diff options
Diffstat (limited to 'src/armnnOnnxParser/OnnxParser.cpp')
| -rw-r--r-- | src/armnnOnnxParser/OnnxParser.cpp | 110 |
1 files changed, 101 insertions, 9 deletions
diff --git a/src/armnnOnnxParser/OnnxParser.cpp b/src/armnnOnnxParser/OnnxParser.cpp index 3588975897..eb24bb5425 100644 --- a/src/armnnOnnxParser/OnnxParser.cpp +++ b/src/armnnOnnxParser/OnnxParser.cpp @@ -60,6 +60,25 @@ armnn::INetworkPtr IOnnxParser::CreateNetworkFromString(const std::string& proto return pOnnxParserImpl->CreateNetworkFromString(protoText); } +armnn::INetworkPtr IOnnxParser::CreateNetworkFromBinaryFile( + const char* graphFile, + const std::map<std::string, armnn::TensorShape>& inputShapes) +{ + return pOnnxParserImpl->CreateNetworkFromBinaryFile(graphFile, inputShapes); +} + +armnn::INetworkPtr IOnnxParser::CreateNetworkFromTextFile(const char* graphFile, + const std::map<std::string, armnn::TensorShape>& inputShapes) +{ + return pOnnxParserImpl->CreateNetworkFromTextFile(graphFile, inputShapes); +} + +armnn::INetworkPtr IOnnxParser::CreateNetworkFromString(const std::string& protoText, + const std::map<std::string, armnn::TensorShape>& inputShapes) +{ + return pOnnxParserImpl->CreateNetworkFromString(protoText, inputShapes); +} + BindingPointInfo IOnnxParser::GetNetworkInputBindingInfo(const std::string& name) const { return pOnnxParserImpl->GetNetworkInputBindingInfo(name); @@ -287,12 +306,18 @@ armnn::TensorInfo ToTensorInfo(const std::string& name, std::vector<unsigned int } } - // To avoid crashes by trivial tensors + // Scalar Tensor if (shape.empty()) { return TensorInfo(TensorShape(Dimensionality::Scalar), type); } + // Dynamic Tensor + if(std::find(shape.begin(), shape.end(), 0) != shape.end()) + { + return TensorInfo(TensorShape(Dimensionality::NotSpecified), type); + } + return TensorInfo(TensorShape(static_cast<unsigned int>(shape.size()), shape.data()), type); } @@ -469,7 +494,9 @@ std::vector<TensorInfo> OnnxParserImpl::ComputeOutputInfo(std::vector<std::strin outNames.end(), [this](std::string name) { - return (m_TensorsInfo.count(name) == 0 || m_TensorsInfo[name].m_info == nullptr); + return (m_TensorsInfo.count(name) == 0 || m_TensorsInfo[name].m_info == nullptr + || m_TensorsInfo[name].m_info->GetShape().GetDimensionality() == + Dimensionality::NotSpecified); }); std::vector<TensorInfo> outInfo; //if the output info(s) are not here, we need to compute them @@ -521,6 +548,8 @@ void OnnxParserImpl::ResetParser() { m_Network = armnn::INetworkPtr(nullptr, nullptr); m_Graph = nullptr; + m_InputInfos.clear(); + m_OutputInfos.clear(); } void OnnxParserImpl::Cleanup() @@ -529,6 +558,7 @@ void OnnxParserImpl::Cleanup() m_TensorsInfo.clear(); m_OutputsMap.clear(); m_OutputsFusedAndUsed.clear(); + m_InputShapes.clear(); } template<typename T> @@ -692,6 +722,14 @@ INetworkPtr OnnxParserImpl::CreateNetworkFromTextFile(const char* graphFile) return CreateNetworkFromModel(*modelProto); } +INetworkPtr OnnxParserImpl::CreateNetworkFromTextFile(const char* graphFile, + const std::map<std::string, armnn::TensorShape>& inputShapes) +{ + ResetParser(); + m_InputShapes = inputShapes; + ModelPtr modelProto = LoadModelFromTextFile(graphFile); + return CreateNetworkFromModel(*modelProto); +} ModelPtr OnnxParserImpl::LoadModelFromBinaryFile(const char* graphFile) { @@ -728,6 +766,15 @@ INetworkPtr OnnxParserImpl::CreateNetworkFromBinaryFile(const char* graphFile) return CreateNetworkFromModel(*modelProto); } +INetworkPtr OnnxParserImpl::CreateNetworkFromBinaryFile(const char* graphFile, + const std::map<std::string, armnn::TensorShape>& inputShapes) +{ + ResetParser(); + m_InputShapes = inputShapes; + ModelPtr modelProto = LoadModelFromBinaryFile(graphFile); + return CreateNetworkFromModel(*modelProto); +} + ModelPtr OnnxParserImpl::LoadModelFromString(const std::string& protoText) { if (protoText == "") @@ -754,6 +801,15 @@ INetworkPtr OnnxParserImpl::CreateNetworkFromString(const std::string& protoText return