diff options
Diffstat (limited to 'src/armnnOnnxParser/OnnxParser.cpp')
| -rw-r--r-- | src/armnnOnnxParser/OnnxParser.cpp | 27 |
1 files changed, 18 insertions, 9 deletions
diff --git a/src/armnnOnnxParser/OnnxParser.cpp b/src/armnnOnnxParser/OnnxParser.cpp index 89be4289d6..0c1af03af4 100644 --- a/src/armnnOnnxParser/OnnxParser.cpp +++ b/src/armnnOnnxParser/OnnxParser.cpp @@ -234,17 +234,28 @@ armnn::TensorInfo ToTensorInfo(const onnx::ValueInfoProto& info) shapeDims.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(onnxShape.dim(i).dim_value()))); } + if (shapeDims.empty()) + { + shapeDims.push_back(1); + } + 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))); } + if (shapeDims.empty()) + { + shapeDims.push_back(1); + } + return ToTensorInfo(tensor.name(), shapeDims, tensor.data_type()); } @@ -624,7 +635,7 @@ void OnnxParser::LoadGraph() const std::string& operation = node.op_type(); // check which layers we handled already (add and matmul fused as FC) - if(operation == "MatMul" ) + if (operation == "MatMul" ) { if(m_OutputsFusedAndUsed[nodeIndex].inputForNodes != m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.size()) { @@ -881,7 +892,6 @@ void OnnxParser::CreateConstantLayer(const std::string& tensorName, const std::s void OnnxParser::ParseConstant(const onnx::NodeProto& node) { CHECK_VALID_SIZE(static_cast<size_t>(node.attribute_size()), 1); - if (!node.attribute(0).has_t()) { throw ParseException(boost::str( @@ -897,9 +907,10 @@ void OnnxParser::ParseConstant(const onnx::NodeProto& node) //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()); CreateConstantLayer(node.output(0), node.name()); - } void OnnxParser::ParseMaxPool(const onnx::NodeProto& node) @@ -1505,11 +1516,9 @@ void OnnxParser::ParseAdd(const onnx::NodeProto& node) // register the input connection -> for constant inputs, we need to make a newDim constant layer if(m_TensorsInfo[inputs.first].isConstant()) { - CreateConstantLayer(inputs.first, boost::str(boost::format("Add:constant_of_%1%") % node.input(0))); } if(m_TensorsInfo[inputs.second].isConstant()) { - CreateConstantLayer(inputs.second, boost::str(boost::format("Add:constant_of_%1%") % node.input(1))); } RegisterInputSlots(layer, {inputs.first, inputs.second}); @@ -1653,16 +1662,16 @@ void OnnxParser::RegisterOutputSlots(IConnectableLayer* layer, const std::vector m_TensorConnections[tensorId] = TensorSlots(); } - TensorSlots & tensorSlots = m_TensorConnections[tensorId]; + TensorSlots& tensorSlots = m_TensorConnections[tensorId]; // assuming there is only one producer for that tensor if (tensorSlots.outputSlot != nullptr) { throw ParseException(boost::str( boost::format("Another layer has already registered itself as the producer of " - "tensor:%2% %3%") % - tensorId % - CHECK_LOCATION().AsString())); + "tensor:%1% %2%") + % tensorId + % CHECK_LOCATION().AsString())); } tensorSlots.outputSlot = slot; } |