IVGCVSW-2645 Add Serializer & Deserializer for Pooling2d
Change-Id: Iba41da3cccd539a0175f2ed0ff9a8b6a23c5fb6f
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Signed-off-by: Saoirse Stewart <saoirse.stewart@arm.com>
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6503e6c..4295623 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -163,6 +163,8 @@
src/armnnSerializer/Schema_generated.h
src/armnnSerializer/Serializer.hpp
src/armnnSerializer/Serializer.cpp
+ src/armnnSerializer/SerializerUtils.hpp
+ src/armnnSerializer/SerializerUtils.cpp
src/armnnDeserializeParser/DeserializeParser.hpp
src/armnnDeserializeParser/DeserializeParser.cpp
)
@@ -554,6 +556,7 @@
src/armnnSerializer/test/SerializerTests.cpp
src/armnnDeserializeParser/test/DeserializeAdd.cpp
src/armnnDeserializeParser/test/DeserializeMultiplication.cpp
+ src/armnnDeserializeParser/test/DeserializePooling2d.cpp
src/armnnDeserializeParser/test/ParserFlatbuffersSerializeFixture.hpp
src/armnnDeserializeParser/test/SchemaSerialize.s
)
diff --git a/src/armnnDeserializeParser/DeserializeParser.cpp b/src/armnnDeserializeParser/DeserializeParser.cpp
index eb7bcca..f47c23f 100644
--- a/src/armnnDeserializeParser/DeserializeParser.cpp
+++ b/src/armnnDeserializeParser/DeserializeParser.cpp
@@ -136,6 +136,7 @@
// register supported layers
m_ParserFunctions[Layer_AdditionLayer] = &DeserializeParser::ParseAdd;
m_ParserFunctions[Layer_MultiplicationLayer] = &DeserializeParser::ParseMultiplication;
+ m_ParserFunctions[Layer_Pooling2dLayer] = &DeserializeParser::ParsePooling2d;
m_ParserFunctions[Layer_SoftmaxLayer] = &DeserializeParser::ParseSoftmax;
}
@@ -153,6 +154,8 @@
return graphPtr->layers()->Get(layerIndex)->layer_as_MultiplicationLayer()->base();
case Layer::Layer_OutputLayer:
return graphPtr->layers()->Get(layerIndex)->layer_as_OutputLayer()->base()->base();
+ case Layer::Layer_Pooling2dLayer:
+ return graphPtr->layers()->Get(layerIndex)->layer_as_Pooling2dLayer()->base();
case Layer::Layer_SoftmaxLayer:
return graphPtr->layers()->Get(layerIndex)->layer_as_SoftmaxLayer()->base();
case Layer::Layer_NONE:
@@ -356,7 +359,6 @@
}
std::ifstream file(fileName, std::ios::binary);
fileContent = std::string((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
-
return LoadGraphFromBinary(reinterpret_cast<const uint8_t*>(fileContent.c_str()), fileContent.size());
}
@@ -581,8 +583,8 @@
auto outputs = GetOutputs(m_Graph, layerIndex);
CHECK_VALID_SIZE(outputs.size(), 1);
- auto layerName = boost::str(boost::format("Addition:%1%") % layerIndex);
- IConnectableLayer* layer = m_Network->AddAdditionLayer(layerName.c_str());
+ m_layerName = boost::str(boost::format("Addition:%1%") % layerIndex);
+ IConnectableLayer* layer = m_Network->AddAdditionLayer(m_layerName.c_str());
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
@@ -601,8 +603,8 @@
auto outputs = GetOutputs(m_Graph, layerIndex);
CHECK_VALID_SIZE(outputs.size(), 1);
- auto layerName = boost::str(boost::format("Multiplication:%1%") % layerIndex);
- IConnectableLayer* layer = m_Network->AddMultiplicationLayer(layerName.c_str());
+ m_layerName = boost::str(boost::format("Multiplication:%1%") % layerIndex);
+ IConnectableLayer* layer = m_Network->AddMultiplicationLayer(m_layerName.c_str());
