diff options
Diffstat (limited to 'src')
-rw-r--r-- | src/armnn/Network.cpp | 1 | ||||
-rw-r--r-- | src/armnn/optimizations/All.hpp | 2 | ||||
-rw-r--r-- | src/armnn/optimizations/FoldPadIntoConvolution2d.hpp | 93 | ||||
-rw-r--r-- | src/armnn/optimizations/FoldPadIntoLayer2d.hpp | 117 | ||||
-rw-r--r-- | src/armnn/test/OptimizerTests.cpp | 85 |
5 files changed, 95 insertions, 203 deletions
diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp index 8dad3bba56..9373a6ac15 100644 --- a/src/armnn/Network.cpp +++ b/src/armnn/Network.cpp @@ -1544,7 +1544,6 @@ IOptimizedNetworkPtr Optimize(const INetwork& inNetwork, TransposeAsReshape(), OptimizeConsecutiveReshapes(), FoldPadIntoConvolution2d(), - FoldPadIntoPooling2d(), PermuteAndBatchToSpaceAsDepthToSpace(), TransposeAndBatchToSpaceAsDepthToSpace(), FuseBatchNormIntoConvolution2DFloat32(), diff --git a/src/armnn/optimizations/All.hpp b/src/armnn/optimizations/All.hpp index 5decc7c969..d042616ba4 100644 --- a/src/armnn/optimizations/All.hpp +++ b/src/armnn/optimizations/All.hpp @@ -9,7 +9,7 @@ #include "ConvertConstants.hpp" #include "ConvertFp32NetworkToBf16.hpp" #include "ConvertFp32NetworkToFp16.hpp" -#include "FoldPadIntoLayer2d.hpp" +#include "FoldPadIntoConvolution2d.hpp" #include "FuseBatchNorm.hpp" #include "MovePermuteUp.hpp" #include "MoveTransposeUp.hpp" diff --git a/src/armnn/optimizations/FoldPadIntoConvolution2d.hpp b/src/armnn/optimizations/FoldPadIntoConvolution2d.hpp new file mode 100644 index 0000000000..5def6dfdd2 --- /dev/null +++ b/src/armnn/optimizations/FoldPadIntoConvolution2d.hpp @@ -0,0 +1,93 @@ +// +// 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 +{ + +class FoldPadIntoConvolution2dImpl +{ +public: + + void Run(Graph& graph, InputSlot& connection) const + { + Layer& base = connection.GetConnectedOutputSlot()->GetOwningLayer(); + Layer& child = connection.GetOwningLayer(); + + ARMNN_ASSERT(base.GetType() == LayerType::Pad); + ARMNN_ASSERT(child.GetType() == LayerType::Convolution2d); + + PadLayer* padLayer = PolymorphicDowncast<PadLayer*>(&base); + Convolution2dLayer* convolution2dLayer = PolymorphicDowncast<Convolution2dLayer*>(&child); + + OutputSlot* parentOut = base.GetInputSlot(0).GetConnectedOutputSlot(); + + const std::string name = std::string("folded-") + base.GetName() + std::string("-into-") + child.GetName(); + Convolution2dDescriptor descriptor = convolution2dLayer->GetParameters(); + + auto padList = padLayer->GetParameters().m_PadList; + + armnn::DataLayout dataLayout = descriptor.m_DataLayout; + + // In Convolution2dDescriptor, 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; + } + + auto& newConv2dLayer = *graph.InsertNewLayer<Convolution2dLayer>(base.GetInputSlot(0), + descriptor, + name.c_str()); + + // Copy weights and bias to the new convolution layer + ARMNN_ASSERT_MSG(convolution2dLayer->m_Weight != nullptr, + "FoldPadIntoConvolution2d: Weights data should not be null."); + newConv2dLayer.m_Weight = std::move(convolution2dLayer->m_Weight); + if (descriptor.m_BiasEnabled) + { + ARMNN_ASSERT_MSG(convolution2dLayer->m_Bias != nullptr, + "FoldPadIntoConvolution2d: Bias data should not be null if bias is enabled."); + newConv2dLayer.m_Bias = std::move(convolution2dLayer->m_Bias); + } + + // Reconnects with original parent. + newConv2dLayer.GetOutputSlot().MoveAllConnections(*parentOut); + // Parent is now the new convolution2d layer. + parentOut = &newConv2dLayer.