IVGCVSW-5719 Remove Quantizer
Signed-off-by: Kevin May <kevin.may@arm.com>
Change-Id: I8a29df03afdd6f1cc8413b219e179272cd2d51cf
diff --git a/CMakeLists.txt b/CMakeLists.txt
index df34d28..1234e69 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -149,60 +149,6 @@
set_target_properties(armnnTfParser PROPERTIES VERSION ${TF_PARSER_LIB_VERSION} SOVERSION ${TF_PARSER_LIB_SOVERSION})
endif()
-if(BUILD_ARMNN_QUANTIZER AND ARMNNREF)
- if(NOT BUILD_ARMNN_SERIALIZER)
- message(ERROR, "In order to build the ArmNN Quantization Tool you must set BUILD_ARMNN_SERIALIZER = YES")
- endif()
-
- set(armnn_quantizer_sources)
- list(APPEND armnn_quantizer_sources
- src/armnnQuantizer/QuantizationDataSet.hpp
- src/armnnQuantizer/QuantizationDataSet.cpp
- src/armnnQuantizer/QuantizationInput.hpp
- src/armnnQuantizer/QuantizationInput.cpp
- src/armnnQuantizer/CommandLineProcessor.hpp
- src/armnnQuantizer/CommandLineProcessor.cpp
- )
-
- add_library_ex(armnnQuantizer SHARED ${armnn_quantizer_sources})
-
- target_include_directories(armnnQuantizer PRIVATE include/armnnDeserializer)
- target_include_directories(armnnQuantizer PRIVATE src/armnnUtils)
- target_include_directories(armnnQuantizer PRIVATE src/armnn)
- target_include_directories(armnnQuantizer PRIVATE third-party)
-
- include_directories(SYSTEM "${FLATBUFFERS_INCLUDE_PATH}")
- set_target_properties(armnnQuantizer PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR})
-
- target_link_libraries(armnnQuantizer armnn)
-
- add_executable_ex(ArmnnQuantizerMain
- src/armnnQuantizer/ArmNNQuantizerMain.cpp)
-
- # cmake does not allow same executable and library name
- # workaround that by rename that using set_target_properties
- set_target_properties(ArmnnQuantizerMain
- PROPERTIES OUTPUT_NAME ArmnnQuantizer)
-
- target_include_directories(ArmnnQuantizerMain PRIVATE include/armnnDeserializer)
- target_include_directories(ArmnnQuantizerMain PRIVATE src/armnn)
-
- target_link_libraries(ArmnnQuantizerMain
- armnnQuantizer
- armnnSerializer
- armnn
- armnnUtils
- ${FLATBUFFERS_LIBRARY})
-
- if(Threads_FOUND AND (NOT ("${CMAKE_SYSTEM_NAME}" STREQUAL Android)))
- target_link_libraries(ArmnnQuantizerMain ${CMAKE_THREAD_LIBS_INIT})
- endif()
-
- set_target_properties(armnnQuantizer PROPERTIES VERSION ${GENERIC_LIB_VERSION} SOVERSION ${GENERIC_LIB_SOVERSION})
-
-endif()
-
-
list(APPEND armnn_sources
include/armnn/ArmNN.hpp
include/armnn/BackendHelper.hpp
@@ -388,8 +334,6 @@
src/armnn/Descriptors.cpp
src/armnn/DeviceSpec.hpp
src/armnn/DllExport.hpp
- src/armnn/DynamicQuantizationStrategy.cpp
- src/armnn/DynamicQuantizationStrategy.hpp
src/armnn/Exceptions.cpp
src/armnn/ExecutionFrame.cpp
src/armnn/ExecutionFrame.hpp
@@ -413,11 +357,6 @@
src/armnn/Logging.cpp
src/armnn/Network.cpp
src/armnn/Network.hpp
- src/armnn/NetworkQuantizationScheme.hpp
- src/armnn/NetworkQuantizer.cpp
- src/armnn/NetworkQuantizer.hpp
- src/armnn/NetworkQuantizerUtils.cpp
- src/armnn/NetworkQuantizerUtils.hpp
src/armnn/NetworkUtils.cpp
src/armnn/NetworkUtils.hpp
src/armnn/Observable.cpp
@@ -426,14 +365,10 @@
src/armnn/Optimizer.hpp
src/armnn/OutputHandler.cpp
src/armnn/OutputHandler.hpp
- src/armnn/OverrideInputRangeVisitor.cpp
- src/armnn/OverrideInputRangeVisitor.hpp
src/armnn/Profiling.cpp
src/armnn/ProfilingEvent.cpp
src/armnn/ProfilingEvent.hpp
src/armnn/Profiling.hpp
- src/armnn/QuantizerStrategy.hpp
- src/armnn/QuantizerStrategy.cpp
src/armnn/Runtime.cpp
src/armnn/Runtime.hpp
src/armnn/RangeTracker.cpp
@@ -441,8 +376,6 @@
src/armnn/ResolveType.hpp
src/armnn/SerializeLayerParameters.cpp
src/armnn/SerializeLayerParameters.hpp
- src/armnn/StaticRangeStrategy.cpp
- src/armnn/StaticRangeStrategy.hpp
src/armnn/SubgraphView.cpp
src/armnn/SubgraphView.hpp
src/armnn/SubgraphViewSelector.cpp
@@ -717,7 +650,6 @@
if(ARMNNREF)
list(APPEND unittest_sources
src/armnn/test/DebugCallbackTest.cpp
- src/armnn/test/QuantizerTest.cpp
src/armnn/test/RuntimeTests.cpp
src/armnn/test/RuntimeTests.hpp
)
@@ -859,12 +791,6 @@
)
endif()
- if(BUILD_ARMNN_QUANTIZER AND ARMNNREF)
- list(APPEND unittest_sources
- src/armnnQuantizer/test/QuantizationDataSetTests.cpp
- )
- endif()
-
if(BUILD_ARMNN_SERIALIZER AND ARMNNREF)
enable_language(ASM)
list(APPEND unittest_sources
@@ -984,12 +910,6 @@
target_link_libraries(UnitTests armnnSerializer)
endif()
- if(BUILD_ARMNN_QUANTIZER AND ARMNNREF)
- target_include_directories(UnitTests SYSTEM PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/src/armnnQuantizer)
- target_include_directories(UnitTests SYSTEM PRIVATE "${FLATBUFFERS_INCLUDE_PATH}")
- target_link_libraries(UnitTests armnnQuantizer armnnSerializer)
- endif()
-
if(BUILD_ONNX_PARSER)
target_link_libraries(UnitTests armnnOnnxParser)
endif()
diff --git a/cmake/GlobalConfig.cmake b/cmake/GlobalConfig.cmake
index 0319748..ca02284 100644
--- a/cmake/GlobalConfig.cmake
+++ b/cmake/GlobalConfig.cmake
@@ -19,7 +19,6 @@
# options used for tensorflow lite support
option(BUILD_TF_LITE_PARSER "Build Tensorflow Lite parser" OFF)
option(BUILD_ARMNN_SERIALIZER "Build Armnn Serializer" OFF)
-option(BUILD_ARMNN_QUANTIZER "Build ArmNN quantizer" OFF)
option(BUILD_ACCURACY_TOOL "Build Accuracy Tool" OFF)
option(FLATC_DIR "Path to Flatbuffers compiler" OFF)
option(TF_LITE_GENERATED_PATH "Tensorflow lite generated C++ schema location" OFF)
@@ -419,10 +418,6 @@
message(STATUS "Armnn Serializer support is disabled")
endif()
-if(NOT BUILD_ARMNN_QUANTIZER)
- message(STATUS "ArmNN Quantizer support is disabled")
-endif()
-
if(NOT BUILD_PYTHON_WHL)
message(STATUS "PyArmNN wheel package is disabled")
endif()
diff --git a/include/armnn/INetwork.hpp b/include/armnn/INetwork.hpp
index 2cdbe1a..0dc1ba4 100644
--- a/include/armnn/INetwork.hpp
+++ b/include/armnn/INetwork.hpp
@@ -694,7 +694,6 @@
protected:
~INetwork();
- friend class NetworkQuantizer;
friend void VisitLayersTopologically(const INetwork* inputNetwork, IStrategy& strategy);
friend class TestConnectionPreservation;
friend TensorInfo GetInputTensorInfo(const INetwork* network);
diff --git a/include/armnnQuantizer/INetworkQuantizer.hpp b/include/armnnQuantizer/INetworkQuantizer.hpp
deleted file mode 100644
index 5fc5763..0000000
--- a/include/armnnQuantizer/INetworkQuantizer.hpp
+++ /dev/null
@@ -1,57 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include <armnn/INetwork.hpp>
-#include <armnn/Types.hpp>
-#include <armnn/Tensor.hpp>
-
-namespace armnn
-{
-
-struct QuantizerOptions
-{
- QuantizerOptions() : QuantizerOptions(DataType::QAsymmU8, false) {}
-
- QuantizerOptions(DataType activationFormat) : QuantizerOptions(activationFormat, false) {}
-
- QuantizerOptions(DataType activationFormat, bool preserveType)
- : m_ActivationFormat(activationFormat)
- , m_PreserveType(preserveType) {}
-
- DataType m_ActivationFormat;
- bool m_PreserveType;
-};
-
-using INetworkQuantizerPtr = std::unique_ptr<class INetworkQuantizer, void(*)(INetworkQuantizer* quantizer)>;
-
-/// Quantizer class Quantizes a float32 InputNetwork
-class INetworkQuantizer
-{
-public:
- /// Create Quantizer object and return raw pointer
- static INetworkQuantizer* CreateRaw(INetwork* inputNetwork, const QuantizerOptions& options = QuantizerOptions());
-
- /// Create Quantizer object wrapped in unique_ptr
- static INetworkQuantizerPtr Create(INetwork* inputNetwork, const QuantizerOptions& options = QuantizerOptions());
-
- /// Destroy Quantizer object
- static void Destroy(INetworkQuantizer* quantizer);
-
- /// Overrides the default quantization values for the input layer with the given id
- virtual void OverrideInputRange(LayerBindingId layerId, float min, float max) = 0;
-
- /// Refine input network with a set of refinement data for specified LayerBindingId
- virtual void Refine(const InputTensors& inputTensors) = 0;
-
- /// Extract final quantized network
- virtual INetworkPtr ExportNetwork() = 0;
-
-protected:
- virtual ~INetworkQuantizer() {}
-};
-
-} //namespace armnn
diff --git a/src/armnn/DynamicQuantizationStrategy.cpp b/src/armnn/DynamicQuantizationStrategy.cpp
deleted file mode 100644
index d354a0e..0000000
--- a/src/armnn/DynamicQuantizationStrategy.cpp
+++ /dev/null
@@ -1,276 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "DynamicQuantizationStrategy.hpp"
-#include "NetworkUtils.hpp"
-
-#include <armnn/Descriptors.hpp>
-#include <armnn/utility/IgnoreUnused.hpp>
-#include <armnn/utility/PolymorphicDowncast.hpp>
-#include <armnn/Types.hpp>
-
-#include <limits>
-
-namespace armnn
-{
-DynamicQuantizationStrategy::DynamicQuantizationStrategy(RangeTracker& rangeTracker, Graph& graph)
- : m_RangeTracker(rangeTracker),
- m_Graph(graph)
-{}
-
-void DynamicQuantizationStrategy::SetRange(const IConnectableLayer* layer, unsigned int outputIdx, float min, float max)
-{
- m_RangeTracker.SetRange(layer, outputIdx, min, max);
-}
-
-void DynamicQuantizationStrategy::ForwardParentParameters(const IConnectableLayer* layer)
-{
- for (unsigned int i = 0; i < layer->GetNumInputSlots(); ++i)
- {
- const IOutputSlot *outputSlot = layer->GetInputSlot(i).GetConnection();
- LayerGuid previousLayerId = outputSlot->GetOwningLayerGuid();
- unsigned int ownerIndex = outputSlot->CalculateIndexOnOwner();
- const auto parentRange = m_RangeTracker.GetRange(previousLayerId, ownerIndex);
- SetRange(layer, i, parentRange.first, parentRange.second);
- }
-}
-
-void DynamicQuantizationStrategy::AddToCalibratedLayers(const IConnectableLayer* layer)
-{
- m_LayersToCalibrate.push_back(layer);
-}
-
-void DynamicQuantizationStrategy::AddToNonCalibratedLayers(const IConnectableLayer* layer)
-{
- m_LayersNotToCalibrate.push_back(layer);
-}
-
-void DynamicQuantizationStrategy::FinishStrategy()
-{
- for (const IConnectableLayer* layer : m_LayersToCalibrate)
- {
- std::vector<DebugLayer*> newDebugLayers = InsertDebugLayerAfter(
- m_Graph, *PolymorphicDowncast<Layer*>(const_cast<IConnectableLayer*>(layer)));
- // record them so we can take them out again efficiently afterward
- m_DebugLayers.insert(std::end(m_DebugLayers), std::begin(newDebugLayers), std::end(newDebugLayers));
- }
-}
-
-void DynamicQuantizationStrategy::RemoveDebugLayers()
-{
- for (DebugLayer* debugLayer : m_DebugLayers)
- {
- OutputSlot& proceedingOutputSlot = *debugLayer->GetInputSlot(0).GetConnectedOutputSlot();
- proceedingOutputSlot.Disconnect(debugLayer->GetInputSlot(0));
-
- for (InputSlot* succeedingInputSlot : debugLayer->GetOutputSlot(0).GetConnections())
- {
- debugLayer->GetOutputSlot(0).Disconnect(*succeedingInputSlot);
- proceedingOutputSlot.Connect(*succeedingInputSlot);
- }
- m_Graph.EraseLayer(debugLayer);
- }
- m_DebugLayers.clear();
-}
-
-void DynamicQuantizationStrategy::VisitNonCalibratedLayers() {
- RemoveDebugLayers();
- for (const IConnectableLayer* layer : m_LayersNotToCalibrate)
- {
- ForwardParentParameters(layer);
- }
-}
-
-
-void DynamicQuantizationStrategy::ExecuteStrategy(const armnn::IConnectableLayer* layer,
- const BaseDescriptor& descriptor,
- const std::vector<armnn::ConstTensor>& constants,
- const char* name,
- const armnn::LayerBindingId id)
-{
- IgnoreUnused(name);
- IgnoreUnused(id);
- IgnoreUnused(descriptor);
-
- switch (layer->GetType())
- {
- case armnn::LayerType::Activation :
- {
- const ActivationDescriptor& activationDescriptor = static_cast<const ActivationDescriptor&>(descriptor);
- switch (activationDescriptor.m_Function)
- {
- // Range is 0, 15 for Abs, Linear, ReLu and Soft ReLu
- case ActivationFunction::Abs:
- case ActivationFunction::Linear:
- case ActivationFunction::ReLu:
- case ActivationFunction::SoftReLu:
- SetRange(layer, 0, 0.f, 15.f);
- break;
- case ActivationFunction::BoundedReLu:
- SetRange(layer, 0, 0.f, activationDescriptor.m_A);
- break;
- case ActivationFunction::TanH:
- SetRange(layer, 0, -1.f, 1.f);
- break;
- case ActivationFunction::LeakyReLu:
