| // |
| // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "../Graph.hpp" |
| #include "../Network.hpp" |
| #include "../NetworkQuantizerUtils.hpp" |
| #include "../OverrideInputRangeVisitor.hpp" |
| |
| #include <armnn/INetwork.hpp> |
| #include <armnn/Tensor.hpp> |
| #include <armnn/Types.hpp> |
| #include <armnn/utility/IgnoreUnused.hpp> |
| #include <armnn/utility/PolymorphicDowncast.hpp> |
| #include <armnnQuantizer/INetworkQuantizer.hpp> |
| #include <QuantizeHelper.hpp> |
| |
| #include <boost/test/unit_test.hpp> |
| |
| #include <unordered_map> |
| |
| namespace armnn |
| { |
| using MinMaxRange = std::pair<float, float>; |
| using MinMaxRanges = std::vector<MinMaxRange>; |
| using MinMaxRangeMap = std::unordered_map<LayerGuid, MinMaxRanges>; |
| |
| const float g_AsymmU8QuantizationBase = 255.0f; |
| // Coinciding with calcution which for AsymmS8 which calculates scale on an unsigned basis |
| const float g_AsymmS8QuantizationBase = 255.0f; |
| const float g_SymmS8QuantizationBase = 127.0f; |
| const float g_SymmS16QuantizationBase = 32767.0f; |
| const float g_TestTolerance = 0.000001f; |
| |
| class TestConnectionPreservation : public LayerVisitorBase<VisitorNoThrowPolicy> |
| { |
| public: |
| TestConnectionPreservation(INetwork* network) |
| : LayerVisitorBase<VisitorNoThrowPolicy>() |
| , m_Network(network) |
| {} |
| |
| void VisitAdditionLayer(const IConnectableLayer* layer, const char*) override |
| { |
| CheckLayerName(layer->GetInputSlot(0).GetConnection()->GetOwningLayerGuid(), "reLU1"); |
| CheckLayerName(layer->GetInputSlot(1).GetConnection()->GetOwningLayerGuid(), "reLU2"); |
| } |
| |
| void CheckLayerName(LayerGuid guid, std::string expectedName) |
| { |
| auto graph = m_Network->pNetworkImpl->GetGraph(); |
| bool guidFound = false; |
| for (Layer* layer : graph) |
| { |
| if (layer->GetGuid() == guid) |
| { |
| BOOST_CHECK_EQUAL(layer->GetName(), expectedName.c_str()); |
| guidFound = true; |
| break; |
| } |
| } |
| if (!guidFound) |
| { |
| BOOST_FAIL("No layer matching the GUID was found"); |
| } |
| } |
| |
| private: |
| INetwork* m_Network; |
| }; |
| |
| void VisitLayersTopologically(const INetwork* inputNetwork, IStrategy& visitor) |
| { |
| auto graph = inputNetwork->pNetworkImpl->GetGraph().TopologicalSort(); |
| |
| ApplyStrategyToLayers(graph, visitor); |
| } |
| |
| TensorInfo GetInputTensorInfo(const INetwork* network) |
| { |
| for (auto&& inputLayer : network->pNetworkImpl->GetGraph().GetInputLayers()) |
| { |
| ARMNN_ASSERT_MSG(inputLayer->GetNumOutputSlots() == 1, "Input layer should have exactly 1 output slot"); |
| return inputLayer->GetOutputSlot(0).GetTensorInfo(); |
| } |
| throw InvalidArgumentException("Network has no input layers"); |
| } |
| |
| TensorInfo GetInputTensorInfo(const NetworkImpl* network) |
| { |
| for (auto&& inputLayer : network->GetGraph().GetInputLayers()) |
| { |
| ARMNN_ASSERT_MSG(inputLayer->GetNumOutputSlots() == 1, "Input layer should have exactly 1 output slot"); |
| return inputLayer->GetOutputSlot(0).GetTensorInfo(); |
| } |
| throw InvalidArgumentException("Network has no input layers"); |
| } |
| |
| BOOST_AUTO_TEST_SUITE(Quantizer) |
| |
| class TestQuantization : public IStrategy |
| { |
| public: |
| TestQuantization(const TensorShape &inputShape, const TensorShape &outputShape) |
| : m_InputShape(inputShape), m_OutputShape(outputShape), m_QuantizerOptions(QuantizerOptions()) |
| {} |
| |
| TestQuantization(const QuantizerOptions& options, const TensorShape& inputShape, const TensorShape& outputShape) |
| : m_InputShape(inputShape) |
| , m_OutputShape(outputShape) |
| , m_QuantizerOptions(options) {} |
| |
| void ExecuteStrategy(const armnn::IConnectableLayer *layer, |
| const BaseDescriptor &descriptor, |
| const std::vector<armnn::ConstTensor> &constants, |
| const char *name, |
| const armnn::LayerBindingId id) override |
| { |
| IgnoreUnused(id, name); |
| |
| if (layer->GetType() == armnn::LayerType::Output) |
| { |
| const TensorInfo &info = layer->GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| BOOST_TEST(m_OutputShape == info.GetShape()); |
| return; |
| } |
| |
| const TensorInfo& info = layer->GetOutputSlot(0).GetTensorInfo(); |
| |
| switch (layer->GetType()) |
| { |
| case armnn::LayerType::BatchToSpaceNd : |
| case armnn::LayerType::Permute : |
| case armnn::LayerType::Pooling2d : |
| case armnn::LayerType::Reshape : |
| case armnn::LayerType::Resize : |
| case armnn::LayerType::SpaceToBatchNd : |
| case armnn::LayerType::Splitter : |
| case armnn::LayerType::StridedSlice : |
| { |
| CheckDefaultQuantizationSettings(info); |
| break; |
| } |
| case armnn::LayerType::Addition : |
| { |
| |
| // Based off default static range [-20.0f, 20.0f] |
| TestQuantizationParams(info, {40.0f / g_AsymmU8QuantizationBase, 128}, |
| {40.0f / g_AsymmS8QuantizationBase, 0}, |
| {20.0f / g_SymmS8QuantizationBase, 0}, |
| {20.0f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| case armnn::LayerType::Activation : |
| { |
| const ActivationDescriptor& activationDescriptor = static_cast<const ActivationDescriptor&>(descriptor); |
| |
| switch (activationDescriptor.m_Function) |
| { |
| case ActivationFunction::BoundedReLu : |
| { |
| // Based off default static range [0.0f, 3.5f] |
| TestQuantizationParams(info, {3.5f / g_AsymmU8QuantizationBase, 0}, |
| {3.5f / g_AsymmS8QuantizationBase, -128}, |
| {3.5f / g_SymmS8QuantizationBase, 0}, |
| {3.5f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| case ActivationFunction::Elu : |
| { |
| TestQuantizationParams( |
| info, {30.0f / g_AsymmU8QuantizationBase, 128}, |
| {30.0f / g_AsymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| case ActivationFunction::HardSwish : |
| { |
| TestQuantizationParams(info, {30.0f / g_AsymmU8QuantizationBase, 128}, |
| {30.0f / g_AsymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| case ActivationFunction::LeakyReLu : |
| { |
| // Based off default static range [-5.0f, 15.0f] |
| TestQuantizationParams(info, {20.0f / g_AsymmU8QuantizationBase, 64}, |
| {20.0f / g_AsymmS8QuantizationBase,-64}, |
| {15.0f / g_SymmS8QuantizationBase , 0}, |
| {15.0f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| case ActivationFunction::TanH : |
| { |
| TestQuantizationParams(info, {2.0f / g_AsymmU8QuantizationBase, 128}, |
| {2.0f / g_AsymmS8QuantizationBase, 0}, |
| {1.0f / g_SymmS8QuantizationBase , 0}, |
| {1.0f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| default: |
| { |
| // Based off default static range [0.0f, 15.0f] |
| TestQuantizationParams(info, {15.0f / g_AsymmU8QuantizationBase, 0}, |
| {15.0f / g_AsymmS8QuantizationBase, -128}, |
| {15.0f / g_SymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| } |
| break; |
| } |
| case armnn::LayerType::ArgMinMax : |
| { |
| const ArgMinMaxDescriptor& argMinMaxDescriptor = static_cast<const ArgMinMaxDescriptor&>(descriptor); |
| |
| if(argMinMaxDescriptor.m_Function == ArgMinMaxFunction::Max) |
| { |
| break; |
| } |
| TestQuantizationParams(info, |
| { 30.0f / g_AsymmU8QuantizationBase, 128 }, |
| { 30.0f / g_AsymmS8QuantizationBase, 0}, |
| { 15.0f / g_SymmS8QuantizationBase, 0}, |
| { 15.0f / g_SymmS16QuantizationBase, 0 }); |
| break; |
| } |
| case armnn::LayerType::BatchNormalization : |
| { |
| |
| // Based off default static range [-15.0f, 15.0f] |
| TestQuantizationParams( |
| info, {30.0f / g_AsymmU8QuantizationBase, 128}, |
| {30.0f / g_AsymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS16QuantizationBase, 0}); |
| |
| // Test constants |
| TestConstantQuantizationParams(constants[0].GetInfo(), {3.0f / g_AsymmU8QuantizationBase, 85}); |
| TestConstantQuantizationParams(constants[1].GetInfo(), {3.0f / g_AsymmU8QuantizationBase, 85}); |
| TestConstantQuantizationParams(constants[2].GetInfo(), {3.0f / g_AsymmU8QuantizationBase, 85}); |
| TestConstantQuantizationParams(constants[3].GetInfo(), {3.0f / g_AsymmU8QuantizationBase, 85}); |
| break; |
| } |
| case armnn::LayerType::Comparison : |
| { |
| |
| const OffsetScalePair qAsymmU8Params{ 30.0f / g_AsymmU8QuantizationBase, 128 }; |
| const OffsetScalePair qAsymmS8Params { 30.0f / g_AsymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS8Params { 15.0f / g_SymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS16Params{ 15.0f / g_SymmS16QuantizationBase, 0 }; |
| |
| TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params); |
| |
| break; |
| } |
| case armnn::LayerType::Constant : |
| { |
| |
| // Based off the range of values in the const tensor used for the test: [-2.0f, 6.0f] |
| TestQuantizationParams(info, {8.0f / g_AsymmU8QuantizationBase, 64}, |
| {8.0f / g_AsymmS8QuantizationBase, -64}, |
| {6.0f / g_SymmS8QuantizationBase, 0}, |
| {6.0f / g_SymmS16QuantizationBase, 0}); |
| |
| break; |
| } |
| case armnn::LayerType::Convolution2d : |
| { |
| if (constants.size() == 1) |
| { |
| TestQuantizationOnLayersWithBiases(layer, constants[0], armnn::EmptyOptional()); |
| } |
| else if (constants.size() == 1) |
| { |
| TestQuantizationOnLayersWithBiases(layer, constants[0], constants[1]); |
