IVGCVSW-5592 Implement Pimpl Idiom for Caffe and Onnx Parsers
Signed-off-by: Kevin May <kevin.may@arm.com>
Change-Id: I760dc4f33c0f87113cda2fa924da70f2e8c19025
diff --git a/src/armnnOnnxParser/OnnxParser.cpp b/src/armnnOnnxParser/OnnxParser.cpp
index f3d0a73..9f5aa19 100644
--- a/src/armnnOnnxParser/OnnxParser.cpp
+++ b/src/armnnOnnxParser/OnnxParser.cpp
@@ -20,6 +20,51 @@
namespace armnnOnnxParser
{
+
+IOnnxParser::IOnnxParser() : pOnnxParserImpl(new OnnxParserImpl()) {}
+
+IOnnxParser::~IOnnxParser() = default;
+
+IOnnxParser* IOnnxParser::CreateRaw()
+{
+ return new IOnnxParser();
+}
+
+IOnnxParserPtr IOnnxParser::Create()
+{
+ return IOnnxParserPtr(CreateRaw(), &IOnnxParser::Destroy);
+}
+
+void IOnnxParser::Destroy(IOnnxParser* parser)
+{
+ delete parser;
+}
+
+armnn::INetworkPtr IOnnxParser::CreateNetworkFromBinaryFile(const char* graphFile)
+{
+ return pOnnxParserImpl->CreateNetworkFromBinaryFile(graphFile);
+}
+
+armnn::INetworkPtr IOnnxParser::CreateNetworkFromTextFile(const char* graphFile)
+{
+ return pOnnxParserImpl->CreateNetworkFromTextFile(graphFile);
+}
+
+armnn::INetworkPtr IOnnxParser::CreateNetworkFromString(const std::string& protoText)
+{
+ return pOnnxParserImpl->CreateNetworkFromString(protoText);
+}
+
+BindingPointInfo IOnnxParser::GetNetworkInputBindingInfo(const std::string& name) const
+{
+ return pOnnxParserImpl->GetNetworkInputBindingInfo(name);
+}
+
+BindingPointInfo IOnnxParser::GetNetworkOutputBindingInfo(const std::string& name) const
+{
+ return pOnnxParserImpl->GetNetworkOutputBindingInfo(name);
+}
+
namespace
{
void CheckValidDataType(std::initializer_list<onnx::TensorProto::DataType> validInputTypes,
@@ -357,25 +402,25 @@
} //namespace
-const std::map<std::string, OnnxParser::OperationParsingFunction> OnnxParser::m_ParserFunctions = {
- { "BatchNormalization", &OnnxParser::ParseBatchNormalization},
- { "GlobalAveragePool", &OnnxParser::ParseGlobalAveragePool},
- { "AveragePool", &OnnxParser::ParseAveragePool },
- { "Clip", &OnnxParser::ParseClip },
- { "Constant", &OnnxParser::ParseConstant },
- { "MaxPool", &OnnxParser::ParseMaxPool },
- { "Reshape", &OnnxParser::ParseReshape },
- { "Sigmoid", &OnnxParser::ParseSigmoid },
- { "Tanh", &OnnxParser::ParseTanh },
- { "Relu", &OnnxParser::ParseRelu },
- { "LeakyRelu", &OnnxParser::ParseLeakyRelu },
- { "Conv", &OnnxParser::ParseConv },
- { "Add", &OnnxParser::ParseAdd },
- { "Flatten", &OnnxParser::ParseFlatten},
+const std::map<std::string, OnnxParserImpl::OperationParsingFunction> OnnxParserImpl::m_ParserFunctions = {
+ { "BatchNormalization", &OnnxParserImpl::ParseBatchNormalization},
+ { "GlobalAveragePool", &OnnxParserImpl::ParseGlobalAveragePool},
+ { "AveragePool", &OnnxParserImpl::ParseAveragePool },
+ { "Clip", &OnnxParserImpl::ParseClip },
+ { "Constant", &OnnxParserImpl::ParseConstant },
+ { "MaxPool", &OnnxParserImpl::ParseMaxPool },
+ { "Reshape", &OnnxParserImpl::ParseReshape },
+ { "Sigmoid", &OnnxParserImpl::ParseSigmoid },
+ { "Tanh", &OnnxParserImpl::ParseTanh },
+ { "Relu", &OnnxParserImpl::ParseRelu },
+ { "LeakyRelu", &OnnxParserImpl::ParseLeakyRelu },
+ { "Conv", &OnnxParserImpl::ParseConv },
+ { "Add", &OnnxParserImpl::ParseAdd },
+ { "Flatten", &OnnxParserImpl::ParseFlatten},
};
template<typename TypePair, typename Location>
-void OnnxParser::ValidateInputs(const onnx::NodeProto& node,