- SetRange(layer, 0, -5.f, 15.f);
- break;
- default:
- SetRange(layer, 0, -15.f, 15.f);
- break;
- }
- break;
- }
- case armnn::LayerType::Addition :
- {
- SetRange(layer, 0, -20.f, 20.f);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::ArgMinMax :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::BatchNormalization :
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Normalization:
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Convolution2d:
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::DepthwiseConvolution2d:
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::FullyConnected :
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Permute :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::SpaceToBatchNd :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Pooling2d :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Softmax :
- {
- SetRange(layer, 0, 0.f, 1.f);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Constant :
- {
- if (constants[0].GetDataType() != DataType::Float32)
- {
- throw InvalidArgumentException("Quantization is supported only for FP32 tensors");
- }
-
- // Work out the range based on the input constants
- unsigned int inputNumElements = constants[0].GetNumElements();
- const float* inputData = reinterpret_cast<const float*>(constants[0].GetMemoryArea());
-
- float min = std::numeric_limits<float>::max();
- float max = std::numeric_limits<float>::lowest();
-
- for (unsigned int i = 0; i < inputNumElements; i++)
- {
- const float inputValue = inputData[i];
-
- min = std::min(min, inputValue);
- max = std::max(max, inputValue);
- }
- SetRange(layer, 0, min, max);
- break;
- }
- case armnn::LayerType::Concat :
- {
- float min = std::numeric_limits<float>::max();
- float max = std::numeric_limits<float>::lowest();
- for (unsigned int i = 0; i < layer->GetNumInputSlots(); ++i)
- {
- const IOutputSlot* outputSlot = layer->GetInputSlot(i).GetConnection();
- LayerGuid layerId = outputSlot->GetOwningLayerGuid();
- unsigned int slotIndex = outputSlot->CalculateIndexOnOwner();
- RangeTracker::MinMaxRange range = m_RangeTracker.GetRange(layerId, slotIndex);
- min = std::min(min, range.first);
- max = std::max(max, range.second);
- }
- SetRange(layer, 0, min, max);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Reshape :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Splitter :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Resize :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::StridedSlice :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::BatchToSpaceNd :
- {
- AddToNonCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Input :
- {
- SetRange(layer, 0, -0.0f, 0.0f);
- AddToCalibratedLayers(layer);
- break;
- }
- case armnn::LayerType::Output :
- {
- AddToNonCalibratedLayers(layer);
- m_OutputLayers.push_back(id);
- break;
- }
- default:
- {}
- }
-}
-
-const std::vector<LayerBindingId>& DynamicQuantizationStrategy::GetOutputLayers()
-{
- return m_OutputLayers;
-}
-
-} //namespace armnn
diff --git a/src/armnn/DynamicQuantizationStrategy.hpp b/src/armnn/DynamicQuantizationStrategy.hpp
deleted file mode 100644
index aa77a4b..0000000
--- a/src/armnn/DynamicQuantizationStrategy.hpp
+++ /dev/null
@@ -1,59 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include "armnn/LayerVisitorBase.hpp"
-#include "RangeTracker.hpp"
-#include "layers/DebugLayer.hpp"
-
-#include <armnn/INetwork.hpp>
-#include <armnnQuantizer/INetworkQuantizer.hpp>
-
-namespace armnn
-{
-
-/// Visitor class implementation to gather the TensorInfo for LayerBindingID for creation of ConstTensor for Refine.
-class DynamicQuantizationStrategy : public armnn::IStrategy
-{
-public:
-
- DynamicQuantizationStrategy(RangeTracker& rangeTracker, Graph& graph);
- ~DynamicQuantizationStrategy() = default;
-
- virtual void ExecuteStrategy(const armnn::IConnectableLayer* layer,
- const armnn::BaseDescriptor& descriptor,
- const std::vector<armnn::ConstTensor>& constants,
- const char* name,
- const armnn::LayerBindingId id = 0) override;
-
- const std::vector<armnn::LayerBindingId>& GetOutputLayers();
- void VisitNonCalibratedLayers();
- void FinishStrategy() override;
-
-
-private:
- /// Set the range for an output slot on a layer
- void SetRange(const IConnectableLayer* layer, unsigned int outputIdx, float min, float max);
-
- void ForwardParentParameters(const IConnectableLayer* layer);
-
- /// Mapping from a layer Guid to an array of ranges for outputs
- RangeTracker& m_RangeTracker;
-
- Graph& m_Graph;
-
- std::vector<const IConnectableLayer*> m_LayersToCalibrate;
- std::vector<const IConnectableLayer*> m_LayersNotToCalibrate;
- std::vector<DebugLayer*> m_DebugLayers;
-
- std::vector<armnn::LayerBindingId> m_OutputLayers;
- void AddToCalibratedLayers(const IConnectableLayer* layer);
- void AddToNonCalibratedLayers(const IConnectableLayer* layer);
- void RemoveDebugLayers();
-
-
-};
-} //namespace armnn
diff --git a/src/armnn/NetworkQuantizationScheme.hpp b/src/armnn/NetworkQuantizationScheme.hpp
deleted file mode 100644
index a78fd72..0000000
--- a/src/armnn/NetworkQuantizationScheme.hpp
+++ /dev/null
@@ -1,157 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include <armnn/Types.hpp>
-
-#include <cmath>
-#include <algorithm>
-
-namespace armnn
-{
-
-using OffsetScalePair = std::pair<float, int>;
-
-struct IQuantizationScheme
-{
- virtual OffsetScalePair ComputeScheme(double min, double max) const = 0;
-
- virtual int NumBits() const = 0;
-
- virtual DataType GetDataType() const = 0;
-
- virtual ~IQuantizationScheme() {}
-};
-
-struct QAsymmU8QuantizationScheme : IQuantizationScheme
-{
- OffsetScalePair ComputeScheme(double min, double max) const override
- {
- if (min > max)
- {
- throw InvalidArgumentException("min > max will result in invalid quantization.");
- }
-
- double highest = (1 << NumBits()) - 1;
-
- min = std::min(0.0, min); // min <= 0.0
- max = std::max(0.0, max); // max >= 0.0
-
- // To avoid dividing by zero when quantizing a zero filled tensor
- if (min == 0.0 && max == 0.0)
- {
- max = 1.0;
- }
-
- // Assumes quantization range [0-highest]
- double scale = (max-min) / highest;
- double offset = -min / scale;
-
- // Clamp offset [0-highest]
- offset = std::max(0.0, std::min(highest, offset));
-
- return std::make_pair(static_cast<float>(scale), static_cast<int>(std::round(offset)));
- }
-
- int NumBits() const override { return 8; }
-
- DataType GetDataType() const override { return DataType::QAsymmU8; }
-};
-
-struct QAsymmS8QuantizationScheme : IQuantizationScheme
-{
- OffsetScalePair ComputeScheme(double min, double max) const override
- {
- if (min > max)
- {
- throw InvalidArgumentException("min > max will result in invalid quantization.");
- }
-
- double highest = (1 << NumBits()) - 1;
-
- min = std::min(0.0, min); // min <= 0.0
- max = std::max(0.0, max); // max >= 0.0
-
- // To avoid dividing by zero when quantizing a zero filled tensor
- if (min == 0.0 && max == 0.0)
- {
- max = 1.0;
- }
-
- // Assumes quantization range [0-255]
- double scale = (max-min) / highest ;
- double offset = - min / scale;
-
- //Clamp 0 to Highest
- offset = std::max(0.0, std::min(highest, offset));
-
- //-128 on offset to cast to signed range
- return std::make_pair(static_cast<float>(scale), static_cast<int>(std::round(offset)-128));
- }
-
- int NumBits() const override { return 8; }
-
- DataType GetDataType() const override { return DataType::QAsymmS8; }
-};
-
-struct QSymmS8QuantizationScheme : IQuantizationScheme
-{
- OffsetScalePair ComputeScheme(double min, double max) const override
- {
- if (min > max)
- {
- throw InvalidArgumentException("min > max will result in invalid quantization.");
- }
-
- // To avoid dividing by zero when quantizing a zero filled tensor
- if (min == 0.0 && max == 0.0)
- {
- max = 1.0;
- }
-
- double highest = (1 << (NumBits()-1)) - 1; // (numbits-1) accounts for the sign bit
-
- double extent = std::max(std::abs(min), std::abs(max));
- double scale = extent / highest;
-
- return std::make_pair(static_cast<float>(scale), 0);
- }
-
- int NumBits() const override { return 8; }
-
- DataType GetDataType() const override { return DataType::QSymmS8; }
-};
-
-struct QSymm16QuantizationScheme : IQuantizationScheme
-{
- OffsetScalePair ComputeScheme(double min, double max) const override
- {
- if (min > max)
- {
- throw InvalidArgumentException("min > max will result in invalid quantization.");
- }
-
- // To avoid dividing by zero when quantizing a zero filled tensor
- if (min == 0.0 && max == 0.0)
- {
- max = 1.0;
- }
-
- double highest = (1 << (NumBits()-1)) - 1; // (numbits-1) accounts for the sign bit
-
- double extent = std::max(std::abs(min), std::abs(max));
- double scale = extent / highest;
-
- return std::make_pair(static_cast<float>(scale), 0);
-
- }
-
- int NumBits() const override { return 16; }
-
- DataType GetDataType() const override { return DataType::QSymmS16; }
-};
-
-} // namespace armnn
diff --git a/src/armnn/NetworkQuantizer.cpp b/src/armnn/NetworkQuantizer.cpp
deleted file mode 100644
index fd4486b..0000000
--- a/src/armnn/NetworkQuantizer.cpp
+++ /dev/null
@@ -1,190 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "NetworkQuantizer.hpp"
-#include "NetworkQuantizerUtils.hpp"
-#include "Graph.hpp"
-#include "Layer.hpp"
-#include "Network.hpp"
-#include "DynamicQuantizationStrategy.hpp"
-#include "StaticRangeStrategy.hpp"
-#include "QuantizerStrategy.hpp"
-#include "OverrideInputRangeVisitor.hpp"
-
-#include <TensorIOUtils.hpp>
-
-#include <armnn/ILayerVisitor.hpp>
-#include <armnn/INetwork.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/Types.hpp>
-
-#include <armnnUtils/TensorUtils.hpp>
-#include <armnn/utility/PolymorphicDowncast.hpp>
-
-#include <mapbox/variant.hpp>
-
-#include <vector>
-#include <cmath>
-
-namespace armnn
-{
-
-using TContainer =
- mapbox::util::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>, std::vector<int8_t>>;
-
-INetworkQuantizer* INetworkQuantizer::CreateRaw(INetwork* inputNetwork, const QuantizerOptions& options)
-{
- return new NetworkQuantizer(inputNetwork, options);
-}
-
-INetworkQuantizerPtr INetworkQuantizer::Create(INetwork* inputNetwork, const QuantizerOptions& options)
-{
- return INetworkQuantizerPtr(CreateRaw(inputNetwork, options), &INetworkQuantizer::Destroy);
-}
-
-void INetworkQuantizer::Destroy(INetworkQuantizer *quantizer)
-{
- delete PolymorphicDowncast<NetworkQuantizer*>(quantizer);
-}
-
-void NetworkQuantizer::OverrideInputRange(LayerBindingId layerId, float min, float max)
-{
- const Graph& graph = m_InputNetwork->pNetworkImpl->GetGraph();
- auto inputLayers = graph.GetInputLayers();
-
- // Walk the input layers of the graph and override the quantization parameters of the one with the given id
- OverrideInputRangeVisitor overrideInputRangeVisitor(m_Ranges, layerId, RangeTracker::MinMaxRange{min, max});
- VisitLayers(inputLayers, overrideInputRangeVisitor);
-}
-
-void NetworkQuantizer::Refine(const InputTensors& inputTensors)
-{
- // The first time Refine is called the m_Runtime and the DynamicQuantizationStrategy
- // will not have been created. Need to get the environment set up, Runtime loaded,
- // DynamicQuantizationStrategy created and run over the network to initialise itself
- // and the RangeTracker the Debug callback registered and an initial inference
- // done to set up the first min/max values
- if (!m_Runtime)
- {
- m_RefineCount = 0;
- m_Ranges.SetDynamicMode(true);
- const Graph& cGraph = m_InputNetwork->pNetworkImpl->GetGraph().TopologicalSort();
-
- // need to insert Debug layers in the DynamicQuantizationStrategy
- Graph& graph = const_cast<Graph&>(cGraph);
-
- // Initialize RangeTracker to the default values for each layer.
