| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #include "OnnxParser.hpp" |
| |
| #include "armnnOnnxParser/Version.hpp" |
| |
| #include <armnn/Descriptors.hpp> |
| #include <armnn/utility/Assert.hpp> |
| #include <armnn/utility/NumericCast.hpp> |
| #include <ParserHelper.hpp> |
| #include <VerificationHelpers.hpp> |
| |
| #include <fmt/format.h> |
| |
| #include <google/protobuf/text_format.h> |
| #include <google/protobuf/io/zero_copy_stream_impl.h> |
| |
| #include <iostream> |
| #include <numeric> |
| #include <armnnUtils/Permute.hpp> |
| |
| using namespace armnn; |
| |
| 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, |
| const onnx::TensorProto::DataType actualValue, |
| const char* validExpr, |
| std::string nodeName, |
| std::string tensorName, |
| const armnn::CheckLocation& location) |
| { |
| bool isValid = std::any_of(validInputTypes.begin(), |
| validInputTypes.end(), |
| [&actualValue](onnx::TensorProto::DataType x) { return x == actualValue; } ); |
| if (!isValid) |
| { |
| throw ParseException( |
| fmt::format("Datatype {} is not valid for tensor '{}' of node '{}', not in {{{}}}. {}", |
| onnx::TensorProto::DataType_Name(actualValue), |
| tensorName, |
| nodeName, |
| validExpr, |
| location.AsString())); |
| } |
| } |
| |
| #define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL, ...) \ |
| CheckValidDataType({__VA_ARGS__}, ACTUAL, #__VA_ARGS__, NODE, TENSOR, CHECK_LOCATION()) |
| |
| using StrTypeListPair = std::pair<const char*, std::initializer_list<onnx::TensorProto::DataType>>; |
| #define STR_LIST(...) StrTypeListPair(#__VA_ARGS__, {__VA_ARGS__}) |
| |
| template <typename Callable> |
| void ReadMandatoryNodeAttributeImpl(const onnx::NodeProto& node, |
| const std::string& attribName, |
| onnx::AttributeProto::AttributeType expectedType, |
| Callable callable) |
| { |
| auto attribs = node.attribute(); |
| int attriNum = 0; |
| while (attriNum < node.attribute_size()) |
| { |
| if (attribs.Get(attriNum).name() == attribName) |
| { |
| if (attribs.Get(attriNum).type() == expectedType) |
| { |
| callable(attribs.Get(attriNum)); |
| } |
| else |
| { |
| throw ParseException(fmt::format("Attribute {} of node {} expected to have {} as " |
| "onnx::AttributeProto::AttributeType, but found {} instead {}", |
| attribName, |
| node.name(), |
| onnx::AttributeProto::AttributeType_Name(expectedType), |
| onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type()), |
| CHECK_LOCATION().AsString())); |
| } |
| break; |
| } |
| ++attriNum; |
| } |
| if (attriNum == node.attribute_size()) |
| { |
| throw ParseException(fmt::format("Could not find required attribute {} in node {} {}", |
| attribName, node.name(), CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| template <typename Callable> |
| void ReadOptionalNodeAttributeImpl(const onnx::NodeProto& node, |
| const std::string& attribName, |
| onnx::AttributeProto::AttributeType expectedType, |
| Callable callable) |
| { |
| auto attribs = node.attribute(); |
| for (int attriNum = 0; attriNum < node.attribute_size(); ++attriNum) |
| { |
| if (attribs.Get(attriNum).name() == attribName) |
| { |
| if (attribs.Get(attriNum).type() == expectedType) |
| { |
| callable(attribs.Get(attriNum)); |
| } |
| else |
| { |
| throw ParseException( |
| fmt::format("Attribute {} of node {} expected to have {} as onnx::AttributeProto::AttributeType, " |
| "but found {} instead {}", |
| attribName, |
| node.name(), |
| onnx::AttributeProto::AttributeType_Name(expectedType), |
| onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type()), |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| } |
| } |
| |
| int ReadMandatoryNodeIntAttribute(const onnx::NodeProto& node, |
| const std::string& name) |
| { |
| int attribValue = 0; |
| ReadMandatoryNodeAttributeImpl(node, name, onnx::AttributeProto::INT, |
| [&attribValue](const onnx::AttributeProto& attrValue) |
| { |
| attribValue = CHECKED_INT32(attrValue.i()); |
| }); |
| return attribValue; |
| } |
| |
| int64_t ReadOptionalNodeInt64Attribute(const onnx::NodeProto& node, |
| const std::string& name, |
| const int64_t defaultValue = 0) |
| { |
| int64_t attribValue = defaultValue; |
| ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INT, |
| [&attribValue](const onnx::AttributeProto& attrValue) |
| { |
| attribValue = attrValue.i(); |
| }); |
| return attribValue; |
| } |
| |
| std::vector<uint32_t> ReadMandatoryNodeUint32ListAttribute(const onnx::NodeProto& node, |
| const std::string& name) |
| { |
| std::vector<uint32_t> attriList; |
| ReadMandatoryNodeAttributeImpl(node, name, onnx::AttributeProto::INTS, |
| [&attriList](const onnx::AttributeProto& attrValue) |
| { |
| for (int attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum) |
| { |
| attriList.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(attrValue.ints().Get(attriNum)))); |
| } |
| }); |
| return attriList; |
| } |
| |
| uint32_t ReadOptionalNodeUint32Attribute(const onnx::NodeProto& node, |
| const std::string& name, |
| const uint32_t defaultVal = 0u) |
| { |
| uint32_t attribValue = defaultVal; |
| ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INT, |
| [&attribValue](const onnx::AttributeProto& attrValue) |
| { |
| attribValue = CHECKED_NON_NEGATIVE(CHECKED_INT32((attrValue.i()))); |
| }); |
| return attribValue; |
| } |
| |
| std::vector<uint32_t> ReadOptionalNodeUint32ListAttribute(const onnx::NodeProto& node, |
| const std::string& name) |
| { |
| std::vector<uint32_t> attriList; |
| ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INTS, |
| [&attriList](const onnx::AttributeProto& attrValue) |
| { |
| for (int attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum) |
| { |
| attriList.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(attrValue.ints().Get(attriNum)))); |
| } |
| }); |
| |
| return attriList; |
| } |
| |
| float ReadOptionalNodeFloatAttribute(const onnx::NodeProto& node, |
| const std::string& name, |
| const float defaultValue = 0.0f) |
| { |
| float attribValue = defaultValue; |
| ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::FLOAT, |
| [&attribValue](const onnx::AttributeProto& attrValue) |
| { |
| attribValue = attrValue.f(); |
| }); |
| return attribValue; |
| } |
| |
| std::string ReadOptionalNodeStringAttribute(const onnx::NodeProto& node, const std::string& name) |
| { |
| std::string attribValue = ""; |
| ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::STRING, |
| [&attribValue](const onnx::AttributeProto& attrValue) |
| { |
| attribValue = attrValue.s(); |
| }); |
| return attribValue; |
| } |
| |
| armnn::TensorInfo ToTensorInfo(const std::string& name, std::vector<unsigned int>& shape, int data_type) |
| { |
| DataType type; |
| switch(data_type) |
| { |
| case onnx::TensorProto::FLOAT: |
| { |
| type = DataType::Float32; |
| break; |
| } |
| case onnx::TensorProto::INT32: |
| case onnx::TensorProto::INT64: |
| { |
| type = DataType::Signed32; |
| break; |
| } |
| default: |
| { |
| throw ParseException( |
| fmt::format("'{}' is not a currently supported datatype for tensor {}." |
| " Supported dataTypes are FLOAT, INT32 and INT64. {}", |
| onnx::TensorProto::DataType_Name(static_cast<onnx::TensorProto::DataType>(data_type)), |
| name, |
| CHECK_LOCATION().AsString() )); |
| } |
| } |
| |
| // To avoid crashes by trivial tensors |
| if (shape.empty()) |
| { |
| return TensorInfo(TensorShape(Dimensionality::Scalar), type); |
| } |
| |
| return TensorInfo(TensorShape(static_cast<unsigned int>(shape.size()), shape.data()), type); |
| } |
| |
| armnn::TensorInfo ToTensorInfo(const onnx::ValueInfoProto& info) |
| { |
| const onnx::TensorShapeProto onnxShape = info.type().tensor_type().shape(); |
| std::vector<unsigned int> shapeDims; |
| for (int i = 0; i < onnxShape.dim_size(); ++i) |
| { |
| shapeDims.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(onnxShape.dim(i).dim_value()))); |
| } |
| |
| return ToTensorInfo(info.name(), shapeDims, info.type().tensor_type().elem_type()); |
| } |
| |
| armnn::TensorInfo ToTensorInfo(const onnx::TensorProto& tensor) |
| { |
| std::vector<unsigned int> shapeDims; |
| |
| for (auto dim: tensor.dims()) |
| { |
| shapeDims.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(dim))); |
| } |
| |
| return ToTensorInfo(tensor.name(), shapeDims, tensor.data_type()); |
| } |
| |
| std::string TensorInfoAsString(const TensorInfo& info, |
| const std::string& name, |
| const onnx::TensorProto::DataType& type) |
| { |
| const TensorShape shape = info.GetShape(); |
| std::stringstream ss; |
| ss << "tensor '" << name << "' contains " |
| << onnx::TensorProto::DataType_Name(type) |
| << " and has shape ["; |
| |
| for (uint32_t i = 0; i < shape.GetNumDimensions() - 1; ++i) |
| { |
| ss << shape[i] << ", "; |
| } |
| ss << shape[shape.GetNumDimensions() - 1] << "]"; |
| return ss.str(); |
| } |
| |
| void CalcPadding(uint32_t inputSize, |
| uint32_t filterSize, |
| uint32_t stride, |
| uint32_t dilation, |
| uint32_t* paddingFront, |
| uint32_t* paddingBack, |
| bool isUpper) |
| { |
| uint32_t outputSize = (inputSize + stride - 1) / stride; |
| uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1); |