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
@@ -611,6 +613,119 @@
RegisterOutputSlots(layerIndex, layer);
}
+armnn::Pooling2dDescriptor DeserializeParser::GetPoolingDescriptor(DeserializeParser::PoolingDescriptor pooling2dDesc,
+ unsigned int layerIndex)
+{
+ armnn::Pooling2dDescriptor desc;
+
+ switch (pooling2dDesc->poolType())
+ {
+ case PoolingAlgorithm_Average:
+ {
+ desc.m_PoolType = armnn::PoolingAlgorithm::Average;
+ m_layerName = boost::str(boost::format("AveragePool2D:%1%") % layerIndex);
+ break;
+ }
+ case PoolingAlgorithm_Max:
+ {
+ desc.m_PoolType = armnn::PoolingAlgorithm::Max;
+ m_layerName = boost::str(boost::format("MaxPool2D:%1%") % layerIndex);
+ break;
+ }
+ default:
+ {
+ BOOST_ASSERT_MSG(false, "Unsupported pooling algorithm");
+ }
+ }
+
+ switch (pooling2dDesc->outputShapeRounding())
+ {
+ case OutputShapeRounding_Floor:
+ {
+ desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
+ break;
+ }
+ case OutputShapeRounding_Ceiling:
+ {
+ desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling;
+ break;
+ }
+ default:
+ {
+ BOOST_ASSERT_MSG(false, "Unsupported output shape rounding");
+ }
+ }
+
+ switch (pooling2dDesc->paddingMethod())
+ {
+ case PaddingMethod_Exclude:
+ {
+ desc.m_PaddingMethod = armnn::PaddingMethod::Exclude;
+ break;
+ }
+ case PaddingMethod_IgnoreValue:
+ {
+ desc.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
+ break;
+ }
+ default:
+ {
+ BOOST_ASSERT_MSG(false, "Unsupported padding method");
+ }
+ }
+
+ switch (pooling2dDesc->dataLayout())
+ {
+ case DataLayout_NCHW:
+ {
+ desc.m_DataLayout = armnn::DataLayout::NCHW;
+ break;
+ }
+ case DataLayout_NHWC:
+ {
+ desc.m_DataLayout = armnn::DataLayout::NHWC;
+ break;
+ }
+ default:
+ {
+ BOOST_ASSERT_MSG(false, "Unsupported data layout");
+ }
+ }
+
+ desc.m_PadRight = pooling2dDesc->padRight();
+ desc.m_PadLeft = pooling2dDesc->padLeft();
+ desc.m_PadBottom = pooling2dDesc->padBottom();
+ desc.m_PadTop = pooling2dDesc->padTop();
+ desc.m_StrideX = pooling2dDesc->strideX();
+ desc.m_StrideY = pooling2dDesc->strideY();
+ desc.m_PoolWidth = pooling2dDesc->poolWidth();
+ desc.m_PoolHeight = pooling2dDesc->poolHeight();
+
+ return desc;
+}
+
+void DeserializeParser::ParsePooling2d(unsigned int layerIndex)
+{
+ CHECK_LAYERS(m_Graph, 0, layerIndex);
+
+ auto pooling2dDes = m_Graph->layers()->Get(layerIndex)->layer_as_Pooling2dLayer()->descriptor();
+
+ auto inputs = GetInputs(m_Graph, layerIndex);
+ CHECK_VALID_SIZE(inputs.size(), 1);
+
+ auto outputs = GetOutputs(m_Graph, layerIndex);
+ CHECK_VALID_SIZE(outputs.size(), 1);
+ auto outputInfo = ToTensorInfo(outputs[0]);
+
+ auto pooling2dDescriptor = GetPoolingDescriptor(pooling2dDes, layerIndex);
+
+ IConnectableLayer* layer = m_Network->AddPooling2dLayer(pooling2dDescriptor, m_layerName.c_str());
+ layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+ RegisterInputSlots(layerIndex, layer);
+ RegisterOutputSlots(layerIndex, layer);
+}
+
void DeserializeParser::ParseSoftmax(unsigned int layerIndex)
{
CHECK_LAYERS(m_Graph, 0, layerIndex);
diff --git a/src/armnnDeserializeParser/DeserializeParser.hpp b/src/armnnDeserializeParser/DeserializeParser.hpp
index ddd02ab..1edb5a9 100644