| } |
| break; |
| } |
| case armnn::LayerType::DepthwiseConvolution2d : |
| { |
| if (constants.size() == 2) |
| { |
| TestQuantizationOnLayersWithBiases(layer, constants[0], constants[1]); |
| } |
| else if (constants.size() == 1) |
| { |
| TestQuantizationOnLayersWithBiases(layer, constants[0], armnn::EmptyOptional()); |
| } |
| break; |
| } |
| case armnn::LayerType::DepthToSpace : |
| { |
| const OffsetScalePair qAsymmU8Params{30.0f / g_AsymmU8QuantizationBase, 128}; |
| const OffsetScalePair qAsymmS8Params{30.0f / g_AsymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS8Params{15.0f / g_SymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS16Params{15.0f / g_SymmS16QuantizationBase, 0}; |
| |
| TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params); |
| break; |
| } |
| case armnn::LayerType::FullyConnected : |
| { |
| if (constants.size() == 2) |
| { |
| TestQuantizationOnLayersWithBiases(layer, constants[0], constants[1]); |
| } |
| else if (constants.size() == 1) |
| { |
| TestQuantizationOnLayersWithBiases(layer, constants[0], armnn::EmptyOptional()); |
| } |
| |
| break; |
| } |
| case armnn::LayerType::Fill : |
| { |
| const OffsetScalePair qAsymmU8Params{ 30.0f / g_AsymmU8QuantizationBase, 128 }; |
| const OffsetScalePair qAsymmS8Params { 30.0f / g_AsymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS8Params { 15.0f / g_SymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS16Params{ 15.0f / g_SymmS16QuantizationBase, 0 }; |
| |
| TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params); |
| break; |
| } |
| case armnn::LayerType::Input : |
| { |
| BOOST_TEST(m_InputShape == info.GetShape()); |
| // Based off current default [-15.0f, 15.0f] |
| TestQuantizationParams(info, {30.0f / g_AsymmU8QuantizationBase, 128}, |
| {30.0f / g_AsymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| case armnn::LayerType::InstanceNormalization : |
| { |
| const OffsetScalePair qAsymmU8Params{ 30.0f / g_AsymmU8QuantizationBase, 128 }; |
| const OffsetScalePair qAsymmS8Params { 30.0f / g_AsymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS8Params { 15.0f / g_SymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS16Params{ 15.0f / g_SymmS16QuantizationBase, 0 }; |
| |
| TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params); |
| break; |
| } |
| case armnn::LayerType::LogSoftmax : |
| { |
| const OffsetScalePair qAsymmU8Params{ 30.0f / g_AsymmU8QuantizationBase, 128 }; |
| const OffsetScalePair qAsymmS8Params { 30.0f / g_AsymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS8Params { 15.0f / g_SymmS8QuantizationBase, 0}; |
| const OffsetScalePair qSymmS16Params{ 15.0f / g_SymmS16QuantizationBase, 0 }; |
| |
| TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params); |
| break; |
| } |
| case armnn::LayerType::Slice : |
| { |
| const OffsetScalePair qAsymmU8Params{ 30.0f / g_AsymmU8QuantizationBase, 128 }; |
| const OffsetScalePair qAsymmS8Params{ 30.0f / g_AsymmS8QuantizationBase, 0 }; |
| const OffsetScalePair qSymmS8Params { 15.0f / g_SymmS8QuantizationBase, 0 }; |
| const OffsetScalePair qSymmS16Params{ 15.0f / g_SymmS16QuantizationBase, 0 }; |
| |
| TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params); |
| break; |
| } |
| case armnn::LayerType::Softmax : |
| { |
| // Based off default static range [0.0f, 1.0f] |
| TestQuantizationParams(info, {1.0f / g_AsymmU8QuantizationBase, 0}, |
| {1.0f / g_AsymmS8QuantizationBase, -128}, |
| {1.0f / g_SymmS8QuantizationBase, 0}, |
| {1.0f / g_SymmS16QuantizationBase, 0}); |
| break; |
| } |
| case armnn::LayerType::SpaceToDepth : |
| { |
| TestQuantizationParams(info, |
| { 30.0f / g_AsymmU8QuantizationBase, 128 }, |
| { 30.0f / g_AsymmS8QuantizationBase, 0 }, |
| { 15.0f / g_SymmS8QuantizationBase, 0 }, |
| { 15.0f / g_SymmS16QuantizationBase, 0 }); |
| |
| break; |
| } |
| case armnn::LayerType::Stack : |
| { |
| TensorInfo outputInfo = layer->GetOutputSlot(0).GetTensorInfo(); |
| |
| TestQuantizationParams(outputInfo, |
| { 30.0f / g_AsymmU8QuantizationBase, 128 }, |
| { 30.0f / g_AsymmS8QuantizationBase, 0}, |
| { 15.0f / g_SymmS8QuantizationBase, 0}, |
| { 15.0f / g_SymmS16QuantizationBase, 0 }); |
| break; |
| } |
| case armnn::LayerType::TransposeConvolution2d : |
| { |
| if (constants.size() == 2) |
| { |
| TestQuantizationOnLayersWithBiases(layer, constants[0], constants[1]); |
| } |
| else if (constants.size() == 1) |
| { |
| TestQuantizationOnLayersWithBiases(layer, constants[0], armnn::EmptyOptional()); |
| } |
| break; |
| } |
| default: |
| { |
| throw UnimplementedException("Unimplemented layer encountered"); |
| } |
| } |
| } |
| |
| |
| protected: |
| |
| void CheckDefaultQuantizationSettings(const TensorInfo& info) |
| { |
| TestQuantizationParams(info, {20.0f / g_AsymmU8QuantizationBase, 64}, |
| {20.0f / g_AsymmS8QuantizationBase,-64}, |
| {15.0f / g_SymmS8QuantizationBase, 0}, |
| {15.0f / g_SymmS16QuantizationBase, 0}); |
| } |
| |
| void TestQuantizationParams(const TensorInfo& info, |
| const OffsetScalePair& qAsymmU8Params, |
| const OffsetScalePair& qAsymmS8Params, |
| const OffsetScalePair& qSymmS8Params, |
| const OffsetScalePair& qSymmS16Params) |
| { |
| switch (m_QuantizerOptions.m_ActivationFormat) |
| { |
| case DataType::QAsymmU8: |
| TestQuantizationParamsImpl( |
| info, DataType::QAsymmU8, qAsymmU8Params.first, qAsymmU8Params.second); |
| break; |
| case DataType::QAsymmS8: |
| TestQuantizationParamsImpl( |
| info, DataType::QAsymmS8, qAsymmS8Params.first, qAsymmS8Params.second); |
| break; |
| case DataType::QSymmS8: |
| TestQuantizationParamsImpl( |
| info, DataType::QSymmS8, qSymmS8Params.first, qSymmS8Params.second); |
| break; |
| case DataType::QSymmS16: |
| TestQuantizationParamsImpl( |
| info, DataType::QSymmS16, qSymmS16Params.first, qSymmS16Params.second); |
| break; |
| default: |
| throw InvalidArgumentException("Unsupported quantization target"); |
| } |
| } |
| |
| void TestDifferentQuantizationScale(const TensorInfo& info0, const TensorInfo& info1) |
| { |
| BOOST_TEST(info0.GetQuantizationScale() != info1.GetQuantizationScale()); |
| } |
| |
| void TestConstantQuantizationParams(const TensorInfo& info, |
| const OffsetScalePair& params, |
| DataType dataType = DataType::QAsymmU8) |
| { |
| IgnoreUnused(dataType); |
| TestQuantizationParamsImpl(info, dataType, params.first, params.second); |
| } |
| |
| void TestBiasQuantizationParams(const TensorInfo& info, |
| const OffsetScalePair& qAsymmU8Params, |
| const OffsetScalePair& qAsymmS8Params, |
| const OffsetScalePair& qSymmS8Params, |
| const OffsetScalePair& qSymmS16Params, |
| DataType dataType = DataType::QAsymmU8) |
| { |
| switch (m_QuantizerOptions.m_ActivationFormat) |
| { |
| case DataType::QAsymmU8: |
| TestQuantizationParamsImpl(info, dataType, qAsymmU8Params.first, qAsymmU8Params.second); |
| break; |
| case DataType::QAsymmS8: |
| TestQuantizationParamsImpl(info, dataType, qAsymmS8Params.first, qAsymmS8Params.second); |
| break; |
| case DataType::QSymmS8: |
| TestQuantizationParamsImpl(info, dataType, qSymmS8Params.first, qSymmS8Params.second); |
| break; |
| case DataType::QSymmS16: |
| TestQuantizationParamsImpl(info, dataType, qSymmS16Params.first, qSymmS16Params.second); |
| break; |
| default: |
| throw InvalidArgumentException("Unsupported quantization target"); |
| } |
| } |
| |
| void TestQuantizationOnLayersWithBiases(const IConnectableLayer* layer, |
| const ConstTensor& weights, |
| const Optional<ConstTensor>& biases) |
| { |
| TensorInfo info = layer->GetOutputSlot(0).GetTensorInfo(); |
| float inputScaleQAsymmU8 = 30.0f / g_AsymmU8QuantizationBase; |
| float inputScaleQAsymmS8 = 30.0f / g_AsymmS8QuantizationBase; |
| float inputScaleQSymmS8 = 15.0f / g_SymmS8QuantizationBase; |
| float inputScaleQSymmS16 = 15.0f / g_SymmS16QuantizationBase; |
| float weightsScale = 3.0f / g_AsymmU8QuantizationBase; |
| |
| // Based off default static range [-15.0f, 15.0f] |
| TestQuantizationParams(info, {inputScaleQAsymmU8, 128}, |
| {inputScaleQAsymmS8, 0}, |
| {inputScaleQSymmS8, 0}, |
| {inputScaleQSymmS16, 0}); |
| |
| TestConstantQuantizationParams(weights.GetInfo(), {weightsScale, 85}); |
| |
| if (biases.has_value()) |
| { |
| TestBiasQuantizationParams(biases.value().GetInfo(), |
| {inputScaleQAsymmU8 * weightsScale, 0}, |
| {inputScaleQAsymmS8 * weightsScale, 0}, |
| {inputScaleQSymmS8 * weightsScale, 0}, |
| {inputScaleQSymmS16 * weightsScale, 0}, |
| DataType::Signed32); |
| } |
| } |
| |
| TensorShape m_InputShape; |
| TensorShape m_OutputShape; |
| |
| private: |
| void TestQuantizationParamsImpl(const TensorInfo& info, DataType dataType, float scale, int32_t offset) |
| { |
| BOOST_TEST((info.GetDataType() == dataType)); |
| BOOST_TEST(info.GetQuantizationOffset() == offset); |
| BOOST_CHECK_CLOSE(info.GetQuantizationScale(), scale, g_TestTolerance); |
| } |
| |
| QuantizerOptions m_QuantizerOptions; |
| }; |
| |
| void TestNetwork(INetwork* network, const TensorShape inShape, const TensorShape outShape) |
| { |