| uint32_t temp = (outputSize - 1) * stride + dilatedSize; |
| *paddingFront = (temp - inputSize) / 2; |
| *paddingBack = *paddingFront; |
| if((temp - inputSize) % 2 == 1) |
| { |
| if (isUpper) |
| { |
| *paddingBack += 1; |
| } |
| else |
| { |
| *paddingFront += 1; |
| } |
| } |
| } |
| |
| TensorInfo ComputeReshapeInfo(const TensorShape& targetShapeTensor, |
| const TensorShape& inShape, |
| const std::string& outName, |
| DataType dataType = DataType::Float32) |
| { |
| std::vector<int> targetDims; |
| for(uint i = 0; i < targetShapeTensor.GetNumDimensions(); ++i) |
| { |
| int val = CHECKED_INT32(targetShapeTensor[i]); |
| if(val == 0) |
| { |
| targetDims.push_back(static_cast<int>(inShape[static_cast<uint>(i)])); |
| } |
| else |
| { |
| targetDims.push_back(val); |
| } |
| } |
| |
| std::vector<unsigned int> outDims(targetDims.begin(), targetDims.end()); |
| const auto stretchDim = std::find(targetDims.begin(), targetDims.end(), -1); |
| if (stretchDim != targetDims.end()) |
| { |
| if (std::find(std::next(stretchDim), targetDims.end(), -1) != targetDims.end()) |
| { |
| std::stringstream ss; |
| ss << "[ "; |
| for(uint i = 0; i < targetDims.size() - 1; ++i) |
| { |
| ss << targetDims[i] << ", "; |
| } |
| ss << targetDims[targetDims.size() - 1] << " ]"; |
| |
| throw ParseException( |
| fmt::format("Error during creation of reshaped tensor '{}'. At most one component of shape can be " |
| " -1 and here, shape is {} {}", |
| outName, |
| ss.str(), |
| CHECK_LOCATION().AsString())); |
| } |
| |
| auto targetNumElements = armnn::numeric_cast<unsigned int>(std::accumulate(targetDims.begin(), targetDims.end(), |
| -1, std::multiplies<int32_t>())); |
| auto stretchIndex = static_cast<size_t>(std::distance(targetDims.begin(), stretchDim)); |
| outDims[stretchIndex] = inShape.GetNumElements() / targetNumElements; |
| } |
| TensorShape outShape = TensorShape{static_cast<unsigned int>(outDims.size()), outDims.data()}; |
| return TensorInfo(outShape, dataType); |
| } |
| |
| } //namespace |
| |
| 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 }, |
| { "Shape", &OnnxParserImpl::ParseShape }, |
| { "Gather", &OnnxParserImpl::ParseGather }, |
| { "Unsqueeze", &OnnxParserImpl::ParseUnsqueeze }, |
| { "Concat", &OnnxParserImpl::ParseConcat }, |
| { "Gemm", &OnnxParserImpl::ParseGemm } |
| }; |
| |
| template<typename TypePair, typename Location> |
| void OnnxParserImpl::ValidateInputs(const onnx::NodeProto& node, |
| TypePair validInputs, |
| const Location& location) |
| { |
| for(auto input : node.input()) |
| { |
| CheckValidDataType(validInputs.second, |
| m_TensorsInfo[input].m_dtype, |
| validInputs.first, |
| node.name(), |
| input, |
| location); |
| } |
| } |
| |
| #define VALID_INPUTS(NODE, VALID_INPUTS) \ |
| OnnxParserImpl::ValidateInputs(NODE, \ |
| VALID_INPUTS, \ |
| CHECK_LOCATION()) |
| |
| std::vector<TensorInfo> OnnxParserImpl::ComputeOutputInfo(std::vector<std::string> outNames, |
| const IConnectableLayer* layer, |
| std::vector<TensorShape> inputShapes, |
| const onnx::TensorProto::DataType& dataType) |
| { |
| ARMNN_ASSERT(! outNames.empty()); |
| bool needCompute = std::any_of(outNames.begin(), |
| outNames.end(), |
| [this](std::string name) |
| { |
| return (m_TensorsInfo.count(name) == 0 || m_TensorsInfo[name].m_info == nullptr); |
| }); |
| std::vector<TensorInfo> outInfo; |
| //if the output info(s) are not here, we need to compute them |
| std::vector<TensorShape> inferredShapes; |
| DataType armnnType = DataType::Float32; |
| if(needCompute) { |
| inferredShapes = layer->InferOutputShapes(inputShapes); |
| ARMNN_ASSERT(inferredShapes.size() == outNames.size()); |
| switch (dataType) { |
| case onnx::TensorProto::FLOAT: { |
| armnnType = DataType::Float32; |
| break; |
| } |
| case onnx::TensorProto::INT32: |
| case onnx::TensorProto::INT64: { |
| armnnType = DataType::Signed32; |
| break; |
| } |
| default: { |
| throw ParseException( |
| fmt::format("'{}' is not a currently supported datatype for {}." |
| " Supported dataTypes are FLOAT, INT32 and INT64. {}", |
| onnx::TensorProto::DataType_Name(static_cast<onnx::TensorProto::DataType>(dataType)), |
| layer->GetName(), |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| } |
| for (uint i = 0; i < outNames.size(); ++i) |
| { |
| if(needCompute) |
| { |
| m_TensorsInfo[outNames[i]] = OnnxTensor(); |
| m_TensorsInfo[outNames[i]].m_info = std::make_unique<TensorInfo>( |
| TensorInfo(inferredShapes[i], armnnType)); |
| m_TensorsInfo[outNames[i]].m_dtype = dataType; |
| } |
| outInfo.push_back(*m_TensorsInfo[outNames[i]].m_info); |
| } |
| return outInfo; |
| } |
| |
| OnnxParserImpl::OnnxParserImpl() |
| : m_Network(nullptr, nullptr) |
| { |
| } |
| |
| void OnnxParserImpl::ResetParser() |
| { |
| m_Network = armnn::INetworkPtr(nullptr, nullptr); |
| m_Graph = nullptr; |
| } |
| |
| void OnnxParserImpl::Cleanup() |
| { |
| m_TensorConnections.clear(); |
| m_TensorsInfo.clear(); |
| m_OutputsMap.clear(); |
| m_OutputsFusedAndUsed.clear(); |
| } |
| |
| template<typename T> |
| std::pair<armnn::ConstTensor, std::unique_ptr<T[]>> |
| CreateConstTensorImpl(const T* bufferPtr, |
| armnn::TensorInfo& tensorInfo, |
| const armnn::Optional<armnn::PermutationVector&> permutationVector) |
| { |
| ARMNN_ASSERT_MSG(bufferPtr != nullptr, fmt::format("Buffer for permutation is null").c_str()); |
| |
| std::unique_ptr<T[]> data(new T[tensorInfo.GetNumElements()]); |
| |
| if (permutationVector.has_value() && permutationVector.value().GetSize() > 0) |
| { |
| tensorInfo = armnnUtils::Permuted(tensorInfo, permutationVector.value()); |
| armnnUtils::Permute(tensorInfo.GetShape(), permutationVector.value(), |
| reinterpret_cast<const T*>(bufferPtr), data.get(), sizeof(T)); |
| } |
| else |
| { |
| ::memcpy(data.get(), bufferPtr, tensorInfo.GetNumBytes()); |
| } |
| |
| return std::make_pair(ConstTensor(tensorInfo, data.get()), std::move(data)); |
| } |
| |
| std::pair<ConstTensor, std::unique_ptr<float[]>> |
| OnnxParserImpl::CreateConstTensor(const std::string name, |
| armnn::Optional<armnn::PermutationVector&> permutationVector) |
| { |
| TensorInfo tensorInfo = *m_TensorsInfo[name].m_info; |
| onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor; |
| |
| //ONNX can have Float16 and double constant nodes but ArmNN only supports float32 |
| CHECK_VALID_DATATYPE(name, onnxTensor.name(), |
| static_cast<onnx::TensorProto::DataType>(onnxTensor.data_type()), onnx::TensorProto::FLOAT); |
| |
| // Makes sure IsConstant flag is set. |
| tensorInfo.SetConstant(); |
| |
| // Const tensors requires at least a list of values |
| if (tensorInfo.GetNumElements() == 0) |
| { |
| throw ParseException(fmt::format("No tensor data found for Const tensor '{}' {}", |
| name, |
| CHECK_LOCATION().AsString())); |
| } |
| |
| auto srcData = onnxTensor.float_data().data(); |
| // Copy the value list entries into the destination |
| if (!onnxTensor.has_raw_data()) |
| { |
| if(tensorInfo.GetNumElements() != static_cast<uint>(onnxTensor.float_data_size())) |
| { |
| throw ParseException( |
| fmt::format("The number of data provided ({}) does not match the tensor '{}' number of " |
| "elements ({}) {}", |
| onnxTensor.float_data_size(), |
| name, |
| tensorInfo.GetNumElements(), |
| CHECK_LOCATION().AsString())); |
| } |
| return CreateConstTensorImpl<float>(srcData, tensorInfo, permutationVector); |
| } |
| else |
| { |
| return CreateConstTensorImpl<float>(reinterpret_cast<const float*>(onnxTensor.raw_data().c_str()), |
| tensorInfo, |
| permutationVector); |
| } |
| } |
| |
| std::pair<ConstTensor, std::unique_ptr<int32_t[]>> |
| OnnxParserImpl::CreateInt64ConstTensor(const std::string name, |
| armnn::Optional<armnn::PermutationVector&> permutationVector) |
| { |
| TensorInfo tensorInfo = *m_TensorsInfo[name].m_info; |
| onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor; |
| |
| CHECK_VALID_DATATYPE(name, onnxTensor.name(), |
| static_cast<onnx::TensorProto::DataType>(onnxTensor.data_type()), onnx::TensorProto::INT64); |
| |
| // Makes sure IsConstant flag is set. |
| tensorInfo.SetConstant(); |
| uint numElements = tensorInfo.GetNumElements(); |
| |
| // Const tensors requires at least a list of values |
| if (numElements == 0) |
| { |
| throw ParseException(fmt::format("No tensor data found for Const tensor '{}' {}", |
| name, |
| CHECK_LOCATION().AsString())); |
| } |
| |
| // Copy the value list entries into the destination |
| if (!onnxTensor.has_raw_data()) |
| { |
| auto srcData = onnxTensor.int64_data().data(); |
| if(numElements != static_cast<uint>(onnxTensor.int64_data_size())) |
| { |
| throw ParseException( |
| fmt::format("The number of data provided ({}) does not match the tensor '{}' number of " |
| "elements ({}) {}", |
| onnxTensor.int64_data_size(), |
| name, |
| tensorInfo.GetNumElements(), |
| CHECK_LOCATION().AsString())); |
| } |
| |
| std::vector<int32_t> int32Data; |
| for(uint i = 0; i < numElements; i++) |
| { |
| int32_t int32Value = CHECKED_INT32(srcData[i]); |
| int32Data.push_back(int32Value); |
| } |
| |
| return CreateConstTensorImpl<int32_t>(int32Data.data(), tensorInfo, permutationVector); |
