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//
// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/StrategyBase.hpp>
#include <armnn/Descriptors.hpp>
#include <optimizations/FoldPadIntoLayer2d.hpp>
namespace armnn
{
namespace
{
/// Checks if a Layer has a DataLayout that is either NCHW or NCDHW.
class CheckForNCHW : public StrategyBase<NoThrowStrategy>
{
public:
CheckForNCHW()
{}
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::IgnoreUnused(layer, constants, id, name);
switch (layer->GetType())
{
case armnn::LayerType::BatchMatMul:
{
auto desc = static_cast<const armnn::BatchMatMulDescriptor &>(descriptor);
m_Result = desc.m_DataLayoutX == DataLayout::NCHW || desc.m_DataLayoutY == DataLayout::NCHW;
break;
}
case armnn::LayerType::BatchNormalization:
{
CheckDescForNCHW(static_cast<const armnn::BatchNormalizationDescriptor&>(descriptor));
break;
}
case armnn::LayerType::BatchToSpaceNd:
{
CheckDescForNCHW(static_cast<const armnn::BatchToSpaceNdDescriptor&>(descriptor));
break;
}
case armnn::LayerType::Convolution2d:
{
CheckDescForNCHW(static_cast<const armnn::Convolution2dDescriptor&>(descriptor));
break;
}
case armnn::LayerType::Convolution3d:
{
CheckDescForNCHW(static_cast<const armnn::Convolution3dDescriptor&>(descriptor));
break;
}
case armnn::LayerType::DepthwiseConvolution2d:
{
CheckDescForNCHW(static_cast<const armnn::DepthwiseConvolution2dDescriptor&>(descriptor));
break;
}
case armnn::LayerType::InstanceNormalization:
{
CheckDescForNCHW(static_cast<const armnn::InstanceNormalizationDescriptor&>(descriptor));
break;
}
case armnn::LayerType::L2Normalization:
{
CheckDescForNCHW(static_cast<const armnn::L2NormalizationDescriptor&>(descriptor));
break;
}
case armnn::LayerType::Normalization:
{
CheckDescForNCHW(static_cast<const armnn::NormalizationDescriptor&>(descriptor));
break;
}
case armnn::LayerType::Pooling2d:
{
CheckDescForNCHW(static_cast<const armnn::Pooling2dDescriptor&>(descriptor));
break;
}
case armnn::LayerType::Pooling3d:
{
CheckDescForNCHW(static_cast<const armnn::Pooling3dDescriptor&>(descriptor));
break;
}
case armnn::LayerType::SpaceToBatchNd:
{
CheckDescForNCHW(static_cast<const armnn::SpaceToBatchNdDescriptor&>(descriptor));
break;
}
case armnn::LayerType::SpaceToDepth:
{
CheckDescForNCHW(static_cast<const armnn::SpaceToDepthDescriptor&>(descriptor));
break;
}
case armnn::LayerType::StridedSlice:
{
CheckDescForNCHW(static_cast<const armnn::StridedSliceDescriptor&>(descriptor));
break;
}
default:
{
m_Result = false;
}
}
}
/// Returns true if the Layer had a DataLayout and it was NCHW or NCDHW.
/// Returns false if the Layer either doesn't have a DataLayout or if it
/// had a DataLayout that was neither NCHW nor NCDHW.