- // The default values are overwritten by the min/max that is
- // recorded during the first dataset min/max calibration. This
- // initialisation is only required for the first call of Refine().
- m_DynamicQuantizationStrategy = DynamicQuantizationStrategy(m_Ranges, graph);
- ApplyStrategyToLayers(cGraph, m_DynamicQuantizationStrategy.value());
-
- IRuntime::CreationOptions options;
- m_Runtime = IRuntime::Create(options);
-
- // Optimize network - debug already enabled for layers that require quantization
- OptimizerOptions optimizerOptions(false, false);
- std::vector<BackendId> backends = {"CpuRef"};
- IOptimizedNetworkPtr optimizedNet = Optimize(*m_InputNetwork,
- backends,
- m_Runtime->GetDeviceSpec(),
- optimizerOptions);
-
- m_Runtime->LoadNetwork(m_NetworkId, std::move(optimizedNet));
-
- // Debug callback function to refine min/max in RangeTracker
- auto rangeTrackerCallback = [&](LayerGuid guid, unsigned int slotIndex, ITensorHandle *tensorHandle) {
- // Get min/max pair from tensor data
- std::pair<float, float> minMax = armnnUtils::FindMinMax(tensorHandle);
-
- // For first calibration dataset, set min/max range in RangeTracker to
- // min/max ranges gathered during inference
- if (m_RefineCount == 0)
- {
- m_Ranges.ResetMinMax(guid, slotIndex, minMax.first, minMax.second);
- }
- else
- {
- // For every other calibration dataset, only set min/max range if the
- // values gathered are less than / greater than originally recorded.
- m_Ranges.RefineMin(guid, slotIndex, minMax.first);
- m_Ranges.RefineMax(guid, slotIndex, minMax.second);
- }
- };
-
- m_Runtime->RegisterDebugCallback(m_NetworkId, rangeTrackerCallback);
- }
-
- // Create output tensor for EnqueueWorkload
- std::vector<armnn::BindingPointInfo> outputBindings;
- auto outputLayers = m_DynamicQuantizationStrategy.value().GetOutputLayers();
- std::vector<TContainer> outputVectors;
- for (auto outputLayerBindingId : outputLayers)
- {
- auto outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, outputLayerBindingId);
- outputBindings.push_back(std::make_pair(outputLayerBindingId, outputTensorInfo));
- outputVectors.push_back(std::vector<float>(outputTensorInfo.GetNumElements(), 0));
- }
- OutputTensors outputTensors = armnnUtils::MakeOutputTensors<TContainer>(outputBindings, outputVectors);
-
- // Execute EnqueueWorkload with calibration image
- m_Runtime->EnqueueWorkload(m_NetworkId, inputTensors, outputTensors);
- ++m_RefineCount;
-}
-
-INetworkPtr NetworkQuantizer::ExportNetwork()
-{
- const Graph& graph = m_InputNetwork->pNetworkImpl->GetGraph().TopologicalSort();
-
- // Step 1) Walk the graph and populate default min/max values for
- // intermediate tensors, only if Runtime does not exist (created
- // if Refine has been called)
- if (!m_Runtime)
- {
- m_Ranges.SetDynamicMode(false);
- StaticRangeStrategy rangeStrategy(m_Ranges);
- ApplyStrategyToLayers(graph, rangeStrategy);
- }
- else
- {
- // Set min/max range of non-calibrated layers to parent layer's range
- m_DynamicQuantizationStrategy.value().VisitNonCalibratedLayers();
- // now tear down the runtime and the dynamic visitor.
- m_Runtime.reset(nullptr);
- m_DynamicQuantizationStrategy = EmptyOptional();
- m_RefineCount = 0;
- }
-
- // Step 2) Convert input InputNetwork to Quantized InputNetwork
- std::unique_ptr<IQuantizationScheme> quantizationScheme;
- switch (m_Options.m_ActivationFormat)
- {
- case DataType::QAsymmU8:
- quantizationScheme = std::make_unique<QAsymmU8QuantizationScheme>();
- break;
- case DataType::QAsymmS8:
- quantizationScheme = std::make_unique<QAsymmS8QuantizationScheme>();
- break;
- case DataType::QSymmS8:
- quantizationScheme = std::make_unique<QSymmS8QuantizationScheme>();
- break;
- case DataType::QSymmS16:
- quantizationScheme = std::make_unique<QSymm16QuantizationScheme>();
- break;
- default:
- throw InvalidArgumentException("Unsupported quantization target");
- }
-
- QuantizerStrategy quantizerVisitor(m_Ranges, quantizationScheme.get(), m_Options.m_PreserveType);
- ApplyStrategyToLayers(graph, quantizerVisitor);
-
- // clear the ranges
- m_Ranges.Reset();
-
- return quantizerVisitor.RetrieveFinalNetwork();
-}
-
-} //namespace armn
diff --git a/src/armnn/NetworkQuantizer.hpp b/src/armnn/NetworkQuantizer.hpp
deleted file mode 100644
index a07ac88..0000000
--- a/src/armnn/NetworkQuantizer.hpp
+++ /dev/null
@@ -1,61 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include <armnn/INetwork.hpp>
-#include <armnnQuantizer/INetworkQuantizer.hpp>
-#include <armnn/IRuntime.hpp>
-#include <armnn/Types.hpp>
-#include <armnn/Optional.hpp>
-
-#include "DynamicQuantizationStrategy.hpp"
-#include "RangeTracker.hpp"
-
-namespace armnn
-{
-
-class NetworkQuantizer : public INetworkQuantizer
-{
-public:
- NetworkQuantizer(INetwork* inputNetwork, const QuantizerOptions& options)
- : m_InputNetwork(inputNetwork),
- m_NetworkId(0),
- m_Runtime(nullptr, &IRuntime::Destroy),
- m_RefineCount(0),
- m_Options(options) {}
-
- void OverrideInputRange(LayerBindingId layerId, float min, float max) override;
- void Refine(const InputTensors& inputTensors) override;
-
- // Required for testing? Need some way to get min/max in RangeTracker (m_Ranges)
- std::pair<float, float> GetMinMaxRange(LayerGuid guid, unsigned int idx) { return m_Ranges.GetRange(guid, idx); }
- INetworkPtr ExportNetwork() override;
-
-private:
- /// Original input network to quantize
- INetwork* m_InputNetwork;
-
- NetworkId m_NetworkId;
-
- // if we are run in dynamic mode this unique pointer will hold
- // the runtime between invocations of the Refine method.
- IRuntimePtr m_Runtime;
-
- Optional<DynamicQuantizationStrategy> m_DynamicQuantizationStrategy;
-
- // counts the number of times refine is called
- unsigned int m_RefineCount;
-
- /// Mapping from Guid to an array of ranges for outputs
- RangeTracker m_Ranges;
-
- /// Options for the NetworkQuantizer
- QuantizerOptions m_Options;
-
- std::pair<float, float> FindMinMax(ITensorHandle* tensorHandle);
-};
-
-} //namespace armnn
diff --git a/src/armnn/NetworkQuantizerUtils.cpp b/src/armnn/NetworkQuantizerUtils.cpp
deleted file mode 100644
index dd0affd..0000000
--- a/src/armnn/NetworkQuantizerUtils.cpp
+++ /dev/null
@@ -1,43 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "NetworkQuantizerUtils.hpp"
-
-#include <algorithm>
-#include <cmath>
-#include <stdint.h>
-
-namespace armnn
-{
-
-ConstTensor CreateQuantizedConst(const ConstTensor& tensor, std::vector<uint8_t>& backing)
-{
- float scale = 0.0f;
- int offset = 0;
-
- // Reserve the backing memory
- backing.resize(tensor.GetInfo().GetNumElements());
-
- DataType type = tensor.GetInfo().GetDataType();
- switch(type)
- {
- case DataType::Float32:
- {
- QuantizeConstant(static_cast<const float*>(tensor.GetMemoryArea()),
- backing.data(),
- backing.size(),
- scale,
- offset);
- }
- break;
- default:
- ARMNN_ASSERT_MSG(false, "Can't quantize unsupported data type");
- }
-
- TensorInfo qInfo(tensor.GetInfo().GetShape(), DataType::QAsymmU8, scale, offset);
- return ConstTensor(qInfo, backing);
-}
-
-} // namespace armnn
diff --git a/src/armnn/NetworkQuantizerUtils.hpp b/src/armnn/NetworkQuantizerUtils.hpp
deleted file mode 100644
index 5497e1b..0000000
--- a/src/armnn/NetworkQuantizerUtils.hpp
+++ /dev/null
@@ -1,70 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include "NetworkQuantizationScheme.hpp"
-
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-#include <armnn/ILayerVisitor.hpp>
-#include <armnn/IStrategy.hpp>
-#include <armnn/utility/Assert.hpp>
-
-#include <utility>
-#include <limits>
-
-namespace armnn
-{
-
-template<typename srcType>
-void QuantizeConstant(const srcType* src, uint8_t* dst, size_t numElements, float& scale, int& offset)
-{
- ARMNN_ASSERT(src);
- ARMNN_ASSERT(dst);
-
- float min = std::numeric_limits<srcType>::max();
- float max = std::numeric_limits<srcType>::lowest();
- for (size_t i = 0; i < numElements; ++i)
- {
- min = std::min(min, src[i]);
- max = std::max(max, src[i]);
- }
-
- QAsymmU8QuantizationScheme quantizationScheme;
- OffsetScalePair qParams = quantizationScheme.ComputeScheme(min, max);
- scale = qParams.first;
- offset = qParams.second;
-
- for (size_t i = 0; i < numElements; ++i)
- {
- dst[i] = armnn::Quantize<uint8_t>(src[i], scale, offset);
- }
-}
-
-ConstTensor CreateQuantizedConst(const ConstTensor& tensor, std::vector<uint8_t>& backing);
-
-template <typename LayerContainer>
-void VisitLayers(const LayerContainer& layerContainer, ILayerVisitor& visitor)
-{
- visitor.StartVisit();
- for (auto layer : layerContainer)
- {
- layer->Accept(visitor);
- }
- visitor.FinishVisit();
-}
-
-template <typename LayerContainer>
-void ApplyStrategyToLayers(const LayerContainer& layerContainer, IStrategy& strategy)
-{
- for (auto layer : layerContainer)
- {
- layer->ExecuteStrategy(strategy);
- }
- strategy.FinishStrategy();
-}
-
-} // namespace armnn
diff --git a/src/armnn/OverrideInputRangeVisitor.cpp b/src/armnn/OverrideInputRangeVisitor.cpp
deleted file mode 100644
index 6e5137b..0000000
--- a/src/armnn/OverrideInputRangeVisitor.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "OverrideInputRangeVisitor.hpp"
-#include "NetworkQuantizerUtils.hpp"
-#include "Layer.hpp"
-
-#include <armnn/utility/IgnoreUnused.hpp>
-
-namespace armnn
-{
-
-OverrideInputRangeVisitor::OverrideInputRangeVisitor(RangeTracker& ranges,
- LayerBindingId layerId,
- const MinMaxRange& minMaxRange)
- : m_Ranges(ranges)
- , m_LayerId(layerId)
- , m_MinMaxRange(minMaxRange)
-{}
-
-void OverrideInputRangeVisitor::VisitInputLayer(const IConnectableLayer* layer, LayerBindingId id, const char* name)
-{
- IgnoreUnused(name);
- if (m_LayerId == id)
- {
- m_Ranges.SetRange(layer, 0, m_MinMaxRange.first, m_MinMaxRange.second);
- }
-}
-
-} // namespace armnn
diff --git a/src/armnn/OverrideInputRangeVisitor.hpp b/src/armnn/OverrideInputRangeVisitor.hpp
deleted file mode 100644
index 196a3aa..0000000
--- a/src/armnn/OverrideInputRangeVisitor.hpp
+++ /dev/null
@@ -1,93 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include "NetworkQuantizer.hpp"
-#include "armnn/LayerVisitorBase.hpp"
-#include "RangeTracker.hpp"
-
-#include <unordered_map>
-
-namespace armnn
-{
-class OverrideInputRangeStrategy : public IStrategy
