Awesome

EVERYHING IS REALLY OUT-OF-DATE. FOR A MORE UP-TO-DATE BINDINGS I RECOMMEND CHECKING OUT https://github.com/wangkuiyi/gotorch OR OTHER PACKAGES AVAILABLE.

WORK IN PROGRESS... USE AT OWN RISK :-)

go-torch

LibTorch (PyTorch) bindings for Golang. Library is first and foremost designed for running inference against serialized models exported from Python version of PyTorch. Library can also be used to compile TorchScript applications directly from Go.

Installing

$ go get github.com/orktes/go-torch

Usage

go-torch depends on the LibTorch shared library to be available. For more information refer to https://pytorch.org/cppdocs/. The is also an example Dockerfile which is used for executing tests for the library.

import (
    "github.com/orktes/go-torch"
)

Creating Tensors

Supported scalar types:

• torch.Byte uint8
• torch.Char int8
• torch.Int int32
• torch.Long int64
• torch.Float float32
• torch.Double float64

matrix := []float32{
    []float32{1,2,3},
    []float32{4,5,6},
}
tensor, _ := torch.NewTensor(matrix)
tensor.Shape() // [2, 3]
tensor.DType() // torch.Float

Using serialized PyTorch models

For instructions on how to export models for PyTorch refer to the PyTorch documentation

// Load model
module, _ := torch.LoadJITModule("model.pt")

// Create an input tensor
inputTensor, _ := torch.NewTensor([][]float32{
    []float32{1, 2, 3},
})

// Forward propagation
res, _ := module.Forward(inputTensor)

Using TorchScript

TorchScript documentation

Currently supported input and output types

• Tensor
• Tuple (of Tensor and/or nested Tuples)

sumScript = `
def sum(a, b):
    return a + b
`

// Compile TorchScript
module, _ := torch.CompileTorchScript(sumScript)

// Create inputs
a, _ := torch.NewTensor([]float32{1})
b, _ := torch.NewTensor([]float32{2})

res, _ := module.RunMethod("sum", a, b)
fmt.Printf("[1] + [2] = %+v\n", res.(*torch.Tensor).Value())
// output: [1] + [2] = [3]

Acknowledgements

Lots of the functionality related to converting Golang types to PyTorch Tensors are a shameless copy on what Google is doing with their Go Tensorflow bindings. Therefore big part of the credit definetely goes to The TensorFlow Authors.

LICENSE

See here