--- a/src/armnnDeserializeParser/DeserializeParser.hpp
+++ b/src/armnnDeserializeParser/DeserializeParser.hpp
@@ -17,6 +17,7 @@
// Shorthands for deserializer types
using GraphPtr = const armnn::armnnSerializer::SerializedGraph *;
using TensorRawPtr = const armnn::armnnSerializer::TensorInfo *;
+ using PoolingDescriptor = const armnn::armnnSerializer::Pooling2dDescriptor *;
using TensorRawPtrVector = std::vector<TensorRawPtr>;
using LayerRawPtr = const armnn::armnnSerializer::LayerBase *;
using LayerBaseRawPtr = const armnn::armnnSerializer::LayerBase *;
@@ -50,6 +51,8 @@
static LayerBaseRawPtrVector GetGraphOutputs(const GraphPtr& graphPtr);
static LayerBaseRawPtr GetBaseLayer(const GraphPtr& graphPtr, unsigned int layerIndex);
static int32_t GetBindingLayerInfo(const GraphPtr& graphPtr, unsigned int layerIndex);
+ armnn::Pooling2dDescriptor GetPoolingDescriptor(PoolingDescriptor pooling2dDescriptor,
+ unsigned int layerIndex);
private:
// No copying allowed until it is wanted and properly implemented
@@ -65,6 +68,7 @@
void ParseUnsupportedLayer(unsigned int layerIndex);
void ParseAdd(unsigned int layerIndex);
void ParseMultiplication(unsigned int layerIndex);
+ void ParsePooling2d(unsigned int layerIndex);
void ParseSoftmax(unsigned int layerIndex);
void RegisterOutputSlotOfConnection(uint32_t connectionIndex, armnn::IOutputSlot* slot);
@@ -82,6 +86,7 @@
armnn::INetworkPtr m_Network;
GraphPtr m_Graph;
std::vector<LayerParsingFunction> m_ParserFunctions;
+ std::string m_layerName;
/// This holds the data of the file that was read in from CreateNetworkFromBinaryFile
/// Needed for m_Graph to point to
diff --git a/src/armnnDeserializeParser/test/DeserializePooling2d.cpp b/src/armnnDeserializeParser/test/DeserializePooling2d.cpp
new file mode 100644
index 0000000..70b96ba
--- /dev/null
+++ b/src/armnnDeserializeParser/test/DeserializePooling2d.cpp
@@ -0,0 +1,162 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <boost/test/unit_test.hpp>
+#include "ParserFlatbuffersSerializeFixture.hpp"
+#include "../DeserializeParser.hpp"
+
+#include <string>
+#include <iostream>
+
+BOOST_AUTO_TEST_SUITE(DeserializeParser)
+
+struct Pooling2dFixture : public ParserFlatbuffersSerializeFixture
+{
+ explicit Pooling2dFixture(const std::string &inputShape,
+ const std::string &outputShape,
+ const std::string &dataType,
+ const std::string &dataLayout,
+ const std::string &poolingAlgorithm)
+ {
+ m_JsonString = R"(
+ {
+ inputIds: [0],
+ outputIds: [2],
+ layers: [
+ {
+ layer_type: "InputLayer",
+ layer: {
+ base: {
+ layerBindingId: 0,
+ base: {
+ index: 0,
+ layerName: "InputLayer",
+ layerType: "Input",
+ inputSlots: [{
+ index: 0,
+ connection: {sourceLayerIndex:0, outputSlotIndex:0 },
+ }],
+ outputSlots: [ {
+ index: 0,
+ tensorInfo: {
+ dimensions: )" + inputShape + R"(,
+ dataType: )" + dataType + R"(
+ }}]
+ }
+ }}},
+ {
+ layer_type: "Pooling2dLayer",
+ layer: {
+ base: {
+ index: 1,
+ layerName: "Pooling2dLayer",
+ layerType: "Pooling2d",
+ inputSlots: [{
+ index: 0,
+ connection: {sourceLayerIndex:0, outputSlotIndex:0 },
+ }],
+ outputSlots: [ {
+ index: 0,
+ tensorInfo: {
+ dimensions: )" + outputShape + R"(,
+ dataType: )" + dataType + R"(
+
+ }}]},
+ descriptor: {