| } |
| else |
| { |
| auto srcData = reinterpret_cast<const int64_t*>(onnxTensor.raw_data().c_str()); |
| std::vector<int32_t> int32Data; |
| for(uint i = 0; i < numElements; i++) |
| { |
| int32_t int32Value = CHECKED_INT32(srcData[i]); |
| int32Data.push_back(int32Value); |
| } |
| return CreateConstTensorImpl<int32_t>(int32Data.data(), tensorInfo, permutationVector); |
| } |
| } |
| |
| ModelPtr OnnxParserImpl::LoadModelFromTextFile(const char* graphFile) |
| { |
| FILE* fd = fopen(graphFile, "r"); |
| |
| if (fd == nullptr) |
| { |
| throw FileNotFoundException(fmt::format("Invalid (null) filename {}", CHECK_LOCATION().AsString())); |
| } |
| |
| // Parse the file into a message |
| ModelPtr modelProto = std::make_unique<onnx::ModelProto>(); |
| using google::protobuf::io::FileInputStream; |
| std::unique_ptr<FileInputStream> input = std::make_unique<FileInputStream>(fileno(fd)); |
| bool success = google::protobuf::TextFormat::Parse(input.get(), modelProto.get()); |
| fclose(fd); |
| |
| if (!success) |
| { |
| std::stringstream error; |
| error << "Failed to parse graph file"; |
| throw ParseException(fmt::format("{} {}", error.str(), CHECK_LOCATION().AsString())); |
| } |
| return modelProto; |
| } |
| |
| INetworkPtr OnnxParserImpl::CreateNetworkFromTextFile(const char* graphFile) |
| { |
| ResetParser(); |
| ModelPtr modelProto = LoadModelFromTextFile(graphFile); |
| return CreateNetworkFromModel(*modelProto); |
| } |
| |
| |
| ModelPtr OnnxParserImpl::LoadModelFromBinaryFile(const char* graphFile) |
| { |
| FILE* fd = fopen(graphFile, "rb"); |
| |
| if (fd == nullptr) |
| { |
| throw FileNotFoundException(fmt::format("Invalid (null) filename {}", CHECK_LOCATION().AsString())); |
| } |
| |
| // Parse the file into a message |
| ModelPtr modelProto = std::make_unique<onnx::ModelProto>(); |
| |
| google::protobuf::io::FileInputStream inStream(fileno(fd)); |
| google::protobuf::io::CodedInputStream codedStream(&inStream); |
| codedStream.SetTotalBytesLimit(INT_MAX); |
| bool success = modelProto.get()->ParseFromCodedStream(&codedStream); |
| fclose(fd); |
| |
| if (!success) |
| { |
| std::stringstream error; |
| error << "Failed to parse graph file"; |
| throw ParseException(fmt::format("{} {}", error.str(), CHECK_LOCATION().AsString())); |
| } |
| return modelProto; |
| |
| } |
| |
| INetworkPtr OnnxParserImpl::CreateNetworkFromBinaryFile(const char* graphFile) |
| { |
| ResetParser(); |
| ModelPtr modelProto = LoadModelFromBinaryFile(graphFile); |
| return CreateNetworkFromModel(*modelProto); |
| } |
| |
| ModelPtr OnnxParserImpl::LoadModelFromString(const std::string& protoText) |
| { |
| if (protoText == "") |
| { |
| throw InvalidArgumentException(fmt::format("Invalid (empty) string for model parameter {}", |
| CHECK_LOCATION().AsString())); |
| } |
| // Parse the string into a message |
| ModelPtr modelProto = std::make_unique<onnx::ModelProto>(); |
| bool success = google::protobuf::TextFormat::ParseFromString(protoText, modelProto.get()); |
| if (!success) |
| { |
| std::stringstream error; |
| error << "Failed to parse graph file"; |
| throw ParseException(fmt::format("{} {}", error.str(), CHECK_LOCATION().AsString())); |
| } |
| return modelProto; |
| } |
| |
| INetworkPtr OnnxParserImpl::CreateNetworkFromString(const std::string& protoText) |
| { |
| ResetParser(); |
| ModelPtr modelProto = LoadModelFromString(protoText); |
| return CreateNetworkFromModel(*modelProto); |
| } |
| |
| INetworkPtr OnnxParserImpl::CreateNetworkFromModel(onnx::ModelProto& model) |
| { |
| m_Network = INetwork::Create(); |
| try |
| { |
| m_Graph = std::make_unique<onnx::GraphProto>(*model.mutable_graph()); |
| LoadGraph(); |
| } |
| catch (const ParseException& e) |
| { |
| Cleanup(); |
| throw e; |
| } |
| Cleanup(); |
| return std::move(m_Network); |
| } |
| |
| void OnnxParserImpl::LoadGraph() |
| { |
| ARMNN_ASSERT(m_Graph.get() != nullptr); |
| |
| //Fill m_TensorsInfo with the shapes and value of every tensor |
| SetupInfo(m_Graph->mutable_output()); |
| SetupInfo(m_Graph->mutable_input()); |
| SetupInfo(m_Graph->mutable_value_info()); |
| |
| for (auto tensor : m_Graph->initializer()) |
| { |
| m_TensorsInfo[tensor.name()].m_tensor = std::make_unique<const onnx::TensorProto>(tensor); |
| m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(ToTensorInfo(tensor)); |
| m_TensorsInfo[tensor.name()].m_dtype = |
| static_cast<onnx::TensorProto::DataType>(tensor.data_type()); |
| } |
| |
| SetupInputLayers(); |
| SetupOutputLayers(); |
| |
| //Detect FullyConnected layers with bias and update the FusedAndUsed map acccordingly |
| DetectFullyConnected(); |
| |
| //Parsing the graph |
| for(size_t nodeIndex = 0; nodeIndex < static_cast<size_t>(m_Graph->node_size()); nodeIndex++) |
| { |
| auto node = m_Graph->node(static_cast<int>(nodeIndex)); |
| const std::string& operation = node.op_type(); |
| |
| // check which layers we handled already (add and matmul fused as FC) |
| if (operation == "MatMul" ) |
| { |
| if(m_OutputsFusedAndUsed[nodeIndex].inputForNodes != m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.size()) |
| { |
| //Node which can not be fused as a FullyConnected layer (used in layers as a simple matmul output) |
| AddFullyConnected(node); |
| } |
| } |
| else if (!(m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) && operation == "Add") |
| { |
| int matmulIndex = static_cast<int> (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes[0]); |
| AddFullyConnected(m_Graph->node(matmulIndex), &node); |
| } |
| else if (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) //node is not part of a fused layer |
| { |
| auto it = m_ParserFunctions.find(operation); |
| if (it != m_ParserFunctions.end()) |
| { |
| auto func = it->second; |
| (this->*func)(node); |
| } |
| else |
| { |
| throw ParseException(fmt::format("Unsupported operation {} for node '{}' {}", |
| operation, |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| } |
| |
| //Making the connections between outputs and inputs of each layers |
| for (const auto& tensorCon : m_TensorConnections) |
| { |
| if (tensorCon.second.outputSlot != nullptr) |
| { |
| for (size_t inputSlotIdx = 0; inputSlotIdx < tensorCon.second.inputSlots.size(); ++inputSlotIdx) |
| { |
| tensorCon.second.outputSlot->Connect(*(tensorCon.second.inputSlots[inputSlotIdx])); |
| } |
| } |
| } |
| } |
| |
| void OnnxParserImpl::SetupInfo(const google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list) |
| { |
| for (auto tensor : *list) |
| { |
| m_TensorsInfo[tensor.name()] = OnnxTensor(); |
| m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(ToTensorInfo(tensor)); |
| m_TensorsInfo[tensor.name()].m_dtype = |
| static_cast<onnx::TensorProto::DataType>(tensor.type().tensor_type().elem_type()); |
| } |
| } |
| |
| void OnnxParserImpl::DetectFullyConnected() |
| { |
| m_OutputsFusedAndUsed = std::vector<UsageSummary> (static_cast<size_t>(m_Graph->node_size()), UsageSummary()); |
| auto matmulAndConstant = [&](const std::string& constInput, |
| const std::string& matmulInput, |
| int& nodeIndex) |
| { |
| auto matmulIt = m_OutputsMap.find(matmulInput); |
| if(matmulIt != m_OutputsMap.end() && matmulIt->second.first->op_type() == "MatMul" |
| && m_TensorsInfo[constInput].isConstant()) |
| { |
| nodeIndex = matmulIt->second.second; |
| return true; |
| } |
| return false; |
| }; |
| |
| for(int nodeIndex = 0; nodeIndex < m_Graph->node_size(); nodeIndex++) |
| { |
| const onnx::NodeProto* node = &m_Graph->node(nodeIndex); |
| for (const std::string& output : node->output()) |
| { |
| m_OutputsMap[output] = std::make_pair(node, nodeIndex); |
| } |
| |
| for (const std::string& input : node->input()) //count how many time a node is used as input |
| { |
| auto matmulIt = m_OutputsMap.find(input); |
| if(matmulIt != m_OutputsMap.end()){ |
| ++m_OutputsFusedAndUsed[static_cast<size_t>(matmulIt->second.second)].inputForNodes; //node used |
| } |
| } |
| |
| if (node->op_type() == "Add") |
| { |
| int matmulIndex = 0; |
| if (matmulAndConstant(node->input(0), node->input(1), matmulIndex) || |
| matmulAndConstant(node->input(1), node->input(0), matmulIndex)) |
| { |
| //matmul and add were fused |
| m_OutputsFusedAndUsed[static_cast<size_t>(matmulIndex)].fusedWithNodes |
| .push_back(static_cast<size_t>(nodeIndex)); |
| |
| m_OutputsFusedAndUsed[static_cast<size_t>(nodeIndex)].fusedWithNodes |
| .push_back(static_cast<size_t>(matmulIndex)); |
| } |
| } |
| } |
| |
| for (auto output: m_Graph->output()) { //Add usages as output of the graph in count of usages |
| auto matmulIt = m_OutputsMap.find(output.name()); |
| if(matmulIt != m_OutputsMap.end()){ |
| ++m_OutputsFusedAndUsed[static_cast<size_t>(matmulIt->second.second)].inputForNodes; |
| } |
| } |
| } |
| |
| template<typename 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()) |
| { |
| cstIndex = 0; |
| } |
| else if (m_TensorsInfo[node.input(1)].isConstant()) |
| { |
| cstIndex = 1; |
| } |
| else |
| { |
| throw ParseException(fmt::format("One of the input tensors ('{}' or '{}') should be constant in node '{}' {}", |
| node.input(0), |
| node.input(1), |
