diff options
Diffstat (limited to 'src/armnn/test/optimizations/FuseActivationTests.cpp')
| -rw-r--r-- | src/armnn/test/optimizations/FuseActivationTests.cpp | 185 |
1 files changed, 163 insertions, 22 deletions
diff --git a/src/armnn/test/optimizations/FuseActivationTests.cpp b/src/armnn/test/optimizations/FuseActivationTests.cpp index 24ea8f6680..2352a3c498 100644 --- a/src/armnn/test/optimizations/FuseActivationTests.cpp +++ b/src/armnn/test/optimizations/FuseActivationTests.cpp @@ -8,6 +8,7 @@ #include <Network.hpp> #include <ResolveType.hpp> #include <armnn/INetwork.hpp> +#include "test/GraphUtils.hpp" #include <test/TestUtils.hpp> #include <doctest/doctest.h> @@ -41,6 +42,7 @@ struct Convolution2dTest { using LayerType = Convolution2dLayer; static const bool isElementWise = false; + static const bool isConstTensorAsInputSupported = false; static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin static TensorShape GetOutputShape() { return TensorShape( {1, 3, 3, 4}); } // NHWCout @@ -70,6 +72,16 @@ struct Convolution2dTest return network->AddConvolution2dLayer(descriptor, weights, optionalBias, name); } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network, + float scale = 1.f, + int32_t offset = 0) + { + IgnoreUnused(network); + IgnoreUnused(scale); + IgnoreUnused(offset); + return {}; + } }; template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> @@ -78,6 +90,7 @@ struct DWConvolution2dTest public: using LayerType = DepthwiseConvolution2dLayer; static const bool isElementWise = false; + static const bool isConstTensorAsInputSupported = false; static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // [N,H,W,Cin] static TensorShape GetOutputShape() { return TensorShape( {1, 3, 3, 12}); } // [N,H,W,Cout] @@ -108,6 +121,16 @@ public: return network->AddDepthwiseConvolution2dLayer(descriptor, weights, optionalBias, name); } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network, + float scale = 1.f, + int32_t offset = 0) + { + IgnoreUnused(network); + IgnoreUnused(scale); + IgnoreUnused(offset); + return {}; + } }; template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> @@ -116,6 +139,7 @@ struct FullyConnectedTest public: using LayerType = FullyConnectedLayer; static const bool isElementWise = false; + static const bool isConstTensorAsInputSupported = true; static TensorShape GetInputShape() { return TensorShape( {2, 5, 1, 1}); } // NCinHW static TensorShape GetOutputShape() { return TensorShape( {2, 3}); } // NCout @@ -129,18 +153,31 @@ public: float scale = 1.f, int32_t offset = 0) { + IgnoreUnused(scale); + IgnoreUnused(offset); + FullyConnectedDescriptor descriptor; descriptor.m_BiasEnabled = false; + return network->AddFullyConnectedLayer(descriptor, name); + } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network, + float scale = 1.f, + int32_t offset = 0) + { std::vector<float> weightsData = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, - 11, 12, 13, 14, 15}; + 11, 12, 13, 14, 15}; std::vector<T> weightsVector = armnnUtils::QuantizedVector<T>(weightsData, scale, offset); - TensorInfo weightsInfo(GetWeightsShape(), ArmnnType, scale, offset); + TensorInfo weightsInfo(GetWeightsShape(), ArmnnType, scale, offset, true); ConstTensor weights(weightsInfo, weightsVector); - Optional<ConstTensor> optionalBias; - return network->AddFullyConnectedLayer(descriptor, weights, optionalBias, name); + IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights"); + weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo); + + std::vector<IConnectableLayer*> layers = { weightsLayer }; + return layers; } }; @@ -150,6 +187,7 @@ struct BatchNormTest public: using LayerType = BatchNormalizationLayer; static const bool isElementWise = false; + static const bool isConstTensorAsInputSupported = false; static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout @@ -181,6 +219,16 @@ public: return network->AddBatchNormalizationLayer(descriptor, mean, variance, beta, gamma, name); } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network, + float scale = 1.f, + int32_t offset = 0) + { + IgnoreUnused(network); + IgnoreUnused(scale); + IgnoreUnused(offset); + return {}; + } }; template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> @@ -188,6 +236,7 @@ struct MultiplicationTest { using LayerType = MultiplicationLayer; static const bool isElementWise = true; + static const bool isConstTensorAsInputSupported = false; static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout @@ -205,6 +254,16 @@ struct MultiplicationTest return network->AddMultiplicationLayer(name); } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network, + float scale = 1.f, + int32_t offset = 0) + { + IgnoreUnused(network); + IgnoreUnused(scale); + IgnoreUnused(offset); + return {}; + } }; template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> @@ -212,6 +271,7 @@ struct AdditionTest { using LayerType = AdditionLayer; static const bool isElementWise = true; + static const bool isConstTensorAsInputSupported = false; static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout @@ -229,6 +289,16 @@ struct AdditionTest return network->AddAdditionLayer(name); } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network, + float scale = 1.f, + int32_t offset = 0) + { + IgnoreUnused(network); + IgnoreUnused(scale); + IgnoreUnused(offset); + return {}; + } }; template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> @@ -236,6 +306,7 @@ struct SubtractionTest { using LayerType = SubtractionLayer; static const bool isElementWise = true; + static const bool isConstTensorAsInputSupported = false; static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout @@ -253,6 +324,16 @@ struct SubtractionTest return network->AddSubtractionLayer(name); } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network, + float scale = 1.f, + int32_t offset = 0) + { + IgnoreUnused(network); + IgnoreUnused(scale); + IgnoreUnused(offset); + return {}; + } }; template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> @@ -260,6 +341,7 @@ struct DivisionTest { using LayerType = DivisionLayer; static const bool isElementWise = true; + static const bool isConstTensorAsInputSupported = false; static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout @@ -277,11 +359,21 @@ struct DivisionTest return network->AddDivisionLayer(name); } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network, + float scale = 1.f, + int32_t offset = 0) + { + IgnoreUnused(network); + IgnoreUnused(scale); + IgnoreUnused(offset); + return {}; + } }; template<typename LayerTest, DataType ArmnnType> -INetworkPtr CreatNetwork(ActivationDescriptor activationDescriptor, bool preventFusing, +INetworkPtr CreateNetwork(ActivationDescriptor activationDescriptor, bool preventFusing, float scale, int32_t offset) { // Create a network @@ -300,6 +392,20 @@ INetworkPtr CreatNetwork(ActivationDescriptor activationDescriptor, bool prevent IConnectableLayer* outputLayer = network->AddOutputLayer(0); IConnectableLayer* output2Layer = preventFusing?network->AddOutputLayer(1):nullptr; + // If ConstTensorAsInputs is supported weights and bias are stored as constant layers. + if(LayerTest::isConstTensorAsInputSupported) + { + std::vector<IConnectableLayer*> constantLayers = LayerTest::AddConstantLayers(network.get(), + scale, + offset); + + // Connect constant layers to receiverLayer. + for (unsigned int i = 0; i < constantLayers.size(); ++i) + { + constantLayers[i]->GetOutputSlot(0).Connect(receiverLayer->GetInputSlot(i + 1)); + } + } + // Define layers information TensorInfo inputInfo(LayerTest::GetInputShape(), ArmnnType, scale, offset); TensorInfo outputInfo(LayerTest::GetOutputShape(), ArmnnType, scale, offset); @@ -335,7 +441,7 @@ void