+ poolType: )" + poolingAlgorithm + R"(,
+ outputShapeRounding: "Floor",
+ paddingMethod: Exclude,
+ dataLayout: )" + dataLayout + R"(,
+ padLeft: 0,
+ padRight: 0,
+ padTop: 0,
+ padBottom: 0,
+ poolWidth: 2,
+ poolHeight: 2,
+ strideX: 2,
+ strideY: 2
+ }
+ }},
+ {
+ layer_type: "OutputLayer",
+ layer: {
+ base:{
+ layerBindingId: 0,
+ base: {
+ index: 2,
+ layerName: "OutputLayer",
+ layerType: "Output",
+ inputSlots: [{
+ index: 0,
+ connection: {sourceLayerIndex:1, outputSlotIndex:0 },
+ }],
+ outputSlots: [ {
+ index: 0,
+ tensorInfo: {
+ dimensions: )" + outputShape + R"(,
+ dataType: )" + dataType + R"(
+ },
+ }],
+ }}},
+ }]
+ }
+ )";
+ SetupSingleInputSingleOutput("InputLayer", "OutputLayer");
+ }
+};
+
+struct SimpleAvgPoolingFixture : Pooling2dFixture
+{
+ SimpleAvgPoolingFixture() : Pooling2dFixture("[ 1, 2, 2, 1 ]", "[ 1, 1, 1, 1 ]",
+ "Float32", "NHWC", "Average") {}
+};
+
+struct SimpleAvgPoolingFixture2 : Pooling2dFixture
+{
+ SimpleAvgPoolingFixture2() : Pooling2dFixture("[ 1, 2, 2, 1 ]",
+ "[ 1, 1, 1, 1 ]",
+ "QuantisedAsymm8", "NHWC", "Average") {}
+};
+
+struct SimpleMaxPoolingFixture : Pooling2dFixture
+{
+ SimpleMaxPoolingFixture() : Pooling2dFixture("[ 1, 1, 2, 2 ]",
+ "[ 1, 1, 1, 1 ]",
+ "Float32", "NCHW", "Max") {}
+};
+
+struct SimpleMaxPoolingFixture2 : Pooling2dFixture
+{
+ SimpleMaxPoolingFixture2() : Pooling2dFixture("[ 1, 1, 2, 2 ]",
+ "[ 1, 1, 1, 1 ]",
+ "QuantisedAsymm8", "NCHW", "Max") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(PoolingQuantisedAsymm8Avg, SimpleAvgPoolingFixture)
+{
+ RunTest<4, armnn::DataType::Float32>(0, { 2, 3, 5, 2 }, { 3 });
+}
+
+BOOST_FIXTURE_TEST_CASE(PoolingFloat32Avg, SimpleAvgPoolingFixture2)
+{
+ RunTest<4, armnn::DataType::QuantisedAsymm8>(0,
+ { 20, 40, 60, 80 },
+ { 50 });
+}
+
+BOOST_FIXTURE_TEST_CASE(PoolingQuantisedAsymm8Max, SimpleMaxPoolingFixture)
+{
+ RunTest<4, armnn::DataType::Float32>(0, { 2, 5, 5, 2 }, { 5 });
+}
+
+BOOST_FIXTURE_TEST_CASE(PoolingFloat32Max, SimpleMaxPoolingFixture2)
+{
+ RunTest<4, armnn::DataType::QuantisedAsymm8>(0,
+ { 20, 40, 60, 80 },
+ { 80 });
+}
+
+BOOST_AUTO_TEST_SUITE_END()
+
diff --git a/src/armnnSerializer/Schema.fbs b/src/armnnSerializer/Schema.fbs
index 411b89a..048181a 100644
--- a/src/armnnSerializer/Schema.fbs
+++ b/src/armnnSerializer/Schema.fbs
@@ -17,6 +17,11 @@
Boolean = 4
}
+enum DataLayout : byte {
+ NHWC = 0,
+ NCHW = 1
+}
+
table TensorInfo {
dimensions:[uint];
dataType:DataType;
@@ -67,7 +72,8 @@
Input = 1,
Multiplication = 2,
Output = 3,
- Softmax = 4
+ Pooling2d = 4,
+ Softmax = 5
}
// Base layer table to be used as part of other layers
@@ -97,6 +103,42 @@
base:LayerBase;
}
+table Pooling2dLayer {
+ base:LayerBase;
+ descriptor:Pooling2dDescriptor;
+}
+
+enum PoolingAlgorithm : byte {
+ Max = 0,
+ Average = 1,
+ L2 = 2
+}
+
+enum OutputShapeRounding : byte {
+ Floor = 0,
+ Ceiling = 1
+}
+
+enum PaddingMethod : byte {
+ IgnoreValue = 0,
+ Exclude = 1
+}
+
+table Pooling2dDescriptor {
+ poolType:PoolingAlgorithm;
+ padLeft:uint;
+ padRight:uint;
+ padTop:uint;
+ padBottom:uint;
+ poolWidth:uint;
+ poolHeight:uint;
+ strideX:uint;
+ strideY:uint;
+ outputShapeRounding:OutputShapeRounding;
+ paddingMethod:PaddingMethod;
+ dataLayout:DataLayout;