+void OnnxParserImpl::ValidateInputs(const onnx::NodeProto& node,
TypePair validInputs,
const Location& location)
{
@@ -391,13 +436,13 @@
}
#define VALID_INPUTS(NODE, VALID_INPUTS) \
- OnnxParser::ValidateInputs(NODE, \
+ OnnxParserImpl::ValidateInputs(NODE, \
VALID_INPUTS, \
CHECK_LOCATION())
-std::vector<TensorInfo> OnnxParser::ComputeOutputInfo(std::vector<std::string> outNames,
- const IConnectableLayer* layer,
- std::vector<TensorShape> inputShapes)
+std::vector<TensorInfo> OnnxParserImpl::ComputeOutputInfo(std::vector<std::string> outNames,
+ const IConnectableLayer* layer,
+ std::vector<TensorShape> inputShapes)
{
ARMNN_ASSERT(! outNames.empty());
bool needCompute = std::any_of(outNames.begin(),
@@ -427,33 +472,18 @@
return outInfo;
}
-IOnnxParser* IOnnxParser::CreateRaw()
-{
- return new OnnxParser();
-}
-
-IOnnxParserPtr IOnnxParser::Create()
-{
- return IOnnxParserPtr(CreateRaw(), &IOnnxParser::Destroy);
-}
-
-void IOnnxParser::Destroy(IOnnxParser* parser)
-{
- delete parser;
-}
-
-OnnxParser::OnnxParser()
+OnnxParserImpl::OnnxParserImpl()
: m_Network(nullptr, nullptr)
{
}
-void OnnxParser::ResetParser()
+void OnnxParserImpl::ResetParser()
{
m_Network = armnn::INetworkPtr(nullptr, nullptr);
m_Graph = nullptr;
}
-void OnnxParser::Cleanup()
+void OnnxParserImpl::Cleanup()
{
m_TensorConnections.clear();
m_TensorsInfo.clear();
@@ -461,7 +491,7 @@
m_OutputsFusedAndUsed.clear();
}
-std::pair<ConstTensor, std::unique_ptr<float[]>> OnnxParser::CreateConstTensor(const std::string name)
+std::pair<ConstTensor, std::unique_ptr<float[]>> OnnxParserImpl::CreateConstTensor(const std::string name)
{
const TensorInfo tensorInfo = *m_TensorsInfo[name].m_info;
onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor;
@@ -499,7 +529,7 @@
return std::make_pair(ConstTensor(tensorInfo, tensorData.get()), std::move(tensorData));
}
-ModelPtr OnnxParser::LoadModelFromTextFile(const char* graphFile)
+ModelPtr OnnxParserImpl::LoadModelFromTextFile(const char* graphFile)
{
FILE* fd = fopen(graphFile, "r");
@@ -524,7 +554,7 @@
return modelProto;
}
-INetworkPtr OnnxParser::CreateNetworkFromTextFile(const char* graphFile)
+INetworkPtr OnnxParserImpl::CreateNetworkFromTextFile(const char* graphFile)
{
ResetParser();
ModelPtr modelProto = LoadModelFromTextFile(graphFile);
@@ -532,7 +562,7 @@
}
-ModelPtr OnnxParser::LoadModelFromBinaryFile(const char* graphFile)
+ModelPtr OnnxParserImpl::LoadModelFromBinaryFile(const char* graphFile)
{
FILE* fd = fopen(graphFile, "rb");
@@ -560,14 +590,14 @@
}
-INetworkPtr OnnxParser::CreateNetworkFromBinaryFile(const char* graphFile)
+INetworkPtr OnnxParserImpl::CreateNetworkFromBinaryFile(const char* graphFile)
{
ResetParser();
ModelPtr modelProto = LoadModelFromBinaryFile(graphFile);
return CreateNetworkFromModel(*modelProto);
}
-ModelPtr OnnxParser::LoadModelFromString(const std::string& protoText)
+ModelPtr OnnxParserImpl::LoadModelFromString(const std::string& protoText)
{
if (protoText == "")
{
@@ -586,14 +616,14 @@
return modelProto;
}
-INetworkPtr OnnxParser::CreateNetworkFromString(const std::string& protoText)
+INetworkPtr OnnxParserImpl::CreateNetworkFromString(const std::string& protoText)
{
ResetParser();
ModelPtr modelProto = LoadModelFromString(protoText);
return CreateNetworkFromModel(*modelProto);
}
-INetworkPtr OnnxParser::CreateNetworkFromModel(onnx::ModelProto& model)