| const QuantizerOptions qAsymmU8Options(DataType::QAsymmU8); |
| INetworkPtr quantizedNetworkQAsymmU8 = INetworkQuantizer::Create(network, qAsymmU8Options)->ExportNetwork(); |
| TestQuantization validatorQAsymmU8(inShape, outShape); |
| VisitLayersTopologically(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8); |
| |
| const QuantizerOptions qAsymmS8Options(DataType::QAsymmS8); |
| INetworkPtr quantizedNetworkQAsymmS8 = INetworkQuantizer::Create(network, qAsymmS8Options)->ExportNetwork(); |
| TestQuantization validatorQAsymmS8(qAsymmS8Options, inShape, outShape); |
| VisitLayersTopologically(quantizedNetworkQAsymmS8.get(), validatorQAsymmS8); |
| |
| const QuantizerOptions qSymmS8Options(DataType::QSymmS8); |
| INetworkPtr quantizedNetworkQSymmS8 = INetworkQuantizer::Create(network, qSymmS8Options)->ExportNetwork(); |
| TestQuantization validatorQSymmS8(qSymmS8Options, inShape, outShape); |
| VisitLayersTopologically(quantizedNetworkQSymmS8.get(), validatorQSymmS8); |
| |
| const QuantizerOptions qSymmS16options(DataType::QSymmS16); |
| INetworkPtr quantizedNetworkQSymmS16 = INetworkQuantizer::Create(network, qSymmS16options)->ExportNetwork(); |
| TestQuantization validatorQSymmS16(qSymmS16options, inShape, outShape); |
| VisitLayersTopologically(quantizedNetworkQSymmS16.get(), validatorQSymmS16); |
| } |
| |
| void TestNetwork(INetwork* network, const TensorShape shape) |
| { |
| TestNetwork(network, shape, shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeAddition) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* input1 = network->AddInputLayer(1); |
| IConnectableLayer* addition = network->AddAdditionLayer(); |
| IConnectableLayer* output = network->AddOutputLayer(2); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(addition->GetInputSlot(0)); |
| input1->GetOutputSlot(0).Connect(addition->GetInputSlot(1)); |
| addition->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| const TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| input1->GetOutputSlot(0).SetTensorInfo(info); |
| addition->GetOutputSlot(0).SetTensorInfo(info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| INetworkPtr CreateNetworkWithActivationLayer(const ActivationDescriptor& descriptor, const TensorShape& shape) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* activation = network->AddActivationLayer(descriptor); |
| IConnectableLayer* output = network->AddOutputLayer(2); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(activation->GetInputSlot(0)); |
| activation->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| TensorInfo info(shape, DataType::Float32); |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| activation->GetOutputSlot(0).SetTensorInfo(info); |
| |
| return network; |
| } |
| |
| INetworkPtr CreateNetworkWithArgMinMaxLayer(const ArgMinMaxDescriptor& descriptor, const TensorShape& shape) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* activation = network->AddArgMinMaxLayer(descriptor); |
| IConnectableLayer* output = network->AddOutputLayer(2); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(activation->GetInputSlot(0)); |
| activation->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| TensorInfo inInfo(shape, DataType::Float32); |
| input0->GetOutputSlot(0).SetTensorInfo(inInfo); |
| TensorInfo outInfo({1}, DataType::Signed32); |
| activation->GetOutputSlot(0).SetTensorInfo(outInfo); |
| |
| return network; |
| } |
| |
| INetworkPtr CreateNetworkWithInputOutputLayers() |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| // Add input/output layers |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| IConnectableLayer* output = network->AddOutputLayer(1); |
| |
| // Establish connections |
| inputLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| TensorShape shape{8U}; |
| TensorInfo info(shape, DataType::Float32); |
| inputLayer->GetOutputSlot(0).SetTensorInfo(info); |
| |
| return network; |
| } |
| |
| BOOST_AUTO_TEST_CASE(InputOutputLayerDynamicQuant) |
| { |
| INetworkPtr network = CreateNetworkWithInputOutputLayers(); |
| |
| armnn::TensorInfo tensorInfo = GetInputTensorInfo(network.get()); |
| |
| // Outliers -56 and 98 |
| std::vector<float> inputData({0, 0, 0, -56, 98, 0, 0, 0}); |
| armnn::ConstTensor inputTensor(tensorInfo, inputData.data()); |
| |
| InputTensors inputTensors; |
| inputTensors.push_back(std::make_pair(0, inputTensor)); |
| |
| armnn::INetworkQuantizerPtr quantizer = armnn::INetworkQuantizer::Create(network.get()); |
| |
| quantizer->Refine(inputTensors); |
| |
| // Outliers -77 and 65 |
| std::vector<float> inputData2({0, -77, 0, -56, 65, 0, 0, 0}); |
| armnn::ConstTensor inputTensor2(tensorInfo, inputData2.data()); |
| InputTensors inputTensors2; |
| inputTensors2.push_back(std::make_pair(0, inputTensor2)); |
| |
| quantizer->Refine(inputTensors2); |
| |
| INetworkPtr quantizedNetwork = quantizer->ExportNetwork(); |
| // Output Layer should be quantized for a min max of -77 and 98 |
| // according to QU8 Quantization Scheme |
| std::unique_ptr<IQuantizationScheme> quantizationScheme = std::make_unique<QAsymmU8QuantizationScheme>(); |
| OffsetScalePair qParams = quantizationScheme->ComputeScheme(-77.0, 98.0); |
| |
| class TestOutputStrategy : public IStrategy |
| { |
| public : |
| TestOutputStrategy(const OffsetScalePair& offsetScalePair, const DataType& dataType) : |
| m_OffsetScalePair(offsetScalePair), m_DataType(dataType) {} |
| |
| void ExecuteStrategy(const armnn::IConnectableLayer* layer, |
| const BaseDescriptor& descriptor, |
| const std::vector<armnn::ConstTensor>& constants, |
| const char* name, |
| const armnn::LayerBindingId id) override |
| { |
| IgnoreUnused(name, constants, id, descriptor); |
| |
| switch (layer->GetType()) |
| { |
| case armnn::LayerType::Output : |
| { |
| const TensorInfo &info = layer->GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| BOOST_CHECK_MESSAGE(info.GetDataType() == m_DataType, |
| std::string(armnn::GetDataTypeName(info.GetDataType())) |
| .append(" == ").append(armnn::GetDataTypeName(m_DataType))); |
| // int_32t |
| BOOST_CHECK(info.GetQuantizationOffset() == m_OffsetScalePair.second); |
| // float |
| BOOST_TEST(info.GetQuantizationScale() == m_OffsetScalePair.first, |
| boost::test_tools::tolerance(0.001)); |
| break; |
| } |
| default: |
| {} |
| } |
| } |
| |
| private: |
| const OffsetScalePair m_OffsetScalePair; |
| const DataType m_DataType; |
| }; |
| |
| TestOutputStrategy strategy(qParams, quantizationScheme->GetDataType()); |
| quantizedNetwork->ExecuteStrategy(strategy); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeAbsActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::Abs; |
| descriptor.m_A = 3.5f; |
| descriptor.m_B = -10.0f; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeArgMax) |
| { |
| ArgMinMaxDescriptor descriptor; |
| descriptor.m_Function = ArgMinMaxFunction::Max; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithArgMinMaxLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeLinearActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::Linear; |
| descriptor.m_A = 3.5f; |
| descriptor.m_B = -10.0f; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeReLuActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::ReLu; |
| descriptor.m_A = 3.5f; |
| descriptor.m_B = -10.0f; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeSoftReLuActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::SoftReLu; |
| descriptor.m_A = 3.5f; |
| descriptor.m_B = -10.0f; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeBoundedReluActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::BoundedReLu; |
| descriptor.m_A = 3.5f; |
| descriptor.m_B = -10.0f; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeTanHActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::TanH; |
| descriptor.m_A = 3.5f; |
| descriptor.m_B = -10.0f; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeLeakyReLuActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::LeakyReLu; |
| descriptor.m_A = 3.5f; |
| descriptor.m_B = -10.0f; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| |
| BOOST_AUTO_TEST_CASE(QuantizeELuActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::Elu; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| BOOST_AUTO_TEST_CASE(QuantizeHardSwishActivation) |
| { |
| ActivationDescriptor descriptor; |
| descriptor.m_Function = ActivationFunction::HardSwish; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithActivationLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| |
| BOOST_AUTO_TEST_CASE(QuantizeBatchNorm) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{3U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| std::vector<float> meanData{-1.0f, 1.5f, 2.0f}; |
| std::vector<float> varData{-1.0f, 1.5f, 2.0f}; |
| std::vector<float> betaData{-1.0f, 1.5f, 2.0f}; |
| std::vector<float> gammaData{-1.0f, 1.5f, 2.0f}; |
| |
| ConstTensor mean(info, meanData); |
| ConstTensor var(info, varData); |
| ConstTensor beta(info, betaData); |
| ConstTensor gamma(info, gammaData); |
| |