| node.name(), |
| location.AsString())); |
| } |
| if(constName) |
| { |
| *constName = node.input(cstIndex); |
| } |
| if(inputName) |
| { |
| *inputName = node.input(!cstIndex); |
| } |
| } |
| |
| template<typename Location> |
| void OnnxParserImpl::To1DTensor(const std::string& name, const Location& location) |
| { |
| TensorShape shape = m_TensorsInfo[name].m_info->GetShape(); |
| std::vector<uint32_t> newShape; |
| for(uint i = 0; i < shape.GetNumDimensions() - 1; ++i) |
| { |
| if(shape[i] != 1) |
| { |
| throw ParseException( |
| fmt::format("Only tensors with shape [1, ..., 1, X] can be converted to 1D and {} {}", |
| TensorInfoAsString(*m_TensorsInfo[name].m_info, name, m_TensorsInfo[name].m_dtype), |
| location.AsString())); |
| } |
| } |
| newShape.push_back(shape[shape.GetNumDimensions() - 1]); |
| |
| m_TensorsInfo[name].m_info->SetShape(TensorShape(static_cast<unsigned int>(newShape.size()), newShape.data())); |
| } |
| |
| void OnnxParserImpl::AddConvLayerWithDepthwiseConv(const onnx::NodeProto& node, const Convolution2dDescriptor& convDesc) |
| { |
| ARMNN_ASSERT(node.op_type() == "Conv"); |
| |
| DepthwiseConvolution2dDescriptor desc; |
| desc.m_PadLeft = convDesc.m_PadLeft; |
| desc.m_PadRight = convDesc.m_PadRight; |
| desc.m_PadTop = convDesc.m_PadTop; |
| desc.m_PadBottom = convDesc.m_PadBottom; |
| desc.m_StrideX = convDesc.m_StrideX; |
| desc.m_StrideY = convDesc.m_StrideY; |
| desc.m_BiasEnabled = convDesc.m_BiasEnabled; |
| |
| armnn::IConnectableLayer* layer; |
| |
| // weights come in as [O,1,H,W] from ONNX and need to be converted to ArmNNs dephtwise weights layout [1,H,W,O] |
| armnn::PermutationVector perVec {3,0,1,2}; |
| auto weightTensor = CreateConstTensor(node.input(1), perVec); |
| |
| if (node.input_size() == 3) |
| { |
| if(!m_TensorsInfo[node.input(2)].isConstant()) |
| { |
| throw ParseException(fmt::format("Bias '{}' should be constant in Conv layer '{}' {}", |
| node.input(2), |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| desc.m_BiasEnabled = true; |
| auto biasTensor = CreateConstTensor(node.input(2)); |
| layer = m_Network->AddDepthwiseConvolution2dLayer(desc, |
| weightTensor.first, |
| Optional<ConstTensor>(biasTensor.first), |
| node.name().c_str()); |
| } |
| else |
| { |
| layer = m_Network->AddDepthwiseConvolution2dLayer(desc, |
| weightTensor.first, |
| EmptyOptional(), |
| node.name().c_str()); |
| } |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer, |
| { m_TensorsInfo[node.input(0)].m_info->GetShape(), |
| weightTensor.first.GetInfo().GetShape() }); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| RegisterInputSlots(layer, {node.input(0)}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| void OnnxParserImpl::AddFullyConnected(const onnx::NodeProto& matmulNode, const onnx::NodeProto* addNode) |
| { |
| |
| // find matmul inputs |
| std::string weightName; |
| std::string inputName; |
| CHECK_VALID_SIZE(static_cast<size_t>(matmulNode.input_size()), 2); |
| CHECK_VALID_SIZE(static_cast<size_t>(matmulNode.output_size()), 1); |
| VALID_INPUTS(matmulNode, STR_LIST(onnx::TensorProto::FLOAT)); |
| |
| GetInputAndParam(matmulNode, &inputName, &weightName, CHECK_LOCATION()); |
| |
| FullyConnectedDescriptor desc; |
| desc.m_BiasEnabled = addNode != nullptr; |
| |
| IConnectableLayer* layer = nullptr; |
| if(desc.m_BiasEnabled) |
| { |
| // find bias const |
| std::string biasName; |
| CHECK_VALID_SIZE(static_cast<size_t>(addNode->input_size()), 2); |
| CHECK_VALID_SIZE(static_cast<size_t>(addNode->output_size()), 1); |
| VALID_INPUTS(*addNode, STR_LIST(onnx::TensorProto::FLOAT)); |
| |
| GetInputAndParam(*addNode, nullptr, &biasName, CHECK_LOCATION()); |
| |
| //Output shape is [1, weights[1]] and 1d vec in ONNX can be [1,X] so we convert biases to "armnn" 1D |
| To1DTensor(biasName, CHECK_LOCATION()); |
| TensorInfo weightInfo = *m_TensorsInfo[weightName].m_info; |
| TensorInfo biasInfo = *m_TensorsInfo[biasName].m_info; |
| |
| if (weightInfo.GetShape()[1] != biasInfo.GetShape()[0]) |
| { |
| throw ParseException( |
| fmt::format("Shape of weights '{}' and bias of following Add node '{}' do not match : {}" |
| " and {} ( /!\\ bias should be a 1D tensor) {}", |
| weightName, |
| addNode->name(), |
| TensorInfoAsString(*m_TensorsInfo[weightName].m_info, weightName, |
| m_TensorsInfo[weightName].m_dtype), |
| TensorInfoAsString(*m_TensorsInfo[biasName].m_info, biasName, |
| m_TensorsInfo[biasName].m_dtype ), |
| CHECK_LOCATION().AsString())); |
| } |
| |
| // Just add a FullyConnected layer, weights and biases are handled as inputs now. |
| layer = m_Network->AddFullyConnectedLayer(desc, matmulNode.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({addNode->output(0)}, layer, |
| {m_TensorsInfo[inputName].m_info->GetShape(), |
| m_TensorsInfo[weightName].m_info->GetShape()}); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // Add constant layer to store weights/biases and connect to FullyConnected layer.. |
| if(m_TensorsInfo[weightName].isConstant()) |
| { |
| IConnectableLayer* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(weightName).first); |
| |
| weightInfo.SetConstant(); |
| weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo); |
| weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| } |
| |
| if(m_TensorsInfo[biasName].isConstant()) |
| { |
| IConnectableLayer* biasLayer = m_Network->AddConstantLayer(CreateConstTensor(biasName).first); |
| |
| biasInfo.SetConstant(); |
| biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo); |
| biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| } |
| |
| RegisterInputSlots(layer, {inputName, weightName, biasName}); |
| RegisterOutputSlots(layer, {addNode->output(0)}); |
| } |
| else |
| { |
| layer = m_Network->AddFullyConnectedLayer(desc, matmulNode.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({matmulNode.output(0)}, layer, |
| {m_TensorsInfo[inputName].m_info->GetShape(), |
| m_TensorsInfo[weightName].m_info->GetShape()}); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // Add constant layer to store weights and connect to FullyConnected layer. |
| if(m_TensorsInfo[weightName].isConstant()) |
| { |
| TensorInfo weightInfo = *m_TensorsInfo[weightName].m_info; |
| IConnectableLayer* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(weightName).first); |
| |
| weightInfo.SetConstant(); |
| weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo); |
| } |
| |
| RegisterInputSlots(layer, {inputName, weightName}); |
| RegisterOutputSlots(layer, {matmulNode.output(0)}); |
| } |
| } |
| |
| void OnnxParserImpl::AddPoolingLayer(const onnx::NodeProto& node, Pooling2dDescriptor& desc) |
| { |
| |
| CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1); |
| CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| |
| VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| |
| std::vector<uint32_t> kernel_shape = ReadMandatoryNodeUint32ListAttribute(node, "kernel_shape"); //size of pool win |
| std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, "strides"); |
| std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, "pads"); |
| |
| desc.m_OutputShapeRounding = OutputShapeRounding::Floor; |
| desc.m_PoolWidth = kernel_shape[1]; |
| desc.m_PoolHeight = kernel_shape[0]; |
| |
| if(strides.empty()) |
| { |
| desc.m_StrideX = 1; |
| desc.m_StrideY = 1; |
| } |
| else |
| { |
| desc.m_StrideX = strides[1]; |
| desc.m_StrideY = strides[0]; |
| } |
| |
| //Check new padding version first |
| if(pads.empty()) |
| { |
| //Check deprecated version |
| std::string paddingString = ReadOptionalNodeStringAttribute(node, "auto_pad"); |
| if(paddingString != "VALID" && paddingString != "" && paddingString != "NOTSET") |
| { |
| bool isUpper; |
| if( paddingString == "SAME_LOWER") |
| { |
| isUpper = false; |
| } |
| else if (paddingString == "SAME_UPPER") |
| { |
| isUpper = true; |
| } |
| else |
| { |
| throw ParseException(fmt::format("Invalid auto_pad attribute for node {}. " |
| "Only SAME_UPPER, SAME_LOWER or VALID supported and found {} {}", |
| node.name(), |
| paddingString, |
| CHECK_LOCATION().AsString())); |
| } |
| auto inputInfo = *m_TensorsInfo[node.input(0)].m_info; |
| uint32_t inputHeight = inputInfo.GetShape()[2]; |
| uint32_t inputWidth = inputInfo.GetShape()[3]; |
| CalcPadding(inputHeight, |
| desc.m_PoolHeight, |
| desc.m_StrideY, |
| 1u, |
| &desc.m_PadTop, |
| &desc.m_PadBottom, |
| isUpper); |
| CalcPadding(inputWidth, |
| desc.m_PoolWidth, |
| desc.m_StrideX, |
| 1u, |
| &desc.m_PadLeft, |
| &desc.m_PadRight, |
| isUpper); |
| } |
| } |
| else |
| { |
| desc.m_PadTop = pads[0]; |
| desc.m_PadLeft = pads[1]; |
| desc.m_PadBottom = pads[2]; |
| desc.m_PadRight = pads[3]; |
| } |
| |
| IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()}); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| RegisterInputSlots(layer, {node.input(0)}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| 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); |
| |
| TensorShape input0Shape = m_TensorsInfo[input0].m_info->GetShape(); |
| TensorShape input1Shape = m_TensorsInfo[input1].m_info->GetShape(); |
| |