+}
+
table SoftmaxLayer {
base:LayerBase;
descriptor:SoftmaxDescriptor;
@@ -115,6 +157,7 @@
InputLayer,
MultiplicationLayer,
OutputLayer,
+ Pooling2dLayer,
SoftmaxLayer
}
diff --git a/src/armnnSerializer/Serializer.cpp b/src/armnnSerializer/Serializer.cpp
index ba4b369..57228c4 100644
--- a/src/armnnSerializer/Serializer.cpp
+++ b/src/armnnSerializer/Serializer.cpp
@@ -4,9 +4,15 @@
//
#include "Serializer.hpp"
+
+#include "SerializerUtils.hpp"
+
#include <armnn/ArmNN.hpp>
+
#include <iostream>
+
#include <Schema_generated.h>
+
#include <flatbuffers/util.h>
using namespace armnn;
@@ -16,25 +22,6 @@
namespace armnnSerializer
{
-serializer::DataType GetFlatBufferDataType(DataType dataType)
-{
- switch (dataType)
- {
- case DataType::Float32:
- return serializer::DataType::DataType_Float32;
- case DataType::Float16:
- return serializer::DataType::DataType_Float16;
- case DataType::Signed32:
- return serializer::DataType::DataType_Signed32;
- case DataType::QuantisedAsymm8:
- return serializer::DataType::DataType_QuantisedAsymm8;
- case DataType::Boolean:
- return serializer::DataType::DataType_Boolean;
- default:
- return serializer::DataType::DataType_Float16;
- }
-}
-
uint32_t SerializerVisitor::GetSerializedId(unsigned int guid)
{
std::pair<unsigned int, uint32_t> guidPair(guid, m_layerId);
@@ -140,6 +127,33 @@
CreateAnyLayer(flatBufferSoftmaxLayer.o, serializer::Layer::Layer_SoftmaxLayer);
}
+void SerializerVisitor::VisitPooling2dLayer(const IConnectableLayer* layer,
+ const Pooling2dDescriptor& pooling2dDescriptor,
+ const char* name)
+{
+ auto fbPooling2dBaseLayer = CreateLayerBase(layer, serializer::LayerType::LayerType_Pooling2d);
+ auto fbPooling2dDescriptor = serializer::CreatePooling2dDescriptor(
+ m_flatBufferBuilder,
+ GetFlatBufferPoolingAlgorithm(pooling2dDescriptor.m_PoolType),
+ pooling2dDescriptor.m_PadLeft,
+ pooling2dDescriptor.m_PadRight,
+ pooling2dDescriptor.m_PadTop,
+ pooling2dDescriptor.m_PadBottom,
+ pooling2dDescriptor.m_PoolWidth,
+ pooling2dDescriptor.m_PoolHeight,
+ pooling2dDescriptor.m_StrideX,
+ pooling2dDescriptor.m_StrideY,
+ GetFlatBufferOutputShapeRounding(pooling2dDescriptor.m_OutputShapeRounding),
+ GetFlatBufferPaddingMethod(pooling2dDescriptor.m_PaddingMethod),
+ GetFlatBufferDataLayout(pooling2dDescriptor.m_DataLayout));
+
+ auto fbPooling2dLayer = serializer::CreatePooling2dLayer(m_flatBufferBuilder,
+ fbPooling2dBaseLayer,
+ fbPooling2dDescriptor);
+
+ CreateAnyLayer(fbPooling2dLayer.o, serializer::Layer::Layer_Pooling2dLayer);
+}
+
fb::Offset<serializer::LayerBase> SerializerVisitor::CreateLayerBase(const IConnectableLayer* layer,
const serializer::LayerType layerType)
{
diff --git a/src/armnnSerializer/Serializer.hpp b/src/armnnSerializer/Serializer.hpp
index ec26dc1..169ed09 100644
--- a/src/armnnSerializer/Serializer.hpp
+++ b/src/armnnSerializer/Serializer.hpp
@@ -60,6 +60,10 @@
const armnn::SoftmaxDescriptor& softmaxDescriptor,
const char* name = nullptr) override;
+ void VisitPooling2dLayer(const armnn::IConnectableLayer* layer,
+ const armnn::Pooling2dDescriptor& pooling2dDescriptor,
+ const char* name = nullptr) override;
+
private:
/// Creates the Input Slots and Output Slots and LayerBase for the layer.