| BatchNormalizationDescriptor desc; |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* batchNorm = network->AddBatchNormalizationLayer(desc, mean, var, beta, gamma); |
| IConnectableLayer* output = network->AddOutputLayer(1); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(batchNorm->GetInputSlot(0)); |
| batchNorm->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| batchNorm->GetOutputSlot(0).SetTensorInfo(info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeDepthToSpace) |
| { |
| const TensorShape inputShape { 1, 2, 2, 4 }; |
| const TensorShape outputShape{ 1, 4, 4, 1 }; |
| |
| const TensorInfo inputInfo (inputShape, DataType::Float32); |
| const TensorInfo outputInfo(outputShape, DataType::Float32); |
| |
| INetworkPtr network = INetwork::Create(); |
| const DepthToSpaceDescriptor descriptor(2, armnn::DataLayout::NHWC); |
| |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| IConnectableLayer* depthToSpaceLayer = network->AddDepthToSpaceLayer(descriptor); |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| |
| inputLayer->GetOutputSlot(0).Connect(depthToSpaceLayer->GetInputSlot(0)); |
| depthToSpaceLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| depthToSpaceLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| TestNetwork(network.get(), inputShape, outputShape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(OverrideInputRangeEmptyNetwork) |
| { |
| RangeTracker ranges; |
| RangeTracker::MinMaxRange minMaxRange(-12.3f, 45.6f); // Range to use for the override |
| |
| NetworkImpl network; // Empty network |
| auto inputLayers = network.GetGraph().GetInputLayers(); // Empty list of input layers |
| |
| OverrideInputRangeStrategy overrideInputRangeStrategy(ranges, 0, minMaxRange); |
| ApplyStrategyToLayers(inputLayers, overrideInputRangeStrategy); |
| |
| BOOST_CHECK(ranges.IsEmpty()); // Check that the map of ranges remained untouched |
| } |
| |
| BOOST_AUTO_TEST_CASE(OverrideInputRangeNoInputLayers) |
| { |
| RangeTracker ranges; |
| MinMaxRange minMaxRange(-12.3f, 45.6f); // Range to use for the override |
| |
| NetworkImpl network; |
| network.AddAdditionLayer(); // Network with no input layers |
| auto inputLayers = network.GetGraph().GetInputLayers(); // Empty list of input layers |
| |
| OverrideInputRangeStrategy overrideInputRangeStrategy(ranges, 0, minMaxRange); |
| ApplyStrategyToLayers(inputLayers, overrideInputRangeStrategy); |
| |
| BOOST_CHECK(ranges.IsEmpty()); // Check that the map of ranges remained untouched |
| } |
| |
| BOOST_AUTO_TEST_CASE(OverrideInputRangeInputLayers) |
| { |
| RangeTracker ranges; |
| MinMaxRange minMaxRange(-12.3f, 45.6f); // Range to use for the override |
| |
| NetworkImpl network; |
| |
| // Adding the layers |
| IConnectableLayer* input0 = network.AddInputLayer(0); |
| IConnectableLayer* input1 = network.AddInputLayer(1); |
| IConnectableLayer* addition = network.AddAdditionLayer(); |
| IConnectableLayer* output = network.AddOutputLayer(2); |
| |
| // Connecting the layer |
| input0->GetOutputSlot(0).Connect(addition->GetInputSlot(0)); |
| input1->GetOutputSlot(0).Connect(addition->GetInputSlot(1)); |
| addition->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Setting the TensorInfos |
| TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| input1->GetOutputSlot(0).SetTensorInfo(info); |
| addition->GetOutputSlot(0).SetTensorInfo(info); |
| |
| auto inputLayers = network.GetGraph().GetInputLayers(); // List of input layers |
| |
| // Trying to override the input range for the input layer with binding id 3 (does not exist in the network) |
| OverrideInputRangeStrategy overrideInputRangeStrategy3(ranges, 3, minMaxRange); |
| ApplyStrategyToLayers(inputLayers, overrideInputRangeStrategy3); |
| |
| // Check that the map of ranges remained untouched |
| BOOST_CHECK(ranges.IsEmpty()); |
| |
| // Override the input range for the input layer with binding id 1 |
| OverrideInputRangeStrategy overrideInputRangeStrategy1(ranges, 1, minMaxRange); |
| ApplyStrategyToLayers(inputLayers, overrideInputRangeStrategy1); |
| |
| // Check that the map of ranges has been populated |
| BOOST_CHECK(!ranges.IsEmpty()); |
| |
| // Check that an entry for the input layer with binding id 0 does not exist |
| BOOST_CHECK(!ranges.HasRanges(input0->GetGuid())); |
| |
| // Check that an entry for the input layer with binding id 1 exists |
| BOOST_CHECK(ranges.HasRanges(input1->GetGuid())); |
| |
| // Check the the overridden values are what we intended to set |
| BOOST_CHECK(ranges.GetRange(input1->GetGuid(), 0) == minMaxRange); |
| } |
| |
| INetworkPtr CreateNetworkWithFullyConnectedLayer(const bool biasEnabled, |
| const TensorShape& inputShape, |
| const TensorShape& outputShape) |
| { |
| FullyConnectedDescriptor desc; |
| desc.m_BiasEnabled = biasEnabled; |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorInfo info(inputShape, DataType::Float32); |
| const TensorInfo outputInfo(outputShape, DataType::Float32); |
| |
| std::vector<float> weightsData{-1.0f, 1.5f, 2.0f}; |
| ConstTensor weights(info, weightsData); |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* fullyConnected; |
| Optional<ConstTensor> optionalBias; |
| std::vector<float> biasData{10.0f, 20.0f, 30.0f}; |
| if (desc.m_BiasEnabled) |
| { |
| ConstTensor bias(info, biasData); |
| optionalBias = Optional<ConstTensor>(bias); |
| } |
| fullyConnected = network->AddFullyConnectedLayer(desc, weights, optionalBias); |
| IConnectableLayer* output = network->AddOutputLayer(1); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(fullyConnected->GetInputSlot(0)); |
| fullyConnected->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| fullyConnected->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| return network; |
| } |
| |
| void ValidateFullyConnectedLayer(const bool biasEnabled) |
| { |
| const TensorShape shape{3U}; |
| INetworkPtr network = CreateNetworkWithFullyConnectedLayer(biasEnabled, shape, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeFill) |
| { |
| const TensorShape tensorShape{ 1U }; |
| const TensorInfo tensorInfo(tensorShape, DataType::Float32); |
| |
| INetworkPtr network = INetwork::Create(); |
| |
| FillDescriptor descriptor; |
| descriptor.m_Value = 1; |
| |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| IConnectableLayer* fillLayer = network->AddFillLayer(descriptor); |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| |
| inputLayer->GetOutputSlot(0).Connect(fillLayer->GetInputSlot(0)); |
| fillLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| fillLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| TestNetwork(network.get(), tensorShape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeFullyConnected) |
| { |
| ValidateFullyConnectedLayer(false); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeFullyConnectedBiasEnabled) |
| { |
| ValidateFullyConnectedLayer(true); |
| } |
| |
| void TestQuantizeConvolution2d(bool useBiases) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| TensorShape shape{3U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| std::vector<float> weightsData{-1.0f, 1.5f, 2.0f}; |
| ConstTensor weights(info, weightsData); |
| |
| Convolution2dDescriptor descriptor; |
| descriptor.m_BiasEnabled = useBiases; |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* conv2d; |
| Optional<ConstTensor> optionalBiases; |
| std::vector<float> biasesData{-1.0f, 1.5f, 2.0f}; |
| if (useBiases) |
| { |
| ConstTensor biases(info, biasesData); |
| optionalBiases = Optional<ConstTensor>(biases); |
| } |
| conv2d = network->AddConvolution2dLayer(descriptor, weights, optionalBiases); |
| IConnectableLayer* output = network->AddOutputLayer(1); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(conv2d->GetInputSlot(0)); |
| conv2d->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| conv2d->GetOutputSlot(0).SetTensorInfo(info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeConvolution2d) |
| { |
| TestQuantizeConvolution2d(false); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeConvolution2dWithBiases) |
| { |
| TestQuantizeConvolution2d(true); |
| } |
| |
| void TestQuantizeDepthwiseConvolution2d(bool useBiases) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| TensorShape shape{3U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| std::vector<float> weightsData{-1.0f, 1.5f, 2.0f}; |
| ConstTensor weights(info, weightsData); |
| |
| DepthwiseConvolution2dDescriptor descriptor; |
| descriptor.m_BiasEnabled = useBiases; |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* depthwiseConv2d; |
| Optional<ConstTensor> optionalBiases; |
| std::vector<float> biasesData{-1.0f, 1.5f, 2.0f}; |
| if (useBiases) |
| { |
| ConstTensor biases(info, biasesData); |
| optionalBiases = Optional<ConstTensor>(biases); |
| } |
| depthwiseConv2d = network->AddDepthwiseConvolution2dLayer(descriptor, weights, optionalBiases); |
| IConnectableLayer* output = network->AddOutputLayer(1); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(depthwiseConv2d->GetInputSlot(0)); |
