README
¶
Cross-Platform onnxruntime Wrapper for Go
About
This library seeks to provide an interface for loading and executing neural networks from Go(lang) code, while remaining as simple to use as possible.
The onnxruntime library provides a
way to load and execute ONNX-format neural networks, though the library
primarily supports C and C++ APIs. Several efforts exist to have written
Go(lang) wrappers for the onnxruntime library, but as far as I can tell, none
of these existing Go wrappers support Windows. This is due to the fact that
Microsoft's onnxruntime library assumes the user will be using the MSVC
compiler on Windows systems, while CGo on Windows requires using Mingw.
This wrapper works around the issues by manually loading the onnxruntime
shared library, removing any dependency on the onnxruntime source code beyond
the header files. Naturally, this approach works equally well on non-Windows
systems.
Additionally, this library uses Go's recent addition of generics to support
multiple Tensor data types; see the NewTensor or NewEmptyTensor functions.
Requirements
To use this library, you'll need a version of Go with cgo support. If you are not using an amd64 version of Windows or Linux (or if you want to provide your own library for some other reason), you simply need to provide the correct path to the shared library when initializing the wrapper. This is seen in the first few lines of the following example.
Example Usage
The example illustrates how this library can be used to load and run an ONNX network taking a single input tensor and producing a single output tensor, both of which contain 32-bit floating point values.
The execution results:
[1.184287 0.6071364] 48.917µs
The full documentation can be found at pkg.go.dev.
Documentation
¶
Overview ¶
This library wraps the C "onnxruntime" library maintained at https://github.com/microsoft/onnxruntime. It seeks to provide as simple an interface as possible to load and run ONNX-format neural networks from Go code.
Index ¶
Constants ¶
This section is empty.
Variables ¶
var NotInitializedError error = fmt.Errorf("InitializeRuntime() has either " +
"not yet been called, or did not return successfully")
Functions ¶
func DestroyEnvironment ¶
func DestroyEnvironment() error
Call this function to cleanup the internal onnxruntime environment when it is no longer needed.
func GetTensorElementDataType ¶
func GetTensorElementDataType[T TensorData]() C.ONNXTensorElementDataType
Returns the ONNX enum value used to indicate TensorData type T.
func InitializeEnvironment ¶
func InitializeEnvironment() error
Call this function to initialize the internal onnxruntime environment. If this doesn't return an error, the caller will be responsible for calling DestroyEnvironment to free the onnxruntime state when no longer needed.
func IsInitialized ¶
func IsInitialized() bool
Returns false if the onnxruntime package is not initialized. Called internally by several functions, to avoid segfaulting if InitializeEnvironment hasn't been called yet.
func SetSharedLibraryPath ¶
func SetSharedLibraryPath(path string)
Use this function to set the path to the "onnxruntime.so" or "onnxruntime.dll" function. By default, it will be set to "onnxruntime.so" on non-Windows systems, and "onnxruntime.dll" on Windows. Users wishing to specify a particular location of this library must call this function prior to calling onnxruntime.InitializeEnvironment().
Types ¶
type Session ¶
type Session[T TensorData] struct { // contains filtered or unexported fields }
A wrapper around the OrtSession C struct. Requires the user to maintain all input and output tensors, and to use the same data type for input and output tensors.
func NewSession ¶
func NewSession[T TensorData]( onnxFilePath string, inputNames, outputNames []string, ) (*Session[T], error)
Loads the ONNX network at the given path, and initializes a Session instance. If this returns successfully, the caller must call Destroy() on the returned session when it is no longer needed. We require the user to provide the input and output tensors and names at this point, in order to not need to re-allocate them every time Run() is called. The user instead can just update or access the input/output tensor data after calling Run(). The input and output tensors MUST outlive this session, and calling session.Destroy() will not destroy the input or output tensors.
func NewSessionWithONNXData ¶
func NewSessionWithONNXData[T TensorData](onnxData []byte, inputNames, outputNames []string, inputs, outputs []*Tensor[T]) (*Session[T], error)
The same as NewSession, but takes a slice of bytes containing the .onnx network rather than a file path.
type Shape ¶
type Shape []int64
The Shape type holds the shape of the tensors used by the network input and outputs.
func (Shape) FlattenedSize ¶
Returns the total number of elements in a tensor with the given shape.
type Tensor ¶
type Tensor[T TensorData] struct { // contains filtered or unexported fields }
func NewEmptyTensor ¶
func NewEmptyTensor[T TensorData](s Shape) (*Tensor[T], error)
Creates a new empty tensor with the given shape. The shape provided to this function is copied, and is no longer needed after this function returns.
func NewTensor ¶
func NewTensor[T TensorData](s Shape, data []T) (*Tensor[T], error)
Creates a new tensor backed by an existing data slice. The shape provided to this function is copied, and is no longer needed after this function returns. If the data slice is longer than s.FlattenedSize(), then only the first portion of the data will be used.
func (*Tensor[T]) Clone ¶
Makes a deep copy of the tensor, including its ONNXRuntime value. The Tensor returned by this function must be destroyed when no longer needed. The returned tensor will also no longer refer to the same underlying data; use GetData() to obtain the new underlying slice.
func (*Tensor[T]) GetData ¶
func (t *Tensor[T]) GetData() []T
Returns the slice containing the tensor's underlying data. The contents of the slice can be read or written to get or set the tensor's contents.
type TensorData ¶
This is used as a type constraint for the generic Tensor type.
Source Files
Directories