diff --git a/src/armnnSerializer/SerializerSupport.md b/src/armnnSerializer/SerializerSupport.md
index 617eafb..a94e0ad 100644
--- a/src/armnnSerializer/SerializerSupport.md
+++ b/src/armnnSerializer/SerializerSupport.md
@@ -9,5 +9,6 @@
* Addition
* Multiplication
* Softmax
+* Pooling2d
More machine learning layers will be supported in future releases.
\ No newline at end of file
diff --git a/src/armnnSerializer/SerializerUtils.cpp b/src/armnnSerializer/SerializerUtils.cpp
new file mode 100644
index 0000000..5772eab
--- /dev/null
+++ b/src/armnnSerializer/SerializerUtils.cpp
@@ -0,0 +1,83 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "SerializerUtils.hpp"
+
+namespace armnnSerializer
+{
+
+using namespace armnn;
+namespace serializer = armnn::armnnSerializer;
+
+serializer::DataType GetFlatBufferDataType(DataType dataType)
+{
+ switch (dataType)
+ {
+ case DataType::Float32:
+ return serializer::DataType::DataType_Float32;
+ case DataType::Float16:
+ return serializer::DataType::DataType_Float16;
+ case DataType::Signed32:
+ return serializer::DataType::DataType_Signed32;
+ case DataType::QuantisedAsymm8:
+ return serializer::DataType::DataType_QuantisedAsymm8;
+ case DataType::Boolean:
+ return serializer::DataType::DataType_Boolean;
+ default:
+ return serializer::DataType::DataType_Float16;
+ }
+}
+
+serializer::DataLayout GetFlatBufferDataLayout(DataLayout dataLayout)
+{
+ switch (dataLayout)
+ {
+ case DataLayout::NHWC:
+ return serializer::DataLayout::DataLayout_NHWC;
+ case DataLayout::NCHW:
+ default:
+ return serializer::DataLayout::DataLayout_NCHW;
+ }
+}
+
+serializer::PoolingAlgorithm GetFlatBufferPoolingAlgorithm(PoolingAlgorithm poolingAlgorithm)
+{
+ switch (poolingAlgorithm)
+ {
+ case PoolingAlgorithm::Average:
+ return serializer::PoolingAlgorithm::PoolingAlgorithm_Average;
+ case PoolingAlgorithm::L2:
+ return serializer::PoolingAlgorithm::PoolingAlgorithm_L2;
+ case PoolingAlgorithm::Max:
+ default:
+ return serializer::PoolingAlgorithm::PoolingAlgorithm_Max;
+ }
+}
+
+serializer::OutputShapeRounding GetFlatBufferOutputShapeRounding(OutputShapeRounding outputShapeRounding)
+{
+ switch (outputShapeRounding)
+ {
+ case OutputShapeRounding::Ceiling:
+ return serializer::OutputShapeRounding::OutputShapeRounding_Ceiling;
+ case OutputShapeRounding::Floor:
+ default:
+ return serializer::OutputShapeRounding::OutputShapeRounding_Floor;
+ }
+}
+
+serializer::PaddingMethod GetFlatBufferPaddingMethod(PaddingMethod paddingMethod)
+{
+ switch (paddingMethod)
+ {
+ case PaddingMethod::IgnoreValue:
+ return serializer::PaddingMethod::PaddingMethod_IgnoreValue;
+ case PaddingMethod::Exclude:
+ default:
+ return serializer::PaddingMethod::PaddingMethod_Exclude;
+ }
+}
+
+} // namespace armnnSerializer
\ No newline at end of file
diff --git a/src/armnnSerializer/SerializerUtils.hpp b/src/armnnSerializer/SerializerUtils.hpp
new file mode 100644
index 0000000..72a8806
--- /dev/null
+++ b/src/armnnSerializer/SerializerUtils.hpp
@@ -0,0 +1,25 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include <armnn/ArmNN.hpp>
+
+#include <Schema_generated.h>
+
+namespace armnnSerializer
+{
+
+armnn::armnnSerializer::DataType GetFlatBufferDataType(armnn::DataType dataType);
+
+armnn::armnnSerializer::DataLayout GetFlatBufferDataLayout(armnn::DataLayout dataLayout);
+
+armnn::armnnSerializer::PoolingAlgorithm GetFlatBufferPoolingAlgorithm(armnn::PoolingAlgorithm poolingAlgorithm);
+
+armnn::armnnSerializer::OutputShapeRounding GetFlatBufferOutputShapeRounding(
+ armnn::OutputShapeRounding outputShapeRounding);
+
+armnn::armnnSerializer::PaddingMethod GetFlatBufferPaddingMethod(armnn::PaddingMethod paddingMethod);