| depthwiseConv2d->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| //Set TensorInfo |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| depthwiseConv2d->GetOutputSlot(0).SetTensorInfo(info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeDepthwiseConvolution2d) |
| { |
| TestQuantizeDepthwiseConvolution2d(false); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeDepthwiseConvolution2dWithBiases) |
| { |
| TestQuantizeDepthwiseConvolution2d(true); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeInstanceNormalization) |
| { |
| const TensorShape shape{ 1, 4, 4, 1 }; |
| const TensorInfo tensorInfo(shape, DataType::Float32); |
| |
| INetworkPtr network = INetwork::Create(); |
| |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| IConnectableLayer* instanceNormLayer = network->AddInstanceNormalizationLayer(InstanceNormalizationDescriptor()); |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| |
| inputLayer->GetOutputSlot(0).Connect(instanceNormLayer->GetInputSlot(0)); |
| instanceNormLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| instanceNormLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeLogSoftmax) |
| { |
| const TensorShape tensorShape{ 1U }; |
| const TensorInfo tensorInfo(tensorShape, DataType::Float32); |
| |
| INetworkPtr network = INetwork::Create(); |
| |
| LogSoftmaxDescriptor descriptor; |
| descriptor.m_Beta = 1.0f; |
| |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| IConnectableLayer* logSoftmaxLayer = network->AddLogSoftmaxLayer(descriptor); |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| |
| inputLayer->GetOutputSlot(0).Connect(logSoftmaxLayer->GetInputSlot(0)); |
| logSoftmaxLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| logSoftmaxLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| TestNetwork(network.get(), tensorShape); |
| } |
| |
| INetworkPtr CreateNetworkWithSoftmaxLayer(const SoftmaxDescriptor& descriptor, const TensorShape& shape) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* softmax = network->AddSoftmaxLayer(descriptor); |
| IConnectableLayer* output = network->AddOutputLayer(2); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); |
| softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| TensorInfo info(shape, DataType::Float32); |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| softmax->GetOutputSlot(0).SetTensorInfo(info); |
| |
| return network; |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeSoftmax) |
| { |
| SoftmaxDescriptor descriptor; |
| descriptor.m_Beta = 1.0f; |
| |
| const TensorShape shape{1U}; |
| INetworkPtr network = CreateNetworkWithSoftmaxLayer(descriptor, shape); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeStandIn) |
| { |
| const TensorShape tensorShape{ 1U }; |
| const TensorInfo tensorInfo(tensorShape, DataType::Float32); |
| |
| INetworkPtr network = INetwork::Create(); |
| |
| StandInDescriptor descriptor; |
| descriptor.m_NumInputs = 1; |
| descriptor.m_NumOutputs = 1; |
| |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| IConnectableLayer* standInLayer = network->AddStandInLayer(descriptor); |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| |
| inputLayer->GetOutputSlot(0).Connect(standInLayer->GetInputSlot(0)); |
| standInLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| standInLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| // test QAsymmU8 quantization |
| BOOST_CHECK_THROW(INetworkQuantizer::Create(network.get())->ExportNetwork(), |
| armnn::UnimplementedException); |
| |
| // test QAsymmS8 quantization |
| const QuantizerOptions qAsymmS8Options(DataType::QAsymmS8); |
| BOOST_CHECK_THROW(INetworkQuantizer::Create(network.get(), qAsymmS8Options)->ExportNetwork(), |
| armnn::UnimplementedException); |
| |
| // test QuantisedSymmS16 quantization |
| const QuantizerOptions qSymmS8Options(DataType::QSymmS8); |
| BOOST_CHECK_THROW(INetworkQuantizer::Create(network.get(), qSymmS8Options)->ExportNetwork(), |
| armnn::UnimplementedException); |
| |
| // test QuantisedSymmS16 quantization |
| const QuantizerOptions qSymmS16options(DataType::QSymmS16); |
| BOOST_CHECK_THROW(INetworkQuantizer::Create(network.get(), qSymmS16options)->ExportNetwork(), |
| armnn::UnimplementedException); |
| } |
| |
| IConnectableLayer* CreateStartOfLeakyReluNetwork(INetwork* network, const TensorInfo& info) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::LeakyReLu; |
| activationDescriptor.m_A = 3.5f; |
| activationDescriptor.m_B = -10.0f; |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* activation = network->AddActivationLayer(activationDescriptor); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(activation->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| activation->GetOutputSlot(0).SetTensorInfo(info); |
| |
| return activation; |
| } |
| |
| void CompleteLeakyReluNetwork(INetwork* network, |
| IConnectableLayer* activation, |
| IConnectableLayer* layerUnderTest, |
| const TensorInfo& info) |
| { |
| // Add the output Layer |
| IConnectableLayer* output = network->AddOutputLayer(3); |
| |
| // Establish connections |
| activation->GetOutputSlot(0).Connect(layerUnderTest->GetInputSlot(0)); |
| layerUnderTest->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| //Set TensorInfo |
| layerUnderTest->GetOutputSlot(0).SetTensorInfo(info); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizePermute) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| IConnectableLayer* activation = CreateStartOfLeakyReluNetwork(network.get(), info); |
| |
| // Add the layer under test |
| PermuteDescriptor desc; |
| IConnectableLayer* permute = network->AddPermuteLayer(desc); |
| |
| CompleteLeakyReluNetwork(network.get(), activation, permute, info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeSpaceToBatch) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| IConnectableLayer* activation = CreateStartOfLeakyReluNetwork(network.get(), info); |
| |
| // Add the layer under test |
| SpaceToBatchNdDescriptor descriptor; |
| IConnectableLayer* spaceToBatch = network->AddSpaceToBatchNdLayer(descriptor); |
| |
| CompleteLeakyReluNetwork(network.get(), activation, spaceToBatch, info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeSpaceToDepth) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{ 1u }; |
| TensorInfo info(shape, DataType::Float32); |
| |
| IConnectableLayer* activation = CreateStartOfLeakyReluNetwork(network.get(), info); |
| IConnectableLayer* spaceToDepth = network->AddSpaceToDepthLayer(SpaceToDepthDescriptor()); |
| |
| CompleteLeakyReluNetwork(network.get(), activation, spaceToDepth, info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizePooling2d) |
| { |
| auto network = INetwork::Create(); |
| |
| TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| Pooling2dDescriptor desc; |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::LeakyReLu; |
| activationDescriptor.m_A = 3.5f; |
| activationDescriptor.m_B = -10.0f; |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* activation = network->AddActivationLayer(activationDescriptor); |
| IConnectableLayer* pooling2d = network->AddPooling2dLayer(desc); |
| IConnectableLayer* output = network->AddOutputLayer(3); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(activation->GetInputSlot(0)); |
| activation->GetOutputSlot(0).Connect(pooling2d->GetInputSlot(0)); |
| pooling2d->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| activation->GetOutputSlot(0).SetTensorInfo(info); |
| pooling2d->GetOutputSlot(0).SetTensorInfo(info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeConstant) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| // Constant layer data |
| std::vector<float> data = {-2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}; |
| const TensorShape shape{1U, 1U, 3U, 3U}; |
| TensorInfo tensorInfo(shape, DataType::Float32); |
| ConstTensor constantTensor(tensorInfo, data); |
| |
| // Add the layers |
| IConnectableLayer* input = network->AddInputLayer(0); |
| IConnectableLayer* constant = network->AddConstantLayer(constantTensor); |
| IConnectableLayer* addition = network->AddAdditionLayer(); |
| IConnectableLayer* output = network->AddOutputLayer(1); |
| |
| // Establish connections |
| input->GetOutputSlot(0).Connect(addition->GetInputSlot(0)); |
| constant->GetOutputSlot(0).Connect(addition->GetInputSlot(1)); |
| addition->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set TensorInfo in the remaining layers |
| input->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| addition->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| constant->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeArgMinMax) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape inputShape{ 1, 1, 1, 5 }; |
| const TensorShape outputShape{ 1, 1, 1 }; |
| |
| TensorInfo inputInfo(inputShape, DataType::Float32); |
| TensorInfo outputInfo(outputShape, DataType::Float32); |
| |
| // Add the input layers |
| IConnectableLayer* input = network->AddInputLayer(0); |
| |
| // Add the layer under test |
