_AUTO_TEST_CASE(FoldPadLayerIntoConvolution2dLayer) }; BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::PadLayer>, - checkSimpleConv2d, - &IsLayerOfType<armnn::OutputLayer>)); + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::PadLayer>, + checkSimpleConv2d, + &IsLayerOfType<armnn::OutputLayer>)); armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FoldPadIntoConvolution2d())); @@ -622,10 +622,10 @@ BOOST_AUTO_TEST_CASE(FoldPadLayerIntoConvolution2dLayer) }; BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - checkPadFoldedIntoConv2d, - &IsLayerOfType<armnn::OutputLayer>)); + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + checkPadFoldedIntoConv2d, + &IsLayerOfType<armnn::OutputLayer>)); } @@ -798,4 +798,294 @@ BOOST_AUTO_TEST_CASE(BackendHintTest) } } +BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_fuse_Test) +{ + using namespace armnn; + // Define layers information + Convolution2dDescriptor convolution2dDescriptor; + convolution2dDescriptor.m_BiasEnabled = false; + convolution2dDescriptor.m_DataLayout = DataLayout::NHWC; + BatchNormalizationDescriptor batchNormDescriptor; + batchNormDescriptor.m_DataLayout = DataLayout::NHWC; + + const unsigned int inputDimensionSizes[] = {1, 4, 4, 3}; // NHWCin + const unsigned int weightsDimensionSizes[] = {1, 2, 2, 3}; // CoutHWCin + const unsigned int outputDimensionSizes[] = {1, 3, 3, 1}; // NHWCout + const unsigned int outputChannelSize[] = {outputDimensionSizes[3]}; // Cout + + TensorInfo inputInfo (4, inputDimensionSizes, DataType::Float32); + TensorInfo outputInfo(4, outputDimensionSizes, DataType::Float32); + + std::vector<float> weightsVector = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; + ConstTensor weights (TensorInfo(4, weightsDimensionSizes, DataType::Float32), weightsVector); + + + std::vector<float> betaVector = {0.1f}; + std::vector<float> gammaVector = {0.5f}; + std::vector<float> meanVector = {0}; + std::vector<float> varianceVector = {1}; + ConstTensor beta (TensorInfo(1, outputChannelSize, DataType::Float32), betaVector); + ConstTensor gamma (TensorInfo(1, outputChannelSize, DataType::Float32), gammaVector); + ConstTensor mean (TensorInfo(1, outputChannelSize, DataType::Float32), meanVector); + ConstTensor variance(TensorInfo(1, outputChannelSize, DataType::Float32), varianceVector); + + // Define the network + Graph graph; + auto input = graph.AddLayer<InputLayer>(0, "input"); + auto conv = graph.AddLayer<Convolution2dLayer>(convolution2dDescriptor, "convolution"); + auto batchNorm = graph.AddLayer<BatchNormalizationLayer>(batchNormDescriptor, "batchNorm"); + auto output = graph.AddLayer<OutputLayer>(0, "output"); + + // Set layer information + input ->GetOutputSlot().SetTensorInfo(inputInfo); + conv ->GetOutputSlot().SetTensorInfo(outputInfo); + batchNorm->GetOutputSlot().SetTensorInfo(outputInfo); + conv ->m_Weight = std::make_unique<ScopedCpuTensorHandle>(weights); + batchNorm->m_Beta = std::make_unique<ScopedCpuTensorHandle>(beta); + batchNorm->m_Gamma = std::make_unique<ScopedCpuTensorHandle>(gamma); + batchNorm->m_Mean = std::make_unique<ScopedCpuTensorHandle>(mean); + batchNorm->m_Variance = std::make_unique<ScopedCpuTensorHandle>(variance); + if (convolution2dDescriptor.m_BiasEnabled) + { + std::vector<float> biasVector = {11}; + ConstTensor bias (TensorInfo(1, outputChannelSize, DataType::Float32), biasVector); + conv->m_Bias = std::make_unique<ScopedCpuTensorHandle>(bias); + } + + // Connect layers + input ->GetOutputSlot(0).Connect(conv ->GetInputSlot(0)); + conv ->GetOutputSlot(0).Connect(batchNorm->GetInputSlot(0)); + batchNorm ->GetOutputSlot(0).Connect(output ->GetInputSlot(0)); + + BOOST_CHECK(4 == graph.GetNumLayers()); + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<Convolution2dLayer>, + &IsLayerOfType<BatchNormalizationLayer>, + &IsLayerOfType<OutputLayer>)); + + // Optimize graph + armnn::Optimizer::Pass(graph, MakeOptimizations(FuseBatchNormIntoConvolution2D())); + + auto checkFusedConv2d = [ ](const armnn::Layer* const layer) -> bool + { + return IsLayerOfType<armnn::Convolution2dLayer>(layer) && + (layer->GetNameStr() == "fused-batchNorm-into-convolution"); + }; + + BOOST_CHECK(3 == graph.GetNumLayers()); + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<InputLayer>, + checkFusedConv2d, + &IsLayerOfType<OutputLayer>)); +} + +BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_notFuse_Test) +{ + // Define the network + Graph graph; + Convolution2dDescriptor convolution2dDescriptor; + BatchNormalizationDescriptor batchNormDescriptor; + + auto input = graph.AddLayer<InputLayer>(0, "input"); + auto conv = graph.AddLayer<Convolution2dLayer>(convolution2dDescriptor, "convolution"); + auto batchNorm = graph.AddLayer<BatchNormalizationLayer>(batchNormDescriptor, "batchNorm"); + auto output = graph.AddLayer<OutputLayer>(0, "output"); + auto output2 = graph.AddLayer<OutputLayer>(1, "output2"); + + // Connect layers + input ->GetOutputSlot(0).Connect(conv ->GetInputSlot(0)); + conv ->GetOutputSlot(0).Connect(batchNorm->GetInputSlot(0)); + batchNorm ->GetOutputSlot(0).Connect(output ->GetInputSlot(0)); + conv ->GetOutputSlot(0).Connect(output2 ->GetInputSlot(0)); + + BOOST_CHECK(5 == graph.GetNumLayers()); + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::Convolution2dLayer>, + &IsLayerOfType<armnn::BatchNormalizationLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + // Optimize graph + armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FuseBatchNormIntoConvolution2D())); + + BOOST_CHECK(5 == graph.GetNumLayers()); + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::Convolution2dLayer>, + &IsLayerOfType<armnn::BatchNormalizationLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); +} + +BOOST_AUTO_TEST_CASE(Fuse_batchNorm_into_Conv2D_Float32_Test) +{ + using namespace armnn; + + // Define layers information + Convolution2dDescriptor convolution2dDescriptor; + convolution2dDescriptor.m_BiasEnabled = false; + convolution2dDescriptor.m_DataLayout = DataLayout::NHWC; + convolution2dDescriptor.m_StrideX = 1; + convolution2dDescriptor.m_StrideY = 1; + BatchNormalizationDescriptor batchNormDescriptor; + batchNormDescriptor.m_DataLayout = DataLayout::NHWC; + + const unsigned int inputDimensionSizes[] = {1, 4, 4, 3}; // NHWCin + const unsigned int weightsDimensionSizes[] = {4, 2, 2, 3}; // CoutHWCin + const unsigned int outputDimensionSizes[] = {1, 3, 3, 4}; // NHWCout + const unsigned int outputChannelSize[] = {outputDimensionSizes[3]}; // Cout + + TensorInfo inputInfo (4, inputDimensionSizes, DataType::Float32); + TensorInfo outputInfo(4, outputDimensionSizes, DataType::Float32); + + std::vector<float> weightsVector = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, + 11, 12, 13, 14, 15, 16, 17, 18, 19, 110, 111, 112, + 21, 22, 23, 24, 25, 26, 27, 28, 29, 210, 211, 212, + 31, 32, 33, 34, 35, 36, 37, 38, 39, 310, 311, 312}; + TensorInfo weightsInfo(4, weightsDimensionSizes, DataType::Float32); + ConstTensor weights (weightsInfo, weightsVector); + std::vector<float> biasVector = {3.3f, 3.2f, 3.1f, 3.0f}; + TensorInfo biasInfo(1, outputChannelSize, DataType::Float32); + ConstTensor bias (biasInfo, biasVector); + Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias); + + std::vector<float> betaVector = {0.0f, 0.2f, 0.3f, 0.4f}; + std::vector<float> gammaVector = {0.5f, 0.6f, 0.7f, 0.8f}; + std::vector<float> meanVector = {0.1f, 0.2f, 0.3f, 0.4f}; + std::vector<float> varianceVector = {1.0f, 1.1f, 1.2f, 1.3f}; + ConstTensor beta (TensorInfo(1, outputChannelSize, DataType::Float32), betaVector); + ConstTensor gamma (TensorInfo(1, outputChannelSize, DataType::Float32), gammaVector); + ConstTensor mean (TensorInfo(1, outputChannelSize, DataType::Float32), meanVector); + ConstTensor variance(TensorInfo(1, outputChannelSize, DataType::Float32), varianceVector); + + auto inputSize = inputDimensionSizes[0]*inputDimensionSizes[1]*inputDimensionSizes[2]*inputDimensionSizes[3]; + auto outputSize = outputDimensionSizes[0]*outputDimensionSizes[1]*outputDimensionSizes[2]*outputDimensionSizes[3]; + + // FIRST NETWORK: Fused + + // Construct ArmNN network + NetworkId networkIdentifier; + INetworkPtr network = INetwork::Create(); + IConnectableLayer *inputLayer = network->AddInputLayer(0); + IConnectableLayer *convLayer = network->AddConvolution2dLayer(convolution2dDescriptor, + weights, + optionalBias, + "convolution"); + IConnectableLayer *batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor, + mean, + variance, + beta, + gamma, + "batchNorm"); + IConnectableLayer *outputLayer = network->AddOutputLayer(0); + + inputLayer ->GetOutputSlot(0).Connect(convLayer ->GetInputSlot(0)); + convLayer ->GetOutputSlot(0).Connect(batchNormLayer->GetInputSlot(0)); + batchNormLayer ->GetOutputSlot(0).Connect(outputLayer ->GetInputSlot(0)); + + // Create ArmNN runtime + IRuntime::CreationOptions options; // default options + IRuntimePtr run = IRuntime::Create(options); + + //Set the tensors in the network. + inputLayer ->GetOutputSlot(0).SetTensorInfo(inputInfo); + convLayer ->GetOutputSlot(0).SetTensorInfo(outputInfo); + batchNormLayer ->GetOutputSlot(0).SetTensorInfo(outputInfo); + + // Optimise ArmNN network + IOptimizedNetworkPtr optNet = Optimize(*network, {Compute::CpuRef}, run->GetDeviceSpec()); + if (!optNet) + { + // This shouldn't happen for this simple sample, with reference backend. + // But in general usage Optimize could fail if the hardware at runtime cannot + // support the model that has been provided. + std::cerr << "Error: Failed to optimise the input network." << std::endl; + } + + // Load graph into runtime + run->LoadNetwork(networkIdentifier, std::move(optNet)); + + //Creates structures for inputs and outputs. + std::vector<float> inputData(inputSize, 128); + std::vector<float> outputData(outputSize); + + InputTensors inputTensors {{0, ConstTensor(run->GetInputTensorInfo (networkIdentifier, 0), inputData.data())}}; + OutputTensors outputTensors{{0, Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}}; + + + // Execute network + run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors); + + // SECOND NETWORK: NotFused + + // Construct ArmNN network + NetworkId networkIdentifierNotFused; + INetworkPtr networkNotFused = INetwork::Create(); + IConnectableLayer *inputLayerNotFused = networkNotFused->AddInputLayer(0); + IConnectableLayer *convLayerNotFused = networkNotFused->AddConvolution2dLayer(convolution2dDescriptor, + weights, + optionalBias, + "convolution"); + IConnectableLayer *batchNormLayerNotFused = networkNotFused->AddBatchNormalizationLayer(batchNormDescriptor, + mean, + variance, + beta, + gamma, + "batchNorm"); + IConnectableLayer *outputLayerNotFused = networkNotFused->AddOutputLayer(0); + IConnectableLayer *output2LayerNotFused = networkNotFused->AddOutputLayer(1); + + + inputLayerNotFused ->GetOutputSlot(0).Connect(convLayerNotFused ->GetInputSlot(0)); + convLayerNotFused ->GetOutputSlot(0).Connect(batchNormLayerNotFused->GetInputSlot(0)); + batchNormLayerNotFused ->GetOutputSlot(0).Connect(outputLayerNotFused ->GetInputSlot(0)); + convLayerNotFused ->GetOutputSlot(0).Connect(output2LayerNotFused ->GetInputSlot(0)); + + // Create ArmNN runtime + IRuntimePtr runNotFused = IRuntime::Create(options); + + //Set the tensors in the network. + inputLayerNotFused ->GetOutputSlot(0).SetTensorInfo(inputInfo); + convLayerNotFused ->GetOutputSlot(0).SetTensorInfo(outputInfo); + batchNormLayerNotFused ->GetOutputSlot(0).SetTensorInfo(outputInfo); + + // Optimise ArmNN network + IOptimizedNetworkPtr optNetNotFused = Optimize(*networkNotFused, {Compute::CpuRef}, runNotFused->GetDeviceSpec()); + if (!optNetNotFused) + { + // This shouldn't happen for this simple sample, with reference backend. + // But in general usage Optimize could fail if the hardware at runtime cannot + // support the model that has been provided. + std::cerr << "Error: Failed to optimise the input network." << std::endl; + } + + // Load graph into runtime + runNotFused->LoadNetwork(networkIdentifierNotFused, std::move(optNetNotFused)); + + //Creates structures for inputs and outputs. + std::vector<float> inputDataNotFused(inputSize, 128); + std::vector<float> outputDataNotFused(outputSize); + std::vector<float> outputData2NotFused(outputSize); + + InputTensors inputTensorsNotFused{ + {0, ConstTensor(runNotFused->GetInputTensorInfo(networkIdentifierNotFused, 0), inputDataNotFused.data())}}; + OutputTensors outputTensorsNotFused{ + {0, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data())}, + {1, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data())}}; + + // Execute network + runNotFused->EnqueueWorkload(networkIdentifierNotFused, inputTensorsNotFused, outputTensorsNotFused); + + // Check the output of the fused-convolution matches with the output of the batchNormm in the "NotFused" network + for (unsigned int n = 0; n < outputData.size(); ++n) + { + BOOST_CHECK_CLOSE(outputData[n], outputDataNotFused[n], 0.001); + } +} + BOOST_AUTO_TEST_SUITE_END() |