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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "Optimization.hpp"
#include <armnn/utility/PolymorphicDowncast.hpp>
namespace armnn
{
namespace optimizations
{
namespace
{
template <typename Layer2dT>
Layer2dT* FoldPadIntoLayer2dImpl(Graph& graph, InputSlot& connection)
{
Layer& base = connection.GetConnectedOutputSlot()->GetOwningLayer();
Layer& child = connection.GetOwningLayer();
ARMNN_ASSERT(base.GetType() == LayerType::Pad);
ARMNN_ASSERT(child.GetType() == LayerEnumOf<Layer2dT>());
PadLayer* padLayer = PolymorphicDowncast<PadLayer*>(&base);
Layer2dT* layer2d = PolymorphicDowncast<Layer2dT*>(&child);
OutputSlot* parentOut = base.GetInputSlot(0).GetConnectedOutputSlot();
const std::string name = std::string("folded-") + base.GetName() + std::string("-into-") + child.GetName();
auto descriptor = layer2d->GetParameters();
auto padList = padLayer->GetParameters().m_PadList;
armnn::DataLayout dataLayout = descriptor.m_DataLayout;
// In Convolution2dDescriptor/Pooling2dDescriptor, padLeft and padRight are defined as paddings
// on width dimension whereas padTop and padBottom - paddings on height dimension, so setting these
// according to data layout
if(dataLayout == armnn::DataLayout::NHWC)
{
descriptor.m_PadLeft = padList[2].first;
descriptor.m_PadRight = padList[2].second;
descriptor.m_PadTop = padList[1].first;
descriptor.m_PadBottom = padList[1].second;
}
else
{
descriptor.m_PadLeft = padList[3].first;
descriptor.m_PadRight = padList[3].second;
descriptor.m_PadTop = padList[2].first;
descriptor.m_PadBottom = padList[2].second;
}
const auto newLayer2d = graph.InsertNewLayer<Layer2dT>(base.GetInputSlot(0), descriptor, name.c_str());
// Reconnects with original parent.
newLayer2d->GetOutputSlot().MoveAllConnections(*parentOut);
// Parent is now the new layer.
parentOut = &newLayer2d->GetOutputSlot();
// Moves connections in child output to parent layer.
// Child layer will be removed as it's left unconnected.
// Base layer will be removed if left unconnected.
child.GetOutputSlot().MoveAllConnections(*parentOut);
return newLayer2d;
}
} // namespace
class FoldPadIntoConvolution2dImpl
{
public:
void Run(Graph& graph, InputSlot& connection) const
{
const auto conv2dLayer = PolymorphicDowncast<Convolution2dLayer*>(&connection.GetOwningLayer());
const auto newConv2dLayer = FoldPadIntoLayer2dImpl<Convolution2dLayer>(graph, connection);
// Copy weights and bias to the new convolution layer
ARMNN_ASSERT_MSG(conv2dLayer->m_Weight != nullptr,
"FoldPadIntoConvolution2d: Weights data should not be null.");
newConv2dLayer->m_Weight = std::move(conv2dLayer->m_Weight);
if (conv2dLayer->GetParameters().m_BiasEnabled)
{
ARMNN_ASSERT_MSG(conv2dLayer->m_Bias != nullptr,
"FoldPadIntoConvolution2d: Bias data should not be null if bias is enabled.");
newConv2dLayer->m_Bias = std::move(conv2dLayer->m_Bias);
}
}
protected:
FoldPadIntoConvolution2dImpl() = default;
~FoldPadIntoConvolution2dImpl() = default;
};
class FoldPadIntoPooling2dImpl
{
public:
void Run(Graph& graph, InputSlot& connection) const
{
FoldPadIntoLayer2dImpl<Pooling2dLayer>(graph, connection);
}
protected:
FoldPadIntoPooling2dImpl() = default;
~FoldPadIntoPooling2dImpl() = default;
};
using FoldPadIntoConvolution2d = OptimizeForConnection<PadLayer, Convolution2dLayer, FoldPadIntoConvolution2dImpl>;
using FoldPadIntoPooling2d = OptimizeForConnection<PadLayer, Pooling2dLayer, FoldPadIntoPooling2dImpl>;
} // namespace optimizations
} // namespace armnn
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