| ArgMinMaxDescriptor argMinMaxDescriptor; |
| argMinMaxDescriptor.m_Function = ArgMinMaxFunction::Max; |
| IConnectableLayer* argMinMaxLayer = network->AddArgMinMaxLayer(argMinMaxDescriptor); |
| |
| // Add the output layers |
| IConnectableLayer* output = network->AddOutputLayer(1); |
| |
| // Establish connections |
| input->GetOutputSlot(0).Connect(argMinMaxLayer->GetInputSlot(0)); |
| argMinMaxLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set tensor info |
| input->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| argMinMaxLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| TestNetwork(network.get(), inputShape, outputShape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeComparison) |
| { |
| const TensorShape tensorShape{ 1u }; |
| const TensorInfo tensorInfo(tensorShape, DataType::Float32); |
| |
| INetworkPtr network = INetwork::Create(); |
| ComparisonDescriptor descriptor(ComparisonOperation::LessOrEqual); |
| |
| IConnectableLayer* inputLayer0 = network->AddInputLayer(0); |
| IConnectableLayer* inputLayer1 = network->AddInputLayer(1); |
| IConnectableLayer* comparisonLayer = network->AddComparisonLayer(descriptor); |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| |
| inputLayer0->GetOutputSlot(0).Connect(comparisonLayer->GetInputSlot(0)); |
| inputLayer1->GetOutputSlot(0).Connect(comparisonLayer->GetInputSlot(1)); |
| comparisonLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| inputLayer0->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| inputLayer1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| comparisonLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| TestNetwork(network.get(), tensorShape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeConcat) |
| { |
| class TestConcatQuantization : public TestQuantization |
| { |
| public: |
| TestConcatQuantization(const TensorShape& inputShape, const TensorShape& outputShape) |
| : TestQuantization(inputShape, outputShape) {} |
| |
| TestConcatQuantization(const QuantizerOptions& options, |
| const TensorShape& inputShape, |
| const TensorShape& outputShape) |
| : TestQuantization(options, inputShape, outputShape) {} |
| |
| void ExecuteStrategy(const armnn::IConnectableLayer* layer, |
| const BaseDescriptor& descriptor, |
| const std::vector<armnn::ConstTensor>& constants, |
| const char* name, |
| const armnn::LayerBindingId id) override |
| { |
| IgnoreUnused(name, constants, id, descriptor); |
| |
| switch (layer->GetType()) |
| { |
| case armnn::LayerType::Input : |
| break; |
| case armnn::LayerType::Output : |
| break; |
| case armnn::LayerType::Concat : |
| { |
| TensorInfo outputInfo = layer->GetOutputSlot(0).GetTensorInfo(); |
| TestQuantizationParams( |
| outputInfo, {60.8f / g_AsymmU8QuantizationBase, 65}, |
| {60.8f / g_SymmS8QuantizationBase, -63}, |
| {45.3f / g_SymmS8QuantizationBase, 0}, |
| {45.3f / g_SymmS16QuantizationBase, 0}); |
| |
| TensorInfo inputInfo0 = layer->GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| TensorInfo inputInfo1 = layer->GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| TensorInfo inputInfo2 = layer->GetInputSlot(2).GetConnection()->GetTensorInfo(); |
| |
| TestDifferentQuantizationScale(inputInfo0, inputInfo1); |
| TestDifferentQuantizationScale(inputInfo0, inputInfo2); |
| TestDifferentQuantizationScale(inputInfo1, inputInfo2); |
| TestDifferentQuantizationScale(inputInfo0, outputInfo); |
| break; |
| } |
| default: |
| {} |
| } |
| } |
| }; |
| |
| INetworkPtr network = INetwork::Create(); |
| |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* input1 = network->AddInputLayer(1); |
| IConnectableLayer* input2 = network->AddInputLayer(2); |
| |
| OriginsDescriptor descriptor(3, 1); |
| IConnectableLayer* concatLayer = network->AddConcatLayer(descriptor); |
| |
| IConnectableLayer* output0 = network->AddOutputLayer(3); |
| |
| // Establish connections |
| input0->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(0)); |
| input1->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(1)); |
| input2->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(2)); |
| concatLayer->GetOutputSlot(0).Connect(output0->GetInputSlot(0)); |
| |
| // Set TensorInfo |
| const TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| input1->GetOutputSlot(0).SetTensorInfo(info); |
| input2->GetOutputSlot(0).SetTensorInfo(info); |
| concatLayer->GetOutputSlot(0).SetTensorInfo(info); |
| |
| const QuantizerOptions qSymmS8Options(DataType::QSymmS8); |
| const QuantizerOptions qSymmS16options(DataType::QSymmS16); |
| INetworkQuantizerPtr quantizerPtrQAsymmU8 = INetworkQuantizer::Create(network.get()); |
| INetworkQuantizerPtr quantizerPtrQSymmS8 = INetworkQuantizer::Create(network.get(), qSymmS8Options); |
| INetworkQuantizerPtr quantizerPtrQSymmS16 = INetworkQuantizer::Create(network.get(), qSymmS16options); |
| // Override the input ranges |
| float min = -15.5f; |
| float max = 45.3f; |
| |
| quantizerPtrQAsymmU8->OverrideInputRange(0, (min + 2.1f), (max - 3.2f)); |
| quantizerPtrQAsymmU8->OverrideInputRange(1, (min + 6.7f), max); |
| quantizerPtrQAsymmU8->OverrideInputRange(2, min, (max - 7.8f)); |
| |
| quantizerPtrQSymmS8->OverrideInputRange(0, (min + 2.1f), (max - 3.2f)); |
| quantizerPtrQSymmS8->OverrideInputRange(1, (min + 6.7f), max); |
| quantizerPtrQSymmS8->OverrideInputRange(2, min, (max - 7.8f)); |
| |
| quantizerPtrQSymmS16->OverrideInputRange(0, (min + 2.1f), (max - 3.2f)); |
| quantizerPtrQSymmS16->OverrideInputRange(1, (min + 6.7f), max); |
| quantizerPtrQSymmS16->OverrideInputRange(2, min, (max - 7.8f)); |
| |
| INetworkPtr quantizedNetworkQAsymmU8 = quantizerPtrQAsymmU8->ExportNetwork(); |
| TestConcatQuantization validatorQAsymmU8(shape, shape); |
| VisitLayersTopologically(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8); |
| |
| INetworkPtr quantizedNetworkQSymmS8 = quantizerPtrQSymmS8->ExportNetwork(); |
| TestConcatQuantization validatorQSymmS8(qSymmS8Options, shape, shape); |
| VisitLayersTopologically(quantizedNetworkQSymmS8.get(), validatorQSymmS8); |
| |
| INetworkPtr quantizedNetworkQSymmS16 = quantizerPtrQSymmS16->ExportNetwork(); |
| TestConcatQuantization validatorQSymmS16(qSymmS16options, shape, shape); |
| VisitLayersTopologically(quantizedNetworkQSymmS16.get(), validatorQSymmS16); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeReshape) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| IConnectableLayer* activation = CreateStartOfLeakyReluNetwork(network.get(), info); |
| |
| // Add the layer under test |
| ReshapeDescriptor descriptor({1, 2, 3, 4}); |
| IConnectableLayer* reshape = network->AddReshapeLayer(descriptor); |
| |
| CompleteLeakyReluNetwork(network.get(), activation, reshape, info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeSplitter) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{3U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| IConnectableLayer* activation = CreateStartOfLeakyReluNetwork(network.get(), info); |
| |
| // Add the layer under test |
| ViewsDescriptor splitterDesc(2,4); |
| IConnectableLayer* splitter = network->AddSplitterLayer(splitterDesc); |
| CompleteLeakyReluNetwork(network.get(), activation, splitter, info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeResize) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| IConnectableLayer* activation = CreateStartOfLeakyReluNetwork(network.get(), info); |
| |
| // Add the layer under test |
| ResizeDescriptor descriptor; |
| descriptor.m_TargetHeight = 3; |
| descriptor.m_TargetWidth = 3; |
| IConnectableLayer* resizeLayer = network->AddResizeLayer(descriptor); |
| |
| CompleteLeakyReluNetwork(network.get(), activation, resizeLayer, info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeStridedSlice) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{3U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| IConnectableLayer* activation = CreateStartOfLeakyReluNetwork(network.get(), info); |
| |
| // Add the layer under test |
| StridedSliceDescriptor stridedSliceDesc; |
| IConnectableLayer* stridedSlice = network->AddStridedSliceLayer(stridedSliceDesc); |
| |
| CompleteLeakyReluNetwork(network.get(), activation, stridedSlice, info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeBatchToSpace) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape shape{1U}; |
| TensorInfo info(shape, DataType::Float32); |
| |
| IConnectableLayer* activation = CreateStartOfLeakyReluNetwork(network.get(), info); |
| |
| // Add the layer under test |
| BatchToSpaceNdDescriptor descriptor; |
| IConnectableLayer* batchToSpace = network->AddBatchToSpaceNdLayer(descriptor); |
| |
| CompleteLeakyReluNetwork(network.get(), activation, batchToSpace, info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizePrelu) |
| { |
| class TestPreluQuantization : public TestQuantization |
| { |
| public: |
| TestPreluQuantization(const TensorShape& inputShape, |
| const TensorShape& alphaShape, |
| const TensorShape& outputShape) |
| : TestQuantization(inputShape, outputShape) |
