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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Network.hpp"
#include "QuantizerVisitor.hpp"
#include "StaticRangeVisitor.hpp"
#include "NetworkQuantizerUtils.hpp"
namespace armnn
{
QuantizerVisitor::QuantizerVisitor(const StaticRangeVisitor *staticRangeVisitor)
: m_StaticRangeVisitor(staticRangeVisitor)
, m_QuantizedNetwork(INetwork::Create())
{
BOOST_ASSERT(m_StaticRangeVisitor);
}
void QuantizerVisitor::SetQuantizedInputConnections(const IConnectableLayer *srcLayer,
IConnectableLayer *quantizedLayer)
{
for (unsigned int i=0; i < srcLayer->GetNumInputSlots(); i++)
{
const IInputSlot& srcInputSlot = srcLayer->GetInputSlot(i);
const InputSlot* inputSlot = boost::polymorphic_downcast<const InputSlot*>(&srcInputSlot);
const OutputSlot* outputSlot = inputSlot->GetConnectedOutputSlot();
unsigned int slotIdx = outputSlot->CalculateIndexOnOwner();
Layer& layerToFind = outputSlot->GetOwningLayer();
auto found = m_OriginalToQuantizedGuidMap.find(layerToFind.GetGuid());
if (found != m_OriginalToQuantizedGuidMap.end())
{
// 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);
// Fetch the min/max ranges that were computed earlier
auto range = m_StaticRangeVisitor->GetRange(layerToFind.GetGuid(), i);
auto qParams = ComputeQAsymmParams(8, range.first, range.second);
// Set the quantization params
TensorInfo info(newOutputSlot.GetTensorInfo());
info.SetDataType(DataType::QuantisedAsymm8);
info.SetQuantizationOffset(qParams.first);
info.SetQuantizationScale(qParams.second);
newOutputSlot.SetTensorInfo(info);
}
else
{
// Error in graph traversal order
BOOST_ASSERT_MSG(false, "Error in graph traversal");
}
}
}
void QuantizerVisitor::RecordLayer(const IConnectableLayer* srcLayer, IConnectableLayer* quantizedLayer)
{
m_OriginalToQuantizedGuidMap[srcLayer->GetGuid()] = quantizedLayer->GetGuid();
m_QuantizedGuidToLayerMap[quantizedLayer->GetGuid()] = quantizedLayer;
}
void QuantizerVisitor::VisitAdditionLayer(const IConnectableLayer *layer, const char *name)
{
IConnectableLayer* newLayer = m_QuantizedNetwork->AddAdditionLayer(name);
RecordLayer(layer, newLayer);
SetQuantizedInputConnections(layer, newLayer);
}
void QuantizerVisitor::VisitActivationLayer(const IConnectableLayer *layer,
const ActivationDescriptor& activationDescriptor,
const char *name)
{
IConnectableLayer* newLayer = m_QuantizedNetwork->AddActivationLayer(activationDescriptor, name);
RecordLayer(layer, newLayer);
SetQuantizedInputConnections(layer, newLayer);
}
void QuantizerVisitor::VisitInputLayer(const IConnectableLayer *layer, LayerBindingId id, const char *name)
{
IConnectableLayer* newLayer = m_QuantizedNetwork->AddInputLayer(id, name);
RecordLayer(layer, newLayer);
}
void QuantizerVisitor::VisitOutputLayer(const IConnectableLayer *layer, LayerBindingId id, const char *name)
{
IConnectableLayer* newLayer = m_QuantizedNetwork->AddOutputLayer(id, name);
RecordLayer(layer, newLayer);
SetQuantizedInputConnections(layer, newLayer);
}
void QuantizerVisitor::VisitBatchNormalizationLayer(const IConnectableLayer *layer,
const BatchNormalizationDescriptor& desc,
const ConstTensor& mean,
const ConstTensor& variance,
const ConstTensor& beta,
const ConstTensor& gamma,
const char *name)
{
std::vector<uint8_t> meanBacking;
ConstTensor qMean = CreateQuantizedConst(mean, meanBacking);
std::vector<uint8_t> varianceBacking;
ConstTensor qVariance = CreateQuantizedConst(variance, varianceBacking);
std::vector<uint8_t> betaBacking;
ConstTensor qBeta = CreateQuantizedConst(beta, betaBacking);
std::vector<uint8_t> gammaBacking;
ConstTensor qGamma = CreateQuantizedConst(variance, gammaBacking);
IConnectableLayer* newLayer = m_QuantizedNetwork->AddBatchNormalizationLayer(desc,
qMean,
qVariance,
qBeta,
qGamma,
name);
RecordLayer(layer, newLayer);
SetQuantizedInputConnections(layer, newLayer);
}
} //namespace armnn
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