| if(input1Shape.GetNumDimensions() < input0Shape.GetNumDimensions()) |
| { |
| auto outputName = fmt::format("reshape_output_{}", input1); |
| PrependForBroadcast(outputName, input1, input0); |
| inputs.second = outputName; |
| } |
| else if(input0Shape.GetNumDimensions() < input1Shape.GetNumDimensions()) |
| { |
| auto outputName = fmt::format("reshape_output_{}", input0); |
| PrependForBroadcast(outputName, input0, input1); |
| inputs.first = outputName; |
| } |
| return inputs; |
| } |
| |
| void OnnxParserImpl::CreateConstantLayer(const std::string& tensorName, const std::string& layerName) |
| { |
| auto armnnTensor = CreateConstTensor(tensorName); |
| IConnectableLayer* layer = m_Network->AddConstantLayer(armnnTensor.first, layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(armnnTensor.first.GetInfo()); |
| RegisterOutputSlots(layer, {tensorName}); |
| } |
| |
| void OnnxParserImpl::CreateInt64ConstantLayer(const std::string& tensorName, const std::string& layerName) |
| { |
| auto armnnTensor = CreateInt64ConstTensor(tensorName); |
| IConnectableLayer* layer = m_Network->AddConstantLayer(armnnTensor.first, layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(armnnTensor.first.GetInfo()); |
| RegisterOutputSlots(layer, {tensorName}); |
| } |
| |
| 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; |
| reshapeDesc.m_TargetShape = outputTensorInfo.GetShape(); |
| |
| IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| RegisterInputSlots(layer, {inputName}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, {outputName}); |
| } |
| |
| 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); |
| |
| VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| |
| ActivationDescriptor desc; |
| desc.m_Function = func; |
| |
| if (func == ActivationFunction::BoundedReLu) |
| { |
| if (node.input_size() == 1 && node.attribute_size() > 0) |
| { |
| desc.m_A = ReadOptionalNodeFloatAttribute(node, "max", std::numeric_limits<float>::max()); |
| desc.m_B = ReadOptionalNodeFloatAttribute(node, "min", std::numeric_limits<float>::lowest()); |
| } |
| else |
| { |
| desc.m_A = node.input(2).empty() ? std::numeric_limits<float>::max() : std::stof(node.input(2)); |
| desc.m_B = node.input(1).empty() ? std::numeric_limits<float>::lowest() : std::stof(node.input(1)); |
| } |
| } |
| |
| IConnectableLayer* const layer = m_Network->AddActivationLayer(desc, node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({ node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()}); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| RegisterInputSlots(layer, {node.input(0)}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| void OnnxParserImpl::ParseClip(const onnx::NodeProto& node) |
| { |
| ParseActivation(node, ActivationFunction::BoundedReLu); |
| } |
| |
| void OnnxParserImpl::ParseSigmoid(const onnx::NodeProto& node) |
| { |
| ParseActivation(node, ActivationFunction::Sigmoid); |
| } |
| |
| void OnnxParserImpl::ParseTanh(const onnx::NodeProto& node) |
| { |
| ParseActivation(node, ActivationFunction::TanH); |
| } |
| |
| void OnnxParserImpl::ParseRelu(const onnx::NodeProto& node) |
| { |
| ParseActivation(node, ActivationFunction::ReLu); |
| } |
| |
| void OnnxParserImpl::ParseLeakyRelu(const onnx::NodeProto& node) |
| { |
| ParseActivation(node, ActivationFunction::LeakyReLu); |
| } |
| |
| 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); |
| |
| VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| |
| // TODO: unify broadcast validation code across layers |
| // tracked by: IVGCVSW-1576 |
| |
| // Checking broadcast compatibility : only scalar or 1D tensors |
| auto inputs = AddPrepareBroadcast(node.input(0), node.input(1)); |
| auto input0 = *m_TensorsInfo[inputs.first].m_info; |
| auto input1 = *m_TensorsInfo[inputs.second].m_info; |
| ARMNN_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions()); |
| |
| unsigned int numDims = input0.GetNumDimensions(); |
| for (unsigned int i = 0; i < numDims; i++) |
| { |
| unsigned int dim0 = input0.GetShape()[i]; |
| unsigned int dim1 = input1.GetShape()[i]; |
| if (dim0 != dim1 && dim0 != 1 && dim1 != 1) |
| { |
| throw ParseException( |
| fmt::format("Broadcast is only supported for scalar or 1D tensors in Add node '{}'. " |
| "Input dimensions should either match or one should be of size 1 and here, " |
| "{} and {} {}", |
| node.name(), |
| TensorInfoAsString(*m_TensorsInfo[inputs.first].m_info, inputs.first, |
| m_TensorsInfo[inputs.first].m_dtype), |
| TensorInfoAsString(*m_TensorsInfo[inputs.second].m_info, inputs.second, |
| m_TensorsInfo[inputs.second].m_dtype), |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| |
| IConnectableLayer* layer = m_Network->AddAdditionLayer(node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer, |
| { m_TensorsInfo[inputs.first].m_info->GetShape(), |
| m_TensorsInfo[inputs.second].m_info->GetShape() }); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection -> for constant inputs, we need to make a newDim constant layer |
| if(m_TensorsInfo[inputs.first].isConstant()) { |
| CreateConstantLayer(inputs.first, fmt::format("Add:constant_of_{}", node.input(0))); |
| } |
| if(m_TensorsInfo[inputs.second].isConstant()) { |
| CreateConstantLayer(inputs.second, fmt::format("Add:constant_of_{}", node.input(1))); |
| } |
| RegisterInputSlots(layer, {inputs.first, inputs.second}); |
| |
| // register the output connection |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| void OnnxParserImpl::ParseAveragePool(const onnx::NodeProto& node) |
| { |
| Pooling2dDescriptor desc; |
| desc.m_PoolType = PoolingAlgorithm::Average; |
| |
| uint32_t count_include_pad = 0; |
| count_include_pad = ReadOptionalNodeUint32Attribute(node, "count_include_pad"); |
| if(count_include_pad) { |
| desc.m_PaddingMethod = PaddingMethod::IgnoreValue; |
| } |
| AddPoolingLayer(node, desc); |
| } |
| |
| void OnnxParserImpl::ParseBatchNormalization(const onnx::NodeProto& node) |
| { |
| //IGNORE momentum parameter and spatial parameters |
| |
| CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 5); |
| CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| |
| VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| for(int ind = 1; ind < node.input_size(); ++ind) |
| { |
| auto tensor = node.input(ind); |
| if(! m_TensorsInfo[tensor].isConstant()) |
| { |
| throw ParseException( |
| fmt::format("Input tensor '{}' should be constant in BatchNormalization node '{}' {}", |
| tensor, |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| float epsilon = ReadOptionalNodeFloatAttribute(node, "epsilon", 1e-5f); |
| BatchNormalizationDescriptor desc; |
| desc.m_Eps = epsilon; |
| |
| auto scaleTensor = CreateConstTensor(node.input(1)); |
| auto biasTensor = CreateConstTensor(node.input(2)); |
| auto meanTensor = CreateConstTensor(node.input(3)); |
| auto varTensor = CreateConstTensor(node.input(4)); |
| |
| IConnectableLayer* layer = m_Network->AddBatchNormalizationLayer(desc, |
| meanTensor.first, |
| varTensor.first, |
| biasTensor.first, |
| scaleTensor.first, |
| node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()}); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| RegisterInputSlots(layer, {node.input(0)}); //don't register constant inputs |
| |
| // register the output connection |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| void OnnxParserImpl::ParseConcat(const onnx::NodeProto& node) |
| { |
| CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| |
| uint32_t numConcatView = static_cast<uint32_t>(node.input_size()); |
| uint32_t inputRank = m_TensorsInfo[node.input(0)].m_info->GetNumDimensions(); |
| |
| int axisInt = ReadMandatoryNodeIntAttribute(node, "axis"); |
| |
| unsigned int concatDimInput = static_cast<unsigned int>( |
| (static_cast<int>(inputRank) + axisInt) % static_cast<int>(inputRank)); |
| |
| OriginsDescriptor concatDescriptor(numConcatView, inputRank); |
| concatDescriptor.SetConcatAxis(concatDimInput); |
| |
| unsigned int mergeDimOrigin = 0; |
| |
| std::vector<TensorShape> inputShapes; |
| std::vector<std::string> tensorIds; |
| |
| for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) |
| { |
| std::string nodeName = node.input(static_cast<int>(viewIndex)); |
| auto inputTensorInfo = *m_TensorsInfo[nodeName].m_info; |
| inputShapes.push_back(inputTensorInfo.GetShape()); |
| tensorIds.push_back(nodeName); |
| |
| // Set up concatDescriptor view origin |
| armnnUtils::ProcessConcatInputTensorInfo( |
| inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin); |
| } |
| |
| IConnectableLayer* layer = m_Network->AddConcatLayer(concatDescriptor, node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, inputShapes, |
| m_TensorsInfo[node.input(0)].m_dtype); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| RegisterInputSlots(layer, tensorIds); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, { node.output(0) }); |
| } |
| |
| void OnnxParserImpl::ParseConstant(const onnx::NodeProto& node) |
| { |
| CHECK_VALID_SIZE(static_cast<size_t>(node.attribute_size()), 1); |
| if (!node.attribute(0).has_t()) |
| { |