| , m_AlphaShape(alphaShape) |
| {} |
| |
| TestPreluQuantization(const QuantizerOptions& options, |
| const TensorShape& inputShape, |
| const TensorShape& alphaShape, |
| const TensorShape& outputShape) |
| : TestQuantization(options, inputShape, outputShape) |
| , m_AlphaShape(alphaShape) |
| {} |
| |
| void ExecuteStrategy(const armnn::IConnectableLayer* layer, |
| const BaseDescriptor& descriptor, |
| const std::vector<armnn::ConstTensor>& constants, |
| const char* name, |
| const armnn::LayerBindingId id) override |
| { |
| IgnoreUnused(name, constants, id, descriptor); |
| |
| switch (layer->GetType()) |
| { |
| case armnn::LayerType::Input : |
| { |
| const TensorInfo& info = layer->GetOutputSlot(0).GetTensorInfo(); |
| |
| switch (id) |
| { |
| case 0: // Input |
| BOOST_TEST(m_InputShape == info.GetShape()); |
| break; |
| case 1: // Alpha |
| BOOST_TEST(m_AlphaShape == info.GetShape()); |
| break; |
| default: |
| throw InvalidArgumentException("Invalid layer binding id for PReLU layer"); |
| } |
| |
| // Based off current default [-15.0f, 15.0f] |
| TestQuantizationParams(info, |
| { 30.0f / g_AsymmU8QuantizationBase, 128 }, // QASymmU8 |
| { 30.0f / g_AsymmS8QuantizationBase, 0}, // QASymmS8 |
| { 15.0f / g_SymmS8QuantizationBase, 0}, // QSymmS8 |
| { 15.0f / g_SymmS16QuantizationBase, 0 }); // QSymmS16 |
| break; |
| } |
| case armnn::LayerType::Output : |
| { |
| const TensorInfo& info = layer->GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| BOOST_TEST(m_OutputShape == info.GetShape()); |
| break; |
| } |
| case armnn::LayerType::Prelu : |
| { |
| const TensorInfo& info = layer->GetOutputSlot(0).GetTensorInfo(); |
| TestQuantizationParams(info, |
| { 30.0f / g_AsymmU8QuantizationBase, 128 }, // QASymmU8 |
| { 30.0f / g_AsymmS8QuantizationBase, 0}, // QAsymmS8 |
| { 15.0f / g_SymmS8QuantizationBase, 0}, // QSymmS8 |
| { 15.0f / g_SymmS16QuantizationBase, 0 }); // QSymmS16 |
| break; |
| } |
| default: |
| {} |
| } |
| } |
| |
| private: |
| TensorShape m_AlphaShape; |
| }; |
| |
| INetworkPtr network = INetwork::Create(); |
| |
| const TensorShape inputShape{ 4, 1, 2 }; |
| const TensorShape alphaShape{ 5, 4, 3, 1 }; |
| const TensorShape outputShape{ 5, 4, 3, 2 }; |
| TensorInfo inputInfo(inputShape, DataType::Float32); |
| TensorInfo alphaInfo(alphaShape, DataType::Float32); |
| TensorInfo outputInfo(outputShape, DataType::Float32); |
| |
| // Add the input layers |
| IConnectableLayer* input = network->AddInputLayer(0); |
| IConnectableLayer* alpha = network->AddInputLayer(1); |
| |
| // Add the layer under test |
| IConnectableLayer* prelu = network->AddPreluLayer("prelu"); |
| |
| // Add the output layers |
| IConnectableLayer* output = network->AddOutputLayer(0); |
| |
| // Establish connections |
| input->GetOutputSlot(0).Connect(prelu->GetInputSlot(0)); |
| alpha->GetOutputSlot(0).Connect(prelu->GetInputSlot(1)); |
| prelu->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // Set tensor info |
| input->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| alpha->GetOutputSlot(0).SetTensorInfo(alphaInfo); |
| prelu->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| INetworkPtr quantizedNetworkQAsymmU8 = INetworkQuantizer::Create(network.get())->ExportNetwork(); |
| TestPreluQuantization validatorQAsymmU8(inputShape, alphaShape, outputShape); |
| VisitLayersTopologically(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8); |
| |
| const QuantizerOptions qAsymmS8Options(DataType::QAsymmS8); |
| INetworkPtr quantizedNetworkQAsymmS8 = INetworkQuantizer::Create(network.get(), qAsymmS8Options)->ExportNetwork(); |
| TestPreluQuantization validatorQAsymmS8(qAsymmS8Options, inputShape, alphaShape, outputShape); |
| VisitLayersTopologically(quantizedNetworkQAsymmS8.get(), validatorQAsymmS8); |
| |
| const QuantizerOptions qSymmS8Options(DataType::QSymmS8); |
| INetworkPtr quantizedNetworkQSymmS8 = INetworkQuantizer::Create(network.get(), qSymmS8Options)->ExportNetwork(); |
| TestPreluQuantization validatorQSymmS8(qSymmS8Options, inputShape, alphaShape, outputShape); |
| VisitLayersTopologically(quantizedNetworkQSymmS8.get(), validatorQSymmS8); |
| |
| const QuantizerOptions qSymmS16options(DataType::QSymmS16); |
| INetworkPtr quantizedNetworkQSymmS16 = INetworkQuantizer::Create(network.get(), qSymmS16options)->ExportNetwork(); |
| TestPreluQuantization validatorQSymmS16(qSymmS16options, inputShape, alphaShape, outputShape); |
| VisitLayersTopologically(quantizedNetworkQSymmS16.get(), validatorQSymmS16); |
| } |
| |
| void TestQuantizeTransposeConvolution2d(bool useBiases) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| TensorShape shape{ 3 }; |
| TensorInfo info(shape, DataType::Float32); |
| |
| std::initializer_list<float> floatData{ -1.0f, 1.5f, 2.0f }; |
| std::vector<float> weightsData(floatData); |
| ConstTensor weights(info, weightsData); |
| |
| TransposeConvolution2dDescriptor descriptor; |
| descriptor.m_BiasEnabled = useBiases; |
| |
| // construct network |
| IConnectableLayer* input = network->AddInputLayer(0); |
| Optional<ConstTensor> optionalBiases; |
| std::vector<float> biasesData(floatData); |
| if (useBiases) |
| { |
| ConstTensor biases(info, biasesData); |
| optionalBiases = Optional<ConstTensor>(biases); |
| } |
| IConnectableLayer* transposeConv2d = network->AddTransposeConvolution2dLayer(descriptor, weights, optionalBiases); |
| IConnectableLayer* output = network->AddOutputLayer(1); |
| |
| input->GetOutputSlot(0).Connect(transposeConv2d->GetInputSlot(0)); |
| transposeConv2d->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| input->GetOutputSlot(0).SetTensorInfo(info); |
| transposeConv2d->GetOutputSlot(0).SetTensorInfo(info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeTransposeConvolution2d) |
| { |
| TestQuantizeTransposeConvolution2d(false); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeTransposeConvolution2dWithBiases) |
| { |
| TestQuantizeTransposeConvolution2d(true); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeStack) |
| { |
| class TestStackQuantization : public TestQuantization |
| { |
| public: |
| TestStackQuantization(const TensorShape& inputShape, |
| const TensorShape& outputShape) |
| : TestQuantization(inputShape, outputShape) {} |
| |
| TestStackQuantization(const QuantizerOptions& options, |
| const TensorShape& inputShape, |
| const TensorShape& outputShape) |
| : TestQuantization(options, inputShape, outputShape) {} |
| |
| void ExecuteStrategy(const armnn::IConnectableLayer* layer, |
| const BaseDescriptor& descriptor, |
| const std::vector<armnn::ConstTensor>& constants, |
| const char* name, |
| const armnn::LayerBindingId id) override |
| { |
| IgnoreUnused(name, constants, id, descriptor); |
| |
| switch (layer->GetType()) |
| { |
| case armnn::LayerType::Input : |
| { |
| break; |
| } |
| case armnn::LayerType::Output : |
| { |
| break; |
| } |
| case armnn::LayerType::Stack : |
| { |
| TensorInfo outputInfo = layer->GetOutputSlot(0).GetTensorInfo(); |
| |
| TestQuantizationParams(outputInfo, |
| { 30.0f / g_AsymmU8QuantizationBase, 128 }, |
| { 30.0f / g_AsymmS8QuantizationBase, 0}, |
| { 15.0f / g_SymmS8QuantizationBase, 0}, |
| { 15.0f / g_SymmS16QuantizationBase, 0 }); |
| break; |
| } |
| default: |
| {} |
| } |
| } |
| }; |
| |
| INetworkPtr network = INetwork::Create(); |
| |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* input1 = network->AddInputLayer(1); |
| |
| const TensorShape inputShape{ 3, 4, 5 }; |
| const TensorShape outputShape{ 3, 4, 2, 5 }; |
| |
| StackDescriptor descriptor(2, 2, inputShape); |
| IConnectableLayer* stackLayer = network->AddStackLayer(descriptor); |
| |
| IConnectableLayer* output = network->AddOutputLayer(0); |
| |
| input0->GetOutputSlot(0).Connect(stackLayer->GetInputSlot(0)); |
| input1->GetOutputSlot(0).Connect(stackLayer->GetInputSlot(1)); |
| stackLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| INetworkPtr quantizedNetworkQAsymmU8 = INetworkQuantizer::Create(network.get())->ExportNetwork(); |
| TestStackQuantization validatorQAsymmU8(inputShape, outputShape); |
| VisitLayersTopologically(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8); |
| |
| const QuantizerOptions qAsymmS8Options(DataType::QAsymmS8); |
| INetworkPtr quantizedNetworkQAsymmS8 = INetworkQuantizer::Create(network.get(), qAsymmS8Options)->ExportNetwork(); |
| TestStackQuantization validatorQAsymmS8(qAsymmS8Options, inputShape, inputShape); |
| VisitLayersTopologically(quantizedNetworkQAsymmS8.get(), validatorQAsymmS8); |
| |
| const QuantizerOptions qSymmS8Options(DataType::QSymmS8); |
| INetworkPtr quantizedNetworkQSymmS8 = INetworkQuantizer::Create(network.get(), qSymmS8Options)->ExportNetwork(); |
| TestStackQuantization validatorQSymmS8(qSymmS8Options, inputShape, inputShape); |
| VisitLayersTopologically(quantizedNetworkQSymmS8.get(), validatorQSymmS8); |
| |
| const QuantizerOptions qSymmS16options(DataType::QSymmS16); |
| INetworkPtr quantizedNetworkQSymmS16 = INetworkQuantizer::Create(network.get(), qSymmS16options)->ExportNetwork(); |
