diff options
Diffstat (limited to 'src/armnn/test/OptimizerTests.cpp')
| -rw-r--r-- | src/armnn/test/OptimizerTests.cpp | 69 |
1 files changed, 29 insertions, 40 deletions
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp index 8845dae6c8..0179589bf4 100644 --- a/src/armnn/test/OptimizerTests.cpp +++ b/src/armnn/test/OptimizerTests.cpp @@ -255,8 +255,6 @@ BOOST_AUTO_TEST_CASE(InsertConvertersTest) &IsLayerOfType<armnn::OutputLayer>)); } - - void CreateConvolution2dGraph(Graph &graph, const unsigned int* inputShape, const unsigned int* weightsShape, const unsigned int* outputShape, DataLayout dataLayout = DataLayout::NCHW) @@ -308,8 +306,8 @@ BOOST_AUTO_TEST_CASE(Conv2dValidateTensorShapesFromInputsNhwc) } void CreateDepthwiseConvolution2dGraph(Graph &graph, const unsigned int* inputShape, - const unsigned int* weightsShape, const unsigned int* outputShape, - DataLayout dataLayout = DataLayout::NCHW) + const unsigned int* weightsShape, const unsigned int* outputShape, + DataLayout dataLayout = DataLayout::NCHW) { armnn::TensorInfo inputInfo(4, inputShape, DataType::Float32); armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32); @@ -357,7 +355,7 @@ BOOST_AUTO_TEST_CASE(DepthwiseConv2dValidateTensorShapesFromInputsNhwc) BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); } -void CreatePooling2dGraph(Graph &graph, const unsigned int* inputShape, const unsigned int* outputShape, +void CreatePooling2dGraph(Graph& graph, const unsigned int* inputShape, const unsigned int* outputShape, DataLayout dataLayout = DataLayout::NCHW) { armnn::TensorInfo inputInfo(4, inputShape, DataType::Float32); @@ -405,7 +403,7 @@ BOOST_AUTO_TEST_CASE(Pooling2dValidateTensorShapesFromInputsNhwc) BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); } -void CreateResizeBilinearGraph(Graph &graph, const unsigned int* inputShape, const unsigned int* outputShape, +void CreateResizeBilinearGraph(Graph& graph, const unsigned int* inputShape, const unsigned int* outputShape, DataLayout dataLayout = DataLayout::NCHW) { TensorInfo inputInfo(4, inputShape, DataType::Float32); @@ -448,7 +446,6 @@ BOOST_AUTO_TEST_CASE(ResizeBilinearValidateTensorShapesFromInputsNhwc) BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); } - void CreateGatherGraph(Graph& graph, const armnn::TensorInfo& paramsInfo, const armnn::TensorInfo& indicesInfo, const armnn::TensorInfo& outputInfo) { @@ -547,7 +544,6 @@ BOOST_AUTO_TEST_CASE(FoldPadLayerIntoConvolution2dLayer) const unsigned int weightsShape[] = { 1, 2, 3, 3 }; const unsigned int outputShape[] = { 1, 2, 1, 1 }; - armnn::TensorInfo inputInfo(4, inputShape, DataType::Float32); armnn::TensorInfo paddedInfo(4, paddedShape, DataType::Float32); armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32); @@ -628,9 +624,6 @@ BOOST_AUTO_TEST_CASE(FoldPadLayerIntoConvolution2dLayer) &IsLayerOfType<armnn::OutputLayer>)); } - - - class MockLayerSupport : public LayerSupportBase { public: bool IsInputSupported(const TensorInfo& /*input*/, @@ -686,7 +679,6 @@ public: }; }; - BOOST_AUTO_TEST_CASE(BackendHintTest) { class TestBackendAssignment : public LayerVisitorBase<VisitorNoThrowPolicy> @@ -764,7 +756,6 @@ BOOST_AUTO_TEST_CASE(BackendHintTest) input->GetOutputSlot(0).Connect(act->GetInputSlot(0)); act->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - auto optNet = IOptimizedNetworkPtr(new OptimizedNetwork(std::move(graph)), &IOptimizedNetwork::Destroy); OptimizedNetwork* optNetObjPtr = PolymorphicDowncast<OptimizedNetwork*>(optNet.get()); @@ -772,7 +763,6 @@ BOOST_AUTO_TEST_CASE(BackendHintTest) // Get the optimized graph Graph& optGraph = optNetObjPtr->GetGraph(); - std::vector<BackendId> prefs{"MockBackend", "CustomBackend"}; BackendIdSet