CreateNetworkFromModel(*modelProto); } +INetworkPtr OnnxParserImpl::CreateNetworkFromString(const std::string& protoText, + const std::map<std::string, armnn::TensorShape>& inputShapes) +{ + ResetParser(); + m_InputShapes = inputShapes; + ModelPtr modelProto = LoadModelFromString(protoText); + return CreateNetworkFromModel(*modelProto); +} + INetworkPtr OnnxParserImpl::CreateNetworkFromModel(onnx::ModelProto& model) { m_Network = INetwork::Create(); @@ -843,6 +899,13 @@ void OnnxParserImpl::LoadGraph() } } } + + // Get output info. + for(int outputIndex = 0; outputIndex < m_Graph->output_size(); ++outputIndex) + { + auto output = m_Graph->output(outputIndex); + m_OutputInfos[output.name()] = *m_TensorsInfo[output.name()].m_info; + } } void OnnxParserImpl::SetupInfo(const google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list) @@ -2172,13 +2235,31 @@ void OnnxParserImpl::SetupInputLayers() for(int inputIndex = 0; inputIndex < m_Graph->input_size(); ++inputIndex) { auto input = m_Graph->input(inputIndex); - if (! m_TensorsInfo[input.name()].isConstant()) + if (!m_TensorsInfo[input.name()].isConstant()) { IConnectableLayer* layer = - m_Network->AddInputLayer(static_cast<armnn::LayerBindingId>(inputIndex), input.name().c_str()); - auto tensorInfo = ToTensorInfo(input); + m_Network->AddInputLayer(static_cast<armnn::LayerBindingId>(inputIndex), input.name().c_str()); + TensorInfo tensorInfo = *m_TensorsInfo[input.name()].m_info; + if (tensorInfo.GetShape().GetDimensionality() == Dimensionality::NotSpecified) + { + if (m_InputShapes.find(input.name()) == m_InputShapes.end()) + { + throw ParseException(fmt::format("The parser does not support dynamic tensor, " + "please specify input shape for {}. {}", + input.name(), + CHECK_LOCATION().AsString())); + } + else + { + tensorInfo.SetShape(m_InputShapes[input.name()]); + m_TensorsInfo[input.name()].m_info = std::make_unique<TensorInfo>(tensorInfo); + } + + } layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); + m_InputInfos[input.name()] = tensorInfo; + RegisterOutputSlots(layer,{ input.name() }); } } @@ -2211,7 +2292,7 @@ void OnnxParserImpl::RegisterInputSlot(IConnectableLayer* layer, if (it == m_TensorConnections.end()) { - //First time seing this tensor, we need to map it + //First time seeing this tensor, we need to map it m_TensorConnections[tensorId] = TensorSlots(); } m_TensorConnections[tensorId].inputSlots.push_back(slot); @@ -2238,7 +2319,7 @@ void OnnxParserImpl::RegisterInputSlots(IConnectableLayer* layer, const std::vec if (it == m_TensorConnections.end()) { - //First time seing this tensor, we need to map it + // First time seing this tensor, we need to map it m_TensorConnections[tensorId] = TensorSlots(); } m_TensorConnections[tensorId].inputSlots.push_back(slot); @@ -2282,6 +2363,7 @@ void OnnxParserImpl::RegisterOutputSlots(IConnectableLayer* layer, const std::ve } tensorSlots.outputSlot = slot; } + } BindingPointInfo OnnxParserImpl::GetNetworkInputBindingInfo(const std::string& name) const @@ -2291,7 +2373,12 @@ BindingPointInfo OnnxParserImpl::GetNetworkInputBindingInfo(const std::string& n auto input = m_Graph->input(i); if(input.name() == name) { - return std::make_pair(static_cast<armnn::LayerBindingId>(i), ToTensorInfo(input)); + auto it = m_InputInfos.find(name); + + if (it != m_InputInfos.end()) + { + return std::make_pair(static_cast<armnn::LayerBindingId>(i), it->second); + } } } throw InvalidArgumentException(fmt::format("The input layer '{}' does not exist {}", @@ -2305,7 +2392,12 @@ BindingPointInfo OnnxParserImpl::GetNetworkOutputBindingInfo(const std::string& auto output = m_Graph->output(i); if(output.name() == name) { - return std::make_pair(static_cast<armnn::LayerBindingId>(i), ToTensorInfo(output)); + auto it = m_OutputInfos.find(name); + + if (it != m_OutputInfos.end()) + { + return std::make_pair(static_cast<armnn::LayerBindingId>(i), it->second); + } } } throw InvalidArgumentException(fmt::format("The output layer '{}' does not exist {}", |