-{
-private:
- using MinMaxRange = RangeTracker::MinMaxRange;
-public :
- OverrideInputRangeStrategy(RangeTracker& ranges,
- LayerBindingId layerId,
- const MinMaxRange& minMaxRange)
- : m_Ranges(ranges)
- , m_LayerId(layerId)
- , m_MinMaxRange(minMaxRange){}
-
- ~OverrideInputRangeStrategy() = default;
-
- 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 :
- {
- if (m_LayerId == id)
- {
- m_Ranges.SetRange(layer, 0, m_MinMaxRange.first, m_MinMaxRange.second);
- }
- break;
- }
- default:
- {
- std::cout << "dont know this one" << std::endl;
- }
- }
- }
-
-private:
- /// Mapping from a layer Guid to an array of ranges for outputs
- RangeTracker& m_Ranges;
-
- /// The id of the input layer of which to override the input range
- LayerBindingId m_LayerId;
-
- /// The new input range to be applied to the input layer
- MinMaxRange m_MinMaxRange;
-};
-
-
-
-/// Visitor object for overriding the input range of the quantized input layers in a network
-class OverrideInputRangeVisitor : public LayerVisitorBase<VisitorNoThrowPolicy>
-{
-private:
- using MinMaxRange = RangeTracker::MinMaxRange;
-
-public:
- OverrideInputRangeVisitor(RangeTracker& ranges,
- LayerBindingId layerId,
- const MinMaxRange& minMaxRange);
- ~OverrideInputRangeVisitor() = default;
-
- void VisitInputLayer(const IConnectableLayer* layer, LayerBindingId id, const char* name = nullptr) override;
-
-private:
- /// Mapping from a layer Guid to an array of ranges for outputs
- RangeTracker& m_Ranges;
-
- /// The id of the input layer of which to override the input range
- LayerBindingId m_LayerId;
-
- /// The new input range to be applied to the input layer
- MinMaxRange m_MinMaxRange;
-};
-
-} // namespace armnn
diff --git a/src/armnn/QuantizerStrategy.cpp b/src/armnn/QuantizerStrategy.cpp
deleted file mode 100644
index df20749..0000000
--- a/src/armnn/QuantizerStrategy.cpp
+++ /dev/null
@@ -1,519 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "QuantizerStrategy.hpp"
-#include "armnn/utility/PolymorphicDowncast.hpp"
-
-namespace armnn
-{
-
-QuantizerStrategy::QuantizerStrategy(const RangeTracker& rangeTracker,
- const IQuantizationScheme* quantizationScheme,
- bool preserveType)
- : m_Ranges(rangeTracker)
- , m_QuantizedNetwork(INetwork::Create())
- , m_QuantizationScheme(quantizationScheme)
- , m_PreserveType(preserveType)
-{
-}
-
-void QuantizerStrategy::SetQuantizedInputConnections(const IConnectableLayer* srcLayer,
- IConnectableLayer* quantizedLayer)
-{
- ARMNN_ASSERT(srcLayer);
- for (unsigned int i = 0; i < srcLayer->GetNumInputSlots(); i++)
- {
- const IInputSlot& srcInputSlot = srcLayer->GetInputSlot(i);
- const InputSlot* inputSlot = static_cast<const InputSlot*>(&srcInputSlot);
- ARMNN_ASSERT(inputSlot);
- const OutputSlot* outputSlot = inputSlot->GetConnectedOutputSlot();
-
- ARMNN_ASSERT(outputSlot);
- unsigned int slotIdx = outputSlot->CalculateIndexOnOwner();
- Layer& layerToFind = outputSlot->GetOwningLayer();
-
- auto found = m_OriginalToQuantizedGuidMap.find(layerToFind.GetGuid());
- if (found == m_OriginalToQuantizedGuidMap.end())
- {
- // Error in graph traversal order
- ARMNN_ASSERT_MSG(false, "Error in graph traversal");
- return;
- }
-
- // Connect the slots in the quantized model
- IConnectableLayer* prevQuantizedLayer = m_QuantizedGuidToLayerMap[found->second];
- IInputSlot& newInputSlot = quantizedLayer->GetInputSlot(i);
- IOutputSlot& newOutputSlot = prevQuantizedLayer->GetOutputSlot(slotIdx);
- newOutputSlot.Connect(newInputSlot);
- TensorInfo info(outputSlot->GetTensorInfo());
-
- // Only try to set quantization params on tensors that can be quantized
- if (inputSlot->GetConnectedOutputSlot()->GetTensorInfo().GetDataType() != DataType::Boolean &&
- inputSlot->GetConnectedOutputSlot()->GetTensorInfo().GetDataType() != DataType::Signed32 &&
- inputSlot->GetConnectedOutputSlot()->GetTensorInfo().GetDataType() != DataType::Signed64)
- {
- // Fetch the min/max ranges that were computed earlier
- auto range = m_Ranges.GetRange(layerToFind.GetGuid(), slotIdx);
- OffsetScalePair qParams = m_QuantizationScheme->ComputeScheme(range.first, range.second);
- info.SetDataType(m_QuantizationScheme->GetDataType());
- info.SetQuantizationOffset(qParams.second);
- info.SetQuantizationScale(qParams.first);
- }
- newOutputSlot.SetTensorInfo(info);
- }
-}
-
-ConstTensor QuantizerStrategy::CreateQuantizedBias(const IConnectableLayer* srcLayer,
- const ConstTensor& weights,
- const Optional<ConstTensor>& biases,
- std::vector<int32_t>& backing)
-{
- ARMNN_ASSERT(srcLayer);
- const IInputSlot& srcInputSlot = srcLayer->GetInputSlot(0);
- auto inputSlot = static_cast<const InputSlot*>(&srcInputSlot);
- ARMNN_ASSERT(inputSlot);
- const OutputSlot* outputSlot = inputSlot->GetConnectedOutputSlot();
-
- ARMNN_ASSERT(outputSlot);
- unsigned int slotIdx = outputSlot->CalculateIndexOnOwner();
- Layer& layerToFind = outputSlot->GetOwningLayer();
-
- auto found = m_OriginalToQuantizedGuidMap.find(layerToFind.GetGuid());
- if (found == m_OriginalToQuantizedGuidMap.end())
- {
- // Error in graph traversal order
- ARMNN_ASSERT_MSG(false, "Error in graph traversal");
- return biases.value();
- }
-
- // Fetch the min/max ranges that were computed earlier
- auto range = m_Ranges.GetRange(layerToFind.GetGuid(), slotIdx);
- OffsetScalePair qParams = m_QuantizationScheme->ComputeScheme(range.first, range.second);
-
- // Get the quantization scale based on input and weight scale
- float scale = qParams.first * weights.GetInfo().GetQuantizationScale();
-
- // Set up quantized bias tensor info and allocate space
- TensorInfo qInfo(biases.value().GetInfo().GetShape(), DataType::Signed32, scale, 0);
- backing.resize(biases.value().GetInfo().GetNumElements());
-
- // Convert values to int32
- for (size_t i = 0; i < backing.size(); ++i)
- {
- float fp32Value = static_cast<const float*>(biases.value().GetMemoryArea())[i];
- backing[i] = armnn::numeric_cast<int32_t>(fp32Value * ( 1 / scale ));
- }
-
- return ConstTensor(qInfo, backing);
-}
-
-void QuantizerStrategy::RecordLayer(const IConnectableLayer* srcLayer, IConnectableLayer* quantizedLayer)
-{
- m_OriginalToQuantizedGuidMap.insert(std::make_pair(srcLayer->GetGuid(), quantizedLayer->GetGuid()));
- m_QuantizedGuidToLayerMap.insert(std::make_pair(quantizedLayer->GetGuid(), quantizedLayer));
-}
-
-void QuantizerStrategy::ExecuteStrategy(const armnn::IConnectableLayer *layer,
- const BaseDescriptor& descriptor,
- const std::vector<armnn::ConstTensor> &constants,
- const char *name,
- const armnn::LayerBindingId id)
-{
- IgnoreUnused(id);
-
- IConnectableLayer* newLayer;
-
- switch (layer->GetType())
- {
- case armnn::LayerType::Addition :
- {
- newLayer = m_QuantizedNetwork->AddAdditionLayer(name);
- break;
- }
- case armnn::LayerType::Activation :
- {
- const ActivationDescriptor& activationDescriptor = static_cast<const ActivationDescriptor&>(descriptor);
- newLayer = m_QuantizedNetwork->AddActivationLayer(activationDescriptor, name);
- break;
- }
- case armnn::LayerType::ArgMinMax :
- {
- ArgMinMaxDescriptor argMinMaxDescriptor = static_cast<const ArgMinMaxDescriptor&>(descriptor);
- newLayer = m_QuantizedNetwork->AddArgMinMaxLayer(argMinMaxDescriptor, name);
- break;
- }
- case armnn::LayerType::BatchNormalization :
- {
-
- BatchNormalizationDescriptor batchNormalizationDescriptor =
- static_cast<const BatchNormalizationDescriptor&>(descriptor);
- std::vector<uint8_t> meanBacking;
- ConstTensor qMean = CreateQuantizedConst(constants[0], meanBacking);
-
- std::vector<uint8_t> varianceBacking;
- ConstTensor qVariance = CreateQuantizedConst(constants[1], varianceBacking);
-
- std::vector<uint8_t> betaBacking;
- ConstTensor qBeta = CreateQuantizedConst(constants[2], betaBacking);
-
- std::vector<uint8_t> gammaBacking;
- ConstTensor qGamma = CreateQuantizedConst(constants[3], gammaBacking);
-
- newLayer = m_QuantizedNetwork->AddBatchNormalizationLayer(batchNormalizationDescriptor,
- qMean,
- qVariance,
- qBeta,
- qGamma,
- name);
- break;
- }
- case armnn::LayerType::BatchToSpaceNd :
- {
- BatchToSpaceNdDescriptor batchToSpaceNdDescriptor =
- static_cast<const BatchToSpaceNdDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddBatchToSpaceNdLayer(batchToSpaceNdDescriptor, name);
- break;
- }
- case armnn::LayerType::Comparison :
- {
- ComparisonDescriptor comparisonDescriptor =static_cast<const ComparisonDescriptor&>(descriptor);
- newLayer = m_QuantizedNetwork->AddComparisonLayer(comparisonDescriptor, name);
- break;
- }
- case armnn::LayerType::Concat :
- {
- OriginsDescriptor originsDescriptor = static_cast<const OriginsDescriptor&>(descriptor);
- newLayer = m_QuantizedNetwork->AddConcatLayer(originsDescriptor, name);
- break;
- }
- case armnn::LayerType::Constant :
- {
- std::vector<uint8_t> inputBacking;
- ConstTensor qInput = CreateQuantizedConst(constants[0], inputBacking);
-
- newLayer = m_QuantizedNetwork->AddConstantLayer(qInput, name);
- break;
- }
- case armnn::LayerType::Convolution2d :
- {
- const armnn::Optional<ConstTensor> biases = constants.size() == 1 ?
- armnn::Optional<ConstTensor>{} :
- armnn::Optional<ConstTensor>(constants[1]);
-
- std::vector<uint8_t> weightsBacking;
- ConstTensor qWeights = CreateQuantizedConst(constants[0], weightsBacking);
- Optional<ConstTensor> optionalQBiases;
- std::vector<int32_t> biasesBacking;
-
- if (biases.has_value())
- {
- ConstTensor qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);
- optionalQBiases = Optional<ConstTensor>(qBiases);
- }
- Convolution2dDescriptor convolution2dDescriptor = static_cast<const Convolution2dDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddConvolution2dLayer(convolution2dDescriptor,
- qWeights,
- optionalQBiases,
- name);
- break;
- }
- case armnn::LayerType::DepthToSpace :
- {
- DepthToSpaceDescriptor depthToSpaceDescriptor = static_cast<const DepthToSpaceDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddDepthToSpaceLayer(depthToSpaceDescriptor, name);
- break;
- }
- case armnn::LayerType::DepthwiseConvolution2d :
- {
- DepthwiseConvolution2dDescriptor depthwiseConvolution2dDescriptor =
- static_cast<const DepthwiseConvolution2dDescriptor&>(descriptor);
-
- const armnn::Optional<ConstTensor> biases = constants.size() == 1 ?