availableBackends = {"CustomBackend", "MockBackend"}; @@ -799,13 +789,13 @@ BOOST_AUTO_TEST_CASE(BackendHintTest) } // Tests that OptimizeForExclusiveConnections works, fusing when needed, using BatchNorm fusing as example -BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_fuse_Test) +BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnectionsFuseTest) { using namespace armnn; // Define layers information Convolution2dDescriptor convolution2dDescriptor; convolution2dDescriptor.m_BiasEnabled = false; - convolution2dDescriptor.m_DataLayout = DataLayout::NHWC; + convolution2dDescriptor.m_DataLayout = DataLayout::NHWC; BatchNormalizationDescriptor batchNormDescriptor; batchNormDescriptor.m_DataLayout = DataLayout::NHWC; @@ -814,32 +804,31 @@ BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_fuse_Test) const unsigned int outputDimensionSizes[] = {1, 3, 3, 1}; // NHWCout const unsigned int outputChannelSize[] = {outputDimensionSizes[3]}; // Cout - TensorInfo inputInfo (4, inputDimensionSizes, DataType::Float32); + 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> 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); + 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"); + 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); + 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); @@ -849,8 +838,8 @@ BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_fuse_Test) 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); + ConstTensor bias(TensorInfo(1, outputChannelSize, DataType::Float32), biasVector); + conv->m_Bias = std::make_unique<ScopedCpuTensorHandle>(bias); } // Connect layers @@ -867,12 +856,12 @@ BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_fuse_Test) &IsLayerOfType<OutputLayer>)); // Optimize graph - armnn::Optimizer::Pass(graph, MakeOptimizations(FuseBatchNormIntoConvolution2D())); + armnn::Optimizer::Pass(graph, MakeOptimizations(FuseBatchNormIntoConvolution2DFloat32())); - auto checkFusedConv2d = [ ](const armnn::Layer* const layer) -> bool + auto checkFusedConv2d = [](const armnn::Layer* const layer)->bool { return IsLayerOfType<armnn::Convolution2dLayer>(layer) && - (layer->GetNameStr() == "fused-batchNorm-into-convolution"); + (layer->GetNameStr() == "fused-batchNorm-into-convolution"); }; BOOST_CHECK(3 == graph.GetNumLayers()); @@ -884,11 +873,11 @@ BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_fuse_Test) } // Tests that OptimizeForExclusiveConnections works, not fusing when not needed, using BatchNorm fusing as example -BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_notFuse_Test) +BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnectionsWithoutFuseTest) { // Define the network - Graph graph; - Convolution2dDescriptor convolution2dDescriptor; + Graph graph; + Convolution2dDescriptor convolution2dDescriptor; BatchNormalizationDescriptor batchNormDescriptor; auto input = graph.AddLayer<InputLayer>(0, "input"); @@ -912,7 +901,7 @@ BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_notFuse_Test) &IsLayerOfType<armnn::OutputLayer>, &IsLayerOfType<armnn::OutputLayer>)); // Optimize graph - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FuseBatchNormIntoConvolution2D())); + armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FuseBatchNormIntoConvolution2DFloat32())); BOOST_CHECK(5 == graph.GetNumLayers()); BOOST_TEST(CheckSequence(graph.cbegin(), @@ -923,4 +912,4 @@ BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnections_notFuse_Test) &IsLayerOfType<armnn::OutputLayer>, &IsLayerOfType<armnn::OutputLayer>)); } -BOOST_AUTO_TEST_SUITE_END() +BOOST_AUTO_TEST_SUITE_END()
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