diff options
Diffstat (limited to 'src/armnn/test/optimizations/FuseBatchNormTests.cpp')
| -rw-r--r-- | src/armnn/test/optimizations/FuseBatchNormTests.cpp | 124 |
1 files changed, 100 insertions, 24 deletions
diff --git a/src/armnn/test/optimizations/FuseBatchNormTests.cpp b/src/armnn/test/optimizations/FuseBatchNormTests.cpp index 70cffea2b2..b28bb17773 100644 --- a/src/armnn/test/optimizations/FuseBatchNormTests.cpp +++ b/src/armnn/test/optimizations/FuseBatchNormTests.cpp @@ -24,6 +24,7 @@ class Conv2dTest public: using ConvDescriptorType = armnn::Convolution2dDescriptor; using ConvLayerType = armnn::Convolution2dLayer; + static const bool isConstTensorAsInputSupported = false; static IConnectableLayer *AddConvolution(INetwork *network, const Convolution2dDescriptor &descriptor, @@ -33,6 +34,19 @@ public: { return network->AddConvolution2dLayer(descriptor, weights, biases, name); } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork *network, + const Convolution2dDescriptor &descriptor, + const ConstTensor &weights, + const Optional<ConstTensor> &biases) + { + IgnoreUnused(network); + IgnoreUnused(descriptor); + IgnoreUnused(weights); + IgnoreUnused(biases); + + return {}; + } }; class DepthwiseConv2dTest @@ -40,6 +54,7 @@ class DepthwiseConv2dTest public: using ConvDescriptorType = armnn::DepthwiseConvolution2dDescriptor; using ConvLayerType = armnn::DepthwiseConvolution2dLayer; + static const bool isConstTensorAsInputSupported = true; static IConnectableLayer *AddConvolution(INetwork *network, const DepthwiseConvolution2dDescriptor &descriptor, @@ -47,7 +62,29 @@ public: const Optional<ConstTensor> &biases, const char *name) { - return network->AddDepthwiseConvolution2dLayer(descriptor, weights, biases, name); + IgnoreUnused(weights); + IgnoreUnused(biases); + + return network->AddDepthwiseConvolution2dLayer(descriptor, name); + } + + static std::vector<IConnectableLayer*> AddConstantLayers(INetwork *network, + const DepthwiseConvolution2dDescriptor &descriptor, + const ConstTensor &weights, + const Optional<ConstTensor> &biases) + { + auto weightsLayer = network->AddConstantLayer(weights, "Weights"); + weightsLayer->GetOutputSlot(0).SetTensorInfo(weights.GetInfo()); + std::vector<IConnectableLayer*> layers = {weightsLayer}; + + if (descriptor.m_BiasEnabled) + { + auto biasLayer = network->AddConstantLayer(biases.value(), "Bias"); + biasLayer->GetOutputSlot(0).SetTensorInfo(biases.value().GetInfo()); + layers.emplace_back(biasLayer); + } + + return layers; } }; @@ -73,7 +110,7 @@ template <typename Conv2dTest, armnn::DataType ArmnnType, typename ConvDescriptorType = typename Conv2dTest::ConvDescriptorType, typename T = armnn::ResolveType<ArmnnType>> -INetworkPtr CreatNetwork(bool depthwise, bool preventFusing) +INetworkPtr CreateNetwork(bool depthwise, bool preventFusing) { // Define layers information ConvDescriptorType convolution2dDescriptor; @@ -110,11 +147,6 @@ INetworkPtr CreatNetwork(bool depthwise, bool preventFusing) TensorInfo weightsInfo(4, weightsDimensionSizes, ArmnnType, 0.0f, 0, true); ConstTensor weights(weightsInfo, weightsVector); - std::vector<T> biasVector = GetVector<T>(outputDimensionSizes[3], 3.3f, 0.1f); - TensorInfo biasInfo(1, outputChannelSize, ArmnnType, 0.0f, 0, true); - ConstTensor bias(biasInfo, biasVector); - Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias); - std::vector<T> betaVector = GetVector<T>(outputDimensionSizes[3], 0.0f, 0.2f); std::vector<T> gammaVector = GetVector<T>(outputDimensionSizes[3], 0.5f, 0.1f); std::vector<T> meanVector = GetVector<T>(outputDimensionSizes[3], 0.1f, 0.1f); @@ -133,7 +165,7 @@ INetworkPtr CreatNetwork(bool depthwise, bool preventFusing) IConnectableLayer* convLayer = Conv2dTest::AddConvolution(network.get(), convolution2dDescriptor, weights, - optionalBias, + Optional<ConstTensor>(), "convolution"); IConnectableLayer* batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor, @@ -151,6 +183,21 @@ INetworkPtr CreatNetwork(bool depthwise, bool preventFusing) output2Layer = network->AddOutputLayer(1); } + // If ConstTensorAsInputs is supported weights and bias are stored as constant layers. + if (Conv2dTest::isConstTensorAsInputSupported) + { + std::vector<IConnectableLayer*> constantLayers = Conv2dTest::AddConstantLayers(network.get(), + convolution2dDescriptor, + weights, + Optional<ConstTensor>()); + + // Connect constant layers to receiverLayer. + for (unsigned int i = 0; i < constantLayers.size(); ++i) + { + constantLayers[i]->GetOutputSlot(0).Connect(convLayer->GetInputSlot(i + 1)); + } + } + // Set layer information inputLayer ->GetOutputSlot(0).SetTensorInfo(inputInfo); convLayer ->GetOutputSlot(0).SetTensorInfo(outputInfo); @@ -178,7 +225,7 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b { // FIRST NETWORK: Fused // Construct ArmNN network - INetworkPtr networkFused = CreatNetwork<Conv2dTest, ArmnnType>(depthwise, false); + INetworkPtr networkFused = CreateNetwork<Conv2dTest, ArmnnType>(depthwise, false); // Create ArmNN runtime IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions()); // default options @@ -194,12 +241,26 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b (layer->GetNameStr() == "fused-batchNorm-into-convolution"); }; - CHECK(3 == graphFused.GetNumLayers()); - CHECK(CheckSequence(graphFused.cbegin(), - graphFused.cend(), - &IsLayerOfType<InputLayer>, - checkFusedConv2d, - &IsLayerOfType<OutputLayer>)); + if (Conv2dTest::isConstTensorAsInputSupported) + { + CHECK(5 == graphFused.GetNumLayers()); + CHECK(CheckSequence(graphFused.cbegin(), + graphFused.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<ConstantLayer>, + &IsLayerOfType<ConstantLayer>, + checkFusedConv2d, + &IsLayerOfType<OutputLayer>)); + } + else + { + CHECK(3 == graphFused.GetNumLayers()); + CHECK(CheckSequence(graphFused.cbegin(), + graphFused.cend(), + &IsLayerOfType<InputLayer>, + checkFusedConv2d, + &IsLayerOfType<OutputLayer>)); + } // Load network into runtime NetworkId networkIdentifier; @@ -227,7 +288,7 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b // SECOND NETWORK: NotFused // Construct ArmNN network - INetworkPtr networkNotFused = CreatNetwork<Conv2dTest, ArmnnType>(depthwise, true); + INetworkPtr networkNotFused = CreateNetwork<Conv2dTest, ArmnnType>(depthwise, true); // Create ArmNN runtime IRuntimePtr runNotFused = IRuntime::Create(IRuntime::CreationOptions()); // default options @@ -237,14 +298,29 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b Graph& graphNotFused = GetGraphForTesting(optNetNotFused.get()); - CHECK(5 == graphNotFused.GetNumLayers()); - CHECK(CheckSequence(graphNotFused.cbegin(), - graphNotFused.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<ConvLayerType>, - &IsLayerOfType<armnn::BatchNormalizationLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>)); + if (Conv2dTest::isConstTensorAsInputSupported) + { + CHECK(6 == graphNotFused.GetNumLayers()); + CHECK(CheckSequence(graphNotFused.cbegin(), + graphNotFused.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::ConstantLayer>, + &IsLayerOfType<ConvLayerType>, + &IsLayerOfType<armnn::BatchNormalizationLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + } + else + { + CHECK(5 == graphNotFused.GetNumLayers()); + CHECK(CheckSequence(graphNotFused.cbegin(), + graphNotFused.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<ConvLayerType>, + &IsLayerOfType<armnn::BatchNormalizationLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + } // Load network into runtime NetworkId networkIdentifierNotFused; |