- armnn::Optional<ConstTensor>{} :
- armnn::Optional<ConstTensor>(constants[1]);
-
- std::vector<uint8_t> weightsBacking;
- ConstTensor qWeights = CreateQuantizedConst(constants[0], weightsBacking);
- Optional<ConstTensor> optionalQBiases;
- std::vector<int32_t> biasesBacking;
-
- if (biases.has_value())
- {
- ConstTensor qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);
- optionalQBiases = Optional<ConstTensor>(qBiases);
- }
-
- newLayer = m_QuantizedNetwork->AddDepthwiseConvolution2dLayer(
- depthwiseConvolution2dDescriptor,
- qWeights,
- optionalQBiases,
- name);
- break;
- }
- case armnn::LayerType::ElementwiseUnary :
- {
- ElementwiseUnaryDescriptor elementwiseUnaryDescriptor =
- static_cast<const ElementwiseUnaryDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddElementwiseUnaryLayer(elementwiseUnaryDescriptor, name);
- break;
- }
- case armnn::LayerType::Fill :
- {
- FillDescriptor fillDescriptor = static_cast<const FillDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddFillLayer(fillDescriptor, name);
- break;
- }
- case armnn::LayerType::FullyConnected :
- {
- FullyConnectedDescriptor fullyConnectedDescriptor =
- static_cast<const FullyConnectedDescriptor&>(descriptor);
-
- const armnn::Optional<ConstTensor> biases = constants.size() == 1 ?
- armnn::Optional<ConstTensor>{} :
- armnn::Optional<ConstTensor>(constants[1]);
-
- std::vector<uint8_t> weightsBacking;
- ConstTensor qWeights = CreateQuantizedConst(constants[0], weightsBacking);
- Optional<ConstTensor> optionalQBiases;
- std::vector<int32_t> biasesBacking;
-
- if (biases.has_value())
- {
- ConstTensor qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);
- optionalQBiases = Optional<ConstTensor>(qBiases);
- }
-
- newLayer = m_QuantizedNetwork->AddFullyConnectedLayer(fullyConnectedDescriptor,
- qWeights,
- optionalQBiases,
- name);
- break;
- }
- case armnn::LayerType::Input :
- {
- const DataType dataType = layer->GetOutputSlot(0).GetTensorInfo().GetDataType();
- IConnectableLayer* inputLayer = m_QuantizedNetwork->AddInputLayer(id, name);
-
- if (m_PreserveType && (dataType == DataType::Float32 || dataType == DataType::Float16))
- {
- IConnectableLayer* quantizeLayer = m_QuantizedNetwork->AddQuantizeLayer();
- inputLayer->GetOutputSlot(0).Connect(quantizeLayer->GetInputSlot(0));
- inputLayer->GetOutputSlot(0).SetTensorInfo(layer->GetOutputSlot(0).GetTensorInfo());
- RecordLayer(layer, quantizeLayer);
- return;
- }
- else
- {
- RecordLayer(layer, inputLayer);
- return;
- }
- }
- case armnn::LayerType::InstanceNormalization :
- {
- InstanceNormalizationDescriptor instanceNormalizationDescriptor =
- static_cast<const InstanceNormalizationDescriptor&>(descriptor);
-
- newLayer =
- m_QuantizedNetwork->AddInstanceNormalizationLayer(instanceNormalizationDescriptor, name);
- break;
- }
- case armnn::LayerType::LogSoftmax :
- {
- LogSoftmaxDescriptor logSoftmaxDescriptor = static_cast<const LogSoftmaxDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddLogSoftmaxLayer(logSoftmaxDescriptor, name);
- break;
- }
- case armnn::LayerType::Mean :
- {
- MeanDescriptor meanDescriptor = static_cast<const MeanDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddMeanLayer(meanDescriptor, name);
- break;
- }
- case armnn::LayerType::Multiplication :
- {
- newLayer = m_QuantizedNetwork->AddMultiplicationLayer(name);
- break;
- }
- case armnn::LayerType::Normalization :
- {
- NormalizationDescriptor normalizationDescriptor = static_cast<const NormalizationDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddNormalizationLayer(normalizationDescriptor, name);
- break;
- }
- case armnn::LayerType::Output :
- {
- const TensorInfo& info = layer->GetInputSlot(0).GetConnection()->GetTensorInfo();
- const DataType& dataType = info.GetDataType();
- newLayer = m_QuantizedNetwork->AddOutputLayer(id, name);
-
- if (m_PreserveType && (dataType == DataType::Float32 || dataType == DataType::Float16))
- {
- IConnectableLayer* dequantizeLayer = m_QuantizedNetwork->AddDequantizeLayer();
- RecordLayer(layer, dequantizeLayer);
- SetQuantizedInputConnections(layer, dequantizeLayer);
- dequantizeLayer->GetOutputSlot(0).Connect(newLayer->GetInputSlot(0));
- dequantizeLayer->GetOutputSlot(0).SetTensorInfo(info);
- return;
- }
- else
- {
- break;
- }
- }
- case armnn::LayerType::Pad :
- {
- PadDescriptor padDescriptor = static_cast<const PadDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddPadLayer(padDescriptor, name);
- break;
- }
- case armnn::LayerType::Permute :
- {
- PermuteDescriptor permuteDescriptor = static_cast<const PermuteDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddPermuteLayer(permuteDescriptor, name);
- break;
- }
- case armnn::LayerType::Pooling2d :
- {
- Pooling2dDescriptor pooling2dDescriptor = static_cast<const Pooling2dDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddPooling2dLayer(pooling2dDescriptor, name);
- break;
- }
- case armnn::LayerType::Prelu :
- {
- newLayer = m_QuantizedNetwork->AddPreluLayer(name);
- break;
- }
- case armnn::LayerType::Reshape :
- {
- ReshapeDescriptor reshapeDescriptor = static_cast<const ReshapeDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddReshapeLayer(reshapeDescriptor, name);
- break;
- }
- case armnn::LayerType::Resize :
- {
-
- ResizeBilinearDescriptor resizeBilinearDescriptor =
- static_cast<const ResizeBilinearDescriptor&>(descriptor);
-
- ResizeDescriptor resizeDescriptor;
- resizeDescriptor.m_Method = ResizeMethod::Bilinear;
- resizeDescriptor.m_TargetWidth = resizeBilinearDescriptor.m_TargetWidth;
- resizeDescriptor.m_TargetHeight = resizeBilinearDescriptor.m_TargetHeight;
- resizeDescriptor.m_DataLayout = resizeBilinearDescriptor.m_DataLayout;
-
- newLayer = m_QuantizedNetwork->AddResizeLayer(resizeDescriptor, name);
- break;
- }
- case armnn::LayerType::Slice :
- {
- SliceDescriptor sliceDescriptor = static_cast<const SliceDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddSliceLayer(sliceDescriptor, name);
- break;
- }
- case armnn::LayerType::Softmax :
- {
- SoftmaxDescriptor softmaxDescriptor = static_cast<const SoftmaxDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddSoftmaxLayer(softmaxDescriptor, name);
- break;
- }
- case armnn::LayerType::SpaceToBatchNd :
- {
- SpaceToBatchNdDescriptor spaceToBatchNdDescriptor =
- static_cast<const SpaceToBatchNdDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddSpaceToBatchNdLayer(spaceToBatchNdDescriptor, name);
- break;
- }
- case armnn::LayerType::SpaceToDepth :
- {
- SpaceToDepthDescriptor spaceToDepthDescriptor = static_cast<const SpaceToDepthDescriptor&>(descriptor);
- newLayer = m_QuantizedNetwork->AddSpaceToDepthLayer(spaceToDepthDescriptor, name);
- break;
- }
- case armnn::LayerType::Splitter :
- {
- SplitterDescriptor splitterDescriptor = static_cast<const SplitterDescriptor&>(descriptor);
- newLayer = m_QuantizedNetwork->AddSplitterLayer(splitterDescriptor, name);
- break;
- }
- case armnn::LayerType::Stack :
- {
- StackDescriptor stackDescriptor = static_cast<const StackDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddStackLayer(stackDescriptor, name);
- break;
- }
- case armnn::LayerType::StridedSlice :
- {
- StridedSliceDescriptor stridedSliceDescriptor = static_cast<const StridedSliceDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddStridedSliceLayer(stridedSliceDescriptor, name);
- break;
- }
- case armnn::LayerType::Subtraction :
- {
- newLayer = m_QuantizedNetwork->AddSubtractionLayer( name);
- break;
- }
- case armnn::LayerType::TransposeConvolution2d :
- {
-
- const armnn::Optional<ConstTensor> biases = constants.size() == 1 ?
- armnn::Optional<ConstTensor>{} :
- armnn::Optional<ConstTensor>(constants[1]);
- // quantize weights
- std::vector<uint8_t> weightsBacking;
- ConstTensor qWeights = CreateQuantizedConst(constants[0], weightsBacking);
-
- // quantize biases
- std::vector<int32_t> biasesBacking;
- Optional<ConstTensor> optionalQBiases;
- if (biases.has_value())
- {
- ConstTensor qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);
- optionalQBiases = Optional<ConstTensor>(qBiases);
- }
-
- TransposeConvolution2dDescriptor transposeConvolution2dDescriptor =
- static_cast<const TransposeConvolution2dDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddTransposeConvolution2dLayer(transposeConvolution2dDescriptor,
- qWeights,
- optionalQBiases,
- name);
- break;
- }
- case armnn::LayerType::Transpose :
- {
- TransposeDescriptor transposeDescriptor = static_cast<const TransposeDescriptor&>(descriptor);
-
- newLayer = m_QuantizedNetwork->AddTransposeLayer(transposeDescriptor, name);
- break;
- }
- default:
- {
- throw UnimplementedException("Unimplemented layer encountered");
- }
- }
- RecordLayer(layer, newLayer);
- SetQuantizedInputConnections(layer, newLayer);
-}
-
-}
-
diff --git a/src/armnn/QuantizerStrategy.hpp b/src/armnn/QuantizerStrategy.hpp
deleted file mode 100644
index f782959..0000000
--- a/src/armnn/QuantizerStrategy.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "Network.hpp"
-#include "NetworkQuantizerUtils.hpp"
-#include "StaticRangeStrategy.hpp"
-
-#include <armnn/utility/NumericCast.hpp>
-#include <armnn/utility/PolymorphicDowncast.hpp>
-
-namespace armnn
-{
-class QuantizerStrategy : public IStrategy
-{
-public :
- QuantizerStrategy(const RangeTracker& rangeTracker,
- const IQuantizationScheme* quantizationScheme,
- bool preserveType);
-
- ~QuantizerStrategy() = default;
-
- void ExecuteStrategy(const armnn::IConnectableLayer* layer,
- const BaseDescriptor& descriptor,
- const std::vector<armnn::ConstTensor>& constants,
- const char* name,
- const armnn::LayerBindingId id) override;
-
- /// Extract the quantized network
- INetworkPtr RetrieveFinalNetwork() { return std::move(m_QuantizedNetwork); }
-
-private:
- /// Connects the layer to preceeding layers and sets the quantization parameters based on recorded ranges
- void SetQuantizedInputConnections(const IConnectableLayer* srcLayer, IConnectableLayer* quantizedLayer);
-
- /// Record the guids so we can easily find the layers later
- void RecordLayer(const IConnectableLayer* srcLayer, IConnectableLayer* qLayer);
-
- /// Sets the bias quantization scale based on input and weight scales
- ConstTensor CreateQuantizedBias(const IConnectableLayer* srcLayer,
- const ConstTensor& weights,
- const Optional<ConstTensor>& biases,
- std::vector<int32_t>& weightsBacking);
-
- /// Reference to the static range visitor used to retrieve the quantization ranges
- const RangeTracker& m_Ranges;
-
- /// Quantized version of the model we are building up
- INetworkPtr m_QuantizedNetwork;
-
- /// Mapping from input network guids to quantized network guids
- std::unordered_map<LayerGuid, LayerGuid> m_OriginalToQuantizedGuidMap;
-
- /// Mapping from guid to layer in quantized network
- std::unordered_map<LayerGuid, IConnectableLayer*> m_QuantizedGuidToLayerMap;
-
- const IQuantizationScheme* m_QuantizationScheme;
-
- const bool m_PreserveType;
-};
-
-} //namespace armnn
\ No newline at end of file
diff --git a/src/armnn/StaticRangeStrategy.cpp b/src/armnn/StaticRangeStrategy.cpp
deleted file mode 100644
index 84b8d24..0000000
--- a/src/armnn/StaticRangeStrategy.cpp
+++ /dev/null
@@ -1,193 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "StaticRangeStrategy.hpp"
-
-#include <armnn/utility/IgnoreUnused.hpp>
-#include <armnn/Descriptors.hpp>
-#include <armnn/Types.hpp>
-
-#include <limits>
-
-namespace armnn
-{
-
-StaticRangeStrategy::StaticRangeStrategy(RangeTracker& rangeTracker)
- : m_RangeTracker(rangeTracker)
-{}
-
-void StaticRangeStrategy::SetRange(const IConnectableLayer* layer, unsigned int outputIdx, float min, float max)
-{
- m_RangeTracker.SetRange(layer, outputIdx, min, max);
-}
-
-void StaticRangeStrategy::ForwardParentParameters(const IConnectableLayer* layer)
-{