+INetworkPtr OnnxParserImpl::CreateNetworkFromModel(onnx::ModelProto& model)
{
m_Network = INetwork::Create();
try
@@ -610,7 +640,7 @@
return std::move(m_Network);
}
-void OnnxParser::LoadGraph()
+void OnnxParserImpl::LoadGraph()
{
ARMNN_ASSERT(m_Graph.get() != nullptr);
@@ -684,7 +714,7 @@
}
}
-void OnnxParser::SetupInfo(const google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list)
+void OnnxParserImpl::SetupInfo(const google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list)
{
for (auto tensor : *list)
{
@@ -695,7 +725,7 @@
}
}
-void OnnxParser::DetectFullyConnected()
+void OnnxParserImpl::DetectFullyConnected()
{
m_OutputsFusedAndUsed = std::vector<UsageSummary> (static_cast<size_t>(m_Graph->node_size()), UsageSummary());
auto matmulAndConstant = [&](const std::string& constInput,
@@ -753,10 +783,10 @@
}
template<typename Location>
-void OnnxParser::GetInputAndParam(const onnx::NodeProto& node,
- std::string* inputName,
- std::string* constName,
- const Location& location)
+void OnnxParserImpl::GetInputAndParam(const onnx::NodeProto& node,
+ std::string* inputName,
+ std::string* constName,
+ const Location& location)
{
int cstIndex;
if (m_TensorsInfo[node.input(0)].isConstant())
@@ -786,7 +816,7 @@
}
template<typename Location>
-void OnnxParser::To1DTensor(const std::string& name, const Location& location)
+void OnnxParserImpl::To1DTensor(const std::string& name, const Location& location)
{
TensorShape shape = m_TensorsInfo[name].m_info->GetShape();
std::vector<uint32_t> newShape;
@@ -805,7 +835,7 @@
m_TensorsInfo[name].m_info->SetShape(TensorShape(static_cast<unsigned int>(newShape.size()), newShape.data()));
}
-void OnnxParser::AddConvLayerWithDepthwiseConv(const onnx::NodeProto& node, const Convolution2dDescriptor& convDesc)
+void OnnxParserImpl::AddConvLayerWithDepthwiseConv(const onnx::NodeProto& node, const Convolution2dDescriptor& convDesc)
{
ARMNN_ASSERT(node.op_type() == "Conv");
@@ -864,7 +894,7 @@
RegisterOutputSlots(layer, {node.output(0)});
}
-void OnnxParser::AddFullyConnected(const onnx::NodeProto& matmulNode, const onnx::NodeProto* addNode)
+void OnnxParserImpl::AddFullyConnected(const onnx::NodeProto& matmulNode, const onnx::NodeProto* addNode)
{
// find matmul inputs
@@ -941,7 +971,7 @@
}
}
-void OnnxParser::AddPoolingLayer(const onnx::NodeProto& node, Pooling2dDescriptor& desc)
+void OnnxParserImpl::AddPoolingLayer(const onnx::NodeProto& node, Pooling2dDescriptor& desc)
{
CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1);
@@ -1021,8 +1051,8 @@
RegisterOutputSlots(layer, {node.output(0)});
}
-std::pair<std::string, std::string> OnnxParser::AddPrepareBroadcast(const std::string& input0,
- const std::string& input1)
+std::pair<std::string, std::string> OnnxParserImpl::AddPrepareBroadcast(const std::string& input0,
+ const std::string& input1)
{
std::pair<std::string, std::string> inputs = std::make_pair(input0, input1);
@@ -1044,7 +1074,7 @@
return inputs;
}
-void OnnxParser::CreateConstantLayer(const std::string& tensorName, const std::string& layerName)
+void OnnxParserImpl::CreateConstantLayer(const std::string& tensorName, const std::string& layerName)
{
auto armnnTensor = CreateConstTensor(tensorName);
@@ -1053,9 +1083,9 @@
RegisterOutputSlots(layer, {tensorName});
}
-void OnnxParser::CreateReshapeLayer(const std::string& inputName,