FuseActivationIntoPreviousLayerTest(ActivationDescriptor activationDescript { // FIRST NETWORK: Fused // Construct ArmNN network - INetworkPtr networkFused = CreatNetwork<LayerTest, ArmnnType>(activationDescriptor, false, scale, offset); + INetworkPtr networkFused = CreateNetwork<LayerTest, ArmnnType>(activationDescriptor, false, scale, offset); // Create ArmNN runtime IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions()); // default options @@ -350,12 +456,31 @@ void FuseActivationIntoPreviousLayerTest(ActivationDescriptor activationDescript (layer->GetNameStr() == "fused-activation-into-receiverLayer"); }; - CHECK(3 == graphFused.GetNumLayers()); - CHECK(CheckSequence(graphFused.cbegin(), - graphFused.cend(), - &IsLayerOfType<InputLayer>, - checkFusedConv2d, - &IsLayerOfType<OutputLayer>)); + // If ConstTensorAsInputs is supported, weights and bias are stored as constant layers. + if(LayerTest::isConstTensorAsInputSupported) + { + CHECK(4 == graphFused.GetNumLayers()); + CHECK(CheckSequence(graphFused.cbegin(), + graphFused.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<ConstantLayer>, + checkFusedConv2d, + &IsLayerOfType<OutputLayer>)); + + // Check if new constant layer is connected to fused receiver layer. + Layer* fusedReceiverLayer = GetFirstLayerWithName(graphFused, "fused-activation-into-receiverLayer"); + CHECK(fusedReceiverLayer); + CHECK(fusedReceiverLayer->GetInputSlot(1).GetConnection() != nullptr); + } + else + { + CHECK(3 == graphFused.GetNumLayers()); + CHECK(CheckSequence(graphFused.cbegin(), + graphFused.cend(), + &IsLayerOfType<InputLayer>, + checkFusedConv2d, + &IsLayerOfType<OutputLayer>)); + } // Load network into runtime NetworkId networkIdentifier; @@ -376,7 +501,7 @@ void FuseActivationIntoPreviousLayerTest(ActivationDescriptor activationDescript // SECOND NETWORK: NotFused // Construct ArmNN network - INetworkPtr networkNotFused = CreatNetwork<LayerTest, ArmnnType>(activationDescriptor, true, scale, offset); + INetworkPtr networkNotFused = CreateNetwork<LayerTest, ArmnnType>(activationDescriptor, true, scale, offset); // Create ArmNN runtime IRuntimePtr runNotFused = IRuntime::Create(IRuntime::CreationOptions()); // default options @@ -386,14 +511,30 @@ void FuseActivationIntoPreviousLayerTest(ActivationDescriptor activationDescript Graph& graphNotFused = GetGraphForTesting(optNetNotFused.get()); - CHECK(5 == graphNotFused.GetNumLayers()); - CHECK(CheckSequence(graphNotFused.cbegin(), - graphNotFused.cend(), - &IsLayerOfType<InputLayer>, - &IsLayerOfType<LayerType>, - &IsLayerOfType<ActivationLayer>, - &IsLayerOfType<OutputLayer>, - &IsLayerOfType<OutputLayer>)); + // If ConstTensorAsInputs is supported, weights and bias are stored as constant layers. + if(LayerTest::isConstTensorAsInputSupported) + { + CHECK(6 == graphNotFused.GetNumLayers()); + CHECK(CheckSequence(graphNotFused.cbegin(), + graphNotFused.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<ConstantLayer>, + &IsLayerOfType<LayerType>, + &IsLayerOfType<ActivationLayer>, + &IsLayerOfType<OutputLayer>, + &IsLayerOfType<OutputLayer>)); + } + else + { + CHECK(5 == graphNotFused.GetNumLayers()); + CHECK(CheckSequence(graphNotFused.cbegin(), + graphNotFused.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<LayerType>, + &IsLayerOfType<ActivationLayer>, + &IsLayerOfType<OutputLayer>, + &IsLayerOfType<OutputLayer>)); + } // Load network into runtime NetworkId networkIdentifierNotFused; @@ -433,7 +574,7 @@ bool FuseActivationSimpleTest(ActivationDescriptor activationDescriptor, Compute try { // Construct ArmNN network - INetworkPtr networkFused = CreatNetwork<LayerTest, ArmnnType>(activationDescriptor, false, scale, offset); + INetworkPtr networkFused = CreateNetwork<LayerTest, ArmnnType>(activationDescriptor, false, scale, offset); // Create ArmNN runtime IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions()); // default options |