- const auto parentRange = m_RangeTracker.GetRange(layer->GetInputSlot(0).GetConnection()->GetOwningLayerGuid(), 0);
- SetRange(layer, 0, parentRange.first, parentRange.second);
-}
-
-
-void StaticRangeStrategy::ExecuteStrategy(const armnn::IConnectableLayer *layer,
- const BaseDescriptor &descriptor,
- const std::vector<armnn::ConstTensor> &constants,
- const char *name,
- const armnn::LayerBindingId id)
-{
-IgnoreUnused(id, name);
-
-switch (layer->GetType())
-{
- case armnn::LayerType::Activation :
- {
- const ActivationDescriptor& activationDescriptor = static_cast<const ActivationDescriptor&>(descriptor);
-
- switch (activationDescriptor.m_Function)
- {
- // Range is 0, 15 for Abs, Linear, ReLu and Soft ReLu
- case ActivationFunction::Abs:
- case ActivationFunction::Linear:
- case ActivationFunction::ReLu:
- case ActivationFunction::SoftReLu:
- SetRange(layer, 0, 0.f, 15.f);
- break;
- case ActivationFunction::BoundedReLu:
- SetRange(layer, 0, 0.f, activationDescriptor.m_A);
- break;
- case ActivationFunction::TanH:
- SetRange(layer, 0, -1.f, 1.f);
- break;
- case ActivationFunction::LeakyReLu:
- SetRange(layer, 0, -5.f, 15.f);
- break;
- default:
- SetRange(layer, 0, -15.f, 15.f);
- break;
- }
- break;
- }
- case armnn::LayerType::Addition :
- {
- SetRange(layer, 0, -20.f, 20.f);
- break;
- }
- case armnn::LayerType::ArgMinMax :
- {
- ForwardParentParameters(layer);
- break;
- }
- case armnn::LayerType::BatchToSpaceNd :
- {
- ForwardParentParameters(layer);
- break;
- }
- case armnn::LayerType::BatchNormalization :
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- break;
- }
- case armnn::LayerType::Concat :
- {
- float min = std::numeric_limits<float>::max();
- float max = std::numeric_limits<float>::lowest();
- for (unsigned int i = 0; i < layer->GetNumInputSlots(); ++i)
- {
- const IOutputSlot* outputSlot = layer->GetInputSlot(i).GetConnection();
- LayerGuid layerId = outputSlot->GetOwningLayerGuid();
- unsigned int slotIndex = outputSlot->CalculateIndexOnOwner();
- RangeTracker::MinMaxRange range = m_RangeTracker.GetRange(layerId, slotIndex);
- min = std::min(min, range.first);
- max = std::max(max, range.second);
- }
- SetRange(layer, 0, min, max);
- break;
- }
- case armnn::LayerType::Constant :
- {
-
- if (constants[0].GetDataType() != DataType::Float32)
- {
- throw InvalidArgumentException("Quantization is supported only for FP32 tensors");
- }
-
- // Work out the range based on the input constants
- unsigned int inputNumElements = constants[0].GetNumElements();
- const float* inputData = reinterpret_cast<const float*>(constants[0].GetMemoryArea());
-
- float min = std::numeric_limits<float>::max();
- float max = std::numeric_limits<float>::lowest();
-
- for (unsigned int i = 0; i < inputNumElements; i++)
- {
- const float inputValue = inputData[i];
-
- min = std::min(min, inputValue);
- max = std::max(max, inputValue);
- }
- SetRange(layer, 0, min, max);
- break;
- }
- case armnn::LayerType::Convolution2d :
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- break;
- }
- case armnn::LayerType::DepthwiseConvolution2d :
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- break;
- }
- case armnn::LayerType::FullyConnected :
- {
- SetRange(layer, 0, -15.0f, 15.0f);
- break;
- }
- case armnn::LayerType::Permute :
- {
- ForwardParentParameters(layer);
- break;
- }
- case armnn::LayerType::Pooling2d :
- {
- ForwardParentParameters(layer);
- break;
- }
- case armnn::LayerType::Reshape :
- {
- ForwardParentParameters(layer);
- break;
- }
- case armnn::LayerType::Resize :
- {
- ForwardParentParameters(layer);
- break;
- }
- case armnn::LayerType::Splitter :
- {
- ForwardParentParameters(layer);
- break;
- }
- case armnn::LayerType::SpaceToBatchNd :
- {
- ForwardParentParameters(layer);
- break;
- }
- case armnn::LayerType::Softmax :
- {
- SetRange(layer, 0, 0.f, 1.f);
- break;
- }
- case armnn::LayerType::StridedSlice :
- {
- ForwardParentParameters(layer);
- break;
- }
- default:
- {
- }
-}
-}
-
-} //namespace armnn
diff --git a/src/armnn/StaticRangeStrategy.hpp b/src/armnn/StaticRangeStrategy.hpp
deleted file mode 100644
index ed7cf27..0000000
--- a/src/armnn/StaticRangeStrategy.hpp
+++ /dev/null
@@ -1,41 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include "armnn/LayerVisitorBase.hpp"
-#include "RangeTracker.hpp"
-
-#include <armnn/INetwork.hpp>
-#include <armnnQuantizer/INetworkQuantizer.hpp>
-
-
-namespace armnn
-{
-
-class StaticRangeStrategy : public IStrategy
-{
-public:
- StaticRangeStrategy(RangeTracker& rangeTracker);
- ~StaticRangeStrategy() = default;
-
- void ExecuteStrategy(const armnn::IConnectableLayer *layer,
- const BaseDescriptor &descriptor,
- const std::vector<armnn::ConstTensor> &constants,
- const char *name,
- const armnn::LayerBindingId id) override;
-
-private:
- /// Set the range for an output slot on a layer
- void SetRange(const IConnectableLayer* layer, unsigned int outputIdx, float min, float max);
-
- void ForwardParentParameters(const IConnectableLayer* layer);
-
- /// Mapping from a layer Guid to an array of ranges for outputs
- RangeTracker& m_RangeTracker;
-
-};
-
-} //namespace armnn
diff --git a/src/armnn/test/QuantizerTest.cpp b/src/armnn/test/QuantizerTest.cpp
deleted file mode 100644
index a932698..0000000
--- a/src/armnn/test/QuantizerTest.cpp
+++ /dev/null
@@ -1,2220 +0,0 @@
-//
-// 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
diff --git a/src/armnnQuantizer/ArmNNQuantizerMain.cpp b/src/armnnQuantizer/ArmNNQuantizerMain.cpp
deleted file mode 100644
index 49652ef..0000000
--- a/src/armnnQuantizer/ArmNNQuantizerMain.cpp
+++ /dev/null
@@ -1,99 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "CommandLineProcessor.hpp"
-#include <armnnDeserializer/IDeserializer.hpp>
-#include <armnnQuantizer/INetworkQuantizer.hpp>
-#include <armnnSerializer/ISerializer.hpp>
-#include "QuantizationDataSet.hpp"
-#include "QuantizationInput.hpp"
-
-#include <algorithm>
-#include <fstream>
-#include <iostream>
-
-int main(int argc, char* argv[])
-{
- armnnQuantizer::CommandLineProcessor cmdline;
- if (!cmdline.ProcessCommandLine(argc, argv))
- {
- return -1;
- }
- armnnDeserializer::IDeserializerPtr parser = armnnDeserializer::IDeserializer::Create();
- std::ifstream inputFileStream(cmdline.GetInputFileName(), std::ios::binary);
- std::vector<std::uint8_t> binaryContent;
- while (inputFileStream)
- {
- char c;
- inputFileStream.get(c);
- if (inputFileStream)
- {
- binaryContent.push_back(static_cast<std::uint8_t>(c));
- }
- }
- inputFileStream.close();
-
- armnn::QuantizerOptions quantizerOptions;
-
- if (cmdline.GetQuantizationScheme() == "QAsymmS8")
- {
- quantizerOptions.m_ActivationFormat = armnn::DataType::QAsymmS8;
- }
- else if (cmdline.GetQuantizationScheme() == "QSymmS16")
- {
- quantizerOptions.m_ActivationFormat = armnn::DataType::QSymmS16;
- }
- else
- {
- quantizerOptions.m_ActivationFormat = armnn::DataType::QAsymmU8;
- }
-
- quantizerOptions.m_PreserveType = cmdline.HasPreservedDataType();
-
- armnn::INetworkPtr network = parser->CreateNetworkFromBinary(binaryContent);
- armnn::INetworkQuantizerPtr quantizer = armnn::INetworkQuantizer::Create(network.get(), quantizerOptions);
-
- if (cmdline.HasQuantizationData())
- {
- armnnQuantizer::QuantizationDataSet dataSet = cmdline.GetQuantizationDataSet();
- if (!dataSet.IsEmpty())
- {
- // Get the Input Tensor Infos
- armnnQuantizer::InputLayerStrategy inputLayerStrategy;
- network->ExecuteStrategy(inputLayerStrategy);
-
- for (armnnQuantizer::QuantizationInput quantizationInput : dataSet)
- {
- armnn::InputTensors inputTensors;
- std::vector<std::vector<float>> inputData(quantizationInput.GetNumberOfInputs());
- std::vector<armnn::LayerBindingId> layerBindingIds = quantizationInput.GetLayerBindingIds();
- unsigned int count = 0;
- for (armnn::LayerBindingId layerBindingId : quantizationInput.GetLayerBindingIds())
- {
- armnn::TensorInfo tensorInfo = inputLayerStrategy.GetTensorInfo(layerBindingId);
- inputData[count] = quantizationInput.GetDataForEntry(layerBindingId);
- armnn::ConstTensor inputTensor(tensorInfo, inputData[count].data());
- inputTensors.push_back(std::make_pair(layerBindingId, inputTensor));
- count++;
- }
- quantizer->Refine(inputTensors);
- }
- }
- }
-
- armnn::INetworkPtr quantizedNetwork = quantizer->ExportNetwork();
- armnnSerializer::ISerializerPtr serializer = armnnSerializer::ISerializer::Create();
- serializer->Serialize(*quantizedNetwork);
-
- std::string output(cmdline.GetOutputDirectoryName());
- output.append(cmdline.GetOutputFileName());
- std::ofstream outputFileStream;
- outputFileStream.open(output);
- serializer->SaveSerializedToStream(outputFileStream);
- outputFileStream.flush();
- outputFileStream.close();
-
- return 0;
-}
\ No newline at end of file
diff --git a/src/armnnQuantizer/CommandLineProcessor.cpp b/src/armnnQuantizer/CommandLineProcessor.cpp
deleted file mode 100644
index 74ad5e7..0000000
--- a/src/armnnQuantizer/CommandLineProcessor.cpp
+++ /dev/null
@@ -1,186 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "CommandLineProcessor.hpp"
-#include <Filesystem.hpp>
-
-#include <cxxopts/cxxopts.hpp>
-
-namespace armnnQuantizer
-{
-
-bool ValidateOutputDirectory(std::string& dir)
-{
- if (dir.empty())
- {
- std::cerr << "No output directory specified" << std::endl;
- return false;
- }
-
- if (dir[dir.length() - 1] != '/')
- {
- dir += "/";
- }
-
- if (!fs::exists(dir))
- {
- std::cerr << "Output directory [" << dir << "] does not exist" << std::endl;
- return false;
- }
-
- if (!fs::is_directory(dir))
- {
- std::cerr << "Given output directory [" << dir << "] is not a directory" << std::endl;
- return false;
- }
-
- return true;
-}
-
-bool ValidateProvidedFile(const std::string& inputFileName)
-{
- if (!fs::exists(inputFileName))
- {
- std::cerr << "Provided file [" << inputFileName << "] does not exist" << std::endl;
- return false;
- }
-
- if (fs::is_directory(inputFileName))
- {
- std::cerr << "Given file [" << inputFileName << "] is a directory" << std::endl;
- return false;
- }
-
- return true;
-}
-
-bool ValidateQuantizationScheme(const std::string& scheme)
-{
- if (scheme.empty())
- {
- std::cerr << "No Quantization Scheme specified" << std::endl;
- return false;
- }
-
- std::vector<std::string> supportedSchemes =
- {
- "QAsymmS8",
- "QAsymmU8",
- "QSymm16"
- };
-
- auto iterator = std::find(supportedSchemes.begin(), supportedSchemes.end(), scheme);
- if (iterator == supportedSchemes.end())
- {
- std::cerr << "Quantization Scheme [" << scheme << "] is not supported" << std::endl;
- return false;
- }
-
- return true;
-}
-
-bool CommandLineProcessor::ProcessCommandLine(int argc, char* argv[])
-{
- try
- {
- cxxopts::Options options("ArmnnQuantizer","Convert a Fp32 ArmNN model to a quantized ArmNN model.");
-
- options.add_options()
- ("h,help", "Display help messages")
- ("f,infile",
- "Input file containing float 32 ArmNN Input Graph",
- cxxopts::value<std::string>(m_InputFileName))
- ("s,scheme",
- "Quantization scheme,"
- " \"QAsymmU8\" or \"QAsymmS8\" or \"QSymm16\","
- " default value QAsymmU8",
- cxxopts::value<std::string>(m_QuantizationScheme)->default_value("QAsymmU8"))
- ("c,csvfile",
- "CSV file containing paths for RAW input tensors",
- cxxopts::value<std::string>(m_CsvFileName)->default_value(""))
- ("p,preserve-data-type",
- "Preserve the input and output data types",
- cxxopts::value<bool>(m_PreserveDataType)->default_value("false"))
- ("d,outdir",
- "Directory that output file will be written to",
- cxxopts::value<std::string>(m_OutputDirectory))
- ("o,outfile",
- "ArmNN output file name",
- cxxopts::value<std::string>(m_OutputFileName));
-
- auto result = options.parse(argc, argv);
-
- if (result.count("help") > 0 || argc <= 1)
- {
- std::cout << options.help() << std::endl;
- return false;
- }
-
- // Check for mandatory single options.