+
+} // namespace armnnSerializer
\ No newline at end of file
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index 5b55682..4b6bf1e 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -15,12 +15,34 @@
#include <vector>
#include <boost/test/unit_test.hpp>
-
#include <flatbuffers/idl.h>
-BOOST_AUTO_TEST_SUITE(SerializerTests)
+using armnnDeserializeParser::IDeserializeParser;
-armnnDeserializeParser::IDeserializeParserPtr g_Parser = armnnDeserializeParser::IDeserializeParser::Create();
+namespace
+{
+
+armnn::INetworkPtr DeserializeNetwork(const std::string& serializerString)
+{
+ std::vector<std::uint8_t> const serializerVector{serializerString.begin(), serializerString.end()};
+ return armnnDeserializeParser::IDeserializeParser::Create()->CreateNetworkFromBinary(serializerVector);
+}
+
+std::string SerializeNetwork(const armnn::INetwork& network)
+{
+ armnnSerializer::Serializer serializer;
+ serializer.Serialize(network);
+
+ std::stringstream stream;
+ serializer.SaveSerializedToStream(stream);
+
+ std::string serializerString{stream.str()};
+ return serializerString;
+}
+
+} // anonymous namespace
+
+BOOST_AUTO_TEST_SUITE(SerializerTests)
BOOST_AUTO_TEST_CASE(SimpleNetworkSerialization)
{
@@ -78,55 +100,47 @@
// Create test network
armnn::INetworkPtr network = armnn::INetwork::Create();
- armnn::IConnectableLayer *const inputLayer = network->AddInputLayer(0);
- armnn::IConnectableLayer *const softmaxLayer = network->AddSoftmaxLayer(descriptor, "softmax");
- armnn::IConnectableLayer *const outputLayer = network->AddOutputLayer(0);
+ armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
+ armnn::IConnectableLayer* const softmaxLayer = network->AddSoftmaxLayer(descriptor, "softmax");
+ armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
inputLayer->GetOutputSlot(0).Connect(softmaxLayer->GetInputSlot(0));
inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
softmaxLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
softmaxLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
- // Serialize
- armnnSerializer::Serializer serializer;
- serializer.Serialize(*network);
- std::stringstream stream;
- serializer.SaveSerializedToStream(stream);
- const std::string serializerString{stream.str()};
-
- // Deserialize
- armnn::INetworkPtr deserializedNetwork =
- g_Parser->CreateNetworkFromBinary({serializerString.begin(), serializerString.end()});
+ // Serialize & deserialize network
+ armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr run = armnn::IRuntime::Create(options);
+ armnn::IRuntimePtr runtime = armnn::IRuntime::Create(options);
armnn::IOptimizedNetworkPtr optimizedNetwork =
- armnn::Optimize(*network, {armnn::Compute::CpuRef}, run->GetDeviceSpec());
+ armnn::Optimize(*network, {armnn::Compute::CpuRef}, runtime->GetDeviceSpec());
BOOST_CHECK(optimizedNetwork);
armnn::IOptimizedNetworkPtr deserializedOptimizedNetwork =
- armnn::Optimize(*deserializedNetwork, {armnn::Compute::CpuRef}, run->GetDeviceSpec());
+ armnn::Optimize(*deserializedNetwork, {armnn::Compute::CpuRef}, runtime->GetDeviceSpec());
BOOST_CHECK(deserializedOptimizedNetwork);
armnn::NetworkId networkId1;
armnn::NetworkId networkId2;
- run->LoadNetwork(networkId1, std::move(optimizedNetwork));
- run->LoadNetwork(networkId2, std::move(deserializedOptimizedNetwork));
+ runtime->LoadNetwork(networkId1, std::move(optimizedNetwork));
+ runtime->LoadNetwork(networkId2, std::move(deserializedOptimizedNetwork));
std::vector<float> inputData(tensorInfo.GetNumElements());
std::iota(inputData.begin(), inputData.end(), 0);
armnn::InputTensors inputTensors1