- const std::string& outputName,
- const std::string& layerName)
+void OnnxParserImpl::CreateReshapeLayer(const std::string& inputName,
+ const std::string& outputName,
+ const std::string& layerName)
{
const TensorInfo outputTensorInfo = *m_TensorsInfo[outputName].m_info;
ReshapeDescriptor reshapeDesc;
@@ -1073,7 +1103,7 @@
RegisterOutputSlots(layer, {outputName});
}
-void OnnxParser::ParseActivation(const onnx::NodeProto& node, const armnn::ActivationFunction func)
+void OnnxParserImpl::ParseActivation(const onnx::NodeProto& node, const armnn::ActivationFunction func)
{
CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1, 3);
CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
@@ -1103,32 +1133,32 @@
RegisterOutputSlots(layer, {node.output(0)});
}
-void OnnxParser::ParseClip(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseClip(const onnx::NodeProto& node)
{
ParseActivation(node, ActivationFunction::BoundedReLu);
}
-void OnnxParser::ParseSigmoid(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseSigmoid(const onnx::NodeProto& node)
{
ParseActivation(node, ActivationFunction::Sigmoid);
}
-void OnnxParser::ParseTanh(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseTanh(const onnx::NodeProto& node)
{
ParseActivation(node, ActivationFunction::TanH);
}
-void OnnxParser::ParseRelu(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseRelu(const onnx::NodeProto& node)
{
ParseActivation(node, ActivationFunction::ReLu);
}
-void OnnxParser::ParseLeakyRelu(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseLeakyRelu(const onnx::NodeProto& node)
{
ParseActivation(node, ActivationFunction::LeakyReLu);
}
-void OnnxParser::ParseAdd(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseAdd(const onnx::NodeProto& node)
{
CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2);
CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
@@ -1186,7 +1216,7 @@
RegisterOutputSlots(layer, {node.output(0)});
}
-void OnnxParser::ParseAveragePool(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseAveragePool(const onnx::NodeProto& node)
{
Pooling2dDescriptor desc;
desc.m_PoolType = PoolingAlgorithm::Average;
@@ -1199,7 +1229,7 @@
AddPoolingLayer(node, desc);
}
-void OnnxParser::ParseBatchNormalization(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseBatchNormalization(const onnx::NodeProto& node)
{
//IGNORE momentum parameter and spatial parameters
@@ -1246,7 +1276,7 @@
RegisterOutputSlots(layer, {node.output(0)});
}
-void OnnxParser::ParseConstant(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseConstant(const onnx::NodeProto& node)
{
CHECK_VALID_SIZE(static_cast<size_t>(node.attribute_size()), 1);
if (!node.attribute(0).has_t())
@@ -1269,7 +1299,7 @@
CreateConstantLayer(node.output(0), node.name());
}
-void OnnxParser::ParseConv(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseConv(const onnx::NodeProto& node)
{
CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2, 3); //input, weight, (bias)
CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
@@ -1462,7 +1492,7 @@
RegisterOutputSlots(layer, {node.output(0)});
}
-void OnnxParser::ParseFlatten(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseFlatten(const onnx::NodeProto& node)
{
CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1);
CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
@@ -1509,7 +1539,7 @@