- std::string mandatorySingleParameters[] = { "infile", "outdir", "outfile" };
- for (auto param : mandatorySingleParameters)
- {
- if (result.count(param) != 1)
- {
- std::cerr << "Parameter \'--" << param << "\' is required but missing." << std::endl;
- return false;
- }
- }
- }
- catch (const cxxopts::OptionException& e)
- {
- std::cerr << e.what() << std::endl << std::endl;
- return false;
- }
- catch (const std::exception& e)
- {
- std::cerr << "Fatal internal error: [" << e.what() << "]" << std::endl;
- return false;
- }
-
- if (!armnnQuantizer::ValidateProvidedFile(m_InputFileName))
- {
- return false;
- }
-
- if (!ValidateQuantizationScheme(m_QuantizationScheme))
- {
- return false;
- }
-
- if (m_CsvFileName != "")
- {
- if (!armnnQuantizer::ValidateProvidedFile(m_CsvFileName))
- {
- return false;
- }
- else
- {
- fs::path csvFilePath(m_CsvFileName);
- m_CsvFileDirectory = csvFilePath.parent_path().c_str();
- }
-
- // If CSV file is defined, create a QuantizationDataSet for specified CSV file.
- m_QuantizationDataSet = QuantizationDataSet(m_CsvFileName);
- }
-
- if (!armnnQuantizer::ValidateOutputDirectory(m_OutputDirectory))
- {
- return false;
- }
-
- std::string output(m_OutputDirectory);
- output.append(m_OutputFileName);
-
- if (fs::exists(output))
- {
- std::cerr << "Output file [" << output << "] already exists" << std::endl;
- return false;
- }
-
- return true;
-}
-
-} // namespace armnnQuantizer
\ No newline at end of file
diff --git a/src/armnnQuantizer/CommandLineProcessor.hpp b/src/armnnQuantizer/CommandLineProcessor.hpp
deleted file mode 100644
index 9de03fb..0000000
--- a/src/armnnQuantizer/CommandLineProcessor.hpp
+++ /dev/null
@@ -1,52 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <string>
-#include <iostream>
-#include <vector>
-#include "QuantizationDataSet.hpp"
-
-namespace armnnQuantizer
-{
-
-// parses the command line to extract
-// * the input file -f containing the serialized fp32 ArmNN input graph (must exist...and be a input graph file)
-// * the csv file -c <optional> detailing the paths for RAW input tensors to use for refinement
-// * the directory -d to place the output file into (must already exist and be writable)
-// * the name of the file -o the quantized ArmNN input graph will be written to (must not already exist)
-// * LATER: the min and max overrides to be applied to the inputs
-// specified as -i <int> (input id) -n <float> (minimum) -x <float> (maximum)
-// multiple sets of -i, -n, -x can appear on the command line but they must match
-// in number i.e. a -n and -x for each -i and the id of the input must correspond
-// to an input layer in the fp32 graph when it is loaded.
-class CommandLineProcessor
-{
-public:
- bool ProcessCommandLine(int argc, char* argv[]);
-
- std::string GetInputFileName() {return m_InputFileName;}
- std::string GetCsvFileName() {return m_CsvFileName;}
- std::string GetCsvFileDirectory() {return m_CsvFileDirectory;}
- std::string GetOutputDirectoryName() {return m_OutputDirectory;}
- std::string GetOutputFileName() {return m_OutputFileName;}
- std::string GetQuantizationScheme() {return m_QuantizationScheme;}
- QuantizationDataSet GetQuantizationDataSet() {return m_QuantizationDataSet;}
- bool HasPreservedDataType() {return m_PreserveDataType;}
- bool HasQuantizationData() {return !m_QuantizationDataSet.IsEmpty();}
-
-protected:
- std::string m_InputFileName;
- std::string m_CsvFileName;
- std::string m_CsvFileDirectory;
- std::string m_OutputDirectory;
- std::string m_OutputFileName;
- std::string m_QuantizationScheme;
- QuantizationDataSet m_QuantizationDataSet;
- bool m_PreserveDataType;
-};
-
-} // namespace armnnQuantizer
-
diff --git a/src/armnnQuantizer/QuantizationDataSet.cpp b/src/armnnQuantizer/QuantizationDataSet.cpp
deleted file mode 100644
index 99fc021..0000000
--- a/src/armnnQuantizer/QuantizationDataSet.cpp
+++ /dev/null
@@ -1,203 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "QuantizationDataSet.hpp"
-
-#include <fmt/format.h>
-
-#include <armnn/utility/StringUtils.hpp>
-#include <armnn/utility/IgnoreUnused.hpp>
-#include <Filesystem.hpp>
-
-namespace armnnQuantizer
-{
-
-QuantizationDataSet::QuantizationDataSet()
-{
-}
-
-QuantizationDataSet::QuantizationDataSet(const std::string csvFilePath):
- m_QuantizationInputs(),
- m_CsvFilePath(csvFilePath)
-{
- ParseCsvFile();
-}
-
-void AddInputData(unsigned int passId,
- armnn::LayerBindingId bindingId,
- const std::string& inputFilePath,
- std::map<unsigned int, QuantizationInput>& passIdToQuantizationInput)
-{
- auto iterator = passIdToQuantizationInput.find(passId);
- if (iterator == passIdToQuantizationInput.end())
- {
- QuantizationInput input(passId, bindingId, inputFilePath);
- passIdToQuantizationInput.emplace(passId, input);
- }
- else
- {
- auto existingQuantizationInput = iterator->second;
- existingQuantizationInput.AddEntry(bindingId, inputFilePath);
- }
-}
-
-QuantizationDataSet::~QuantizationDataSet()
-{
-}
-
-
-/// Visitor class implementation to gather the TensorInfo for LayerBindingID for creation of ConstTensor for Refine.
-
-void InputLayerStrategy::ExecuteStrategy(const armnn::IConnectableLayer* layer,
- const armnn::BaseDescriptor& descriptor,
- const std::vector<armnn::ConstTensor>& constants,
- const char* name,
- const armnn::LayerBindingId id)
-{
- armnn::IgnoreUnused(name, descriptor, constants);
-
- m_TensorInfos.emplace(id, layer->GetOutputSlot(0).GetTensorInfo());
-}
-
-
-
-
-armnn::TensorInfo InputLayerStrategy::GetTensorInfo(armnn::LayerBindingId layerBindingId)
-{
- auto iterator = m_TensorInfos.find(layerBindingId);
- if (iterator != m_TensorInfos.end())
- {
- return m_TensorInfos.at(layerBindingId);
- }
- else
- {
- throw armnn::Exception("Could not retrieve tensor info for binding ID " + std::to_string(layerBindingId));
- }
-}
-
-void InputLayerVisitor::VisitInputLayer(const armnn::IConnectableLayer* layer,
- armnn::LayerBindingId id,
- const char* name)
-{
- armnn::IgnoreUnused(name);
- m_TensorInfos.emplace(id, layer->GetOutputSlot(0).GetTensorInfo());
-}
-
-armnn::TensorInfo InputLayerVisitor::GetTensorInfo(armnn::LayerBindingId layerBindingId)
-{
- auto iterator = m_TensorInfos.find(layerBindingId);
- if (iterator != m_TensorInfos.end())
- {
- return m_TensorInfos.at(layerBindingId);
- }
- else
- {
- throw armnn::Exception("Could not retrieve tensor info for binding ID " + std::to_string(layerBindingId));
- }
-}
-
-
-unsigned int GetPassIdFromCsvRow(std::vector<std::string> tokens, unsigned int lineIndex)
-{
- unsigned int passId;
- try
- {
- passId = static_cast<unsigned int>(std::stoi(tokens[0]));
- }
- catch (const std::invalid_argument&)
- {
- throw armnn::ParseException(fmt::format("Pass ID [{}] is not correct format on CSV row {}",
- tokens[0], lineIndex));
- }
- return passId;
-}
-
-armnn::LayerBindingId GetBindingIdFromCsvRow(std::vector<std::string> tokens, unsigned int lineIndex)
-{
- armnn::LayerBindingId bindingId;
- try
- {
- bindingId = std::stoi(tokens[1]);
- }
- catch (const std::invalid_argument&)
- {
- throw armnn::ParseException(fmt::format("Binding ID [{}] is not correct format on CSV row {}",
- tokens[1], lineIndex));
- }
- return bindingId;
-}
-
-std::string GetFileNameFromCsvRow(std::vector<std::string> tokens, unsigned int lineIndex)
-{
- std::string fileName = armnn::stringUtils::StringTrim(tokens[2]);
-
- if (!fs::exists(fileName))
- {
- throw armnn::ParseException(fmt::format("File [{}] provided on CSV row {} does not exist.",
- fileName, lineIndex));
- }
-
- if (fileName.empty())
- {
- throw armnn::ParseException(fmt::format("Filename cannot be empty on CSV row {} ", lineIndex));
- }
- return fileName;
-}
-
-
-void QuantizationDataSet::ParseCsvFile()
-{
- std::map<unsigned int, QuantizationInput> passIdToQuantizationInput;
-
- if (m_CsvFilePath == "")
- {
- throw armnn::Exception("CSV file not specified.");
- }
-
- std::ifstream inf (m_CsvFilePath.c_str());
- std::string line;
- std::vector<std::string> tokens;
- unsigned int lineIndex = 0;
-
- if (!inf)
- {
- throw armnn::Exception(fmt::format("CSV file {} not found.", m_CsvFilePath));
- }
-
- while (getline(inf, line))
- {
- tokens = armnn::stringUtils::StringTokenizer(line, ",");
-
- if (tokens.size() != 3)
- {
- throw armnn::Exception(fmt::format("CSV file [{}] does not have correct number of entries" \
- "on line {}. Expected 3 entries but was {}.",
- m_CsvFilePath, lineIndex, tokens.size()));
-
- }
-
- unsigned int passId = GetPassIdFromCsvRow(tokens, lineIndex);
- armnn::LayerBindingId bindingId = GetBindingIdFromCsvRow(tokens, lineIndex);
- std::string rawFileName = GetFileNameFromCsvRow(tokens, lineIndex);
-
- AddInputData(passId, bindingId, rawFileName, passIdToQuantizationInput);
-
- ++lineIndex;
- }
-
- if (passIdToQuantizationInput.empty())
- {
- throw armnn::Exception("Could not parse CSV file.");
- }
-
- // Once all entries in CSV file are parsed successfully and QuantizationInput map is populated, populate
- // QuantizationInputs iterator for easier access and clear the map
- for (auto itr = passIdToQuantizationInput.begin(); itr != passIdToQuantizationInput.end(); ++itr)
- {
- m_QuantizationInputs.emplace_back(itr->second);
- }
-}
-
-}
diff --git a/src/armnnQuantizer/QuantizationDataSet.hpp b/src/armnnQuantizer/QuantizationDataSet.hpp
deleted file mode 100644
index 47b893a..0000000
--- a/src/armnnQuantizer/QuantizationDataSet.hpp
+++ /dev/null
@@ -1,71 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include <map>
-#include "QuantizationInput.hpp"
-#include "armnn/LayerVisitorBase.hpp"
-#include "armnn/Tensor.hpp"
-
-namespace armnnQuantizer
-{
-
-/// QuantizationDataSet is a structure which is created after parsing a quantization CSV file.
-/// It contains records of filenames which contain refinement data per pass ID for binding ID.
-class QuantizationDataSet
-{
- using QuantizationInputs = std::vector<armnnQuantizer::QuantizationInput>;
-public:
-
- using iterator = QuantizationInputs::iterator;
- using const_iterator = QuantizationInputs::const_iterator;
-
- QuantizationDataSet();
- QuantizationDataSet(std::string csvFilePath);
- ~QuantizationDataSet();
- bool IsEmpty() const {return m_QuantizationInputs.empty();}
-
- iterator begin() { return m_QuantizationInputs.begin(); }
- iterator end() { return m_QuantizationInputs.end(); }
- const_iterator begin() const { return m_QuantizationInputs.begin(); }
- const_iterator end() const { return m_QuantizationInputs.end(); }
- const_iterator cbegin() const { return m_QuantizationInputs.cbegin(); }
- const_iterator cend() const { return m_QuantizationInputs.cend(); }
-
-private:
- void ParseCsvFile();
-
- QuantizationInputs m_QuantizationInputs;
- std::string m_CsvFilePath;
-};
-
-/// Visitor class implementation to gather the TensorInfo for LayerBindingID for creation of ConstTensor for Refine.