| throw ParseException(fmt::format("Value not found for Constant node '{}' {}", |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| const onnx::TensorProto& onnxTensor = node.attribute(0).t(); |
| |
| //Register this as a m_ConstParam so we know we can use it as a constant param in future layers. |
| m_TensorsInfo[node.output(0)].m_tensor = std::make_unique<const onnx::TensorProto>(onnxTensor); |
| m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(ToTensorInfo(onnxTensor)); |
| m_TensorsInfo[node.output(0)].m_dtype = static_cast<onnx::TensorProto::DataType>(onnxTensor.data_type()); |
| |
| if (m_TensorsInfo[node.output(0)].m_dtype == onnx::TensorProto_DataType_FLOAT) |
| { |
| CreateConstantLayer(node.output(0), node.name()); |
| } |
| else if (m_TensorsInfo[node.output(0)].m_dtype == onnx::TensorProto_DataType_INT64) |
| { |
| CreateInt64ConstantLayer(node.output(0), node.name()); |
| } |
| else |
| { |
| throw ParseException(fmt::format("Data type not support for Constant node '{}' {}", |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| 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); |
| |
| VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| |
| if(m_TensorsInfo[node.input(0)].m_info->GetNumDimensions() != 4) |
| { |
| throw ParseException( |
| fmt::format("ArmNN only supports 2D convolution and Conv layer '{}' input {} {}", |
| node.name(), |
| TensorInfoAsString(*m_TensorsInfo[node.input(0)].m_info, node.input(0), |
| m_TensorsInfo[node.input(0)].m_dtype), |
| CHECK_LOCATION().AsString())); |
| } |
| |
| if(!m_TensorsInfo[node.input(1)].isConstant()) |
| { |
| throw ParseException( |
| fmt::format("Weights '{}' should be constant in Conv layer '{}' {}", |
| node.input(1), |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| |
| auto inputInfo = *m_TensorsInfo[node.input(0)].m_info; |
| |
| Convolution2dDescriptor desc; |
| desc.m_BiasEnabled = false; |
| |
| std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, "strides"); |
| if(strides.empty()) |
| { |
| desc.m_StrideX = 1; |
| desc.m_StrideY = 1; |
| } |
| else |
| { |
| desc.m_StrideX = strides[1]; |
| desc.m_StrideY = strides[0]; |
| } |
| |
| std::vector<uint32_t> dilations = ReadOptionalNodeUint32ListAttribute(node, "dilations"); |
| if(!dilations.empty()) |
| { |
| desc.m_DilationX = dilations[1]; |
| desc.m_DilationY = dilations[0]; |
| } |
| |
| std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, "pads"); |
| //Check new padding version first |
| if(pads.empty()) |
| { |
| //Check deprecated version |
| std::string paddingString = ReadOptionalNodeStringAttribute(node, "auto_pad"); |
| if(paddingString != "VALID" && paddingString != "" && paddingString != "NOTSET") |
| { |
| bool isUpper; |
| if( paddingString == "SAME_LOWER") |
| { |
| isUpper = false; |
| } |
| else if (paddingString == "SAME_UPPER") |
| { |
| isUpper = true; |
| } |
| else |
| { |
| throw ParseException( |
| fmt::format("Invalid auto_pad attribute for node {}. Only SAME_UPPER, SAME_LOWER or VALID " |
| "supported and found {} {}", |
| node.name(), |
| paddingString, |
| CHECK_LOCATION().AsString())); |
| } |
| uint32_t inputHeight = inputInfo.GetShape()[2]; |
| uint32_t inputWidth = inputInfo.GetShape()[3]; |
| |
| uint32_t weightHeight; |
| uint32_t weightWidth; |
| std::vector<uint32_t> kernel_shape = ReadOptionalNodeUint32ListAttribute(node, "kernel_shape"); |
| if (kernel_shape.empty()) |
| { |
| const TensorInfo weightTensorInfo = *m_TensorsInfo[node.input(1)].m_info; |
| weightHeight = weightTensorInfo.GetShape()[2]; |
| weightWidth = weightTensorInfo.GetShape()[3]; |
| } |
| else |
| { |
| weightHeight = kernel_shape[0]; |
| weightWidth = kernel_shape[1]; |
| } |
| CalcPadding(inputHeight, |
| weightHeight, |
| desc.m_StrideY, |
| desc.m_DilationY, |
| &desc.m_PadTop, |
| &desc.m_PadBottom, |
| isUpper); |
| CalcPadding(inputWidth, |
| weightWidth, |
| desc.m_StrideX, |
| desc.m_DilationX, |
| &desc.m_PadLeft, |
| &desc.m_PadRight, |
| isUpper); |
| } |
| } |
| else |
| { |
| desc.m_PadTop = pads[0]; |
| desc.m_PadLeft = pads[1]; |
| desc.m_PadBottom = pads[2]; |
| desc.m_PadRight = pads[3]; |
| } |
| |
| uint32_t group = ReadOptionalNodeUint32Attribute(node, "group", 1); |
| if(group > 1) |
| { |
| if (group > inputInfo.GetShape()[1]) |
| { |
| throw ParseException( |
| fmt::format("Error parsing Convolution node: {}. " |
| "The 'group'={} parameter cannot be larger than the " |
| "channel of the input shape={} (in NCHW format). {}", |
| node.name(), |
| group, |
| inputInfo.GetShape()[1], |
| CHECK_LOCATION().AsString())); |
| } |
| else if (group == inputInfo.GetShape()[1]) |
| { |
| // we use a depthwise convolution here, because the number of groups equals to the |
| // input channels |
| AddConvLayerWithDepthwiseConv(node, desc); |
| return; |
| } |
| else |
| { |
| // TODO: split the input by channels into channels/groups separate convolutions |
| // and concatenate the results afterwards |
| throw ParseException(fmt::format("Error parsing Convolution node: {}. " |
| "The 'group'={} parameter should be 1 or be equal to the " |
| "channel of the input shape={} (in NCHW format). {}", |
| node.name(), |
| group, |
| inputInfo.GetShape()[1], |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| armnn::IConnectableLayer* layer; |
| auto weightTensor = CreateConstTensor(node.input(1)); |
| |
| if (node.input_size() == 3) |
| { |
| if(!m_TensorsInfo[node.input(2)].isConstant()) |
| { |
| throw ParseException(fmt::format("Bias '{}' should be constant in Conv layer '{}' {}", |
| node.input(2), |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| desc.m_BiasEnabled = true; |
| auto biasTensor = CreateConstTensor(node.input(2)); |
| layer = m_Network->AddConvolution2dLayer(desc, |
| weightTensor.first, |
| Optional<ConstTensor>(biasTensor.first), |
| node.name().c_str()); |
| } |
| else |
| { |
| layer = m_Network->AddConvolution2dLayer(desc, |
| weightTensor.first, |
| EmptyOptional(), |
| node.name().c_str()); |
| } |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer, |
| { m_TensorsInfo[node.input(0)].m_info->GetShape(), |
| m_TensorsInfo[node.input(1)].m_info->GetShape() }); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| RegisterInputSlots(layer, {node.input(0)}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| 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); |
| |
| CHECK_VALID_DATATYPE(node.name(), node.input(0), |
| m_TensorsInfo[node.input(0)].m_dtype, |
| onnx::TensorProto::FLOAT); |
| |
| int64_t axis = ReadOptionalNodeInt64Attribute(node, "axis", 1); |
| TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| |
| /// Negative axis conversion |
| if (axis < 0) |
| { |
| axis += inputShape.GetNumDimensions(); |
| } |
| |
| /// Check Axis is within dimensions |
| if (axis < 0 || axis >= inputShape.GetNumDimensions()) |
| { |
| throw ParseException(fmt::format("Axis '{}' invalid. Tensor has '{}' dimensions in FlattenLayer '{}'", |
| axis, inputShape.GetNumDimensions(), node.name())); |
| } |
| |
| /// If axis chosen is 0 dimension1 will always be 1 in output , default dimension2 to 1 because 0 is invalid |
| uint dimension1{1}; |
| uint dimension2{1}; |
| uint i{0}; |
| |
| /// dimension1 = (d_0 * d_1 ... d_(axis-1)) |
| for (i = 0; i < axis; i++){ |
| dimension1 *= inputShape[i]; |
| } |
| |
| /// dimension2 = (d_axis * d_(axis+1) ... d_n) |
| for (i = static_cast<uint>(axis); i < inputShape.GetNumDimensions(); i++){ |
| dimension2 *= inputShape[i]; |
| } |
| |
| TensorShape outputShape{dimension1, dimension2}; |
| |
| auto outInfo = ComputeReshapeInfo(outputShape, inputShape, node.output(0)); |
| m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo); |
| CreateReshapeLayer(node.input(0), node.output(0), node.name()); |
| } |
| |
| void OnnxParserImpl::ParseGather(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); |
| |
| armnn::GatherDescriptor gatherDescriptor; |
| gatherDescriptor.m_Axis = static_cast<int>(ReadOptionalNodeInt64Attribute(node, "axis", 0)); |
| |
| IConnectableLayer* layer = m_Network->AddGatherLayer(gatherDescriptor, node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| const TensorShape& inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| const TensorShape& indicesShape = m_TensorsInfo[node.input(1)].m_info->GetShape(); |
| auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, { inputShape, indicesShape }, |
| m_TensorsInfo[node.input(0)].m_dtype); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| RegisterInputSlots(layer, { node.input(0), node.input(1) }); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, { node.output(0) }); |
| } |
| |
| void OnnxParserImpl::ParseGemm(const onnx::NodeProto& node) |
| { |
| CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2, 3); |
| CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| |
| int transA = static_cast<int>(ReadOptionalNodeUint32Attribute(node, "transA", 0)); |
| int transB = static_cast<int>(ReadOptionalNodeUint32Attribute(node, "transB", 0)); |
| float alpha = ReadOptionalNodeFloatAttribute(node, "alpha", 1.0); |
| float beta = ReadOptionalNodeFloatAttribute(node, "beta", 1.0); |
| bool biasEnabled = node.input_size() == 3; |