CreateReshapeLayer(node.input(0), node.output(0), node.name());
}
-void OnnxParser::ParseGlobalAveragePool(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseGlobalAveragePool(const onnx::NodeProto& node)
{
Pooling2dDescriptor desc = Pooling2dDescriptor();
desc.m_PoolType = PoolingAlgorithm::Average;
@@ -1533,7 +1563,7 @@
RegisterOutputSlots(layer, {node.output(0)});
}
-void OnnxParser::ParseMaxPool(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseMaxPool(const onnx::NodeProto& node)
{
Pooling2dDescriptor desc;
desc.m_PoolType = PoolingAlgorithm::Max;
@@ -1541,7 +1571,7 @@
AddPoolingLayer(node, desc);
}
-void OnnxParser::ParseReshape(const onnx::NodeProto& node)
+void OnnxParserImpl::ParseReshape(const onnx::NodeProto& node)
{
CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2);
CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
@@ -1594,9 +1624,9 @@
}
}
-void OnnxParser::PrependForBroadcast(const std::string& outputName,
- const std::string& input0,
- const std::string& input1)
+void OnnxParserImpl::PrependForBroadcast(const std::string& outputName,
+ const std::string& input0,
+ const std::string& input1)
{
//input0 should be reshaped to have same number of dim as input1
TensorInfo outputTensorInfo = TensorInfo(*m_TensorsInfo[input0].m_info);
@@ -1633,7 +1663,7 @@
}
}
-void OnnxParser::SetupInputLayers()
+void OnnxParserImpl::SetupInputLayers()
{
//Find user input and add their layers
for(int inputIndex = 0; inputIndex < m_Graph->input_size(); ++inputIndex)
@@ -1651,7 +1681,7 @@
}
}
-void OnnxParser::SetupOutputLayers()
+void OnnxParserImpl::SetupOutputLayers()
{
if(m_Graph->output_size() == 0)
{
@@ -1668,7 +1698,7 @@
}
}
-void OnnxParser::RegisterInputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds)
+void OnnxParserImpl::RegisterInputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds)
{
ARMNN_ASSERT(layer != nullptr);
if (tensorIds.size() != layer->GetNumInputSlots())
@@ -1695,7 +1725,7 @@
}
}
-void OnnxParser::RegisterOutputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds)
+void OnnxParserImpl::RegisterOutputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds)
{
ARMNN_ASSERT(layer != nullptr);
if (tensorIds.size() != layer->GetNumOutputSlots())
@@ -1734,7 +1764,7 @@
}
}
-BindingPointInfo OnnxParser::GetNetworkInputBindingInfo(const std::string& name) const
+BindingPointInfo OnnxParserImpl::GetNetworkInputBindingInfo(const std::string& name) const
{
for(int i = 0; i < m_Graph->input_size(); ++i)
{
@@ -1748,7 +1778,7 @@
name, CHECK_LOCATION().AsString()));
}
-BindingPointInfo OnnxParser::GetNetworkOutputBindingInfo(const std::string& name) const
+BindingPointInfo OnnxParserImpl::GetNetworkOutputBindingInfo(const std::string& name) const
{
for(int i = 0; i < m_Graph->output_size(); ++i)
{
@@ -1762,7 +1792,7 @@
name, CHECK_LOCATION().AsString()));
}
-std::vector<std::string> OnnxParser::GetInputs(ModelPtr& model)
+std::vector<std::string> OnnxParserImpl::GetInputs(ModelPtr& model)
{
if(model == nullptr) {
throw InvalidArgumentException(fmt::format("The given model cannot be null {}",
@@ -1786,7 +1816,7 @@
return inputNames;
}
-std::vector<std::string> OnnxParser::GetOutputs(ModelPtr& model)
+std::vector<std::string> OnnxParserImpl::GetOutputs(ModelPtr& model)
{
if(model == nullptr) {
throw InvalidArgumentException(fmt::format("The given model cannot be null {}",