bool Result()
{
return m_Result;
}
private:
template<typename Descriptor>
void CheckDescForNCHW(const Descriptor& descriptor)
{
m_Result = (descriptor.m_DataLayout == DataLayout::NCHW) || (descriptor.m_DataLayout == DataLayout::NCDHW);
}
bool m_Result = false;
};
//
// this helper only works if all layers where the inputs connect to are not selected
//
SubgraphView::IInputSlots CreateIInputsFrom(const std::vector<armnn::IConnectableLayer*>& layers)
{
SubgraphView::IInputSlots result;
for (auto&& layer : layers)
{
for (unsigned int i = 0 ; i < layer->GetNumInputSlots(); ++i)
{
result.push_back(&(layer->GetInputSlot(i)));
}
}
return result;
}
//
// this helper only works if all layers where the outputs connect to are not selected
//
SubgraphView::IOutputSlots CreateIOutputsFrom(const std::vector<armnn::IConnectableLayer*>& layers)
{
SubgraphView::IOutputSlots result;
for (auto &&layer: layers)
{
for (unsigned int i = 0; i < layer->GetNumOutputSlots(); ++i)
{
result.push_back(&(layer->GetOutputSlot(i)));
}
}
return result;
}
}
inline bool IsNCHW(armnn::Layer& layer)
{
CheckForNCHW check;
layer.ExecuteStrategy(check);
return check.Result();
}
inline void ReportUntouchedLayers(OptimizationViews& optimizationViews, std::map<LayerGuid, Layer*> untouched)
{
std::vector<Layer*> untouchedVector;
for (const auto& pair : untouched)
{
Layer* layer = pair.second;
SubgraphView subgraphView({layer},
CreateIInputsFrom({layer}),
CreateIOutputsFrom({layer}));
optimizationViews.AddUntouchedSubgraph(std::move(subgraphView));
}
}
template<typename LayerType>
LayerType* FoldPadLayer(OptimizationViews& optimizationViews,
LayerType* baseLayer,
LayerType* replacementLayer,
PadLayer* padLayer)
{
SubgraphView substitutionSubgraph({padLayer, baseLayer},
CreateIInputsFrom({padLayer}),
CreateIOutputsFrom({baseLayer}));
SubgraphView replacementSubgraph(replacementLayer);
optimizationViews.AddSubstitution({substitutionSubgraph, replacementSubgraph});
return replacementLayer;
}
inline void RemoveReshapeLayer(ReshapeLayer* baseLayer,
std::map<LayerGuid, Layer*>& untouched,
OptimizationViews& optimizationViews)
{
if (baseLayer == nullptr)
{
return;
}
ReshapeDescriptor reshapeDescriptor = baseLayer->GetParameters();
Layer& parentLayer = baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer();
// Cannot currently remove the Reshape if it's connected to an Input, Constant or Splitter
if (parentLayer.GetType() == LayerType::Input || parentLayer.GetType() == LayerType::Constant)
{
return;
}
// Cannot currently remove the Reshape if it's connected to an OutputSlot or Concat
for (unsigned int i = 0; i < baseLayer->GetOutputSlot(0).GetNumConnections(); ++i)
{
Layer& nextLayer = baseLayer->GetOutputSlot(0).GetConnection(i)->GetOwningLayer();
if (nextLayer.GetType() == LayerType::Output)
{
return;
}
}
auto it = untouched.find(baseLayer->GetGuid());
if (it == untouched.end())
{
// Already removed from map
return;
}
untouched.erase(it);
// Override the InputSlot TensorInfos for all the layers connected to the Reshape's OutputSlot
for (unsigned int i = 0; i < baseLayer->GetOutputSlot(0).GetNumConnections(); ++i)
{
Layer& nextLayer = baseLayer->GetOutputSlot(0).GetConnection(i)->GetOwningLayer();
auto inputIndex = baseLayer->GetOutputSlot(0).GetConnection(i)->GetSlotIndex();
TensorInfo reshapeInfo(baseLayer->GetOutputSlot(0).GetTensorInfo());
reshapeInfo.SetShape(reshapeDescriptor.m_TargetShape);
nextLayer.GetInputSlot(inputIndex).SetTensorInfo(reshapeInfo);
}
optimizationViews.AddDeletedSubgraph(baseLayer);
}
template<typename LayerType>
LayerType* FoldPadIntoAveragePool2d(OptimizationViews& optimizationViews,
Pooling2dLayer* baseLayer,
Pooling2dDescriptor& poolDescriptor,
PadLayer* padLayer)
{
IConnectableLayer* replacement =
optimizationViews.GetINetwork()->AddPooling2dLayer(poolDescriptor, "folded-pad-into-pool2d");
LayerType* replacementLayer = PolymorphicDowncast<LayerType*>(replacement);
FoldPadLayer(optimizationViews,
baseLayer,
replacementLayer,
padLayer);
return replacementLayer;
}
} // namespace armnn
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