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); + } +protected: + FoldPadIntoConvolution2dImpl() = default; + ~FoldPadIntoConvolution2dImpl() = default; +}; + +using FoldPadIntoConvolution2d = OptimizeForConnection<PadLayer, Convolution2dLayer, FoldPadIntoConvolution2dImpl>; + +} // namespace optimizations +} // namespace armnn + + diff --git a/src/armnn/optimizations/FoldPadIntoLayer2d.hpp b/src/armnn/optimizations/FoldPadIntoLayer2d.hpp deleted file mode 100644 index cadc2f3017..0000000000 --- a/src/armnn/optimizations/FoldPadIntoLayer2d.hpp +++ /dev/null @@ -1,117 +0,0 @@ -// -// 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 - - diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp index f0d132a561..fa860abb64 100644 --- a/src/armnn/test/OptimizerTests.cpp +++ b/src/armnn/test/OptimizerTests.cpp @@ -624,89 +624,6 @@ BOOST_AUTO_TEST_CASE(FoldPadLayerIntoConvolution2dLayer) &IsLayerOfType<armnn::OutputLayer>)); } -BOOST_AUTO_TEST_CASE(FoldPadLayerIntoPooling2dLayer) -{ - Graph graph; - const unsigned int inputShape[] = { 1, 2, 2, 3 }; - const unsigned int paddedShape[] = { 1, 3, 3, 3 }; - const unsigned int outputShape[] = { 1, 2, 2, 3 }; - - armnn::TensorInfo inputInfo(4, inputShape, DataType::Float32); - armnn::TensorInfo paddedInfo(4, paddedShape, DataType::Float32); - armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32); - - Layer* input = graph.AddLayer<InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(inputInfo); - - PadDescriptor padDescriptor({{ 0, 0 }, { 1, 1 }, { 1, 1 }, { 0, 0 }}); - - PadLayer* padLayer = graph.AddLayer<PadLayer>(padDescriptor, "pad"); - padLayer->GetOutputSlot().SetTensorInfo(paddedInfo); - - Pooling2dDescriptor pooling2dDescriptor; - pooling2dDescriptor.m_PoolWidth = 3; - pooling2dDescriptor.m_PoolHeight = 3; - pooling2dDescriptor.m_StrideX = 1; - pooling2dDescriptor.m_StrideY = 1; - pooling2dDescriptor.m_DataLayout = DataLayout::NHWC; - - Pooling2dLayer* pool2dLayer = graph.AddLayer<Pooling2dLayer>(pooling2dDescriptor, "pool2d"); - pool2dLayer->GetOutputSlot().SetTensorInfo(outputInfo); - - Layer* output = graph.AddLayer<OutputLayer>(0, "output"); - - // Connect up layers - input -> pad -> pool2d -> output - input->GetOutputSlot().Connect(padLayer->GetInputSlot(0)); - padLayer->GetOutputSlot().Connect(pool2dLayer->GetInputSlot(0)); - pool2dLayer->GetOutputSlot().Connect(output->GetInputSlot(0)); - - auto checkSimplePool2d = [&](const armnn::Layer* const layer) - { - const auto pool2dLayer = static_cast<const armnn::Pooling2dLayer*>(layer); - return IsLayerOfType<armnn::Pooling2dLayer>(layer) && - (layer->GetNameStr() == "pool2d") && - (pool2dLayer->GetParameters() == pooling2dDescriptor); - }; - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::PadLayer>, - checkSimplePool2d, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FoldPadIntoPooling2d())); - - auto checkPadFoldedIntoPool2d = [&](const armnn::Layer* const layer) - { - if (!IsLayerOfType<armnn::Pooling2dLayer>(layer) || (layer->GetNameStr() != "folded-pad-into-pool2d")) - { - return false; - } - - const auto pool2dLayer = static_cast<const armnn::Pooling2dLayer*>(layer); - const Pooling2dDescriptor pool2dLayerParams = pool2dLayer->GetParameters(); - - Pooling2dDescriptor pool2dLayerParamsNoPad = pool2dLayerParams; - pool2dLayerParamsNoPad.m_PadLeft = 0; - pool2dLayerParamsNoPad.m_PadRight = 0; - pool2dLayerParamsNoPad.m_PadTop = 0; - pool2dLayerParamsNoPad.m_PadBottom = 0; - - return (pool2dLayerParamsNoPad == pooling2dDescriptor) && - (pool2dLayerParams.m_PadLeft == 1) && - (pool2dLayerParams.m_PadRight == 1) && - (pool2dLayerParams.m_PadTop == 1) && - (pool2dLayerParams.m_PadBottom == 1); - }; - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - checkPadFoldedIntoPool2d, - &IsLayerOfType<armnn::OutputLayer>)); -} - class MockLayerSupport : public LayerSupportBase { public: bool IsInputSupported(const TensorInfo& /*input*/, @@ -995,4 +912,4 @@ BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnectionsWithoutFuseTest) &IsLayerOfType<armnn::OutputLayer>, &IsLayerOfType<armnn::OutputLayer>)); } -BOOST_AUTO_TEST_SUITE_END() +BOOST_AUTO_TEST_SUITE_END()
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