生成onnx模型过程记录

        onnx::ModelProto mymodel;
	onnx::GraphProto graph;
	
	mymodel.clear_graph();
	mymodel.set_producer_name("pytorch 1.3");//important
	onnx::OperatorSetIdProto* myopset = mymodel.add_opset_import();	//important
	
	mymodel.set_allocated_graph(&graph);
	graph.clear_node();
	graph.clear_input();
	graph.clear_output();

	onnx::TypeProto input_type;
	onnx::TypeProto_Tensor input_tensor_type;
	onnx::TensorShapeProto input_shape;

	onnx::ValueInfoProto*input = graph.add_input();
	input->set_name("myinput");
	input->set_allocated_type(&input_type);
	input_type.set_allocated_tensor_type(&input_tensor_type);
	input_tensor_type.set_elem_type(onnx::AttributeProto_AttributeType_FLOAT);
	input_tensor_type.set_allocated_shape(&input_shape);
	onnx::TensorShapeProto_Dimension* dim = input_shape.add_dim();
	dim->set_dim_value(1);
	dim = input_shape.add_dim();
	dim->set_dim_value(512);
	dim = input_shape.add_dim();
	dim->set_dim_value(64);
	dim = input_shape.add_dim();
	dim->set_dim_value(64);

	onnx::TypeProto output_type;
	onnx::TypeProto_Tensor output_tensor_type;
	onnx::TensorShapeProto output_shape;

	onnx::ValueInfoProto*output = graph.add_output();
	output->set_name("output");
	output->set_allocated_type(&output_type);
	output_type.set_allocated_tensor_type(&output_tensor_type);
	output_tensor_type.set_elem_type(onnx::AttributeProto_AttributeType_FLOAT);
	output_tensor_type.set_allocated_shape(&output_shape);
	dim = output_shape.add_dim();
	dim->set_dim_value(1);
	dim = output_shape.add_dim();
	dim->set_dim_value(128);
	dim = output_shape.add_dim();
	dim->set_dim_value(128);
	dim = output_shape.add_dim();
	dim->set_dim_value(128);

	onnx::NodeProto *depthtospace = graph.add_node();
	depthtospace->set_op_type("DepthToSpace");
	onnx::AttributeProto* blocksize = depthtospace->add_attribute();
	blocksize->set_type(onnx::AttributeProto_AttributeType_INT);
	blocksize->set_name("blocksize");
	blocksize->set_i(2);

	onnx::AttributeProto* mode = depthtospace->add_attribute();
	mode->set_type(onnx::AttributeProto_AttributeType_STRING);
	mode->set_name("mode");
	mode->set_s("CRD");

	depthtospace->add_input("myinput");
	depthtospace->add_output("output");
	int fd = open("out.onnx", O_WRONLY | O_CREAT| O_BINARY,0644);
	bool suc = mymodel.SerializeToFileDescriptor(fd);
	close(fd);

目前能够通过netron打开，但是onnxruntime导入模型会有失败的error：

onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : 
INVALID_ARGUMENT : Failed to load model with error: Unknown model file format version.

目前还不知道怎么修改，暂留以待后续解决...

不过可以按照这个方式通过netron显示其它格式的模型（自定义的模型）

问题修改：

1、需要设置 model的ir version

2、需要设置opset_import的domain、version

完成上述修改即可使用onnxruntime对生成的模型进行inference：

        onnx::ModelProto mymodel;
	onnx::GraphProto graph;
							
	mymodel.clear_graph();
	mymodel.set_producer_name("pytorch 1.3");//important
	mymodel.set_ir_version(4);
	onnx::OperatorSetIdProto* myopset = mymodel.add_opset_import();	//important
	myopset->set_domain("com.microsoft"); //"com.microsoft"   "ai.onnx v11"
	myopset->set_version(1);

	mymodel.set_allocated_graph(&graph);
	graph.clear_node();
	graph.clear_input();
	graph.clear_output();

	onnx::TypeProto input_type;
	onnx::TypeProto_Tensor input_tensor_type;
	onnx::TensorShapeProto input_shape;

	onnx::ValueInfoProto*input = graph.add_input();
	input->set_name("myinput");
	input->set_allocated_type(&input_type);
	input_type.set_allocated_tensor_type(&input_tensor_type);
	input_tensor_type.set_elem_type(onnx::AttributeProto_AttributeType_FLOAT);
	input_tensor_type.set_allocated_shape(&input_shape);
	onnx::TensorShapeProto_Dimension* dim = input_shape.add_dim();
	dim->set_dim_value(1);
	dim = input_shape.add_dim();
	dim->set_dim_value(512);
	dim = input_shape.add_dim();
	dim->set_dim_value(64);
	dim = input_shape.add_dim();
	dim->set_dim_value(64);

	onnx::TypeProto output_type;
	onnx::TypeProto_Tensor output_tensor_type;
	onnx::TensorShapeProto output_shape;

	onnx::ValueInfoProto*output = graph.add_output();
	output->set_name("output");
	output->set_allocated_type(&output_type);
	output_type.set_allocated_tensor_type(&output_tensor_type);
	output_tensor_type.set_elem_type(onnx::AttributeProto_AttributeType_FLOAT);
	output_tensor_type.set_allocated_shape(&output_shape);
	dim = output_shape.add_dim();
	dim->set_dim_value(1);
	dim = output_shape.add_dim();
	dim->set_dim_value(128);
	dim = output_shape.add_dim();
	dim->set_dim_value(128);
	dim = output_shape.add_dim();
	dim->set_dim_value(128);

	onnx::NodeProto *depthtospace = graph.add_node();
	depthtospace->set_op_type("DepthToSpace");
	onnx::AttributeProto* blocksize = depthtospace->add_attribute();
	blocksize->set_type(onnx::AttributeProto_AttributeType_INT);
	blocksize->set_name("blocksize");
	blocksize->set_i(2);

	onnx::AttributeProto* mode = depthtospace->add_attribute();
	mode->set_type(onnx::AttributeProto_AttributeType_STRING);
	mode->set_name("mode");
	mode->set_s("CRD");

	depthtospace->add_input("myinput");
	depthtospace->add_output("output");
	int fd = open("out.onnx", O_WRONLY | O_CREAT | O_BINARY, 0644);
	bool suc = mymodel.SerializeToFileDescriptor(fd);
	close(fd);