Revert "Fold PAD into Pooling2d"

This reverts commit 51ce7d487c761358de105f82ff90553570aedac0.

Reason for revert: https://jira.arm.com/browse/IVGCVSW-5798 LargeGraph_TENSOR_FLOAT32 CTS tests failures

Change-Id: Ib031a47f605340b2202ecf074ce96a8b54c51075

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()
\ No newline at end of file