| |
| TensorShape input0Shape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| TensorShape input1Shape = m_TensorsInfo[node.input(1)].m_info->GetShape(); |
| |
| // if transB != 0, add transpose to the input1 (tanspose weight matrix in FullyConnected) |
| armnn::FullyConnectedDescriptor fullyConnectedDescriptor; |
| fullyConnectedDescriptor.m_BiasEnabled = biasEnabled; |
| fullyConnectedDescriptor.m_TransposeWeightMatrix = transB; |
| |
| IConnectableLayer* layer = nullptr; |
| |
| // Just add a FullyConnected layer, weights and biases are handled as inputs now. |
| layer = m_Network->AddFullyConnectedLayer(fullyConnectedDescriptor, node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| // if transA != 0, add transpose to the input0 |
| if (transA != 0) |
| { |
| std::string transAName = "transpose_" + node.input(0); |
| armnn::TransposeDescriptor transposeADescriptor; |
| transposeADescriptor.m_DimMappings = { 1, 0 }; |
| IConnectableLayer* transALayer = m_Network->AddTransposeLayer(transposeADescriptor, transAName.c_str()); |
| ARMNN_ASSERT(transALayer != nullptr); |
| auto transAInfo = ComputeOutputInfo({ transAName }, transALayer, { input0Shape }); |
| transALayer->GetOutputSlot(0).SetTensorInfo(transAInfo[0]); |
| transALayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| RegisterInputSlot(transALayer, node.input(0), 0); |
| input0Shape = transAInfo[0].GetShape(); |
| } |
| else |
| { |
| RegisterInputSlot(layer, node.input(0), 0); |
| } |
| |
| // Add constant layer to store weights/biases and connect to FullyConnected layer. |
| if(m_TensorsInfo[node.input(1)].isConstant()) |
| { |
| IConnectableLayer* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(node.input(1)).first); |
| TensorInfo weightInfo = *m_TensorsInfo[node.input(1)].m_info; |
| weightInfo.SetConstant(); |
| weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo); |
| |
| // if alpha != 1, multiply to the weight |
| if (alpha != 1) |
| { |
| std::string activationName = "activation_" + node.input(1); |
| armnn::ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_A = alpha; |
| activationDescriptor.m_Function = ActivationFunction::Linear; |
| IConnectableLayer* actLayer = m_Network->AddActivationLayer(activationDescriptor, activationName.c_str()); |
| ARMNN_ASSERT(actLayer != nullptr); |
| |
| auto actInfo = ComputeOutputInfo({ activationName }, actLayer, { weightInfo.GetShape() }); |
| actLayer->GetOutputSlot(0).SetTensorInfo(actInfo[0]); |
| actLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| weightsLayer->GetOutputSlot(0).Connect(actLayer->GetInputSlot(0u)); |
| input1Shape = actInfo[0].GetShape(); |
| } |
| else |
| { |
| weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| input1Shape = weightInfo.GetShape(); |
| } |
| } |
| else |
| { |
| // if alpha != 1, multiply to the weight |
| if (alpha != 1) |
| { |
| std::string activationName = "activation_" + node.input(1); |
| armnn::ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_A = alpha; |
| activationDescriptor.m_Function = ActivationFunction::Linear; |
| IConnectableLayer* actLayer = m_Network->AddActivationLayer(activationDescriptor, activationName.c_str()); |
| ARMNN_ASSERT(actLayer != nullptr); |
| |
| auto actInfo = ComputeOutputInfo({ activationName }, actLayer, { input1Shape }); |
| actLayer->GetOutputSlot(0).SetTensorInfo(actInfo[0]); |
| actLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| RegisterInputSlot(actLayer, node.input(1), 0); |
| input1Shape = actInfo[0].GetShape(); |
| } |
| else |
| { |
| RegisterInputSlot(layer, node.input(1), 1); |
| } |
| } |
| |
| if(biasEnabled && m_TensorsInfo[node.input(2)].isConstant()) |
| { |
| To1DTensor(node.input(2), CHECK_LOCATION()); |
| IConnectableLayer* biasLayer = m_Network->AddConstantLayer(CreateConstTensor(node.input(2)).first); |
| TensorInfo biasInfo = *m_TensorsInfo[node.input(2)].m_info; |
| biasInfo.SetConstant(); |
| biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo); |
| |
| // if beta != 1, multiply to the bias |
| if (beta != 1) |
| { |
| std::string activationName = "activation_" + node.input(2); |
| armnn::ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_A = beta; |
| activationDescriptor.m_Function = ActivationFunction::Linear; |
| IConnectableLayer* actLayer = m_Network->AddActivationLayer(activationDescriptor, activationName.c_str()); |
| ARMNN_ASSERT(actLayer != nullptr); |
| |
| auto actInfo = ComputeOutputInfo({ activationName }, actLayer, { biasInfo.GetShape() }); |
| actLayer->GetOutputSlot(0).SetTensorInfo(actInfo[0]); |
| actLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| biasLayer->GetOutputSlot(0).Connect(actLayer->GetInputSlot(0u)); |
| } |
| else |
| { |
| biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| } |
| } |
| else if (biasEnabled) |
| { |
| // Currently we support non-constant tensor of input C (bias) of Gemm when the dimension is 1 |
| if (m_TensorsInfo[node.input(2)].m_info->GetNumDimensions() != 1) |
| { |
| throw ParseException(fmt::format("The parser supports constant or non-constant with 1 dimension for " |
| "Input C of Gemm. Input '{}' in '{}' is not supported '{}'", |
| node.input(2), |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| // if beta != 1, multiply to the bias |
| if (beta != 1) |
| { |
| std::string activationName = "activation_" + node.input(2); |
| armnn::ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_A = beta; |
| activationDescriptor.m_Function = ActivationFunction::Linear; |
| IConnectableLayer* actLayer = m_Network->AddActivationLayer(activationDescriptor, activationName.c_str()); |
| ARMNN_ASSERT(actLayer != nullptr); |
| |
| auto actInfo = ComputeOutputInfo({ activationName }, |
| actLayer, |
| { m_TensorsInfo[node.input(2)].m_info->GetShape() }); |
| actLayer->GetOutputSlot(0).SetTensorInfo(actInfo[0]); |
| actLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| RegisterInputSlot(actLayer, node.input(2), 0); |
| } |
| else |
| { |
| RegisterInputSlot(layer, node.input(2), 2); |
| } |
| } |
| |
| // Set final output of the FullyConnected layer |
| auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer, |
| { input0Shape, input1Shape }); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| void OnnxParserImpl::ParseGlobalAveragePool(const onnx::NodeProto& node) |
| { |
| Pooling2dDescriptor desc = Pooling2dDescriptor(); |
| desc.m_PoolType = PoolingAlgorithm::Average; |
| |
| //kernel size is the same as input |
| TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| desc.m_PoolWidth = inputShape[3]; |
| desc.m_PoolHeight = inputShape[2]; |
| |
| IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {inputShape}); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| RegisterInputSlots(layer, {node.input(0)}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| void OnnxParserImpl::ParseMaxPool(const onnx::NodeProto& node) |
| { |
| Pooling2dDescriptor desc; |
| desc.m_PoolType = PoolingAlgorithm::Max; |
| desc.m_PaddingMethod = PaddingMethod::Exclude; |
| AddPoolingLayer(node, desc); |
| } |
| |
| void OnnxParserImpl::ParseShape(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); |
| |
| IConnectableLayer* layer = m_Network->AddShapeLayer(node.name().c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {inputShape}, onnx::TensorProto::INT64); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| RegisterInputSlots(layer, {node.input(0)}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| RegisterOutputSlots(layer, {node.output(0)}); |
| } |
| |
| 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); |
| |
| CHECK_VALID_DATATYPE(node.name(), node.input(0), |
| m_TensorsInfo[node.input(0)].m_dtype, |
| onnx::TensorProto::FLOAT); //input |
| CHECK_VALID_DATATYPE(node.name(), node.input(1), |
| m_TensorsInfo[node.input(1)].m_dtype, |
| onnx::TensorProto::INT64); //shape |
| |
| if(!m_TensorsInfo[node.input(1)].isConstant()) |
| { |
| throw ParseException(fmt::format("Shape '{}' should be constant in Reshape layer '{}' {}", |
| node.input(1), |
| node.name(), |
| CHECK_LOCATION().AsString())); |
| } |
| |
| if(m_TensorsInfo[node.input(0)].isConstant()) |
| { |
| //make a new cst tensor -> move the data to the output tensor (the shape is already good in the output tensor) |
| if(m_TensorsInfo.count(node.output(0)) == 0) |
| { |
| m_TensorsInfo[node.output(0)] = OnnxTensor(); |
| } |
| m_TensorsInfo[node.output(0)].m_tensor = |
| std::make_unique<onnx::TensorProto>(*m_TensorsInfo[node.input(0)].m_tensor); |
| } |
| else |
| { |
| TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| |
| if(m_TensorsInfo.count(node.output(0)) == 0 || m_TensorsInfo[node.output(0)].m_info == nullptr) |
| { |
| uint64_t dims = static_cast<uint64_t>(m_TensorsInfo[node.input(1)].m_tensor->int64_data_size()); |
| TensorShape targetShape{static_cast<unsigned int>(dims), 1}; |
| |
| for(uint i = 0; i < dims; i++) |
| { |
| int val = CHECKED_INT32(m_TensorsInfo[node.input(1)].m_tensor->int64_data(static_cast<int>(i))); |
| targetShape[i]= static_cast<unsigned int>(val); |
| } |
| |
| auto outInfo = ComputeReshapeInfo(targetShape, inputShape, node.output(0)); |