| TestStackQuantization validatorQSymmS16(qSymmS16options, inputShape, outputShape); |
| VisitLayersTopologically(quantizedNetworkQSymmS16.get(), validatorQSymmS16); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeSlice) |
| { |
| TensorShape shape{ 3 }; |
| TensorInfo info(shape, DataType::Float32); |
| |
| INetworkPtr network = INetwork::Create(); |
| |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| IConnectableLayer* sliceLayer = network->AddSliceLayer(SliceDescriptor()); |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| |
| inputLayer->GetOutputSlot(0).Connect(sliceLayer->GetInputSlot(0)); |
| sliceLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| inputLayer->GetOutputSlot(0).SetTensorInfo(info); |
| sliceLayer->GetOutputSlot(0).SetTensorInfo(info); |
| |
| TestNetwork(network.get(), shape); |
| } |
| |
| std::vector<uint8_t> SetupQuantize(float value) |
| { |
| armnn::TensorInfo inputInfo({ 1, 2, 2 }, armnn::DataType::Float32); |
| inputInfo.SetQuantizationScale(1.0f); |
| inputInfo.SetQuantizationOffset(1); |
| std::vector<float> input({ value, 0.0f, 0.0f, 1.0f }); |
| const std::vector<float> &inputRef = input; |
| |
| auto output = armnnUtils::QuantizedVector<uint8_t>(inputRef, |
| inputInfo.GetQuantizationScale(), |
| inputInfo.GetQuantizationOffset()); |
| |
| return output; |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeInf) |
| { |
| BOOST_CHECK_EQUAL(SetupQuantize(std::numeric_limits<float>::infinity())[0], 255); |
| } |
| |
| BOOST_AUTO_TEST_CASE(QuantizeNegativeInf) |
| { |
| BOOST_CHECK_EQUAL(SetupQuantize(-1 * std::numeric_limits<float>::infinity())[0], 0); |
| } |
| |
| class TestPreserveType : public TestQuantization |
| { |
| public: |
| TestPreserveType(const QuantizerOptions& options, |
| const DataType& dataType, |
| const TensorShape& inputShape, |
| const TensorShape& outputShape) |
| : TestQuantization(options, inputShape, outputShape) |
| , m_DataType(dataType) |
| , m_VisitedQuantizeLayer(false) |
| , m_VisitedDequantizeLayer(false) {} |
| |
| void ExecuteStrategy(const armnn::IConnectableLayer* layer, |
| const BaseDescriptor& descriptor, |
| const std::vector<armnn::ConstTensor>& constants, |
| const char* name, |
| const armnn::LayerBindingId id) override |
| { |
| IgnoreUnused(name, constants, id, descriptor); |
| |
| switch (layer->GetType()) |
| { |
| case armnn::LayerType::Input : |
| { |
| const TensorInfo& info = layer->GetOutputSlot(0).GetTensorInfo(); |
| BOOST_TEST(GetDataTypeName(info.GetDataType()) == GetDataTypeName(m_DataType)); |
| BOOST_TEST(m_InputShape == info.GetShape()); |
| break; |
| } |
| case armnn::LayerType::Output : |
| { |
| const TensorInfo& info = layer->GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| BOOST_TEST(GetDataTypeName(info.GetDataType()) == GetDataTypeName(m_DataType)); |
| BOOST_TEST(m_OutputShape == info.GetShape()); |
| break; |
| } |
| case armnn::LayerType::Quantize : |
| { |
| m_VisitedQuantizeLayer = true; |
| break; |
| } |
| case armnn::LayerType::Dequantize : |
| { |
| m_VisitedDequantizeLayer = true; |
| break; |
| } |
| default: |
| {} |
| } |
| } |
| |
| void CheckQuantizeDequantizeLayerVisited(bool expected) |
| { |
| if (expected) |
| { |
| BOOST_CHECK(m_VisitedQuantizeLayer); |
| BOOST_CHECK(m_VisitedDequantizeLayer); |
| } |
| else |
| { |
| BOOST_CHECK(!m_VisitedQuantizeLayer); |
| BOOST_CHECK(!m_VisitedDequantizeLayer); |
| } |
| } |
| private: |
| const DataType m_DataType; |
| bool m_VisitedQuantizeLayer; |
| bool m_VisitedDequantizeLayer; |
| }; |
| |
| void PreserveTypeTestImpl(const DataType& dataType) |
| { |
| INetworkPtr network = INetwork::Create(); |
| |
| // Add the layers |
| IConnectableLayer* input0 = network->AddInputLayer(0); |
| IConnectableLayer* input1 = network->AddInputLayer(1); |
| IConnectableLayer* addition = network->AddAdditionLayer(); |
| IConnectableLayer* output = network->AddOutputLayer(2); |
| |
| input0->GetOutputSlot(0).Connect(addition->GetInputSlot(0)); |
| input1->GetOutputSlot(0).Connect(addition->GetInputSlot(1)); |
| addition->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| const TensorShape shape{1U, 2U, 3U}; |
| const TensorInfo info(shape, dataType); |
| input0->GetOutputSlot(0).SetTensorInfo(info); |
| input1->GetOutputSlot(0).SetTensorInfo(info); |
| addition->GetOutputSlot(0).SetTensorInfo(info); |
| |
| QuantizerOptions options = dataType == DataType::Float32 ? |
| QuantizerOptions(DataType::QAsymmU8, true) : QuantizerOptions(dataType, true); |
| |
| INetworkPtr quantizedNetworkQAsymmU8 = INetworkQuantizer::Create(network.get(), options)->ExportNetwork(); |
| TestPreserveType validatorQAsymmU8(options, dataType, shape, shape); |
| VisitLayersTopologically(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8); |
| validatorQAsymmU8.CheckQuantizeDequantizeLayerVisited( |
| dataType == DataType::Float32 || dataType == DataType::Float16); |
| } |
| |
| BOOST_AUTO_TEST_CASE(PreserveTypeFloat32) |
| { |
| PreserveTypeTestImpl(DataType::Float32); |
| } |
| |
| BOOST_AUTO_TEST_CASE(PreserveTypeQAsymmU8) |
| { |
| PreserveTypeTestImpl(DataType::QAsymmU8); |
| } |
| |
| BOOST_AUTO_TEST_CASE(PreserveTypeQsymm8) |
| { |
| PreserveTypeTestImpl(DataType::QSymmS8); |
| } |
| |
| BOOST_AUTO_TEST_CASE(PreserveTypeQsymm16) |
| { |
| PreserveTypeTestImpl(DataType::QSymmS16); |
| } |
| |
| BOOST_AUTO_TEST_CASE(TestConnectionPreservationAfterDynamicQuant) |
| { |
| class TestConnectionPreservation : public IStrategy |
| { |
| public: |
| TestConnectionPreservation(const Graph& graph) |
| : m_Graph(graph) |
| {} |
| |
| void ExecuteStrategy(const armnn::IConnectableLayer* layer, |
| const BaseDescriptor& descriptor, |
| const std::vector<armnn::ConstTensor>& constants, |
| const char* name, |
| const armnn::LayerBindingId id) override |
| { |
| IgnoreUnused(name, constants, id, descriptor); |
| |
| switch (layer->GetType()) |
| { |
| case armnn::LayerType::Addition : |
| { |
| CheckLayerName(layer->GetInputSlot(0).GetConnection()->GetOwningLayerGuid(), "reLU1"); |
| CheckLayerName(layer->GetInputSlot(1).GetConnection()->GetOwningLayerGuid(), "reLU2"); |
| break; |
| } |
| default: |
| {} |
| } |
| } |
| |
| void CheckLayerName(LayerGuid guid, std::string expectedName) |
| { |
| bool guidFound = false; |
| for (Layer* layer : m_Graph) |
| { |
| if (layer->GetGuid() == guid) |
| { |
| BOOST_CHECK_EQUAL(layer->GetName(), expectedName.c_str()); |
| guidFound = true; |
| break; |
| } |
| } |
| if (!guidFound) |
| { |
| BOOST_FAIL("No layer matching the GUID was found"); |
| } |
| } |
| private: |
| Graph m_Graph; |
| }; |
| |
| class TestNetwork : public INetwork |
| { |
| public : |
| NetworkImpl* GetPNetworkImpl() |
| { |
| return pNetworkImpl.get(); |
| } |
| }; |
| |
| TestNetwork testNetwork; |
| |
| IConnectableLayer* inputLayer = testNetwork.AddInputLayer(0,"inputLayer1"); |
| armnn::ActivationDescriptor ReLUDesc; |
| ReLUDesc.m_Function = ActivationFunction::ReLu; |
| |
| IConnectableLayer* reLULayer1 = testNetwork.AddActivationLayer(ReLUDesc, "reLU1"); |
| IConnectableLayer* reLULayer2 = testNetwork.AddActivationLayer(ReLUDesc, "reLU2"); |
| IConnectableLayer* addLayer1 = testNetwork.AddAdditionLayer("addLayer1"); |
| IConnectableLayer* outputLayer = testNetwork.AddOutputLayer(0,"outPutLayer1"); |
| |
| inputLayer->GetOutputSlot(0).Connect(reLULayer1->GetInputSlot(0)); |
| reLULayer1->GetOutputSlot(0).Connect(reLULayer2->GetInputSlot(0)); |
| reLULayer1->GetOutputSlot(0).Connect(addLayer1->GetInputSlot(0)); |
| reLULayer2->GetOutputSlot(0).Connect(addLayer1->GetInputSlot(1)); |
| addLayer1->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| inputLayer->GetOutputSlot(0).SetTensorInfo(TensorInfo(TensorShape({1, 2, 2, 1}), DataType::Float32)); |
| reLULayer1->GetOutputSlot(0).SetTensorInfo(TensorInfo(TensorShape({1, 2, 2, 1}), DataType::Float32)); |
| reLULayer2->GetOutputSlot(0).SetTensorInfo(TensorInfo(TensorShape({1, 2, 2, 1}), DataType::Float32)); |
| addLayer1->GetOutputSlot(0).SetTensorInfo(TensorInfo(TensorShape({1, 2, 2, 1}), DataType::Float32)); |
| |
| TestConnectionPreservation strategy1(testNetwork.GetPNetworkImpl()->GetGraph()); |
| VisitLayersTopologically(&testNetwork, strategy1); |
| |
| armnn::INetworkQuantizerPtr quantizer = armnn::INetworkQuantizer::Create(&testNetwork); |
| |
| armnn::TensorInfo tensorInfo = GetInputTensorInfo(&testNetwork); |
| |
| std::vector<float> inputData({0, 2, 0, 4}); |
| armnn::ConstTensor inputTensor(tensorInfo, inputData.data()); |
| |
| InputTensors inputTensors; |
| inputTensors.push_back(std::make_pair(0, inputTensor)); |
| quantizer->Refine(inputTensors); |
| |
| INetworkPtr quantNetwork = quantizer->ExportNetwork(); |
| |
| TestNetwork* testQuantNetwork = static_cast<TestNetwork*>(quantNetwork.get()); |
| |
| TestConnectionPreservation strategy2(testQuantNetwork->GetPNetworkImpl()->GetGraph()); |
| VisitLayersTopologically(quantNetwork.get(), strategy2); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |
| } // namespace armnn |