{
- {0, armnn::ConstTensor(run->GetInputTensorInfo(networkId1, 0), inputData.data())}
+ {0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkId1, 0), inputData.data())}
};
armnn::InputTensors inputTensors2
{
- {0, armnn::ConstTensor(run->GetInputTensorInfo(networkId2, 0), inputData.data())}
+ {0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkId2, 0), inputData.data())}
};
std::vector<float> outputData1(inputData.size());
@@ -134,19 +148,83 @@
armnn::OutputTensors outputTensors1
{
- {0, armnn::Tensor(run->GetOutputTensorInfo(networkId1, 0), outputData1.data())}
+ {0, armnn::Tensor(runtime->GetOutputTensorInfo(networkId1, 0), outputData1.data())}
};
armnn::OutputTensors outputTensors2
{
- {0, armnn::Tensor(run->GetOutputTensorInfo(networkId2, 0), outputData2.data())}
+ {0, armnn::Tensor(runtime->GetOutputTensorInfo(networkId2, 0), outputData2.data())}
};
- run->EnqueueWorkload(networkId1, inputTensors1, outputTensors1);
- run->EnqueueWorkload(networkId2, inputTensors2, outputTensors2);
+ runtime->EnqueueWorkload(networkId1, inputTensors1, outputTensors1);
+ runtime->EnqueueWorkload(networkId2, inputTensors2, outputTensors2);
BOOST_CHECK_EQUAL_COLLECTIONS(outputData1.begin(), outputData1.end(),
outputData2.begin(), outputData2.end());
}
+BOOST_AUTO_TEST_CASE(SimplePooling2dIntegration)
+{
+ armnn::NetworkId networkIdentifier;
+ armnn::IRuntime::CreationOptions options; // default options
+ armnn::IRuntimePtr runtime = armnn::IRuntime::Create(options);
+
+ unsigned int inputShape[] = {1, 2, 2, 1};
+ unsigned int outputShape[] = {1, 1, 1, 1};
+
+ auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
+ auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
+
+ armnn::Pooling2dDescriptor desc;
+ desc.m_DataLayout = armnn::DataLayout::NHWC;
+ desc.m_PadTop = 0;
+ desc.m_PadBottom = 0;
+ desc.m_PadLeft = 0;
+ desc.m_PadRight = 0;
+ desc.m_PoolType = armnn::PoolingAlgorithm::Average;
+ desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
+ desc.m_PaddingMethod = armnn::PaddingMethod::Exclude;
+ desc.m_PoolHeight = 2;
+ desc.m_PoolWidth = 2;
+ desc.m_StrideX = 2;
+ desc.m_StrideY = 2;
+
+ armnn::INetworkPtr network = armnn::INetwork::Create();
+ armnn::IConnectableLayer *const inputLayer = network->AddInputLayer(0);
+ armnn::IConnectableLayer *const pooling2dLayer = network->AddPooling2dLayer(desc, "ReshapeLayer");
+ armnn::IConnectableLayer *const outputLayer = network->AddOutputLayer(0);
+
+ inputLayer->GetOutputSlot(0).Connect(pooling2dLayer->GetInputSlot(0));
+ inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
+ pooling2dLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
+ pooling2dLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+ auto deserializeNetwork = DeserializeNetwork(SerializeNetwork(*network));
+
+ //Optimize the deserialized network
+ auto deserializedOptimized = Optimize(*deserializeNetwork, {armnn::Compute::CpuRef},
+ runtime->GetDeviceSpec());
+
+ // Load graph into runtime
+ runtime->LoadNetwork(networkIdentifier, std::move(deserializedOptimized));
+
+ std::vector<float> input1Data(inputTensorInfo.GetNumElements());
+ std::iota(input1Data.begin(), input1Data.end(), 4);
+
+ armnn::InputTensors inputTensors
+ {
+ {0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkIdentifier, 0), input1Data.data())}
+ };
+
+ std::vector<float> outputData(input1Data.size());
+ armnn::OutputTensors outputTensors
+ {
+ {0, armnn::Tensor(runtime->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}
+ };
+
+ runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
+
+ BOOST_CHECK_EQUAL(outputData[0], 5.5);
+}
+
BOOST_AUTO_TEST_SUITE_END()