-class InputLayerStrategy : public armnn::IStrategy
-{
-public:
- virtual void ExecuteStrategy(const armnn::IConnectableLayer* layer,
- const armnn::BaseDescriptor& descriptor,
- const std::vector<armnn::ConstTensor>& constants,
- const char* name,
- const armnn::LayerBindingId id = 0) override;
-
- armnn::TensorInfo GetTensorInfo(armnn::LayerBindingId);
-private:
- std::map<armnn::LayerBindingId, armnn::TensorInfo> m_TensorInfos;
-};
-
-
-/// Visitor class implementation to gather the TensorInfo for LayerBindingID for creation of ConstTensor for Refine.
-class InputLayerVisitor : public armnn::LayerVisitorBase<armnn::VisitorNoThrowPolicy>
-{
-public:
- void VisitInputLayer(const armnn::IConnectableLayer *layer, armnn::LayerBindingId id, const char* name);
- armnn::TensorInfo GetTensorInfo(armnn::LayerBindingId);
-private:
- std::map<armnn::LayerBindingId, armnn::TensorInfo> m_TensorInfos;
-};
-
-} // namespace armnnQuantizer
\ No newline at end of file
diff --git a/src/armnnQuantizer/QuantizationInput.cpp b/src/armnnQuantizer/QuantizationInput.cpp
deleted file mode 100644
index bb7aff1..0000000
--- a/src/armnnQuantizer/QuantizationInput.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "QuantizationInput.hpp"
-
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "armnn/Exceptions.hpp"
-
-namespace armnnQuantizer
-{
-
-QuantizationInput::QuantizationInput(const unsigned int passId,
- const armnn::LayerBindingId bindingId,
- const std::string fileName):
- m_PassId(passId)
-{
- m_LayerBindingIdToFileName.emplace(bindingId, fileName);
-}
-
-QuantizationInput::QuantizationInput(const QuantizationInput& other)
-{
- m_PassId = other.GetPassId();
- m_LayerBindingIdToFileName.clear();
- for (armnn::LayerBindingId bindingId : other.GetLayerBindingIds())
- {
- std::string filename = other.GetFileName(bindingId);
- AddEntry(bindingId, filename);
- }
-}
-
-void QuantizationInput::AddEntry(const armnn::LayerBindingId bindingId, const std::string fileName)
-{
- m_LayerBindingIdToFileName.emplace(bindingId, fileName);
-}
-
-std::vector<float> QuantizationInput::GetDataForEntry(const armnn::LayerBindingId bindingId) const
-{
- if (m_LayerBindingIdToFileName.at(bindingId).empty())
- {
- throw armnn::Exception("Layer binding ID not found");
- }
-
- std::string fileName = m_LayerBindingIdToFileName.at(bindingId);
- std::ifstream in(fileName.c_str(), std::ifstream::binary);
- if (!in.is_open())
- {
- throw armnn::Exception("Failed to open input tensor file " + fileName);
- }
-
- std::string line;
- std::vector<float> values;
- char* pEnd;
-
- while (std::getline(in, line, ' '))
- {
- values.emplace_back(std::strtof(line.c_str(), &pEnd));
- }
- return values;
-}
-
-std::vector<armnn::LayerBindingId> QuantizationInput::GetLayerBindingIds() const
-{
- std::vector<armnn::LayerBindingId> layerBindingIDs;
-
- for (auto iterator = m_LayerBindingIdToFileName.begin(); iterator != m_LayerBindingIdToFileName.end(); ++iterator)
- {
- layerBindingIDs.emplace_back(iterator->first);
- }
- return layerBindingIDs;
-}
-
-unsigned long QuantizationInput::GetNumberOfInputs() const
-{
- return m_LayerBindingIdToFileName.size();
-}
-
-unsigned int QuantizationInput::GetPassId() const
-{
- return m_PassId;
-}
-
-std::string QuantizationInput::GetFileName(const armnn::LayerBindingId bindingId) const
-{
- auto iterator = m_LayerBindingIdToFileName.find(bindingId);
- if (iterator != m_LayerBindingIdToFileName.end())
- {
- return m_LayerBindingIdToFileName.at(bindingId);
- }
- else
- {
- throw armnn::Exception("Could not retrieve filename for binding ID " + std::to_string(bindingId));
- }
-}
-
-QuantizationInput::~QuantizationInput() noexcept
-{
-}
-
-}
\ No newline at end of file
diff --git a/src/armnnQuantizer/QuantizationInput.hpp b/src/armnnQuantizer/QuantizationInput.hpp
deleted file mode 100644
index 1bfe84b..0000000
--- a/src/armnnQuantizer/QuantizationInput.hpp
+++ /dev/null
@@ -1,54 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include <map>
-#include <armnn/Types.hpp>
-#include <armnnQuantizer/INetworkQuantizer.hpp>
-
-namespace armnnQuantizer
-{
-
-/// QuantizationInput for specific pass ID, can list a corresponding raw data file for each LayerBindingId.
-class QuantizationInput
-{
-public:
-
- /// Constructor for QuantizationInput
- QuantizationInput(const unsigned int passId,
- const armnn::LayerBindingId bindingId,
- const std::string fileName);
-
- QuantizationInput(const QuantizationInput& other);
-
- // Add binding ID to image tensor filepath entry
- void AddEntry(const armnn::LayerBindingId bindingId, const std::string fileName);
-
- // Retrieve tensor data for entry with provided binding ID
- std::vector<float> GetDataForEntry(const armnn::LayerBindingId bindingId) const;
-
- /// Retrieve Layer Binding IDs for this QuantizationInput.
- std::vector<armnn::LayerBindingId> GetLayerBindingIds() const;
-
- /// Get number of inputs for this QuantizationInput.
- unsigned long GetNumberOfInputs() const;
-
- /// Retrieve Pass ID for this QuantizationInput.
- unsigned int GetPassId() const;
-
- /// Retrieve filename path for specified Layer Binding ID.
- std::string GetFileName(const armnn::LayerBindingId bindingId) const;
-
- /// Destructor
- ~QuantizationInput() noexcept;
-
-private:
- unsigned int m_PassId;
- std::map<armnn::LayerBindingId, std::string> m_LayerBindingIdToFileName;
-
-};
-
-}
\ No newline at end of file
diff --git a/src/armnnQuantizer/README.md b/src/armnnQuantizer/README.md
deleted file mode 100644
index 6100372..0000000
--- a/src/armnnQuantizer/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# The ArmnnQuantizer
-
-The `ArmnnQuantizer` is a program for loading a 32-bit float network into ArmNN and converting it into a quantized asymmetric 8-bit or quantized symmetric 16-bit network.
-It supports static quantization by default, dynamic quantization is enabled if CSV file of raw input tensors is provided. Run the program with no arguments to see command-line help.
-
-**NOTE**: The ArmnnQuantizer is deprecated in Arm NN 21.02 and will be removed in 21.05.
-
-|Cmd:|||
-| ---|---|---|
-| -h | --help | Display help messages |
-| -f | --infile | Input file containing float 32 ArmNN Input Graph |
-| -s | --scheme | Quantization scheme, "QAsymm8" or "QSymm16". Default value: QAsymm8 |
-| -c | --csvfile | CSV file containing paths for raw input tensors for dynamic quantization. If unset, static quantization is used |
-| -p | --preserve-data-type | Preserve the input and output data types. If unset, input and output data types are not preserved |
-| -d | --outdir | Directory that output file will be written to |
-| -o | --outfile | ArmNN output file name |
-
-Example usage: <br>
-<code>./ArmnnQuantizer -f /path/to/armnn/input/graph/ -s "QSymm16" -c /path/to/csv/file -p 1 -d /path/to/output -o outputFileName</code>
diff --git a/src/armnnQuantizer/test/QuantizationDataSetTests.cpp b/src/armnnQuantizer/test/QuantizationDataSetTests.cpp
deleted file mode 100644
index 9e71f68..0000000
--- a/src/armnnQuantizer/test/QuantizationDataSetTests.cpp
+++ /dev/null
@@ -1,140 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <boost/test/unit_test.hpp>
-
-#include "../QuantizationDataSet.hpp"
-
-#include <armnn/Optional.hpp>
-#include <Filesystem.hpp>
-#include <iostream>
-#include <fstream>
-#include <vector>
-#include <map>
-
-
-using namespace armnnQuantizer;
-
-struct CsvTestHelper {
-
- CsvTestHelper()
- {
- BOOST_TEST_MESSAGE("setup fixture");
- }
-
- ~CsvTestHelper()
- {
- BOOST_TEST_MESSAGE("teardown fixture");
- TearDown();
- }
-
- std::string CreateTempCsvFile(std::map<int, std::vector<float>> csvData)
- {
- fs::path fileDir = fs::temp_directory_path();
- fs::path p = armnnUtils::Filesystem::NamedTempFile("Armnn-QuantizationCreateTempCsvFileTest-TempFile.csv");
-
- fs::path tensorInput1{fileDir / "input_0_0.raw"};
- fs::path tensorInput2{fileDir / "input_1_0.raw"};
- fs::path tensorInput3{fileDir / "input_2_0.raw"};
-
- try
- {
- std::ofstream ofs{p};
-
- std::ofstream ofs1{tensorInput1};
- std::ofstream ofs2{tensorInput2};
- std::ofstream ofs3{tensorInput3};
-
-
- for(auto entry : csvData.at(0))
- {
- ofs1 << entry << " ";
- }
- for(auto entry : csvData.at(1))
- {
- ofs2 << entry << " ";
- }
- for(auto entry : csvData.at(2))
- {
- ofs3 << entry << " ";
- }
-
- ofs << "0, 0, " << tensorInput1.c_str() << std::endl;
- ofs << "2, 0, " << tensorInput3.c_str() << std::endl;
- ofs << "1, 0, " << tensorInput2.c_str() << std::endl;
-
- ofs.close();
- ofs1.close();
- ofs2.close();
- ofs3.close();
- }
- catch (std::exception &e)
- {
- std::cerr << "Unable to write to file at location [" << p.c_str() << "] : " << e.what() << std::endl;
- BOOST_TEST(false);
- }
-
- m_CsvFile = p;
- return p.string();
- }
-
- void TearDown()
- {
- RemoveCsvFile();
- }
-
- void RemoveCsvFile()
- {
- if (m_CsvFile)
- {
- try
- {
- fs::remove(m_CsvFile.value());
- }
- catch (std::exception &e)
- {
- std::cerr << "Unable to delete file [" << m_CsvFile.value() << "] : " << e.what() << std::endl;
- BOOST_TEST(false);
- }
- }
- }
-
- armnn::Optional<fs::path> m_CsvFile;
-};
-
-
-BOOST_AUTO_TEST_SUITE(QuantizationDataSetTests)
-
-BOOST_FIXTURE_TEST_CASE(CheckDataSet, CsvTestHelper)
-{
-
- std::map<int, std::vector<float>> csvData;
- csvData.insert(std::pair<int, std::vector<float>>(0, { 0.111111f, 0.222222f, 0.333333f }));
- csvData.insert(std::pair<int, std::vector<float>>(1, { 0.444444f, 0.555555f, 0.666666f }));
- csvData.insert(std::pair<int, std::vector<float>>(2, { 0.777777f, 0.888888f, 0.999999f }));
-
- std::string myCsvFile = CsvTestHelper::CreateTempCsvFile(csvData);
- QuantizationDataSet dataSet(myCsvFile);
- BOOST_TEST(!dataSet.IsEmpty());
-
- int csvRow = 0;
- for(armnnQuantizer::QuantizationInput input : dataSet)
- {
- BOOST_TEST(input.GetPassId() == csvRow);
-
- BOOST_TEST(input.GetLayerBindingIds().size() == 1);
- BOOST_TEST(input.GetLayerBindingIds()[0] == 0);
- BOOST_TEST(input.GetDataForEntry(0).size() == 3);
-
- // Check that QuantizationInput data for binding ID 0 corresponds to float values
- // used for populating the CSV file using by QuantizationDataSet
- BOOST_TEST(input.GetDataForEntry(0).at(0) == csvData.at(csvRow).at(0));
- BOOST_TEST(input.GetDataForEntry(0).at(1) == csvData.at(csvRow).at(1));
- BOOST_TEST(input.GetDataForEntry(0).at(2) == csvData.at(csvRow).at(2));
- ++csvRow;
- }
-}
-
-BOOST_AUTO_TEST_SUITE_END();
\ No newline at end of file
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 308b885..0b26384 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -243,7 +243,7 @@
AccuracyTool(ModelAccuracyTool)
endif()
-if(BUILD_ARMNN_QUANTIZER)
+if(BUILD_ACCURACY_TOOL)
macro(ImageTensorExecutor executorName)
target_link_libraries(${executorName} ${CMAKE_THREAD_LIBS_INIT})
addDllCopyCommands(${executorName})
diff --git a/tests/ImageTensorGenerator/README.md b/tests/ImageTensorGenerator/README.md
index a7e149c..b6bc312 100644
--- a/tests/ImageTensorGenerator/README.md
+++ b/tests/ImageTensorGenerator/README.md
@@ -4,7 +4,7 @@
Build option:
To build ModelAccuracyTool, pass the following options to Cmake:
-* -DBUILD_ARMNN_QUANTIZER=1
+* -DBUILD_ACCURACY_TOOL=1
|Cmd:|||
| ---|---|---|