| m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo); |
| } |
| |
| CreateReshapeLayer(node.input(0), node.output(0), node.name()); |
| } |
| } |
| |
| void OnnxParserImpl::ParseUnsqueeze(const onnx::NodeProto& node) |
| { |
| CHECK_VALID_SIZE(armnn::numeric_cast<size_t>(node.input_size()), 1, 2); |
| CHECK_VALID_SIZE(armnn::numeric_cast<size_t>(node.output_size()), 1); |
| |
| TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| std::vector<uint32_t> dims; |
| if (node.input_size() == 1 && node.attribute_size() > 0) |
| { |
| dims = ReadMandatoryNodeUint32ListAttribute(node, "axes"); |
| } |
| else |
| { |
| CHECK_VALID_DATATYPE(node.name(), node.input(1), |
| m_TensorsInfo[node.input(1)].m_dtype, |
| onnx::TensorProto::INT64); //axes |
| |
| auto int64Axes = m_TensorsInfo[node.input(1)].m_tensor->int64_data().data(); |
| uint numDim = armnn::numeric_cast<uint>(m_TensorsInfo[node.input(1)].m_tensor->int64_data_size()); |
| |
| for(uint i = 0; i < numDim; i++) |
| { |
| uint32_t uint32Value = CHECKED_NON_NEGATIVE(CHECKED_INT32(int64Axes[i])); |
| dims.push_back(uint32Value); |
| } |
| } |
| |
| // Ensure that the axes are sorted |
| std::sort(dims.begin(), dims.end()); |
| |
| std::vector<unsigned int> targetShape; |
| |
| if (inputShape.GetDimensionality() != Dimensionality::Scalar) |
| { |
| for(uint i = 0; i < inputShape.GetNumDimensions(); i++) |
| { |
| targetShape.push_back(inputShape[i]); |
| } |
| } |
| |
| for(uint i = 0; i < dims.size(); i++) |
| { |
| targetShape.insert(targetShape.begin() + armnn::numeric_cast<int>(dims[i]), 1); |
| } |
| |
| auto outInfo = ComputeReshapeInfo(TensorShape(static_cast<unsigned int>(targetShape.size()), targetShape.data()), |
| inputShape, node.output(0), m_TensorsInfo[node.input(0)].m_info->GetDataType()); |
| m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo); |
| m_TensorsInfo[node.output(0)].m_dtype = m_TensorsInfo[node.input(0)].m_dtype; |
| |
| CreateReshapeLayer(node.input(0), node.output(0), node.name()); |
| } |
| |
| 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); |
| |
| TensorShape input0Shape = m_TensorsInfo[input0].m_info->GetShape(); |
| TensorShape input1Shape = m_TensorsInfo[input1].m_info->GetShape(); |
| |
| uint32_t diff = input1Shape.GetNumDimensions() - input0Shape.GetNumDimensions(); |
| std::vector<uint32_t> newShape; |
| while(diff > 0) |
| { |
| newShape.push_back(1); |
| diff--; |
| } |
| for (uint dim = 0; dim < input0Shape.GetNumDimensions(); ++dim) |
| { |
| newShape.push_back(input0Shape[dim]); |
| } |
| outputTensorInfo.SetShape(TensorShape(static_cast<unsigned int>(newShape.size()), newShape.data())); |
| |
| //add the new tensor to m_TensorsInfo |
| m_TensorsInfo[outputName] = OnnxTensor(); |
| m_TensorsInfo[outputName].m_info = std::make_unique<TensorInfo>(outputTensorInfo); |
| |
| //add reshape layer if the parent was not constant... |
| if( ! m_TensorsInfo[input0].isConstant()) |
| { |
| CreateReshapeLayer(input0, outputName, fmt::format("Add:reshapeOf{}", input0)); |
| } |
| else //make it constant and it will be create in Add |
| { |
| m_TensorsInfo[outputName].m_tensor = std::make_unique<onnx::TensorProto>(*m_TensorsInfo[input0].m_tensor); |
| |
| } |
| } |
| |
| void OnnxParserImpl::SetupInputLayers() |
| { |
| //Find user input and add their layers |
| for(int inputIndex = 0; inputIndex < m_Graph->input_size(); ++inputIndex) |
| { |
| auto input = m_Graph->input(inputIndex); |
| if (! m_TensorsInfo[input.name()].isConstant()) |
| { |
| IConnectableLayer* layer = |
| m_Network->AddInputLayer(static_cast<armnn::LayerBindingId>(inputIndex), input.name().c_str()); |
| auto tensorInfo = ToTensorInfo(input); |
| layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| RegisterOutputSlots(layer,{ input.name() }); |
| } |
| } |
| } |
| |
| void OnnxParserImpl::SetupOutputLayers() |
| { |
| if(m_Graph->output_size() == 0) |
| { |
| throw ParseException(fmt::format("The given model does not have any outputs {}", CHECK_LOCATION().AsString())); |
| } |
| |
| for(int outputIndex = 0; outputIndex < m_Graph->output_size(); ++outputIndex) |
| { |
| IConnectableLayer* layer = |
| m_Network->AddOutputLayer(static_cast<armnn::LayerBindingId>(outputIndex), |
| m_Graph->output(outputIndex).name().c_str()); |
| |
| RegisterInputSlots(layer, { m_Graph->output(outputIndex).name() }); |
| } |
| } |
| |
| void OnnxParserImpl::RegisterInputSlot(IConnectableLayer* layer, |
| const std::string& tensorId, |
| unsigned int slotIndex) |
| { |
| armnn::IInputSlot* slot = &(layer->GetInputSlot(slotIndex)); |
| |
| auto it = m_TensorConnections.find(tensorId); |
| |
| if (it == m_TensorConnections.end()) |
| { |
| //First time seing this tensor, we need to map it |
| m_TensorConnections[tensorId] = TensorSlots(); |
| } |
| m_TensorConnections[tensorId].inputSlots.push_back(slot); |
| } |
| |
| void OnnxParserImpl::RegisterInputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds) |
| { |
| ARMNN_ASSERT(layer != nullptr); |
| if (tensorIds.size() != layer->GetNumInputSlots()) |
| { |
| throw ParseException( |
| fmt::format("The number of tensor inputs ({}) does not match the number expected ({}) {}", |
| tensorIds.size(), |
| layer->GetNumInputSlots(), |
| CHECK_LOCATION().AsString())); |
| } |
| |
| for (unsigned int slotIndex = 0; slotIndex < layer->GetNumInputSlots(); ++slotIndex) |
| { |
| std::string tensorId = tensorIds[slotIndex]; |
| armnn::IInputSlot* slot = &(layer->GetInputSlot(slotIndex)); |
| |
| auto it = m_TensorConnections.find(tensorId); |
| |
| if (it == m_TensorConnections.end()) |
| { |
| //First time seing this tensor, we need to map it |
| m_TensorConnections[tensorId] = TensorSlots(); |
| } |
| m_TensorConnections[tensorId].inputSlots.push_back(slot); |
| } |
| } |
| |
| void OnnxParserImpl::RegisterOutputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds) |
| { |
| ARMNN_ASSERT(layer != nullptr); |
| if (tensorIds.size() != layer->GetNumOutputSlots()) |
| { |
| throw ParseException( |
| fmt::format("The number of tensor outputs ({}) does not match the number expected ({}) {} ", |
| tensorIds.size(), |
| layer->GetNumOutputSlots(), |
| CHECK_LOCATION().AsString())); |
| } |
| |
| for (unsigned int slotIndex = 0; slotIndex < layer->GetNumOutputSlots(); ++slotIndex) |
| { |
| std::string tensorId = tensorIds[slotIndex]; |
| armnn::IOutputSlot* slot = &(layer->GetOutputSlot(slotIndex)); |
| |
| auto it = m_TensorConnections.find(tensorId); |
| |
| if (it == m_TensorConnections.end()) |
| { |
| //First time seing this tensor, we need to map it |
| m_TensorConnections[tensorId] = TensorSlots(); |
| } |
| |
| TensorSlots& tensorSlots = m_TensorConnections[tensorId]; |
| |
| // assuming there is only one producer for that tensor |
| if (tensorSlots.outputSlot != nullptr) |
| { |
| throw ParseException(fmt::format("Another layer has already registered itself as the producer of " |
| "tensor:{} {}", |
| tensorId, |
| CHECK_LOCATION().AsString())); |
| } |
| tensorSlots.outputSlot = slot; |
| } |
| } |
| |
| BindingPointInfo OnnxParserImpl::GetNetworkInputBindingInfo(const std::string& name) const |
| { |
| for(int i = 0; i < m_Graph->input_size(); ++i) |
| { |
| auto input = m_Graph->input(i); |
| if(input.name() == name) |
| { |
| return std::make_pair(static_cast<armnn::LayerBindingId>(i), ToTensorInfo(input)); |
| } |
| } |
| throw InvalidArgumentException(fmt::format("The input layer '{}' does not exist {}", |
| name, CHECK_LOCATION().AsString())); |
| } |
| |
| BindingPointInfo OnnxParserImpl::GetNetworkOutputBindingInfo(const std::string& name) const |
| { |
| for(int i = 0; i < m_Graph->output_size(); ++i) |
| { |
| auto output = m_Graph->output(i); |
| if(output.name() == name) |
| { |
| return std::make_pair(static_cast<armnn::LayerBindingId>(i), ToTensorInfo(output)); |
| } |
| } |
| throw InvalidArgumentException(fmt::format("The output layer '{}' does not exist {}", |
| name, CHECK_LOCATION().AsString())); |
| } |
| |
| std::vector<std::string> OnnxParserImpl::GetInputs(ModelPtr& model) |
| { |
| if(model == nullptr) { |
| throw InvalidArgumentException(fmt::format("The given model cannot be null {}", |
| CHECK_LOCATION().AsString())); |
| } |
| |
| std::vector<std::string> inputNames; |
| std::map<std::string, bool> isConstant; |
| for(auto tensor : model->graph().initializer()) |
| { |
| isConstant[tensor.name()] = true; |
| } |
| for(auto input : model->graph().input()) |
| { |
| auto it = isConstant.find(input.name()); |
| if(it == isConstant.end()) |
| { |
| inputNames.push_back(input.name()); |
| } |
| } |
| return inputNames; |
| } |
| |
| std::vector<std::string> OnnxParserImpl::GetOutputs(ModelPtr& model) |
| { |
| if(model == nullptr) { |
| throw InvalidArgumentException(fmt::format("The given model cannot be null {}", |
| CHECK_LOCATION().AsString())); |
| } |
| |
| std::vector<std::string> outputNames; |
| for(auto output : model->graph().output()) |
| { |
| outputNames.push_back(output.name()); |
| } |
| return outputNames; |
| } |
| |
| const std::string OnnxParserImpl::GetVersion() |
| { |
| return ONNX_PARSER_VERSION; |
| } |
| |
| } // namespace armnnOnnxParser |